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An Improved Power System Operation For Better Voltage Stability Through FACTs Devices 1 B. Mallikarjuna, 2A.Bhaskar Abstract:-Voltage stability plays an important role in the operation of power system. Now a dayโs voltage instability problems in a power system have become one of the most important concerns in the power industries. The introduction of emerging Flexible AC Transmission Systems (FACTS) technology improves the stability, reduces the losses. FACTS devices are used to control the voltage, current, impedance, phase angle and to damp the oscillations. To place these FACTS devices at optimal location with optimal rating in the electrical networks, some optimization techniques are proposed. In this paper Genetic algorithm optimization technique is proposed. The load flow analysis (Newton Raphson technique) is done for an IEEE-14 bus test system to verify the losses. MATLAB coding is developed for simulation. Keywords: FACTS devices, Stability indices, Genetic Algorithm, Particle Swarm Optimization, Optimal location and optimal value I. INTRODUCTION Most of the large power system blackouts are caused by heavily stressed system with large amount of real and reactive power demand and low voltage condition. When the voltages at power system buses are low, the losses of test system are increased. This study is devoted to develop a technique for improving the voltage stability and minimizing the losses and hence eliminate voltage instability in a power system [1], [2]. Application of FACTS devices are currently pursued very intensively to achieve better control over the Since last twenty years, new techniques have been developed, such as Tabu Search method (TS), Simulated Annealing (SA), Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) etc, for finding operating range of different FACTS devices. Minimization of transmission loss is solved by using the optimization techniques. The SVC is related to reactive power control at load buses and the TCSC is related to real power transmission increase through impedance control in line. The IEEE standard tested power system has been considered as test system to investigate the effect of considering TCSC and SVC on power loss minimization and voltage stability. This paper deals with the optimal location and operating range of SVC and TCSC devices with the consideration of stability improvement and active power loss reduction of electrical network. The operating range of FACTS devices are obtained by performing optimization technique name as Genetic Algorithm (GA) [4]. II. ABOUT FACTS DEVICES The proper utilization of FACTS devices gives the following benefits [3]. 1) Improved voltage stability 2) Enhanced power transfer capacity of the transmission network 3) Reduced transmission losses 4) Improved controllability and system security In this paper two typical FACTS devices have been considered: SVC (Static Var Compensator) and TCSC (Thyristor Controlled Series Capacitor) A. Static VAR Compensator (SVC): 1 B.Mallikarjuna , M.Tech Student, Department of EEE (Electrical Power Engineering), Narayana College of Engineering and Technology, JNTUA ,Anantapur ,Nellore district ,AndhraPradesh ,India ,(e-mail: [email protected]). 2A.Bhaskar, Associate Professor, Department of EEE, Narayana college of engineering and technology ,JNTUA, Anantapur, Nellore, Nellore district, Andhra Pradesh, India (e-mail: [email protected]). Transmission lines for manipulating power flows. There are several kinds of FACTS devices [3]. Thyristor-Controlled Series Capacitors (TCSC) and Static Var Compensator (SVC) can exert a voltage in series with the line and they can control the active power through a transmission line. The optimal operation of the power system networks have been based on economic criterion. Now other criterion such as improving voltage profile and minimizing power loss of transmission line are taken into consideration. The Flexible AC Transmission System (FACTS) have been considered to maximize the use of existing transmission facilities. SVC (shunt compensating