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  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 169 OPTIMAL PLACEMENT OF SVC BY USING ABC ALGORITHM Mohammad Rafee Shaik1 , Dr. A. Srinivasula Reddy2 1 Asst. Professor, Jijiga University, Ethiopia, 2 Professor and Principal, CMR Engineering College, A.P. India, ABSTRACT In modern power systems, due to uncertainty of the load curve and power transfers between various utilities and loads create block out situations. In these situations the Flexible AC transmission system (FACTS) controllers play an important role in power system security enhancement. However, these controllers must be placed optimally due to their high capital investment. FACTS devices can regulate the active and reactive power control as well as adaptive to voltage-magnitude control simultaneously because of their flexibility and fast control characteristics. Placement of these devices at optimal location can lead to control in line flow and maintain bus voltages at required level and so improve the voltage profile, to improve load transfer capability, decreasing the losses in the system and operate the system within stable regions. This paper proposes a systematic method for finding optimal location of SVC to improve voltage profile of a power system by implementing Artificial Bee Colony (ABC) Algorithm. An OPF with SVC using ABC algorithm is considered for simulation and compared with existing literature. Effectiveness of the proposed method is demonstrated on IEEE 30-bus test system. Keywords: ABC Algorithm, FACTS Devices, Optimization SVC, Voltage Profile. . I. INTRODUCTION In recent years power demand has increased substantially while the expansion of power generation and transmission has been limited due to limited resources and environmental restrictions. As a consequence some transmission lines are heavily loaded and system stability becomes a power transfer limiting factor. Flexible AC transmission system (FACTS) controllers are mainly used for solving various power system steady state control problems. However recent studies reveal that FACTS controllers could be employed to enhance power system stability in addition to their main function of power flow control. It is known that the power flow through an AC transmission line is a function of line impedance, the magnitude and the phase angle between the sending and the receiving end voltages. By proper coordination of FACTS devices in the power system network, both the INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING AND TECHNOLOGY (IJARET) ISSN 0976 - 6480 (Print) ISSN 0976 - 6499 (Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME: www.iaeme.com/ijaret.asp Journal Impact Factor (2014): 7.8273 (Calculated by GISI) www.jifactor.com IJARET © I A E M E
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 170 active and reactive power flow in the lines can be controlled. FACTS devices improves power transmission capacity, voltage profile, enhancing power system stability [5].FACTS devices include static var compensator (SVC), thyristor controlled series compensator (TCSC), unified power flow controller (UPFC) etc. SVC and Statcom are connected in shunt with the system to improve voltage profile by injecting or absorbing the reactive power [6]. Like other FACTS devices, SVC is an expensive device; therefore it is important to find the optimal location and its size in a power system, so that voltage profile may be improved effectively. In [4], optimal placement of SVC based on reactive power spot price is discussed. In [8], a method optimal placement of SVC for static and dynamic voltage security enhancement has been developed. In [8, 9], new SVC models and their implementation in Newton-Raphson load flow and optimal power flow algorithms has been is developed. This paper focuses on the placement of SVC, for improving the voltage profile and reducing the real power losses. SVC is a shunt FACTS device which is designed to maintain the voltage profile in a power system under normal/contingency conditions. In practical power systems, all