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International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter Using an Artificial Neural Network C.Dinakaran, Assistant Professor, Dept. of EEE, Sri Venkateswara College of Engg. & Tech., Chittoor [email protected] Abstract — In recent years, the demand for higher power ratings for industries has increased and multilevel inverter system has become a solution for high power applications. A multi level inverter system not only achieves high power ratings but also implement the use of renewable energy sources. As multilevel inverter systems are utilized in high power applications and reliability of the power electronics equipment. For example, industrial applications such as manufacturing industries are dependent upon induction motors and their inverter systems for manufacturing process control. In general, the conventional protection systems are passive devices such as fuses, overload relays and circuit breakers to protect the inverter system. Whenever a fault occurs, the protective devices will disconnect power sources from multilevel inverter system. Downtime of manufacturing equipment results in huge loss. Many engineers and researchers have focused on incipient fault detection and preventive maintenance to avoid inverter and motor faults. The most important factor is to operate the system continuously during abnormal conditions. In order to maintain continuous operation for a multilevel inverter system, knowledge of fault behavior, fault prediction and fault diagnosis are necessary. Faults should be detected as soon as possible after they occur, because if a motor drive runs continuously under abnormal conditions, the drive or motor may quickly fail. In this paper a fault diagnostic system in a multilevel- inverter using neural network is developed. The performance of proposed system is verified by MATLAB/SIMULINK, PROTEUS software and Hardware setup. Key Words — Diagnostic system, Fault Diagnosis, Multi string multilevel inverter, Artificial Neural Network. I. INTRODUCTION Now a day’s day to day, the demand for higher power ratings in industries. Medium voltage level in drives and utility applications require medium voltage and megawatt power level. High rating level is normally interfaced with a medium voltage network. For inverter drive a medium voltage grid, it is troublesome to connect only one power semiconductor switch directly. As a result, a multilevel power converter structure has been introduced as an alternative in high power and medium voltage situations and also multilevel inverter drive (MLID) systems have become a solution for high power drive applications [1] – [2]. A cascaded Multi Level Induction Drive is a general fit for large automotive all-electric drives because of the high VA rating possible and because it uses several DC voltage sources which would be available from batteries or fuel cells. A multilevel inverter not only achieves high power ratings, but also enables the use of renewable energy sources such as photovoltaic, wind and fuel cells that can be easily interfaced to a multilevel converter system for a high power application [3]. Multilevel converter topologies have been developed during the last two decades, the elementary concept of a multilevel converter to achieve higher power is to use a series of power semiconductor switches with several low voltage DC sources to perform the power conversion by synthesizing a staircase voltage waveform [4]. Capacitors, batteries and renewable energy voltage sources can be used as the multiple DC voltage sources. The commutations of the power switches aggregate these multiple DC sources in order to achieve high voltage at the output [5]. However, the rated voltage of the power semiconductor switches depends only upon the rating of the DC voltage sources to which they are connected [6]. The main disadvantage of multilevel inverters is that they use a high number of power semiconductors, switches for this