type) and TCSC (series compensating type) have been used in this paper. SVC is shunt connected type FACTS device whose output is adjusted to exchange capacitive or inductive and is used to control reactive power in network. The SVC consist thyristor controlled or switched reactor (TSR) and thyristor switched capacitor (TSC). TSR is used to absorbing reactive power and TSC is used to supply the reactive power under abnormal conditions of network. The Fig.1 shows the schematic diagram of SVC connected to an electrical network. The operating range of SVC is -100Mvar to 100Mvar Fig 1: Schematic diagram of SVC ๐ Thyristor Controlled Series Capacitor (TCSC): ๐ฟ๐ =| 1 โ โ๐=1 ๐น๐๐ The following figure2 represents the modeling of TCSC. TCSC is series connected type FACTS device. ๐๐ ๐๐ | Where n is the total number of buses. g is the no of generators connected in the system. And j=g+1โฆ.n. The values of ๐น๐๐ can be obtained from Y bus matrix. ๐น๐๐ = [๐๐ฟ๐ฟ ]โ1 [๐๐ฟ๐บ ] Fig 2: Modeling of TCSC The TCSC consist of a capacitor bank and a thyristor controlled inductive branch connected in parallel and connected in series to the transmission line. Its aims to directly control the overall series line impedance of the transmission line to improves power transfer capacity of the line., The operating range of TCSC is given by -0.7Xl to 0.2Xl. III. STABILITY INDICES In power system, the stability level of all buses and the weakest bus among them are identified with the help of the stability indices [6]. Leeโs stability margin, Schlueterโs stability indicator, and Kasselโs bus stability index, Voltage Stability Index (VSI) [7] and Line Stability Index (LSI) [8] are the various types of stability indices used in power system to monitor the system stability. In this paper VSI and LSI are proposed to find the stability levels of all bus and lines simultaneously. A. Line Stability Index (LSI): A.Y. Goharriz, R. Asghari [8] formulated a line stability index based on the power transmission concept in a single line. The line stability index, for this model, can be defined as: ๐ฟ๐๐ = 4๐๐ (๐๐ ๐ ๐๐(๐โ๐ฟ)) 2 (1) Where, Vs is the sending end voltage Q is the reactive power at receiving end X is the reactance at receiving end ๐ Is the impedance angle ๐ฟ Is the angle difference between the voltage and the receiving end voltage. supply L๐๐ calls the stability index of that line. It is used to find the stability index for each line connected between two bus bars in an interconnected network. Based on the stability indices of lines, voltage collapse can be predicted. When the stability index L๐๐ less than 1, the system is stable and when this index exceeds the value 1, the whole system loses its stability and voltage collapse occurs. B. Voltage Stability Index (VSI): Voltage Stability Index is used to calculate the stability indices for all the load buses connected in an IEEE 14 bus network [8]. For a given system operating condition, by using the load flow results obtained from Newton Raphson Technique, the Voltage Stability index (L index) for load buses is to be computed as (2) (3) Where ๐๐ฟ๐ฟ and ๐๐ฟ๐บ are corresponding partitioned portions of the Y-bus matrix. The L-indices for a given load condition are computed for all load buses. The L index gives a scalar number to each load bus. If the index value (L index) is moving towards zero, then the system is considered as stable and also improves system security. When this index value moves away from zero, the stability of system is relatively decreases then the system is considered as unstable. The L indices are calculated for all the load buses and the maximum of the L indices gives the proximity to the system to voltage collapse. IV. OPTIMIZATION TECHNIQUES In this paper Genetic algorithm and Particle swarm Optimization techniques are used for obtaining the optimal location and operating range of FACTS devices. A. Genetic Algorithm (GA): Genetic Algorithm (GAs) is efficient search methods based on principles of natural selection and genetics [9]. They are being applied successfully to find acceptable solutions to problems