* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Validation of the Measurement Characteristics in an Instrument for
Audio power wikipedia , lookup
Ground (electricity) wikipedia , lookup
Electrical engineering wikipedia , lookup
Electronic engineering wikipedia , lookup
Spectral density wikipedia , lookup
History of electric power transmission wikipedia , lookup
Oscilloscope history wikipedia , lookup
Stray voltage wikipedia , lookup
Analog-to-digital converter wikipedia , lookup
Buck converter wikipedia , lookup
Immunity-aware programming wikipedia , lookup
Power electronics wikipedia , lookup
Pulse-width modulation wikipedia , lookup
Electronic musical instrument wikipedia , lookup
Distribution management system wikipedia , lookup
Switched-mode power supply wikipedia , lookup
Voltage optimisation wikipedia , lookup
Power engineering wikipedia , lookup
Rectiverter wikipedia , lookup
Alternating current wikipedia , lookup
energies Article Validation of the Measurement Characteristics in an Instrument for Power Quality Estimation—A Case Study † Romuald Masnicki Marine Electrical Engineering Faculty, Gdynia Maritime University, 81-225 Gdynia, Poland; [email protected]; Tel.: +48-58-5586-666 † This is an expanded version of a conference paper: Masnicki, R. Validation of the measurement algorithms in instrument for power quality estimation. In Proceedings of the IEEE 16th International Conference on Environment and Electrical Engineering (EEEIC), Florence, Italy, 7–10 June 2016. Academic Editor: Rodolfo Araneo Received: 15 February 2017; Accepted: 6 April 2017; Published: 15 April 2017 Abstract: An acceptable quality of electrical energy is seen today as an important component of ecology. Several instruments for estimating the quality of electrical power have been elaborated. Each supplier assures that the instrument meets the applicable standards and that the uncertainty of the measurement results obtained using the instrument does not exceed the established levels. The accuracy of the measurement results depends on a couple of things, e.g., the correctness of the measurement algorithms implemented in the instrument and the quality of its calibration. In this paper, the basic features of an “estimator/analyzer” (E/A) instrument, as well as the calibration methods of the instrument, the verification of its measurement algorithms, and also the obtained exemplary results, are shown. The proposal of the strategy of the reliable validation of embedded measurement algorithms for the identification of parameters characterizing electrical power quality in the power grid is discussed. Keywords: electrical power quality estimation; measurement algorithm; measurement track examination; validation procedures 1. Introduction Ecology is the study of environmental systems, and is not only interested in the individual components of nature, but also in how the parts interact. Industrial ecology is a discipline that has recently been developed, particularly in Europe. Industrial ecology is an interdisciplinary framework for designing and operating industrial systems as living systems interdependent from natural systems. Its aim, among others, is to follow the energy use throughout the industrial processes, in order to improve the efficiency of energy producing. Many ecological and environmental impacts are the side products of electrical power generation, delivery, and consumer systems [1]. The electrical power system can be regarded as a large network system consisting of electrical power generators, voltage transformers, connecting cables, and power consumers [2]. The environmental impact of the production and consumption of electrical energy is becoming more significant because modern society is using an increasing amount of electrical power [3]. This power is usually produced at power plants that convert some other kind of energy into electrical power: conventional, nuclear, and renewable energy sources (RES). Each system has pros and cons, but many of them may pose a threat to the environment. Additionally, the common use of electronic power converters affects the deterioration of electrical power quality. This lower quality of power places a greater burden on the environment [4]. Energies 2017, 10, 536; doi:10.3390/en10040536 www.mdpi.com/journal/energies Energies 2017, 10, 536 2 of 16 The electrical power quality is a set of parameters describing the properties of the process of provision energy to the user under operating conditions, determining the continuity of the supply (power outage), and characterizing the supply voltage (its value and frequency, shape of waveform, and asymmetry in the multi-phase systems) [5]. The voltage waveforms in the electrical network, often thought of as composed of pure sinusoidal signals, can contain a wide variety of disturbances [6]. The main phenomena related to power quality include: • • • • • Harmonic and other derogations from the expected frequency of the alternating voltage, Voltage fluctuations, particularly those causing flickering lights, Voltage dips and short interruptions, Asymmetry within the three-phase system, Surges, having the characteristics of high frequency. A set of standards has been introduced to cover this field. They define the types and sizes of disturbance, and also the tolerance of various types of equipment to the possible disturbances that may be encountered. The principal standards in this field are the series of IEC 61000 [7], norms EN 50160 [8] and IEEE 1159 [9]. Electrical energy is a product, and like all other products, should meet the relevant quality requirements [10]. A proper operation of electrical equipment requires that the supply voltage only varies within a specified range around the nominal value [11,12]. The presence of disturbances in the electrical network (deterioration of the electrical power quality) is associated with a decrease in the energy production and conversion efficiency [13]. On the other hand, disturbances in the supply voltage mean that the components consuming electrical energy may reach an excessive usage level. It is important to note that the production technologies of the equipment for the generation of electrical energy from renewable sources are not fully “green”. The RES devices also introduce some disturbances to the electrical network. To minimize the influence of these disturbances, some protectors or noise filters are used [4,14]. A proper choice of equipment can be achieved after the determination of parameters of electrical disturbances as a result of the assessment of the electrical power quality. Commercially available instruments for electrical power quality estimations also measure other parameters characterizing the power quality. For example, the: • • • • • • Effective value of current and voltage, Active, reactive, and apparent power and energy, Frequency, Power factors: cosφ, tgφ, or λ, rms value of given harmonics for voltage and current, Total harmonic distortion (THD) coefficients for voltage and current. In [15], the results of an interesting experiment relating to the designation of power quality parameters were shown. Ten instruments used in the experiment (Table 1), owned by various institutions, measured the same set of parameters describing the electrical power quality. “Own design” means the prototype instrument, developed in the scientific institution and taking part in the experiment. The measurements included the network frequency, voltage rms values, total harmonic distortion, rms values of the dominant harmonics, and also the voltage fluctuation parameters: short and long term flicker severity. The measurements were carried out at bus-bars of the medium voltage 6 kV, by means of voltage transformers. Energies 2017, 10, 536 3 of 16 Table 1. List of instruments used in the measurements [15]. Energies 2017, 10, 536 No. Type of Instrument Owner 3 of 16 1 Panensa Table MEF 1. List of instruments Technical University Radom, Radom, used in the of measurements [15]. Poland 2 Memobox 686 Technical University of Radom, Radom, Poland Type of Instrument 3 No. Own design Technical University ofOwner Lodz, Lodz, Poland Panensa MEF Technical UniversityInstitute, of Radom,Gdansk, Radom, Poland 4 1 Own design Electrotechnical Poland 2 Memobox 686 Technical University of Radom, Radom, Poland 5 Memobox 800 Semicon, Warszawa, Poland 3 Own design Technical University of Lodz, Lodz, Poland Power recorder 1650 University of Mining and Metallurgy, Krakow, Poland 6 4 Own design Electrotechnical Institute, Gdansk, Poland 7 Own design University of Mining and Metallurgy, Krakow, Poland 5 Memobox 800 Semicon, Warszawa, Poland 8 6 Own design Institute of Power Engineering, Katowice, Poland Power recorder 1650 University of Mining and Metallurgy, Krakow, Poland 9 7 Memobox 686 Energopomiar-Elektryka, Gliwice, Poland Own design University of Mining and Metallurgy, Krakow, Poland 10 8 Own GdyniaofMaritime Academy, Gdynia, Poland Owndesign design Institute Power Engineering, Katowice, Poland 9 10 Memobox 686 Own design Energopomiar-Elektryka, Gliwice, Poland Gdynia Maritime Academy, Gdynia, Poland A similar set of ten instruments was used for the following measurements [16]. A schematic diagram of measurement site is shown Figure 1. The measurement points[16]. were on the A the similar set