reason, multilevel inverters may be considered less reliable. Also multilevel inverter systems are utilized in high power applications. Power electronics equipment is more reliability [7]. Generally, the conventional protection systems are passive devices such as fuses, overload relays and circuit breakers to protect the inverter systems and the induction motors [8]-[9]. The protection devices will disconnect the power sources from the multilevel inverter system whenever a fault occurs, stopping the operated process. Downtime of manufacturing equipment can add up to be thousands or hundreds of thousands of dollars per hour. Therefore, fault detection and diagnosis is vital to a company’s bottom line [10]-[11]. Multilevel inverters provide more possibilities in the power circuit to operate under faulty conditions. However, faults should be detected as soon as possible after they occur, because if a motor drive runs continuously under abnormal conditions, the drive or motor may quickly fail. Thus 1850 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ knowledge of fault behaviors, fault prediction and fault diagnosis are necessary [12]-[13]. the vehicle was connected to an AC supply as shown in Fig.2. II. CASCADED H – BRIDGE MULTILEVEL INVERTER DRIVES A single-phase structure of an m-level cascaded inverter is shown in Fig.1. Each separate DC source (SDCS) is connected to a single-phase full-bridge or H-bridge inverter. Each inverter level can generate three different voltage outputs +Vdc, 0 and –Vdc by connecting the DC source to the AC output by different combinations of the four switches S1, S2, S3 and S4. To obtain +Vdc switches S1 and S4 are turned ON, whereas –Vdc can be obtained by turning ON switches S2 and S3. By turning on S1 and S2 or S3 and S4 the output voltage is 0. The AC output of each of the different full-bridge inverter levels are connected in series such that the synthesized voltage waveform is the sum of the inverter outputs. The number of output phase voltage levels ‘m’ in a cascade inverter is defined by m = 2n+1, where ‘n’ is the number of separate DC sources. . Fig. 2 Three Phase Star connection structure for electric vehicle motor drive and battery charging III. GENERAL PROTECTION USED IN A CONVENTIONAL INVERTER DRIVE SYSTEM A conventional inverter drive (CID) has become one of the major applications in industry. Since a CID is used in various industrial applications, the reliability of the power electronic system of a CID is of outstanding importance. An example of an open loop volts/Hz speed control of a CID is shown in Fig.3 as can be seen a CID usually consists of six diodes on the input side, dc link voltage and six semiconductor power switches on the output side as shown in Fig. 3 Shows that the faults occur in the motor, rectifier or inverter. The conventional protection system used in a CID is mostly passive devices such as fuses, circuit breakers (CBs) and overload relays as shown in Fig.4. Fig. 1 Multilevel Inverter Cascaded inverters have also been proposed for use as the main traction drive in electric vehicles, where several batteries or ultra capacitors are well suited to serve as SDCSs. The cascaded inverter could also serve as a rectifier/charger for the batteries of an electric vehicle while Fig. 3 Conventional Voltage Fed PWM Inverter Drive. 1851 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ Fig. 4 A Typical Protection of a conventional voltage fed inverter drive The modern power semiconductor switches for a CID is mostly designed as modular package known as intelligent power module (IPM). An IPM usually combines a single phase or three phase rectifier with three phase inverter, gate drive circuit and protection circuit as one package as depicted in Fig.5 Fig. 6 Structure of Fault Diagnostic System V. NEURAL NETWORK ARCHITECTURE DESIGN Fig. 5 A Typical Protection System of a Conventional Voltage Fed Inverter Drive using Intelligent Power Module IV. FAULT DIAGNOSTIC SYSTEM The structure for a fault