buses have different sensitivity to the power system stability, some buses are more and some are less. If SVC is allocated at more sensitive buses, it will effectively improve the voltage profile stability [10]. The optimal locations of the FACTS devices are obtained by solving the economic dispatch problem plus the cost of these devices making the assumption that all the lines, initially, have these devices. The system load ability was employed as a measure of power system performance [16]. In this paper the optimal placement of SVC is modeled as a multi objective optimization problem and solved by Artificial Bee Colony algorithm [18]. It is tested on IEEE 30 bus system and compared with OPF without placing SVC. II SVC MODELLING II a) SVC Susceptance model A changing susceptance (B) SVC model represents the fundamental frequency equivalent of all shunt modules making up the SVC. This model is an improved version of SVC models. This is giving the shunt compensation for the system. It is shown in the figure 1.In this paper only this model is considered for case studies. Figure 1: Variable susceptance model of SVC [6]
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 171 II b) SVC LOAD FLOW MODELS The circuit shown in Fig. 1 is used to derive the SVC's nonlinear power equations required by Newton's load flow method. The voltages and angles at the buses i and j are Vi, δi and Vj, δj respectively. The real and reactive power flow of the buses i to bus j can be written as gi 1 P cos( ) sin( ) 0, 1,2.. n di i j ij ij ij ij j P VV G B i nδ δ =  − − + = = ∑ (1) gi 1 Q sin( ) cos( ) 0, i=1,2..n n di SVC i j ij ij ij ij j Q Q VV G Bδ δ =  − − − + = ∑ (2) Qmin<QSVC<Qmax (3) Pgi=Generated real power of ‘i’th bus Qgi=Generated reactive power of ‘i’th bus Pdi=Consumed real power of ‘i’th bus Qdi= Consumed reactive power of ‘i’th bus Vi= Voltage of ‘i’th bus Gij=Conductance of line between buses ‘i’ and ‘j’ Bij=Susceptance of line between buses ‘i’ and ‘j’ δij=Phase angle difference between bus voltages ‘i’ and ‘j’ QSVC= SVC capacity (MVAR or p.u) III. PROBLEM FORMULATION III A. Static modeling of SVC and installation cost The implementation of the variable shunt susceptance models in a Newton-Raphson load flow algorithm requires the incorporation of a nonstandard type of bus, namely PVB. This is a controlled bus where the nodal voltage magnitude and active and reactive powers are specified while the SVC’s total susceptance BSVC is handled as state variable. If BSVC is within limits the specified voltage magnitude is attained and the controlled bus remains PVB-type. However, if BSVC goes out of limits, so the bus becomes PQ-type. In this situation, the SVC will act as an unregulated voltage compensator whose production or absorption reactive power capabilities will be a function of the nodal voltage at the SVC point of connection to get the voltage 1.0 p.u. Cost of SVC 2 (cos ) 0.0003( ) 0.2691( ) 188.22SVC t OR OR= − + (4) III B. Transmission Losses The proposed algorithm also considers the transmission loss minimization for selecting optimal location of SVC. The real power losses can be expressed as the algebric sum of generated powers and load powers. gi 1 1 1 1 P cos( ) sin( ) , 1,2.. n n n n di i j ij ij ij ij i i i j TL P VV G B i nδ δ = = = =  = − = + = ∑ ∑ ∑∑ (5)
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 172 III C. Voltage Deviations In a power system, it is desirable to maintain the voltage deviations within ±5%. In this paper, the optimal location and size of SVC is determined by observing minimum value of VD. Voltage deviation is calculated as follows: 2 1 ( ) n ref i i i VD V V = = −∑ (6) Vi –Voltage at i'th bus Vi ref –Reference Voltage at i'th bus III D. Line flow limits In a power system, it is mandatory to maintain the line flows (MVA) within ±5% of the limits. Line flow deviations Voltage deviation is calculated as follows: * * [ *( )( )]ij i i j ij ijLF abs V V V G iB= − − (7) LFij –Line flow at ij'th line LFij ref –Reference Line flow at ij'th bus The line flow deviation are expressed as 2 1 ( ) ln ref ij ij ij LFD LF LF ∈ = −∑ (8) nl is the number of transmission lines in the system. III E) Fuel Cost Minimization Along with Voltage enhancement, transmission loss minimization the economic aspect of fuel cost also considered in this paper. From the case study with IEEE 30 bus system normal monotonic quadratic fuel cost equations are considered. 