of ten instruments wasinused for the following measurements A located schematic secondary side of a supply transformer, at the substation denoted as GSTS-30 kV (phase-to-phase diagram of the measurement site is shown in Figure 1. The measurement points were located on the secondary side=of supply the substation denoted as GSTS-30 (phase-to-phase voltage level Urms 30akV). Tentransformer, measuring at instruments were connected via thekV voltage transformers. voltage level Urms =used 30 kV).inTen measuring instruments connected via the voltage transformers. All instruments both experiments werewere declared as complying with the terms of All instruments used in standards both experiments were declared as complying with of the and the respective IEC 61000-4-15 [17], according to the statements of the theterms designers respective IEC 61000-4-15 standards [17], according to the statements of the designers and manufacturers. Each of the two measurements was carried out by independent experiments. They were manufacturers. Each of the two measurements was carried out by independent experiments. They held at the same point of the supply network at the same time, for each experiment. Although the were held at the same point of the supply network at the same time, for each experiment. Although instruments measured the same voltage signals at the same time, their indications differed significantly, the instruments measured the same voltage signals at the same time, their indications differed independently forindependently each experiment. significantly, for each experiment. Figure 1. Schematic diagram of the measurement site in the electric steelworks power supply system [16]. Figure 1. Schematic diagram of the measurement site in the electric steelworks power supply system [16]. In [18], the analysis of possible reasonsfor forthe the different different indications of of instruments observed in in In [18], the analysis of possible reasons indications instruments observed the aforementioned experimentsisispresented. presented. As error, the authors of the of paper the the aforementioned experiments Asa source a source error, the authors the indicate paper indicate human factor factor (erroneous (erroneous set-up parameters) or design drawbacks of the the human set-upofofthe theinstrument’s instrument’s parameters) or design drawbacks of the manufacturer’s equipment. After an estimation of the relations between the results obtained during manufacturer’s equipment. After an estimation of the relations between the results obtained during the measurements, they formulate the simplified linear formulas showing the dependencies between the measurements, they formulate the simplified linear formulas showing the dependencies between the obtained results and bring the result sets from all of the instruments to a common course, within the obtained results and bring the result sets from all of the instruments to a common course, within the the accepted range of deviations. They concluded that the reason for the deviations in the accepted range of deviations. They concluded reason for the deviations in the measurements measurements results could have originatedthat in the variable amplification and could be due to the results could have originated in variable amplification and could be due to the constant offset of the constant offset of the measurement characteristics of the individual instruments. measurement characteristics of the individual instruments. Energies 2017, 10, 536 4 of 16 The description of effects related to the deviations of the characteristics of the measurement channel (i.e., lost performance or improper calibration) of various devices, causing the deterioration of the operation performance, can be found, e.g., in [19,20]. The vital issue for millions of customers, connected with the calibration quality of energy meters, is presented in [21]. The paper concerns research focused on the accurate assessment of the commonly used static energy meters. In some specific load conditions, their indications can differ by more than 580% when compared to the conventional (electromechanical) energy meters. The research was performed in accordance with the relevant standards of test setups. The research included the energy meters equipped with a variety of current sensors. However, the question, “What is the cause of an error in the processing characteristics of the energy meters?”, remains unanswered. In the context of this investigation, the necessity to measure the quality of the electrical energy is becoming increasingly important, because the number of non-linear and variable-energy loads connected to the electrical network is constantly growing. The descriptions of the methods and systems for electrical power quality assessment, in their various aspects, can be found, e.g., in [22–27]. In [28–30], issues concerning the configuration and operation of the measurement track of the “estimator/analyzer” (E/A) instrument for the designation of electrical power quality coefficients, especially for ship power network estimations, were considered. Additionally, considerations connected with investigating the characteristics of the E/A measurement tracks are described in [31–33]. Further detailed analyses are based on the case study concerning the designed and constructed instrument, in the area under consideration. The author of the present paper uses the term “calibration”, which can be defined as: “a test during which known values of measures are applied to the device input and corresponding output readings are recorded under specified conditions”. A related notion of “validation”, which in engineering means confirming that a product or service meets the needs of its users, is used in this paper. An overview of the selected validation methods can be found in [34]. For the user of a specific instrument, it is difficult to assess the correctness of the instruments’ indications during the measurement operation. They can only relay on the declarations of the instrument manufacturer. This work is focused on the method of reliable validation of the E/A instrument characteristics. The main goal of this paper is to show the solution used in the developed instrument. The paper deals with an additional configuration, necessary to implement the validation procedures of the algorithms applied in the E/A instrument. In Section 2, the measurement functions, as well as the basic measurement algorithms, implemented in the instrument are in short given. Section 3 describes the calibration procedures applied during the examination of the instrument. The constraints of the procedures used are mentioned. Section 4 presents the proposal of a reliable method for the validation of measurement algorithms implemented in the instrument measurement functions as embedded processor software. This work is summarized in Section 5. 2. Measurement Functions of the E/A In this section, the hardware configuration of the E/A and its measurement functions are in short presented. The configuration of the instrument is shown in Figure 2. The voltages from the electrical network (3- or 4-wires) and phase currents from generating sets (up to three) are delivered to the input terminals of the signal conditioning units. The configuration of the signal conditioning units applied in the voltage channels is shown in Figure 3. The voltage dividers (VD) adapt the level of the network voltages to the value acceptable in the next parts of the measurement channels. The galvanic separation (GS) block ensures a safe operation for other circuits of the E/A. The low-pass filter is switched between 10 and 100 kHz, depending on the actual measurement function. The voltage follower (VF) provides the adequate output resistance of this part Energies 2017, 10, 536 5 of 16 of the measurement channel. The output signal is fed to the input of the analog-to-digital converter 5 of 16 (ADC). The network currents are measured using the current probe type LFR 1/15 (Power Electronic Measurements Ltd., Ltd., Nottingham, Nottingham, UK) UK) (based (based on on the the Rogowski Rogowski coil). coil). The The configuration configuration of of the the signal signal Measurements conditioning units applied in the current channels is similar to the signal conditioning unit used in the conditioning units applied in the current channels is similar to the signal conditioning unit used in voltage channels, but in the current channels, the VD blocks are missing. The current probe signals are the voltage channels, but in the current channels, the VD blocks are missing. The current probe signals directly fed fed to the inputs of the are directly to the inputs of GS the blocks. GS blocks. The designation of coefficients describing the the electrical electrical power power quality quality is is carried carried out out using using aa The designation of coefficients describing ADSP-TS201 TigerSHARC TigerSHARC (Analog (Analog Devices, Devices, Norwood, Norwood, MA, MA, USA) USA) digital digital signal signal processor processor (DSP) (DSP) ADSP-TS201 embedded on the DSP Subsystem Module (DSM; STSL stands for “Single TigerSHARC with Logic”) embedded on the DSP Subsystem Module (DSM; STSL stands for “Single TigerSHARC with Logic”) (Kaztek Systems, Acton, MA, MA, USA) The configuration configuration of of the the DSM DSM STSL STSL module (Kaztek Systems, Acton, USA) [35] [35] (Figure (Figure 2). 