Optimal setting and placement of FACTS devices using strength Pareto multi-objective evolutionary algorithm

This work proposes a novel approach for multi-type optimal placement of flexible AC transmission system (FACTS) devices so as to optimize multi-objective voltage stability problem. The current study discusses a way for locating and setting of thyristor controlled series capacitor (TCSC) and static var compensator (SVC) using the multi-objective optimization approach named strength pareto multi-objective evolutionary algorithm (SPMOEA). Maximization of the static voltage stability margin (SVSM) and minimizations of real power losses (RPL) and load voltage deviation (LVD) are taken as the goals or three objective functions, when optimally locating multi-type FACTS devices. The performance and effectiveness of the proposed approach has been validated by the simulation results of the IEEE 30-bus and IEEE 118-bus test systems. The proposed approach is compared with non-dominated sorting particle swarm optimization (NSPSO) algorithm. This comparison confirms the usefulness of the multi-objective proposed technique that makes it promising for determination of combinatorial problems of FACTS devices location and setting in large scale power systems.

[1]  O. Alsac,et al.  Optimal Load Flow with Steady-State Security , 1974 .

[2]  P. Kundur,et al.  Definition and classification of power system stability IEEE/CIGRE joint task force on stability terms and definitions , 2004, IEEE Transactions on Power Systems.

[3]  Tjing T. Lie,et al.  Optimal flexible AC transmission systems (FACTS) devices allocation , 1997 .

[4]  Noureddine Henini,et al.  Location and tuning of TCPSTs and SVCs based on optimal power flow and an improved cross-entropy approach , 2014 .

[5]  M. A. Abido,et al.  Optimal VAR dispatch using a multiobjective evolutionary algorithm , 2005 .

[6]  Fernando L. Alvarado,et al.  SVC placement using critical modes of voltage instability , 1993 .

[7]  Lothar Thiele,et al.  An evolutionary algorithm for multiobjective optimization: the strength Pareto approach , 1998 .

[8]  H. Sasaki,et al.  A New Formulation for FACTS Allocation for Security Enhancement against Voltage Collapse , 2002, IEEE Power Engineering Review.

[9]  Jong-Bae Park,et al.  A New Optimal Routing Algorithm for Loss Minimization and Voltage Stability Improvement in Radial Power Systems , 2007, IEEE Transactions on Power Systems.

[10]  A. Karami,et al.  Artificial bee colony algorithm for solving multi-objective optimal power flow problem , 2013 .

[11]  K.Y. Lee,et al.  Multi-objective VAr Planning with SVC for a Large Power System Using PSO and GA , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[12]  W. F. Long,et al.  Determination of Needed FACTS Controllers That Increase Asset Utilization of Power Systems , 1997 .

[13]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .

[14]  Anirudh Dube,et al.  Location of SVC and UPFC for Real Power Loss Minimization and Stability Enhancement in a Multi Machine Power System using Parametric Approach , 2012 .

[15]  M. A. Abido,et al.  Optimal location and setting of SVC and TCSC devices using non-dominated sorting particle swarm optimization , 2009 .

[16]  S. C. Srivastava,et al.  Optimal Placement of SVC for Static and Dynamic Voltage Security Enhancement , 2005 .

[17]  P. Tiwari,et al.  An Efficient Approach for Optimal Allocation and Parameters Determination of TCSC With Investment Cost Recovery Under Competitive Power Market , 2013, IEEE Transactions on Power Systems.