2 1 gn i i i i i i FC a P b P c = = + +∑ (9) The overall multi objective cost function to be minimized can be summarized as min max min max min max ( , , , ) 0 , 1,2...gi gi gi gi gi gi i i i Min F Subject to F V P Q P P P i n Q Q Q Lineflows Limits V V V δ = ≤ ≤ = ≤ ≤ < ≤ ≤ Where F=FC+TL+VD+LFD+ SVC (cost) ------- (9) F(V,δ,P,Q) is the power flow equations
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), IV. ARTIFICIAL BEE COLONY ALGORITHM [18] In a real bee colony, some tasks are performed by specialized individuals. These specialized bees try to maximize the nectar amount stored in the hive using efficient divisio organization. The Artificial Bee Colony (ABC) algorithm, proposed by Karaboga[18] in 2005 for real-parameter optimization, is a recently introduced optimization algorithm which simulates the foraging behavior of a bee colony .The minim honey bee colony which the ABC algorithm simulates consists of three kinds of bees: employed bees, onlooker bees and scout bees. Half of the colony consists of employed bees, and the other half includes onlooker bees. Employed bees are responsible for exploiting the nectar sources explored before and giving information to the waiting bees (onlooker bees) in the hive about the quality of the food source sites which they are exploiting. Onlooker bees wait in source to exploit based on the information shared by the employed bees. Scouts either randomly search the environment in order to find a new food source depending on an internal motivation or based on possible external clues. The units of the basic ABC algorithm can be explained as follows: IV a) Producing initial food source sites Initial food sources are produced randomly within the range of t parameters as shown in the equation where i = 1…SN, j = 1…D. SN is the number of food sources and parameters. In addition, counters which store the numbers of trials of solutions are reset to 0 in this phase. After initialization, the population of the search processes of the employed bees, the onlooker bees and the scout bees. Termination criteria for the ABC algorithm might be reaching a maximum cycle number ( error tolerance (ϵ). IV b) Sending employed bees to the food source sites In ABC, finding a neighboring food source is defined by υ i j =xi j +ϕ i j (xi j -xk j ) Within the neighborhood of every food source site represented by determined by changing one parameter of range [−1, 1]. After producing υi within the boundaries, a fitness value for a minimization problem can be assigned to the solution υi by (12) where fi is the cost value of the solution directly used as a fitness function. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 173 ARTIFICIAL BEE COLONY ALGORITHM [18] In a real bee colony, some tasks are performed by specialized individuals. These specialized bees try to maximize the nectar amount stored in the hive using efficient division of labour and self organization. The Artificial Bee Colony (ABC) algorithm, proposed by Karaboga[18] in 2005 for parameter optimization, is a recently introduced optimization algorithm which simulates the .The minimal model of swarm-intelligent forage selection in a honey bee colony which the ABC algorithm simulates consists of three kinds of bees: employed bees, onlooker bees and scout bees. Half of the colony consists of employed bees, and the other half nlooker bees. Employed bees are responsible for exploiting the nectar sources explored before and giving information to the waiting bees (onlooker bees) in the hive about the quality of the food source sites which they are exploiting. Onlooker bees wait in the hive and decide on a food source to exploit based on the information shared by the employed bees. Scouts either randomly