2). The module resources is is shown shown in in Figure Figure 4. There is is also also aa field-programmable field-programmable gate gate array array (FPGA) (FPGA) Xilinx Xilinx Spartan-3 Spartan-3 resources 4. There (Xilinx, Inc., Inc., San San Jose, Jose, CA, CA, USA) USA) which which is is used used for for the the data data exchange exchange between between the the DSP DSP and and other other systems systems (Xilinx, of the instrument. For the communication of DSM devices with other E/A circuits, user-defined FPGA of the instrument. For the communication of DSM devices with other E/A circuits, user-defined FPGA input/output (I/O) (I/O)lines lineswere wereused. used. The Theuser userinterface interface(Figure (Figure2), 2), designed designed for for the the control control and and input/output selection of individual measurement functions of the instrument, is supported by the general purpose selection of individual measurement functions of the instrument, is supported by the general purpose processor (GPP) (GPP) LPC3250 LPC3250 (ARM926EJ-S (ARM926EJ-S core) core) (NXP (NXP Semiconductors Semiconductors Netherlands Netherlands B.V., B.V., Eindhoven, Eindhoven, processor The Netherlands). This processor also controls the indication and registration of the measurement data. The Netherlands). This processor also controls the indication and registration of the measurement data. Energies 2017, 10, 536 Bus bars of Main switchboard Estimator/analyzer of power quality L1 L2 L3 N Signal conditioning unit I G11 I G13 I G21 I G23 DSM STSL Module ADC AD7656 FPGA Spartan 3 Xilinx DSP TigerSHARC ADC Signal conditioning unit I G31 I G33 AD7656 RAM Flash memory GPP USB Ethernet keyboard LCD display Figure 2. The hardware structure of instrument [30]. ADC: analog-to-digital converter; DSM STSL: Figure 2. The hardware structure of instrument [30]. ADC: analog-to-digital converter; DSM DSP subsystem module; FPGA: field-programmable gate array; DSP: digital signal processor; RAM: STSL: DSP subsystem module; FPGA: field-programmable gate array; DSP: digital signal processor; random access memory; GPP: general purpose processor; USB: universal serial bus; LCD: liquid RAM: random access memory; GPP: general purpose processor; USB: universal serial bus; LCD: crystal display. liquid crystal display. In Figure 5, the main operations carried out in the measurement track of the instrument, In Figure the main operations carried out in differ the measurement trackrate of the connected with5,data processing, are shown. They in the sampling of instrument, the electricalconnected network with data processing, are shown. They differ in the sampling rate of the electrical network signals, signals, as well as in a set of measured quantities and therefore in the set of designated coefficients. as well as in a set of measured quantities and therefore sampled in the set of The electrical network signals are simultaneously in designated all voltagecoefficients. channels atThe theelectrical rate of network signals are simultaneously sampled in all voltage channels at the rate of 210 kS/s all 210 kS/s or in all of the voltage and current channels at the rate of 25 kS/s, dependingoroninthe of the voltage and current channels at the rate of 25 kS/s, depending on the performed measuring performed measuring function of the E/A. function of the E/A. Energies 2017, 10, 536 6 of 16 Energies 2017, 10, 536 Energies 2017, 10, 536 6 of 16 6 of 16 Energies 2017, 10, 536 6 of 16 Figure 3. The configuration of the signal conditioning units applied in the voltage channels. Figure 3. The configuration ofofthe unitsapplied applied voltage channels. Figure 3. The configuration thesignal signal conditioning conditioning units in in thethe voltage channels. Figure 3. The configuration of the signal conditioning units applied in the voltage channels. Figure 4. The block diagram of the DSM STSL module [35]. Figure 4. The block diagram of the DSM STSL module [35]. Figure 4. The block diagram of the DSM STSL module [35]. Figure 4. The block diagram of the DSM STSL module [35]. Sampling at 210 kS/s Sampling at 210 kS/s Operating options Operating options Operating options Sampling at 210 kS/s Estimator Estimator Estimator Designation and Registration Designation Registration Designation and registration ofand Registration of raw registration raw registration of of ofofraw selected samples selected samples selected samples coefficients coefficients coefficients of electrical of electrical of electrical power quality power quality power quality Sampling at 25 kS/s Sampling at 25 kS/s Sampling at 25 kS/s Analyzer Analyzer Analyzer Designation and Registration Designation and Designation and Registration registration of Registration of raw registration of raw registration of raw selectedofof samples selected samples selected samples coefficients coefficients coefficients of electrical of electrical of electrical power quality power quality power quality Figure Mainoperations operations performed performed ininthe instrument. Figure 5. 5. Main the instrument. Figure 5. 5.Main performedininthe the instrument. Figure Mainoperations operations performed instrument. The algorithms of the digital data processing (DDP) used to determine the the valuevalue of theof the The algorithms of the digital data processing (DDP) used to determine individual parameters of the electrical power quality are based on the common application theof the The algorithms ofof the data processing (DDP) used the value The algorithms of the digital data processing (DDP) used to determine the value ofofthe individual individual parameters thedigital electrical power quality are based onto thedetermine common application of the discrete wavelet transform (DWT), fast Fourier transform (FFT), discrete Fourier transform (DFT), orof the individual parameters of the electrical power quality are based on the common application discrete wavelet transform (DWT), fast Fourier transform (FFT), discrete Fourier transform (DFT), or parameterschirp of the electrical(CZT), power quality are on the common application of the the wavelet discrete wavelet Z-transform performed in abased complementary way [30,36,37]. Namely, discrete wavelet transform (DWT), fast Fourier transform (FFT), discrete Fourier transform (DFT), or chirp Z-transform (CZT), performed in a complementary way [30,36,37]. Namely, the wavelet transform coefficients (DWT), fast Fourier transform (FFT), discrete Fourier transform (DFT), chirp Z-transform after respective levels of wavelet decomposition are used as the input dataorfor various chirp Z-transform (CZT), performed in a complementary wayused [30,36,37]. Namely, wavelet coefficients after levels ofthe wavelet decomposition as the input datathe for various procedures to calculate rms[30,36,37]. values and coefficients in thewavelet Fourier series, in combination (CZT), performed inemployed arespective complementary way Namely,are the coefficients after respective coefficients after respective levels of wavelet decomposition are used as the input data for various procedures employed to calculate the rms values and coefficients in the Fourier series, in combination levelsprocedures of wavelet decomposition as the input data for various employed to employed to calculateare theused rms values and coefficients in the Fourierprocedures series, in combination calculate the rms values and coefficients in the Fourier series, in combination with the transient monitoring algorithm. The main components of the measurement algorithms are shown in Figure 6. These operations are carried out in the DSP (Figure 2). Energies 2017, 10, 536 7 of 16 Energies 2017,with 10, 536 the transient monitoring algorithm. The main components of the measurement algorithms are shown in Figure 6. These operations are carried out in the DSP (Figure 2). Energies 2017, 10, 536 7 of 16 7 of 16 Components above with the transient monitoring algorithm. The main components of the measurement algorithms are 50th harmonic shown in Figure 6. These operations are carried out in the DSP (Figure 2). Range up to 9 kHz Rms value calculation Components above 50th harmonic FFT Range up to 9 kHz Wavelet decomposition Rms value calculation Transient detection and assessment Wavelet decomposition DFT/CZT FFT Spectrum up to 50th harmonic DFT/CZT Figure 6. The diagram of operations performed in the measurement algorithms [30]. Figure 6. The diagram of operations performed measurement algorithms [30]. Transient detection in the Spectrum up to and assessment 3. Procedures for Assessing the Characteristics of the E/A 50th harmonic 3. Procedures To forevaluate Assessing the Characteristics ofofthe E/A measurement the in whole measurement track, the E/A was tested Figurethe 6. The diagram of characteristics operations performed the measurement algorithms [30]. in the laboratory as well as in the ship environment for several different test signals. To evaluate measurement characteristics theE/A whole measurement track, the 3. Procedures for Assessing the Characteristics oftrack the Thethe configuration of each E/A measurementof can be illustrated in simplified form, as E/A shownwas tested in the laboratory as main in the ship foracquisition