in business, engineering, and science. GAs are generally able to find good solutions in reasonable amounts of time, but as they are applied to harder and bigger problems there is an increase in the time required to find adequate solutions. There are several types of GAs are present in the nature. In this paper real coded genetic algorithm is considered. The Fig.4 shows the flow chart of GA. Objective function: In this paper the main objective is to find the optimal location and operating range of different FACTS devices within the equality and inequality constraints. The Objective function is coded mathematically as, f (x) = max (Lj) + min (Losses) (4) Where, Lj is the Stability index for the loads connected in the IEEE 14 bus network. Losses represent total losses in the IEEE 14 bus system. Fitness function: Fitness is a measure of quality which is used to compare different solutions. For maximization problems fitness function is same as objective function. For minimization problems the fitness function is expressed as below. F(x)= 1 1+๐(๐ฅ) (5) In this paper loss reduction is the fitness function. This is obtained from following equation Loss reduction = Before P๐๐๐ ๐ -After P๐๐๐ ๐ (6) Selection: In this technique the values of TCSC and SVC are generated randomly within a given specified limits. These values are called as chromosomes. For each chromosome fitness (loss reduction) is calculated. According to the fitness function the individual chromosomes are sorted out (descending order) Crossover: From the sorted values top two ratings of SVC and TCSC are selected as parental chromosomes to produce new off springs. Fig 4: IEEE 14 bus test system Table1: VSI results for 140% load condition Load bus VSI 4 0.724 5 0.723 7 0.695 9 1.000 10 1.000 11 0.497 12 0.465 13 0.439 14 1.000 At the maximum of VSI the SVC is placed. Fig 3: Flow chart for GA Off spring1 = X*parent1+ (1-X)*parent2 Off spring2 = X*parent2+ (1-X)*parent1 (7) Where โXโ is the Crossover operating point. It is having range 0 to 1. X is selected randomly. Mutation: It is a background operator which produces spontaneous random changes in various chromosomes. Mutation is used to random alteration of chromosomes string positions. New off springs = M*off springs (8) Where M is the probability of mutation is selected randomly. It is having range 0 to 1. The process is repeated up to max no of iterations are up to convergence. V. SIMULATION RESULTS A. EEE 14 bus system: The IEEE 14 bus system includes 5 generator buses, 9 load buses and 20 transmission lines. The representation of IEEE 14 bus test system is shown in bellow figure. Base case results: The load flow is performed for an IEEE14 bus test system. By substituting the corresponding load flow results in the equations (3) and (4) we obtain the LSI and VSI. These are shown in bellow tables (2) and (3). All of these results are for 140% load condition. Table 2: LSI results for 140% load condition Line no LSI Line no LSI 1 0.0031 11 0.0097 2 0.0105 12 0.0185 3 0.0454 13 0.0090 4 0.0133 14 0.0670 5 0.0052 15 0.0124 6 0.0126 16 0.0022 7 0.0001 17 0.0676 8 0.0000 18 0.0084 9 1.5993 19 0.0576 10 0.1879 20 0.1465 From the above table LSI is maximum at 9th line. At that location TCSC is placed. B. Voltage Profile: After finding the location, by placing the corresponding FACTS devices at suitable location the GA is performed their operation for getting the operating range of TCSC and SVC [10]. The corresponding results are shown in bellow. Table 3: Voltage profile Before placing FACTS After placing FACTS devices devices 1.0600 1.0600 1.0150 1.0250 0.9600 0.9800 0.9870 0.9974 0.9746 1.0300 0.9986 1.0500 0.9742 0.9729 0.9958 1.0054 0.9957 0.9562 1.0000 1.0700 1.0656 1.0900 1.0764 1.0650 1.0627 1.0510 1.0461 1.0380 [1] [2] [3] The following figure5 shows the graph representation of voltage profile for before and after placing the FACTS devices. [4] [5] 1.1 before placement of TCSC & SVC after placement of TCSC & SVC 1.08 1.06 [6] voltages 1.04 1.02 [7] 1 0.98 [8] 0.96 0.94 0 2 4 6 8 number of buses 10 12 14 Fig 5: Voltage profile The following