search the environment in order to find a new food source depending on an internal motivation or he units of the basic ABC algorithm can be explained as follows: IV a) Producing initial food source sites Initial food sources are produced randomly within the range of the boundaries of the (10). is the number of food sources and D is the number of optimization parameters. In addition, counters which store the numbers of trials of solutions are reset to 0 in this After initialization, the population of the food sources (solutions) is subjected to repeat cycles of the search processes of the employed bees, the onlooker bees and the scout bees. Termination criteria for the ABC algorithm might be reaching a maximum cycle number (MCN Sending employed bees to the food source sites In ABC, finding a neighboring food source is defined by equation (11) Within the neighborhood of every food source site represented by xi, a food source determined by changing one parameter of xi. ϕij is a uniformly distributed real random number in the within the boundaries, a fitness value for a minimization problem can be is the cost value of the solution υi. For maximization problems, the cost function can be International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – © IAEME In a real bee colony, some tasks are performed by specialized individuals. These specialized n of labour and self- organization. The Artificial Bee Colony (ABC) algorithm, proposed by Karaboga[18] in 2005 for parameter optimization, is a recently introduced optimization algorithm which simulates the intelligent forage selection in a honey bee colony which the ABC algorithm simulates consists of three kinds of bees: employed bees, onlooker bees and scout bees. Half of the colony consists of employed bees, and the other half nlooker bees. Employed bees are responsible for exploiting the nectar sources explored before and giving information to the waiting bees (onlooker bees) in the hive about the quality of the the hive and decide on a food source to exploit based on the information shared by the employed bees. Scouts either randomly search the environment in order to find a new food source depending on an internal motivation or he boundaries of the (10) is the number of optimization parameters. In addition, counters which store the numbers of trials of solutions are reset to 0 in this of the food sources (solutions) is subjected to repeat cycles of the search processes of the employed bees, the onlooker bees and the scout bees. Termination MCN) or meeting an (1 1 ) , a food source υi is is a uniformly distributed real random number in the within the boundaries, a fitness value for a minimization problem can be (12) . For maximization problems, the cost function can be
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), IV c) Calculating probability values involved in probabilistic selection After all employed bees complete their searches, they share their information related to the nectar amounts and the positions of their sources with the onlooker bees on the dance area. IV d) Abandonment criteria: Limit and scout production In a cycle, after all employed bees and onlooker bees complete their searches, the algorithm checks to see if there is any exhausted source to be abandoned. All these units and interactions between them are shown as a flowchart on Fig. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 174 ng probability values involved in probabilistic selection After all employed bees complete their searches, they share their information related to the nectar amounts and the positions of their sources with the onlooker bees on the dance area. ) Abandonment criteria: Limit and scout production In a cycle, after all employed bees and onlooker bees complete their searches, the algorithm checks to see if there is any exhausted source to be abandoned. ns between them are shown as a flowchart on Fig. 2. Fig. 2. Flow chart of ABC Algorithm International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – © IAEME After all employed bees complete their searches, they share their information related to the nectar amounts and the positions of their sources with the onlooker bees on the dance area. (13) In a cycle, after all employed bees and onlooker bees complete their searches, the algorithm