several different test signals. in Figureas 7. well The two parts of environment the track, the data (DA) and the DDP, determine the To evaluate the measurement characteristics of the whole measurement track, the E/A was tested correctness of the measurement result. An evaluation of the measurementincharacteristics of the as shown The configuration of each E/A measurement track can be illustrated simplified form, in the laboratory as well as in the ship environment for several different test signals. designed instrument was made considering these two parts of the track. in Figure 7. The main parts ofE/A themeasurement track, the track datacan acquisition (DA) and the DDP, determine the Thetwo configuration of each be illustrated in simplified form, as shown in Figure The two main parts of the track, the data acquisition and the DDP, determine the correctness of the7.measurement result. An evaluation of the measurement characteristics of the Digital Data(DA) Processing Data Acquisition correctness of the measurement result. An evaluation of the measurement characteristics of the designed instrument was made considering these two parts of the track. designed instrument was made Signal considering these two parts of the track. In conditioning ADC Data Acquisition unit DSP UI Digital Data Processing Figure 7. The simplified block diagram of the digital measurement track. Signal In conditioning DSP UI ADC unit in the DA part of the track is carried by the instantaneous values of Measurement information the analog signals, and the conversion of these signals is performed systemically (in analog track). Figure 7. The simplified block diagram of the digital measurement track. The course of characteristics of the DA may change, of because this part of the measurement Figure 7. The simplified block diagram the digital measurement track. track is susceptible to the influence of many external impacts. The calibration of the DA part is based on the Measurement information in the DA part of the track is carried by the instantaneous values of determination of its actual characteristics of the signal conversion. the analog signals, and the conversion of these signals is performed systemically (in analog track). In the DDP part, the information a form digital representation thethe signals from the DA, values of Measurement information in the DAinpart ofofthe track is carriedofby instantaneous The course of characteristics of the DA may change, because this part of the measurement track is after they have been sampled and converted in signals the ADC,is is performed presented by the stream of digital the analogsusceptible signals, and conversion of these (inondata. analog track). to the the influence of many external impacts. The calibration of thesystemically DA part is based the The processing of data in this part of the track is performed in the software. The characteristics of the determination of its actual characteristics of the signal conversion. The course of characteristics of the DA may change, because this part of the measurement track is DDP, describing the data processing in the software algorithms, are substantially unchanging. The In the DDP part, the information in a form of digital representation of the signals from the DA, susceptiblevalidation to the influence many external The calibration of the DA part is based on the of the DDPof characteristics meansimpacts. checking the correctness (as well as further correction) after they have been sampled and converted in the ADC, is presented by the stream of digital data. of theof measurement algorithms performed in this part of the track. determination its actual characteristics of the signal conversion. The processing of data in this part of the track is performed in the software. The characteristics of the In theDDP, DDP part, the inina the form of digital representation of the signals The from the DA, describing theinformation data processing software algorithms, are substantially unchanging. 3.1. The Calibration of DA Part of Measurement Channel of the DDP characteristics means in checking the correctness (as well further correction) after they validation have been sampled and converted the ADC, is presented byasthe stream of digital data. calibration of the DAperformed part is easy to perform. It track. relys on determining the relationship of theThe measurement in this of the The processing of data in algorithms this part of the track is part performed in the software. The characteristics of between the output signal and the input signal (under stabilized conditions) over the range of the the DDP, describing thecalibration data processing the software algorithms, are substantially unchanging. 3.1. Thesignal. Calibration of DA Part of the Measurement Channel input The of inputinanalog circuits, starting from the voltage dividers (for three The validation of the DDP characteristics correctness well as further voltage channels) and the Rogowski means coils (forchecking six currentthe channels) to the (as ADC’s (Figure 2), wascorrection) The calibration of the DA part is easy to perform. It relys on determining the relationship performed using the pattern sources of the alternating current. In Figure 8, the exemplary of the measurement this(under partvoltage of theand track. between the algorithms output signal performed and the inputin signal stabilized conditions) over the range of the characteristics obtained as a result of the calibration of one of the current probes used, together with input signal. The calibration of the input analog circuits, starting from the voltage dividers (for three a designation of itsPart trendofline, are shown. Figure 9 contains the deviation of the trend line from the 3.1. The Calibration of DA Measurement Channel voltage channels) and the Rogowski coils (for six current channels) to the ADC’s (Figure 2), was probe characteristics. The absolute value of this deviation (∆approx) slightly exceeds the value of 0.8 A. performed using the pattern sources of the alternating voltage and current. In Figure 8, the exemplary The calibration ofobtained the DAaspart is easy perform.ofItone relys oncurrent determining the together relationship characteristics a result of thetocalibration of the probes used, with between designation its input trend line, are shown. 9 contains the deviation of the lineoffrom the outputa signal andofthe signal (underFigure stabilized conditions) over thetrend range thethe input signal. probe characteristics. Theanalog absolutecircuits, value of this deviation (∆approx ) slightly exceeds the value(for of 0.8 A. voltage The calibration of the input starting from the voltage dividers three channels) and the Rogowski coils (for six current channels) to the ADC’s (Figure 2), was performed using the pattern sources of the alternating voltage and current. In Figure 8, the exemplary characteristics obtained as a result of the calibration of one of the current probes used, together with a designation of its trend line, are shown. Figure 9 contains the deviation of the trend line from the probe characteristics. The absolute value of this deviation (∆approx ) slightly exceeds the value of 0.8 A. Taking into account the range of the current probe LFR 1/15 (peak current to 3000 A), it is an acceptable value in the application under consideration. Energies 2017, 10, 536 8 of 16 Energies 2017, 10, 536 8 of 16 Energies 2017, 10, 536account the range of the current probe LFR 1/15 (peak current to 3000 A), it is an8 of 16 Taking into Taking intovalue account range of under the current probe LFR 1/15 (peak current to 3000 A), it is an acceptable in thethe application consideration. acceptable value Energies 2017, 10, in 536the application under consideration. 8 of 16 Taking into account the range of the current probe LFR 1/15 (peak current to 3000 A), it is an acceptable value in the application under consideration. Figure 8. The exemplary calibration report of current probe No. 1. Figure 8. The exemplary calibration report of current probe No. 1. Figure 8. The exemplary calibration report of current probe No. 1. Figure 8. The exemplary calibration report of current probe No. 1. Figure 9. The deviation between the trend line and the current probe characteristics No. 1. Figure 9. The deviation between the trend line and the current probe characteristics No. 1. Figure 9. The deviation between trend line and the current probe characteristics The equations of the trend lines, the obtained in the calibration procedures of the DA No. part1.of the The equations the trend lines, used obtained in the procedures of digital the DAdata part in of the measurement track of (Figure 8), were in the DDPcalibration part for the scaling of measurement track (Figure 8), were used in the DDP part for the scaling of digital data in theof the Figure 9. The deviation between the trend line and the current probe characteristics No. 1. corresponding channels. The equations of the trend lines, obtained in the calibration procedures of the DA part corresponding channels. Current research on the configuration of the E/A is carried out in a way which leads to the measurement track (Figure 8), were used in the DDP part for the scaling of digital data in the The equations of the trend lines, obtained in theis calibration procedures