Evolutionary Programming Approach for Deregulated Power Systems to Optimal Positioning of FACTS Devices

Abstract

From past decade, the major issues involved in deregulated power systems are branch loading and voltage stability. To address this issue, in this paper an evolutionary programming algorithm was proposed for optimal positioning of FACTS devices. The Evolutionary Programming algorithm considers the FACTS devices and line numbers to generate the population. The proposed approach considered three objectives for optimality such as maximization of branch loading, maximization of the voltage stability and minimization of the power loss. The proposed algorithm's performance is compared with the conventional genetic algorithm and the simulation is carried by MATLAB with different cases. With the simultaneous operations of branch loading, voltage stability and loss minimization, the branch loading is increased by 9.6% and the voltage stability is increased by 2.3% and the loss is reduced by 4.8%. The result shows the performance of the proposed model.

Unified Particle Swarm Optimization Based Distributed Generation Placement in Radial Power Systems

“…Past two decades, many power system optimization problems have been solved with the application of artificial intelligence (AI) algorithms [10][11][12][13]. Evolutionary programming method has been employed to identify the optimum location of TCSC and SVC in power system with cost function as active power loss minimization [10].…”

“…Evolutionary programming method has been employed to identify the optimum location of TCSC and SVC in power system with cost function as active power loss minimization [10]. Some of the analysts have executed the use of AI methods to reduce the active power loss in RDS by installing DGs at appropriate buses [11][12][13].…”