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 175 V. CASE STUDIES The proposed algorithm for optimal placement and sizing of SVC has been implemented on IEEE 30 bus system [9, 14]. This system comprises of one slack bus, 5 PV buses, 24 PQ buses and 41 lines. In this case study two different conditions are considered 1. Base case OPF without SVC using ABC algorithm. (ABC solution) 2. Base case OPF with SVC (ABC solution) The optimal location for SVC is found at bus 30 because the voltage deviation is found on it. The size of SVC at bus 29 is slightly smaller than obtained at bus 30, but voltage deviations and real and reactive power losses are slightly greater than that obtained for bus 30. Fig. 3 illustrates the voltage profile of the sample power system without SVC and with SVC TABLE 1: VOLTAGE PROFILE OF IEEE 30-BUS SYSTEM WTHOUT AND WITH SVC Bus Number OPF(IEEE30Bus) ABC solution (p.u) OPF SVC (IEEE30 Bus) ABC solution (p.u) 1 1.06 1.06 2 1.045 1.045 3 1.0253 1.0258 4 1.0167 1.0172 5 1.01 1.01 6 1.0134 1.014 7 1.0042 1.0046 8 1.01 1.01 9 1.0532 1.0542 10 1.0479 1.0497 11 1.082 1.082 12 1.06 1.0609 13 1.071 1.071 14 1.0452 1.0465 15 1.0405 1.0421 16 1.0472 1.0485 17 1.0427 1.0444 18 1.031 1.0327 19 1.0285 1.0302 20 1.0326 1.0343 21 1.0355 1.0379 22 1.036 1.0385 23 1.0299 1.0328 24 1.0241 1.0288 25 1.0192 1.0297 26 1.0016 1.0123 27 1.0248 1.0388 28 1.0093 1.0111 29 1.005 1.027 30 0.9935 1.0242
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 176 TABLE 2: Performance of IEEE 30-Bus System WTH and WITHOUT SVC Pg(MW) OPF(ieee30 bus) ABC solution OPF-SVC ABC solution Pg1 172.6914 177.2639 Pg2 48.9940 48.4640 Pg5 22.0273 21.6943 Pg8 24.0486 21.9405 Pg11 12.9654 12.4202 Pg13 11.8599 11.0687 Pgtotal 292.5866 292.8517 Transmission loss (TL) 9.1866 9.18517 Fuel cost(FC) 802.4918 802.0948 Voltage violations at buses Nil Nil SVC Size (MVAR) 0 4.628 SVC Location - 30 Fig. 3: Graphical representation of voltage profile with bus number 0 5 10 15 20 25 30 0.98 1 1.02 1.04 1.06 1.08 1.1 Bus number voltagesinp.u. voltage profiles for different cases IEEE 30 bus OPF by ABC IEEE 30 bus OPF with SVC by ABC
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 177 VI. CONCLUSION In this paper, a method for optimal placement and sizing of SVC has been proposed for improving voltage profile and performance of a power system. The proposed approach has been implemented on IEEE 30-bus system. The criteria for selection of optimal placement of SVC were to maintain the voltage profile, minimize the voltage deviations and to reduce the power losses under line loadings of a daily load curve. Simulations performed on the test system shows that the optimally placed SVC maintains the voltage profile, minimizes the deviations and also reduces the real and reactive power losses. VII REFERENCES [1] H. Okamoto, A. Kurita and Y. Sekine, “A Method for Identification of Effective Locations of Variable Impedance Apparatus on Enhancement of Steady-State Stability in Large Scale Power Systems,” IEEE Trans. Power System, Vol. 10, No. 3, 1401-1407, 1995. [2] P. Kundur, 1. Paserba, V. Ajjarapu, G. Anderson, A.Bose, C.A. Canizares, N. HatziargYfiou, D. Hill, A.Stankovic, C. Taylor, T. Van Cutsem, and V. Vittal, "Definition and Classification of Power System Instability," IEEE Trans. On Power Systems, Vol.19, No.2, pp.1387-1401, May 2004. [3] Z.T.Faur, "Effects of FACTS Devices on Static Voltage Collapse Phenomena," M.S. issertation, Dept. Elect. Eng., Univ. of Waterloo, 1996. [4] J.G.Singh, S.N.Singh, S.C.Srivastava, “An Approach for optimal Placement of Static Var Compensators Based on Reactive Power Spot Price”, IEEE Trans. On Power Systems, Vol. 22,No. 4, November 2007. [5] CIGRE, Working Group 38-01, Task Force No.2 on SVC Compensators, I.A.B primez., Ed., 1986. [6] N.G.Hingorani and L.Gyugyi, “Understanding FACTS Concepts and