ofthe themeasurement DA part of the Current research on theto configuration theself-calibration E/A carriedofout in a part way of which leads to the provision ofchannels. its input circuits the system forofthe the DA corresponding measurement track (Figure 8), DA were used in self-calibration the DDP part for theDA scaling data provision its input circuits the system forobtained the of the part ofofthe measurement track. Theof DDP processing oftothe data, for the standard test signal Sdigital t, allows oneinto the Current researchchannels. on the configuration of the E/A is carried out in a way which leads to the corresponding track. Thethe DDP processing of the DA and data,correct obtained for the signal St, allows one to designate actual DA characteristics the DA datastandard obtainedtest for the measurement signal provision of its input circuits to the the configuration system for the self-calibration of out thefor DA of the measurement Current on of along the DA E/A is the carried in aofpart way which leads to the designate the DA characteristics andresult, correct data obtained the measurement signal S m, as well asactual toresearch determine the corrected with estimation its uncertainty. The track.detailed DDP processing of theto DA obtained for thethe standard signal S ,measurement allows one to input circuits the data, system for the self-calibration of the test DA of the SmThe , provision as well asof toitsdetermine the corrected result, along with ofpart its[38]. uncertainty. The description of the procedures and the calibration circuit isestimation shown, e.g., in Ittis assumed designate theadditional actual DAoperations characteristics correct the DA data obtained forenable thein measurement signal track. The DDP processing of theand DA data, obtained for the test signal SItt, is allows one toSm , detailed description of the procedures and the circuit isstandard shown, e.g., [38].determination assumed that the performed in thecalibration DDP (shown in Figure 10) the designate the actual characteristics and the DA data obtained foruncertainty. thethe measurement signal as well as to determine theDA corrected result,in along with the estimation of its The detailed that the additional performed thecorrect DDP (shown in Figure 10) enable determination of the uncertainty ofoperations results. S m , as well as to determine the corrected result, along with the estimation of its uncertainty. Thethe of the uncertainty of the results. description of the procedures and the calibration circuit is shown, e.g., in [38]. It is assumed that Sm of the procedures and the calibration circuit is shown, e.g., in [38]. It is assumed detailed description additional operations performed in the DDP (shown inDDP Figure 10) enable the determination of the Corrected Data DA Sm that the additional performed in the DDP (shown in Figure 10) enable the determination result correction Corrected DDP Data uncertainty of the results.operations DA St of the uncertainty of the results. result correction St Real Uncertainty Uncertainty characteristics estimation of result Test Sm Real Uncertainty Uncertainty Corrected DDP Data DA signal characteristics estimation of result Test result correction signal St Figure 10. The idea of the implementation of the auto-calibration procedures in the E/A operations. Real Uncertainty Uncertainty Figure 10. The idea of the auto-calibration E/A operations. characteristics estimationprocedures in of the result Testof the implementation signal Figure 10. The idea of the implementation of the auto-calibration procedures in the E/A operations. Figure 10. The idea of the implementation of the auto-calibration procedures in the E/A operations. Energies 2017, 10, 536 9 of 16 Energies 2017, 10, 536of Characreristics of Measurement Channel 3.2. The Designation 9 of 16 The proper calibration of the instrument’s input Channel circuits seems to be a basic requirement. Probably, 3.2. The Designation of Characreristics of Measurement for most The of the effects described in [15,16], it can be circuits assigned to the change the conversion proper calibration of the instrument’s input seems to be a basicofrequirement. characteristics of most the DA section the devices useditin the the accuracy Probably, for of the effectsof described in [15,16], can beexperiment. assigned to theIndependently, change of the conversion of thecharacteristics designationofofthe a particular quality coefficient also depends on the correctness of the DA section power of the devices used in the experiment. Independently, the accuracy algorithms applied inofthe DDP part of the measurement channels. goal of the performed of the designation a particular power quality coefficient also depends The on the correctness of the algorithms applied in the part of the measurement Theingoal the performed experiments is, most of all, theDDP examination of the algorithmschannels. embedded the of software executed in experiments is, most of all, the examination of the algorithms embedded inconfigurations the software executed in in the DSP. The research was carried out using the four basic arrangement presented the11. DSP. research was carried out the fourin basic arrangement configurations in Figure TheThe estimation procedures areusing performed applications running on a PC.presented The distinction Figure 11. The estimation procedures are performed in applications running on a PC. The distinction of the DA and DDP parts in the measurement channel allows one to apply various test signals and of the DA and DDP parts in the measurement channel allows one to apply various test signals and calibration techniques for the estimation of the measurement features of the E/A instrument. calibration techniques for the estimation of the measurement features of the E/A instrument. (a) (b) (c) (d) Figure 11. Experimental setups for the verification of measurement algorithms: (a) The preliminary Figure 11. Experimental setups for the verification of measurement algorithms: (a) The preliminary assessment of algorithms in the development kit; (b) The estimation of algorithms with the use of assessment of algorithms in the development kit; (b) The estimation of algorithms with the use of pattern analog signals; (c) The estimation with the use of the raw samples of signal and coefficients pattern analog signals; (c) The estimation with the use of the raw samples of signal and coefficients designated in the E/A; and (d) The estimation using the reference instrument. designated in the E/A; and (d) The estimation using the reference instrument. The experimental layout in Figure 11a figuratively shows the preliminary tests of the E/A instrument algorithms. The software designing, well as the initial verification of the measurement The experimental layout in Figure 11a as figuratively shows the preliminary tests of the algorithms implemented in software of the E/A, were carried out using the evaluation board EVALE/A instrument algorithms. The software designing, as well as the initial verification of the TS201S-EZKIT Analog Devices (Analog Devices, Norwood, MA, USA) (Figure 12) [39], equipped measurement algorithms implemented in software of the E/A, were carried out using the evaluation with two TigerSHARC processors, the same as the DSP used in the E/A instrument (Figure 2). Due board EVAL-TS201S-EZKIT Analog Devices (Analog Devices, Norwood, MA, USA) (Figure 12) [39], equipped with two TigerSHARC processors, the same as the DSP used in the E/A instrument (Figure 2). Energies 2017, 10, 536 10 of 16 Due to the different configurations of the peripheral circuits of the DSP processors on the evaluation Energies 2017, 10, 536 10 of 16 board and in the E/A instrument, the verification only included the selected portions of the measurement functions. However, the verification ofofthe foronmeasuring the individual to the different configurations of the peripheral circuits the algorithms DSP processors the evaluation board and in the E/A instrument, thefully verification thestandard selected portions of the measurement factors of energy quality has been carriedonly out included using the sets of digital data. These studies functions.to However, verification of algorithms for measuring thedata individual of energy were important start thetheinstrument, inthe spite of a limited set of test whichfactors cannot cover various quality has been fully carried out using the standard sets of digital data. These studies were important measurement conditions. As a result, the software can be embedded in the DSP on the E/A instrument. to start the instrument, in spite of a limited set of test data which cannot cover various measurement Figure 11b shows the calibration setup using the standard analog test signals. Just as in the conditions. As a result, the software can be embedded in the DSP on the E/A instrument. previous layout, of testing signals issetup limited, calibration to in both Figurethe 11bset shows the calibration usingbut thethe standard analogprocedure test signals.refers Just as the parts of the channel: thelayout, DA and DDP. As signals a standard signal the 3-phase generator type 6590 previous thethe set of testing is limited, butsource, the calibration procedure refers toChroma both parts of the channel: the DASource and the was DDP.used As a standard