Fuzzy based evolutionary algorithm for reactive power optimization with FACTS devices

Abstract In this paper, optimization techniques such as Genetic Algorithm (GA) and Differential Evolution (DE) along with Fuzzy Logic (FL) is used for the optimal setting of power system variables, including Flexible AC Transmission Systems (FACTS) devices. Here, two types of FACTS devices, Thyristor Controlled Series Compensator (TCSC) and Static Var Compensator (SVC) are used for the optimal operation of the power system as well as in reducing congestion in transmission lines. Optimal placement of FACTS devices in the heavily loaded power system reduces transmission loss, controls reactive power flow, improves voltage profile of all nodes and also reduces operating cost. In this proposed approach fuzzy membership function is used for the selection of weak nodes in the power system for the placement of SVC’s as one of the FACTS devices while the location of TCSC’s are determined by the reactive power flow in lines. The proposed technique is compared with other optimization methods using different globally accepted evolutionary algorithms where the nodes are detected by eigen value analysis and the amount of FACTS devices are determined by evolutionary techniques like, Genetic Algorithm (GA), Differential Evolution (DE) and Particle Swarm Optimization (PSO). The superiority of the proposed fuzzy based optimization approach is established by the results and the comparative analysis with other methods.

[1]  Seema Singh,et al.  Optimal location of FACTS devices for congestion management , 2001 .

[2]  C. T. Su,et al.  Fuzzy-Based Voltage/Reactive Power Scheduling for Voltage Security Improvement and Loss Reduction , 2001, IEEE Power Engineering Review.

[3]  Devendra K. Chaturvedi,et al.  Optimal power flow solution using fuzzy evolutionary and swarm optimization , 2013 .

[4]  Seyed Abbas Taher,et al.  Congestion management by determining optimal location of TCSC in deregulated power systems , 2008 .

[5]  Ghadir Radman,et al.  Power flow model/calculation for power systems with multiple FACTS controllers , 2007 .

[6]  Parimal Acharjee.  Identification of maximum loadability limit and weak buses using security constraint genetic algorithm , 2012 .

[7]  José L. Bernal-Agustín,et al.  Optimal parameters of FACTS devices in electric power systems applying evolutionary strategies , 2007 .

[8]  Narayana Prasad Padhy,et al.  Power flow control and solutions with multiple and multi-type FACTS devices , 2005 .

[9]  Malabika Basu,et al.  Optimal power flow with FACTS devices using differential evolution , 2008 .

[10]  L. Tolbert,et al.  Review of reactive power planning: objectives, constraints, and algorithms , 2008, T&D 2008.

[11]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[12]  Innocent Kamwa,et al.  Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface , 2013, IEEE Transactions on Power Systems.

[13]  S. Meikandasivam,et al.  Fine power flow control by split TCSC , 2013 .

[14]  N.G. Hingorani,et al.  High Power Electronics and flexible AC Transmission System , 1988, IEEE Power Engineering Review.

[15]  Bala Venkatesh,et al.  A new optimal reactive power scheduling method for loss minimization and voltage stability margin maximization using successive multi-objective fuzzy LP technique , 2000 .

[16]  C. K. Babulal,et al.  Maximum loadability limit of power system using hybrid differential evolution with particle swarm optimization , 2012 .

[17]  R. Bacher,et al.  Steady-state optimization in power systems with series FACTS devices , 2003 .

[18]  S. M. Shahidehpour,et al.  A fuzzy-based optimal reactive power control , 1993 .

[19]  Bhimrao S. Umre,et al.  Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization , 2012 .

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

[21]  S. Goswami,et al.  Application of DE & PSO Algorithm For The Placement of FACTS Devices For Economic Operation of a Power System , 2012 .

[22]  A. R. Phadke,et al.  Multi-objective fuzzy-GA formulation for optimal placement and sizing of shunt FACTS controller , 2009, 2009 International Conference on Power Systems.

[23]  Provas Kumar Roy,et al.  Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization , 2013 .

[24]  R. H. Liang,et al.  Fuzzy-Based Reactive Power and Voltage Control in a Distribution System , 2002, IEEE Power Engineering Review.

[25]  Georgios C. Stamtsis,et al.  Optimal choice and allocation of FACTS devices in deregulated electricity market using genetic algorithms , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[26]  S. M. Shahidehpour,et al.  Reactive power optimization using fuzzy load representation , 1994 .

[27]  Ramesh C. Bansal,et al.  Loss Sensitivity Approach in Evolutionary Algorithms for Reactive Power Planning , 2009 .

[28]  Ashwani Kumar,et al.  Congestion management with FACTS devices in deregulated electricity markets ensuring loadability limit , 2013 .

[29]  S. C. Srivastava,et al.  Optimal reactive power dispatch considering FACTS devices , 1997 .