diagnostic system is illustrated in Fig.6. The system is composed of four major states: Feature Extraction Neural Network classification Fault Diagnosis Switching pattern calculation with Gate signal output. Focus of this paper is feature extraction, neural classification and Fault Diagnosis. The feature extraction performs the voltage input signal transformation with rated signal values as important features and the output of the transformed signal is transferred to the Neural Network classification. The networks are trained with both normal and abnormal data for the MLID. Thus, the output of this network is nearly 0 and 1 as binary code. The binary code is sent to the fault diagnosis to decode the fault type and its location. Then, the switching pattern is calculated. The architecture of the proposed fault diagnostic neural network is shown in Fig.7. The five multilayer feed forward networks or multilayer perceptron (MLP) are used in this research because the input data contain continuous features. A network has one hidden layer with 40 input nodes corresponding to harmonic order and magnitude, 2 hidden nodes, and 1 output node. The sigmoid activation function is used LOGSIG for hidden nodes and PURELM for an output node. The implementation of the proposed neural network system consisting of five Neural Networks as shown in Fig. 7. The number of nodes for the input and output layers depends on the specific application. The selection of number and dimension in the hidden layer is based on neural network accuracy in preliminary tests. Indeed, optimization of the network architecture is a significant topic in a study of artificial intelligence aspects. Fig. 7 Feed Forward Network 1852 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ VI. RESULTS A. Simulation Result The proposed method has been tested and simulation results are shown in Fig. 8. This model has been implemented using MATLAB/SIMULINK environment with SIMPOWER system toolbox. Fig. 9 PWM Signal for Proposed Five Level Inverter Fig. 10 Output Voltage of Five Level Inverter Fig. 8 Simulation Circuit for Five Level Inverter using Artificial Neural Network Fig. 11 Sinusoidal and Triangular Waveforms Fig. 12 Multilevel Sinusoidal Pulse Width Modulation 1853 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ R8 100 U9(VC) Q12 2N3859A D6 Q8 R7 2N3859A A B 10 C Q13 2N3859A 11 D 12 Q9 VC HIN SD VB HO VS LIN COM 2N3859A 100 U9 DIODE 3 LO 2 6 7 5 1 IR2112 V1 12v R6(1) R6(2) R6 5k Q14 2N3859A Q10 2N3859A Q11 Q15 2N3859A 2N3859A A B A C B D C D U6(MCLR/Vpp/THV) D19(A) R12 D19 10 11 U6 13 14 2 3 4 5 6 7 8 9 10 1 OSC1/CLKIN RB0/INT OSC2/CLKOUT RB1 RB2 RA0/AN0 RB3/PGM RA1/AN1 RB4 RA2/AN2/VREF-/CVREF RA3/AN3/VREF+ RB5 RB6/PGC RA4/T0CKI/C1OUT RB7/PGD RA5/AN4/SS/C2OUT RC0/T1OSO/T1CKI RE0/AN5/RD RC1/T1OSI/CCP2 RE1/AN6/WR RC2/CCP1 RE2/AN7/CS RC3/SCK/SCL RC4/SDI/SDA MCLR/Vpp/THV RC5/SDO RC6/TX/CK RC7/RX/DT RD0/PSP0 RD1/PSP1 Fig. 13 FFT Analysis from Power Graphical User Interface RD2/PSP2 RD3/PSP3 RD4/PSP4 RD5/PSP5 RD6/PSP6 RD7/PSP7 33 12 34 D1(A) 36 37 D18 R1 D1 38 40 3 10 10 17 11 23 25 12 HIN VC SD VB HO VS LIN COM LO 6 11 7 5 1 2 19 12 HIN VC SD 29 7 5 1 IR2112 VS LIN COM LO R2 6 7 10 5 1 11 12 IR2112 U2 DIODE 3 HIN VC SD VB HO VS LIN COM LO 100 U5(VC) 6 D5 7 D3 U3(VC) 5 IR2112 U3 DIODE 3 2 U4(VC) R5 R3 1 D4 100 10 11 12 HIN VC SD VB HO VS LIN COM LO 6 7 10 5 1 11 12 2 U4DIODE 3 100 10 11 30 IR2112 U5 DIODE 3 100 R4 IR2112 27 28 6 U2(VC) 100 21 22 LO 2 U7 DIODE VB HO 2 26 20 COM D2 3 U1 DIODE 15 24 VB HO VS LIN R11 100 39 18 VC SD D18(A) 35 16 HIN 100 U8 DIODE 3 HIN VC SD VB HO VS LIN COM LO 6 12 7 HIN VC SD 6 VB 7 HO 5 VS LIN COM 1 LO 5 1 2 IR2112 PIC16F877A 2 IR2112 Fig. 15 Development of Five Level Inverter in PROTEUS Software Fig. 16 Output Pulse of Five Level Inverter Fig. 14 Neural Network Training B. PROTEUS Model Proteus is the more familiar simulators. It can be used to simulate almost every circuit on electric fields. It is easy to use because of the Graphical User Interface (GUI) that is