signal source, thesource 3-phasewas generator Chroma type others, Programmable AC Power [40]. The same signal also used, among 6590 Programmable AC Power used [40]. The same in signal source was also used, among results in the setup shown in Figure 11b,c.Source In thewas experimental setup Figure 11b, the measurement others, in the setup shown in Figure 11b,c. In the experimental setup in Figure 11b, the measurement from the E/A are assessed in a PC-based estimation system, taking into account the features of the test results from the E/A are assessed in a PC-based estimation system, taking into account the features signal from the Chroma generator. of the test signal from the Chroma generator. Figure 12. The block diagram of evaluation board EVAL-TS201S-EZKIT [39]. CPLD: complex Figure programmable 12. The block diagram evaluation converter; board EVAL-TS201S-EZKIT [39]. CPLD: logic device; DAC: of digital-to-analog ADC: analog-to-digital converter; complex programmable logic device; DAC: digital-to-analog converter; analog-to-digital JTAG: join test action group; LEDs: light-emitting diodes; PBs: push buttons; PLL: ADC: phase-locked loop; FLAGs: processor’s status register; IRQs: interrupt SDRAM:diodes; synchronous converter; JTAG: join test action group; LEDs: requests; light-emitting PBs:dynamic push random buttons; PLL: access memory. phase-locked loop; FLAGs: processor’s status register; IRQs: interrupt requests; SDRAM: synchronous dynamic random access memory. Another way of verifying the measurement characteristics of the E/A was achieved using the registered raw samples of the signals. In the experimental setup shown in Figure 11c, for the stationary test of signals, the digital of the raw samples ofof these Another way verifying therepresentations measurement characteristics thesignals E/A were wasregistered. achieved using For the same signals,of after of instrument operation (Figure 5), particular the registered rawtest samples thechanging signals.theInoption the experimental setup shown in the Figure 11c, for the coefficients are registered. In the estimation arrangement, supported by the PC computer with stationary test signals, the digital representations of the raw samples of these signals were registered. running software package (independently tested using pattern data), the registered raw samples are For the processed same testand signals, after changing the option of instrument operation (Figure 5), the particular then the calculated coefficients are compared with the coefficients obtained from the coefficients are registered. In the estimation arrangement, supported by the PC computer with running examined E/A instrument. software package tested using data), thetriangle registered raw samples are processed Table 2 (independently contains the measurement resultspattern of the three-phase signal (experimental setup as the in Figure 11b), generated usingare a Chroma type with 6590 Programmable ACobtained Power Source and then calculated coefficients compared the coefficients from(Chroma the examined ATE Inc., Taipei Hsien, Taiwan) (declared values of Urms, U1 and THD for standard triangle signal E/A instrument. DST017 from its waveforms library) [40], combined with the results obtained from the E/A instrument Table 2 contains the measurement results of the three-phase triangle signal (experimental setup as and as a result of the raw samples processing in the PC system (experimental setup as in Figure 11c). in Figure 11b), generated using a Chroma type 6590 Programmable AC Power Source (Chroma ATE Inc., Taipei Hsien, Taiwan) (declared values of Urms , U1 and THD for standard triangle signal DST017 from its waveforms library) [40], combined with the results obtained from the E/A instrument and as a result of the raw samples processing in the PC system (experimental setup as in Figure 11c). Energies 2017, 10, 536 11 of 16 Table 2. The results obtained from the E/A and in the PC system for the standard signal DST017 from Energies 2017, 10, 536 11 of 16 the Chroma Programmable AC Power Source. Table 2. The results obtained from the E/A and in the PC system for the standard signal DST017 from Device Size Unit L1 L2 L3 the Chroma Programmable AC Power Source. Device E/A E/A PC PC DST017 DST017 Urms USize 1 Urms THD U1 Urms THD UU 1 rms THD U1 THD Urms UU 1 rms U1 THD THD (V) 220.75 220.56 219.70 Unit (V) (V) (%) (V) (V) (%) (V) (V) (%) (V) (%) (V) (V) (V) (V) (%) (%) L1 219.13 220.75 12.00 219.13 220.80 12.00 219.23 220.80 12.18 219.23 12.18 220.00 220.00 218.42 218.42 12.04 12.04 L2 218.93 220.56 10.24 218.93 220.020 10.24 219.05 220.020 12.23 219.05 12.23 220.00 220.00 218.42 218.42 12.04 12.04 L3 218.10 219.70 11.97 218.10 220.24 11.97 218.68 220.24 12.14 218.68 12.14 220.00 220.00 218.42 218.42 12.04 12.04 The experimental layout shown (as well wellasasininFigure Figure 11c) is free from the signal The experimental layout shownininFigure Figure 11d 11d (as 11c) is free from the signal limitations. TheThe testtest signals were same time, time,totothe theinputs inputs examined limitations. signals wereprovided, provided, at at the the same of of thethe examined E/A E/A instrument and to thetoreference instrument. The reference instrument was based on theon PCIthe eXtensions instrument and the reference instrument. The reference instrument was based PCI eXtensions for Instrumentation (PXI) platform (National Instruments Corporation, Austin, TX, USA) for Instrumentation (PXI) platform (National Instruments Corporation, Austin, TX, USA) equipped with suitable and peripheries and the virtual instrument designed for measuring the power with equipped suitable peripheries the virtual instrument designed for measuring the power quality quality parameters (PCI stands for Peripheral Component Interconnect—personal computer bus). 13, parameters (PCI stands for Peripheral Component Interconnect—personal computer bus). In Figure In Figure 13, the exemplary results of calibration of the phase voltage channels are presented. the exemplary results of calibration of the phase voltage channels are presented. The differences The differences between the rms values, measured by the tested instrument and also the PXI system, between the rms values, measured by the tested instrument and also the PXI system, are shown. are shown. Figure 13. The calibration results of the voltage measurement channel of the instrument. Figure 13. The calibration results of the voltage measurement channel of the instrument. Table 3 contains the absolute errors ∆ representing the deviations between the rms values of the phase voltages, measured by the E/A instrument and the PXI obtained during the the calibration Table 3 contains the absolute errors ∆ representing thesystem, deviations between rms values of of the instrument for differentby frequencies the network voltage, wellsystem, as the relative (percentage) the phase voltages, measured the E/Aofinstrument and theasPXI obtained during the errors δ, adopting the value indicated by the PXI device as the actual value of the voltage, calibration of the instrument for different frequencies of the network voltage, as well as the relative respectively. The values and course of the errors indicate that their main source were the systematic (percentage) errors δ, adopting the value indicated by the PXI device as the actual value of the voltage, effects resulting from the incorrect calibration of the processing characteristics of the measurement respectively. The values and course of the errors indicate that their main source were the systematic channels. The random influences cause the dispersion of errors ∆ as a function of frequency (for L2 effects resulting from the ofAssuming the processing characteristics of the measurement and L3 channels) not incorrect exceeding calibration ∆max = 0.2 V. the uniform distribution of errors, the channels. The random influences cause the dispersion of errors ∆ as a function of frequency (for L2 and standard uncertainty can be calculated using Formula (1): L3 channels) not exceeding ∆max = 0.2 V. Assuming the uniform distribution of errors, the standard Δ (1) uncertainty can be calculated using Formula=(1): ≅ 0.12 √3 ∆max u= √ ∼ = 0.12 V 3 (1) Energies 2017, 10, 536 Energies 2017, 10, 536 12 of 16 Table 3. The voltage deviations with reference to the network frequency. 