results are represents the real and reactive power losses before and after placing the FACTS devices by performing the GA technique. Table 4: before placing FACTS devices Normal case P๐๐๐ ๐ 13.393 Q๐๐๐ ๐ 28.023 140% load condition 30.3052 97.705 Table 5: After placing the FACTS devices Location P๐๐๐ ๐ Q๐๐๐ ๐ Line Bus Normal Fault Normal Fault 9 9 13.391 29.665 28.228 93.402 9 10 13.364 29.650 27.802 93.796 9 14 13.341 29.303 27.791 92.148 From these results at 9th-14th location we get maximum benefits. VI. CONCLUSION In this paper GA technique is proposed for obtaining the operating values of TCSC and SVC, locations are found based on stability indices. From results it is observed that for overloads i.e., 140% of normal loading, the voltage profile of the system is increased and maintained within the specified limits, and the real power losses are also reduced. FUTURE WORK: For the same test system and for same loading condition of this paper, PSO technique is applied and then results of GA and PSO are compared. From the results of both GA and PSO the best one is suggested for placing FACTS devices. Cost function also taken into consideration to obtain the optimum cost of FACTS devices. REFERENCES [9] [10] V.A Preethi, Dr.S.Muralidharan, Mr.S.rajasekar, โApplication of Genetic Algorithm to Power System Voltage Stability Enhancement Using Facts Devices,โ IEEE International Conference on Recent Advancements in Electrical, Electronics and Control Engineering, 15-17 Dec 2011. D. Thukaram, K. Parthasarathy, H.P. Khincha, Narendranath Udupa, A. Bansilal โVoltage stability improvement: case studies of Indian powernetworksโ Department of Electrical Engineering, Indian Institute of Science, Bangalore 560 012, India N.G. Hingorani and L. Gyugyi, "Understanding FACTS: concepts and technology of flexible AC transmission systems," Wiley-IEEE Press, 1999. Stephane Gerbex, Rachid Cherkaoui, and Alain J. Germond, โOptimalLocation of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms,โ IEEE Trans. Power Systems, vol. 16, pp. 537 544, August 2001. G.I.Rashed, H.I.Shaheen, and S.J.Cheng, โOptimal Location and Parameter Setting of TCSC by Both Genetic Algorithm and ParticleSwarm Optimization,โ 2007 Second IEEE Conference on IndustrialElectronics and Applications , pp. 1141โ1147. A.Mohamed and G.B.Jasmon, โDetermining the Weak Segment of aPower System with Voltage Stability Considerations,โ Electric Machines and Power Systems-24:555-568, 1996. P.Kessel and H.Glavitsh, โEstimating the Voltage Stability of a PowerSystem,โ IEEE Transaction on Power Delivery, vol. 3, PWRD-1,July 1986. A.Y. Goharrizi, R. asghari, โA Novel Line Stability Index (NLSI) for Voltage Stability Assessment of Power Systemsโ 7th WSEAS International conference on power systems, Beijing, chaina, sept. 15-17, 2007. S. Durairaj, P.S. Kannan and D. Devaraj, โApplication of Genetic Algorithm to Optimal Reactive Power Dispatch including Voltage Stability Constraint,โ Journal of Energy & Environment, vol. 4, April2005. Kiran kumar kuthadi, N. Suresh โEnhancement of Voltage Stability through Optimal Placement of Facts Controllers in Power Systemsโ American Journal of Sustainable Cities and Society Issue 1 vol 1 July 2012 B.Mallikarjuna has Received B.Tech in Electrical and Electronics Engineering(EEE) from Kottam College of Engineering (KCE), Chinnatekur, Kurnool, and Andhrapradesh, India Affiliated the Jawaharlal Nehru technological university Ananthapur, in 2012, and pursing M. Tech in Electrical Power Engineering from the Narayana College of Engineering and Technology Affiliated to the Jawaharlal Nehru technological university Anantapur, Andhrapradesh, India in 2014, respectively. EMail.Id: [email protected]. A. Bhaskar working as Associate Professor in NARAYANA Engineering College, Nellore, Andhra Pradesh, India. He received his M.Tech degree in Power Electronics and Industrial Drives Engineering from SATYABAMA University, CHENNAI, in 2008. B.Tech in Electrical and Electronics Engineering from Visvodaya Inistitute of Tecnology &Science, Kavali, Affliated JNTUA in 2003, (EMail.id: [email protected] )