Technology of Flexible AC Transmission Systems. Piscataway: IEEE Press1999 [7] M.K.Verma, “Optimal Placement of SVC for static and Dynamic Voltage Security enhancement,” International Journal of Emerging Electric Power systems, Vol. 2, Issue 2, Article 1050,2005. [8] H.Ambriz-Perez, E.Acha, and C.R. Fuerte-Esquivel, “Advanced SVC models for Newton- Raphson Load Flow and Newton Optimal Power Flow studies”, IEEE Trans. on Power Systems 15(1) 129-136. [9] R. Minguez, F.Milano, R Zarate-Minano, A.J.Conejo, “Optimal Network Placement of SVC Devices,’ IEEE Trans. On Power Systems, Vol.22,No.4, Nov.2007. [10] Abdelaziz Laifa and Mohamed Boudour, “Optimal Location of SVC for Voltage Security Enhancement using MOPSO”, Journal of electrical systems, Special Issue N. 01:Nov. 2009. [11] E. Acha, C.R. Fuerte, H. Ambriz and C. Angeles. FACTS : Modelling and Simulation in Power Networks, John Willey & Sons, Ltd. [12] M. Iravani, P. L. Dandeno, and D. Maratukulam, “Application of Static Phase Shifters in Power Systems,” IEEE Trans Power Delivery, Vol. 9, No. 3, 1600-1608, 1994. [13] M. O. Hassan, S. J. Cheng and Z. A. Zakaria, “Steady-State Modeling of SVC and TCSC for Power Flow Analysis”, Int. Multi Conference of Engineers and Computer Scientists 2009, Vol. II, IMECS 2009.Biplab Bhattacharyya, et al. [14] T.T. Lie, W. Deng, “Optimal flexible AC transmission systems (FACTS) devices allocation,” Int. J. Electic. Power Energy System Vol. 19 pp. 125– 134, 1999. [15] E.J. de Oliveira, J.W.M. Lima, “Allocation of FACTS devices in a competitive environment,” in: 13th PSCC, pp.1184– 1190,1999.
  • International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 – 6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 4, April (2014), pp. 169-178 © IAEME 178 [16] Shraddha Udgir, Sarika Varshney & Laxmi Srivastava,’’ Optimal Placement and Sizing of SVC for Improving Voltage Profile of Power System” International Journal of Power System Operation and Energy Management, ISSN (PRINT): 2231–4407, Volume-1, Issue-2, 2011. [17] Ray D. Zimmerman, Carlos E. Murillo-Sánchez & Deqiang (David) Gan “A MATLAB Power System Simulation Package” [18] D. Karaboga, AN IDEA BASED ON HONEY BEE SWARM FOR NUMERICAL OPTIMIZATION,TECHNICAL REPORT-TR06,Erciyes University, Engineering Faculty, Computer Engineering Department 2005. (PDF). [19] Lalit Kumar and Dr. Dheerendra Singh, “Knowledge Extraction from Numerical Data: An ABC Based Approach”, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 1 - 9, ISSN Print: 0976 – 6367, ISSN Online: 0976 – 6375. [20] Dr. M. P. Sharma, Devandra Saini, Swati Harsh and Sarfaraz Nawaz, “Application of SVC for Voltage Control in Wind Farm Power System”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 3, 2013, pp. 95 - 114, ISSN Print: 0976-6545, ISSN Online: 0976-6553. [21] Satyendra Kumar, Dr.Upendra Prasad and Dr.Arbind Kumar Singh, “Reactive Power Management and Voltage Control using Facts Devices”, International Journal of Electrical Engineering & Technology (IJEET), Volume 4, Issue 1, 2013, pp. 184 - 189, ISSN Print: 0976-6545, ISSN Online: 0976-6553. AUTHOR’S DETAIL S. Mohammad Rafee: He is from India. He is persuing his PhD from JNTU Hyderabad, India. He has done his M.Tech from JNTU Anantapur, Andhra Pradesh India. Presently he is working in JIJIGA University, Ethiopia as Asst. Professor in Electrical Engineering Department. His areas of interests are reactive power compensation, power quality, FACTS devices. He also published papers in various international Journals and conferences. Dr. Srinivasula Reddy: He is from India.He has done his PhD from JNTU Anantapur, Andhra Pradesh, India. Presently he is working as Professor and Principal in CMR Engineering College, Hyderabad, A.P., India. He published papers in various international journals, international conferences, national conferences to his credit. He also received many prestageous awards for his contribution in teaching in Electrical Engineering field. His area of interest is power systems, drives, FACTS devices, reactive power compensation, electromagnetic fields concepts.