12 of 16 Table 3. The voltage deviations with reference to the network frequency. f (Hz) Size Unit L1 L2 L3 f (Hz) 45 45 50 50 55 55 Size U E/A UPXI E/A U U∆PXI δ∆ δ UE/A U UPXI E/A U∆PXI δ∆ Unit (V) (V) (V) (V) (V) (%) (V) (%) (V) (V) (V) (V) (V) (%) (V) L1 221.4 221.4 220.9 0.5 220.9 0.23 0.5 0.23 221.4 220.9 221.4 0.5 220.9 0.23 0.5 L2 220.7 220.7 220.4 0.3 220.4 0.14 0.3 0.14 220.9 220.4 220.9 0.5 220.4 0.23 0.5 L3 220.3 220.3 220.8 − 0.5 220.8 −−0.5 0.23 −0.23 220.5 220.8 220.5 − 0.3 220.8 −−0.3 0.14 UE/A δ U E/A UPXI U∆PXI δ ∆ δ (V) (%) (V) (V) (V) (V) (%) (V) (%) 221.4 0.23 220.9 221.4 0.5 220.9 0.23 0.5 0.23 220.8 0.23 220.4 220.8 0.4 220.4 0.19 0.4 0.19 220.3 −0.14 220.8 220.3 − 0.5 220.8 −0.23 −0.5 −0.23 In sum, all of the applied testing methods for the assessment of the measurement track In sum, all the applied methods for theThe assessment of the of measurement track characteristics areofreliable for thetesting specified instrument. obtained results the experimental characteristics reliable for the specified instrument. The obtained the of experimental verification areare positive within an acceptable tolerance range. However,results on theofbasis the results, verification are about positive tolerance range. However, on the basis of the results, no conclusions thewithin degreeanofacceptable possible modification algorithms to improve the measurement no conclusions accuracy can be about drawn.the degree of possible modification algorithms to improve the measurement accuracy Whatcan is be thedrawn. reason for the phenomena described in [15,16]? Can these cases relate to the What is the reason the phenomena in is[15,16]? these cases to the characteristics of the E/A for instrument? Perhaps described the problem that theCan instrument underrelate test and characteristics of the operate E/A instrument? Perhaps the problem the instrument under and the testing arrangement on different data sets. Because is thethat individual coefficients aretest calculated testingthe arrangement on different data sets. Because the individual coefficients are calculated using samples of operate signals from the electrical network, collected in time windows of approximately using the of signals the differ. electrical collected in time windows approximately 200 ms, thesamples sets of the source from data can Fornetwork, specific measurement conditions, theofsmall inaccuracy 200the ms, the sets of the source data differ. For specific measurement conditions, of measurement algorithms may can result in a large difference of indication comparedthe to small other inaccuracy of So, the next measurement algorithms result in large of indication compared to instruments. to the quality of the may calibration ofathe DAdifference part of the instrument, the proper other instruments. So, next to the quality of the of the DA of the instrument, validation of the algorithms performed in the DDPcalibration part (the difficult taskpart to do) is important for the proper validation of the algorithms performed in the DDP part (the difficult task to do) is important quality of the measurement functions of the instrument. for the quality of the measurement functions of the instrument. 4. The Concept of Validation of Measurement Algorithms 4. The Concept is ofthe Validation Measurement Validation techniqueofused to compareAlgorithms the results to be validated with the results obtained through other numerical methods validated. words, with one technique been Validation is the technique usedpreviously to compare the resultsIntoother be validated the results has obtained validated beforenumerical it is used as a reference to validate the second method. through other methods previously validated. In other words, one technique has been The idea of the reliable validation of measurement algorithms, applicable for the E/A instrument validated before it is used as a reference to validate the second method. (and The probably other devices as of well), relies on algorithms, the assumption that for thethe dataset used for idea of for the reliable validation measurement applicable E/A instrument the coefficients the relies examined the asused the for onethe used in the (andcalculation probably forofother devices asinwell), on theinstrument assumption is that thesame dataset calculation reference system. of coefficients in the examined instrument is the same as the one used in the reference system. This assumption assumption is is met met ififthe themeasurement measurementdata, data,processed processedininthe the DSP designation DSP forfor thethe designation of of coefficients, are stored and further used in the reference system to calculate the corresponding coefficients, are stored and further used in the reference system to calculate coefficients (Figure 14). Test signals DDP DA Collection of selected samples & calculated coefficients Estimation Figure 14. Figure 14. The The experimental experimental setup setup for for the the verification verification of of the the measurement measurement algorithms algorithms using using registered registered samples and and the the corresponding corresponding coefficients, coefficients, also also in in addition addition to to the the PC-based PC-based estimation estimation system. system. samples Figure 4 shows the resources of the DSM STSL module applied in the E/A instrument (Figure 2). The module is additionally equipped with 128 MB of the synchronous dynamic random access memory (SDRAM). This memory was not used in the E/A operations, because of the sufficient Energies 2017, 10, 536 13 of 16 Figure 4 shows the resources of the DSM STSL module applied in the E/A instrument (Figure 2). The module is additionally equipped with 128 MB of the synchronous dynamic random access memory (SDRAM). This memory was not used in the E/A operations, because of the sufficient capacity of the internal memory of the DSP (24 Mb) for the calculation of the adopted coefficients of the electrical power quality. The proposed idea of measurement algorithms validation assumes the use of the SDRAM memory (in Figure 4 marked in red ellipse) for storing the selected samples from the ADC, but only those which were used by the DSP for the coefficients calculation, together with the designated coefficients. The capacity of the SDRAM enables one to accumulate the data from several processing cycles, corresponding to measurement windows of approximately 200 ms. For example, for the “estimator” operating option (Figure 5), for memorizing the data collected from the samples from three voltage channels, converted in the ADC at the rate of 210 kS/s, no more than 256 kB of memory is needed for one measurement window. To perform the validation of embedded algorithms, the original source data, together with the DSP processing results, are sent to the external system. In this way, it is possible to assess the real characteristics of data processing, as well as those corresponding to the DA part as embedded in the DSP software, in the DDP part of the instrument. The implementation of the proposed method in the E/A instrument is not connected with any hardware modifications. Only software changes are needed to obtain the fixed goal. The reference system stores the set of data received from the instrument and uses them for the independent off-line calculation of respective coefficients, resulting from the sequentially selected measurement functions of the E/A. The system is under development in the LabVIEW environment. The estimation part of the reference system compares the values of the coefficients designated in the E/A with the results obtained off-line in the PC estimation system. The simple criterion (2) is used for the validation of the examined algorithm implemented in the DSP: ∆i ≥ CiM − CiE (2) where: CiM —the value of coefficient calculated in the E/A, CiE —the value of the coefficient obtained in the estimation operation in the reference system, ∆i —the admissible error specified for the given algorithm under test, and i—the coefficient identifier. To measure the strength of the relation between the set of CiM and CiE coefficients, obtained in the E/A instrument and in the PC estimation system for many measurement windows, the Pearson coefficient [34] will be used. The results, shown in [15,16], indicate that other methods of verification of the sophisticated algorithms, even using reliable examination systems, are not always sufficiently correct. The imperfection of the measurement algorithms is often the result of the rounding used, simplifications, and approximations, and their optimization can help to improve the properties of the measuring instrument. More detailed descriptions of some other aspects concerning data processing in the measurement track of the E/A instrument are analyzed in [28,29,31–33]. 5. Conclusions Nowadays, the exploitation of electrical appliances has a significant impact on our natural environment. The monitoring of electrical power quality is an important part of its protection. The main problem presented in this paper deals with the calibration procedures of the measurement channel and the validation of the measurement algorithms. A case study-based consideration concerning the performed calibration procedures of the E/A is presented, along with the obtained exemplary results. Another approach for the verification of the measurement characteristics, especially applied to the software part of the instrument, is proposed. The concept of the validation of Energies 2017, 10, 536 14 of 16 the measurement algorithms embedded in the software of the instrument for the designation of the coefficients of electrical power quality, as well as its application using the resources available onboard of the DSP module of the elaborated E/A instrument, are discussed. The development of self-calibration procedures and their implementation to the A/D functions, in combination with the validation of the measurement algorithms, can improve the quality of the measurement results. The use of the proposed solutions in the considered instrument certainly cannot solve all of the problems concerning accuracy of the measurement results, but it allows an evaluation of the data processing algorithms implemented in the built-in DSP software and the identification of errors affecting the deteriorations of the instrument’s accuracy. This research is ongoing and the results will be presented in future papers. Acknowledgments: The cost of publishing in open access is covered by the author’s employer (Gdynia Maritime University). Conflicts of Interest: The author declares no conflict of interest. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. Arrillaga, J.; Bradley, D.A.; Bodger, P.S. Power System Harmonics; John Wiley & Sons: New York, NY, USA, 1985; p. 664. Caciotta, M.; Leccese, F.; Trifirò, T. From power quality to perceived power quality. In Proceedings of the IASTED International Conference on Energy and Power Systems EPS 2006, Chiang Mai, Thailand, 29–31 March 2006; Volume 526–119, pp. 94–102. Caciotta, M.; Leccese, F.; Trifirò, T. Curve-fitting-algorithm (CFA) as power quality basic algorithm. In Proceedings of the XVIII International Measurement Confederation (IMEKO) World Congress, Metrology for a Sustainable Development, Rio de Janeiro, Brazil, 17–22 September 2006. Fuchs, E.F.; Masoum, M.A.S. Power Quality in Power Systems and Electrical Machines, 1st ed.; Academic Press: Cambridge, MA, USA, 2008; p. 638. Arrillaga, J.; Watson, N.R.; Chen, S. Power System Quality Assessment; John Wiley & Sons: New York, NY, USA, 2000; p. 300. Caciotta, M.; Grossoni, M.; Leccese, F. Power quality measurements in telecommunication exchanges. In Proceedings of the International Telecommunications Energy Conference, INTELEC 2008, San Diego, CA, USA, 14–18 September 2008. International Electrotechnical Commission (IEC). IEC 61000-4-30:2015. Electromagnetic Compatibility (EMC)—Part 4-30: Testing and Measurement Techniques—Power Quality Measurement Methods; IEC: Genova, Switzerland, 2015. EN 50160, Voltage Characteristics of Electricity Supplied by Public Distribution Systems. Available online: http://www2.schneider-electric.com/library/SCHNEIDER_ELECTRIC/SE_LOCAL/APS/204836_1312/ DraftStandard0026rev2-DraftEN501602005-05.pdf (accessed on 10 March 2017). IEEE 1159-2009—IEEE Recommended Practice for Monitoring Electric Power Quality; IEEE Standards Association: Piscataway, NJ, USA, 2009. Leccese, F. A first analysis of perceived power quality for domestic customers. In Proceedings of the 12th IMEKO TC1 & TC7 Joint Symposium on Man Science & Measurement, Annecy, France, 3–5 September 2008; pp. 246–253. Fuller, J.F.; Fuchs, E.F.; Roesler, D.J. Influence of harmonics on power system distribution protection. IEEE Trans. Power Deliv. 1988, 3, 546–557. [CrossRef] Dugan, R.C.; McDermott, T.E. Operating conflicts for distributed generation on distribution systems. In Proceedings of the IEEE Industry Applications Society (IAS) 2001 Rural Electric Power Conference Record, Little Rock, AR, USA, 29 April–1 May 2001. Mindykowski, J. Assessment of Electric Power Quality in Ship Systems Fitted with Converter Subsystems; Press of Shipbuilding and Shipping Ltd.: Gdańsk, Poland, 2003; p. 276. Leccese, F. Study and characterization of a new protection system against surges and over voltages for domestic telecommunication networks. In Proceedings of the International Telecommunications Energy Conference INTELEC 2007, Rome, Italy, 30 September–4 October 2007; pp. 363–368. Energies 2017, 10, 536 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 15 of 16 Bień, A.; Firlit, A.; Folga, P.; Gallus, E.; Hanzelka, Z.; Hartman, M.; Hashad, M.; Iwaszkiewicz, J.; Jarocha, R.; Mieński, R.; et al. Voltage fluctuation measurement—Experiment in the industrial environment. Electr. Power Qual. Util. EPQU 2001, 7, 9–18. Szlosek, M.; Piekarz, M.; Hanzelka, Z.; Bien, A.; Piatek, M.; Loziak, W.; Pietrucha, R.; Hashad, M.; Wolski, L.; Olczykowski, Z.; et al. Comparative tests of flickermeters. In Proceedings of the 17th International Conference on Electricity Distribution, Barcelona, Spain, 12–15 May 2003. IEC. IEC 61000-4-15:2010. Electromagnetic Compatibility (EMC)—Part 4-15: Testing and Measurement Techniques—Flickermeter—Functional and Design Specifications; IEC: Genova, Switzerland, 2010. Hashad, M.; Hanzelka, Z.; Hartman, M.; Bien, A. The hypothesis for the wrong measurements results obtained during the flickermeter comparative test. In Proceedings of the 6th International Conference Electrical Power Quality and Utilisation, Cracow, Poland, 19–21 September 2001. Hoon, Y.; Radzi, M.A.M.; Hassan, M.K.; Mailah, N.F. DC-link capacitor voltage regulation for three-phase three-level inverter-based shunt active power filter with inverted error deviation control. Energies 2016, 9, 533. [CrossRef] Ok, Y.; Lee, J.; Choi, J. Analysis and solution for operations of overcurrent relay in wind power system. Energies 2016, 9, 458. [CrossRef] Leferink, F.; Keyer, C.; Melentjev, A. Static energy meter errors caused by conducted electromagnetic interference. IEEE Electromagn. Compat. Mag. 2016, 5, 49–55. [CrossRef] Caciotta, M.; Giarnetti, S.; Leccese, F.; Pedruzzi, E. Curve fitting algorithm FPGA implementation. In Proceedings of the 10th International Conference on Environment and Electrical Engineering—EEEIC2011, Rome, Italy, 8–11 May 2011. Caciotta, M.; Giarnetti, S.; Leccese, F.; Trinca, D. Development of an USB Data acquisition system for power quality and smart metering applications. In Proceedings of the 11th International Conference on Environment and Electrical Engineering—EEEIC2012, Venice, Italy, 18–25 May 2012. Di Pasquale, S.; Giarnetti, S.; Leccese, F.; Trinca, D.; Caciotta, M. A new platform for high accuracy power quality measurements: The forensic point of view. In Proceedings of the 20th International Measurement Confederation (IMEKO) Technical Committee 4 (TC4) International Symposium and 18th International Workshop on ADC Modelling and Testing, Benevento, Italy, 15–17 September 2014; pp. 404–409. Di Pasquale, S.; Giarnetti, S.; Leccese, F.; Trinca, D.; Cagnetti, M.; Caciotta, M. A Distributed Web-Based System for Temporal and Spatial Power Quality Analysis. In Power Quality Issues in Distributed Generation; In Tech: Rijeka, Croatia, 2015. [CrossRef] Giarnetti, S.; Leccese, F.; Caciotta, M. Non recursive multiharmonic least-square fitting for frequency estimation for grid frequency estimation. Measurement 2015, 66, 229–237. [CrossRef] Giarnetti, S.; Leccese, F.; Caciotta, M. Non recursive Nonlinear Least Squares for periodic signal fitting. Measurement 2017, 103, 208–216. [CrossRef] Masnicki, R. The fluency of data flow in the instrument for measurement and registration of parameters of the electrical power network. In Proceedings of the 15 IEEE International Conference on Environment and Electrical Engineering, Rome, Italy, 10–13 June 2015; pp. 2040–2044. Masnicki, R.; Mindykowski, J. Coordination of operations in registration channel of data from electrical power system. Measurement 2017, 99, 68–77. [CrossRef] Mindykowski, J.; Tarasiuk, T.; Masnicki, R.; Szweda, M.; Gorniak, M. Universal estimator/analyzer of electrical power quality—Part II—Design and technical implementation. In Scientific Papers of the Gdynia Maritime University; Publishing House of Gdynia Maritime University: Gdynia, Poland, 2010; pp. 21–36. (In Polish) Masnicki, R. Validation of the measurement algorithms in instrument for power quality estimation. In Proceedings of the 16 IEEE International Conference on Environment and Electrical Engineering, Florence, Italy, 6–8 June 2016; pp. 2519–2523. Masnicki, R.; Mindykowski, J. Examination of the instrument for power quality estimation—Case study. In Proceedings of the 21st IMEKO TC4 International Symposium and 19th International Workshop on ADC Modelling and Testing “Understanding the World through Electrical and Electronic Measurement”, Budapest, Hungary, 7–9 September 2016; pp. 240–244. Energies 2017, 10, 536 33. 34. 35. 36. 37. 38. 39. 40. 16 of 16 Masnicki, R. Verification of algorithms in measuring instrument to assess the quality of electrical energy. In Scientific Papers of the Faculty of Electrical and Control Engineering of Gdansk University of Technology; Politechnika Gdańska: Gdansk, Poland, 2016; Volume 49, pp. 63–67. (In Polish) Jauregui, R.; Silva, F. Numerical Validation Methods. In Numerical Analysis—Theory and Application; In Tech: Rijeka, Croatia, 2011. Available online: http://www.intechopen.com/books/numerical-analysis-theoryand-application/numerical-validation-methods (accessed on 30 March 2017). Kaztek Systems. Available online: http://www.kaztek.com/ (accessed on 7 April 2017). Tarasiuk, T. Estimator-analyser of power quality: Part I—Methods and algorithms. Measurement 2011, 44, 238–247. [CrossRef] Tarasiuk, T. Estimator-analyser of power quality: Part II—Hardware and research results. Measurement 2011, 44, 248–258. [CrossRef] Masnicki, R. The improvement of measurement accuracy in microprocessor instruments. In Proceedings of the 4th International Workshop, Compatibility in Power Electronics, CPE 2005, Gdynia, Poland, 1–3 June 2005. Analog Devices. ADSP-TS201S EZ-KIT Lite, Evaluation System Manual. Available online: http://www. analog.com/media/en/dsp-documentation/evaluation-kit-manuals/ (accessed on 7 April 2017). Chroma ATE Inc. Chroma Programmable AC Power Source 6560/6590. In User’s Manual. Available online: http://www.chromaate.com/product/6500_series_Programmable_AC_Source.htm (accessed on 7 April 2017). © 2017 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).