very similar to the real prototype system. Moreover, it can be used to design printed circuit board (PCB). Fig. 17 Output Voltage for Five Level Inverter 1854 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network International Electrical Engineering Journal (IEEJ) Vol. 6 (2015) No.4, pp. 1850-1855 ISSN 2078-2365 http://www.ieejournal.com/ C. Hardware Setup The hardware is implemented for five level inverter IRF 640 IGBT Switches, IN4007 Diode are used to design for high voltage, high current carrying capability. Gate driver IC IR2112 Circuits are used for boosting the pulses which will get from a microcontroller (PIC 16F877A) is used for generating required pulses. application. In most cases a reduction of the rated power is more preferable than a complete shutdown. It is possible that Artificial Intelligence based technique can be applied in condition monitoring and diagnosis. Moreover, the reliability of the system can also be improved by using diagnosis. Therefore, the fault diagnostic system for cascaded H-bridge multilevel inverter based on artificial intelligent technique is implemented in the paper. REFERENCES Fig. 18 Experimental Setup for Cascaded H – Bridge Five Level Inverter Fig. 19 Output Waveform for Cascaded H - Bridge Five Level Inverter VII. CONCLUSION The cascaded H-bridge multilevel inverter is one of optimistic solutions for high power drives application. If a fault (open or short circuit) occurs in a semiconductor power switch in a cell, it will causes an unbalanced output voltage and current, while the traction motor is operating. The unbalanced voltage and current may result in vital damage to the traction motor if the traction motor is run in this state for a long time. Although a cascaded Multilevel Induction Drive (MLID) has the ability to tolerate a fault for some cycles, it would be better if one can detect the fault location. Then, switching patterns and the modulation index of other active cells of the MLID can be adjusted to maintain the operation under balanced load condition, the MLID cannot be operated at full rated power. The amount of reduction of the rated power that can be tolerated depends upon the MLID [1] S.Sunisith, K.S.Mann, Janardhan Rao, “Effective Algorithm for Reducing DC Link Neutral Point Voltage and Total Harmonic Distortion for Five Level Inverter”, International Journal of Engineering Journal (IEEJ), Vol. 5, (2014), No. 11, PP. 1613-1618. [2] S. kumfoi, Leon M. Tolbrt, ‘‘Fault Diagnostic System for a Multilevel Inverter using a Neural Network’’, IEEE Trans. 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He, “A multilevel converter topology with fault-tolerant ability”, IEEE Trans. Power Electron., vol. 20, no. 2, pp. 405–415, Mar. 2005. [9] D. Diallo, M. H. Benbouzid, D. Hamad and X. Pierre, “Fault detection and diagnosis in an induction machine drive: A pattern recognition approach based on concordia stator mean current vector”, IEEE Trans.Energy Conv., vol. 20, no. 3, pp. 512–519, Sep. 2005. [10] F. Blaabjerg and J. K. Pedersen, “A new low-cost, fully fault-protected PWM-VSI inverter with true phase-current information”, IEEE Trans.Power Electron., vol. 12, no. 1, pp. 187–197, Jan. 1997. [11] J. Rodriguez, P. W. Hammond, J. Pontt, R. Musalem, P. Lezana and M. J. Escobar, “Operation of a medium-voltage drive under faulty conditions”, IEEE Trans. Ind. Electron., vol. 52, no. 4, pp. 1080–1085, Aug. 2005. [12] S.K.Meeravali, K.Chandrasekhar, “Power Quality Enhancement using Multilevel Cascaded H – Bridge based D-STATCOM with IRP Theory”, International Journal of Engineering Journal (IEEJ), Vol. 6 (2015), No. 2, PP. 1756-1764. [13] S.Sunisith, M.Saritha, D.Krishna Chaitanya, “Grid Connected Solar System with PWM Operated Thirteen level Inverter using Digital PI Controller, International Journal of Engineering Journal (IEEJ), Vol. 5 (2014), No. 12, PP. 1688-1695. 1855 C.Dinakaran Evaluation of Fault Diagnostic System for a Multistring Multilevel Inverter using an Artificial Neural Network