Optimal Allocation of Multi-Type FACTS Devices for Loadability Improvement in the Power System Using Evolutionary Computation Techniques

Abstract

Flexible Alternating Current Transmission Systems (FACTS) got in the recent years a well known term for higher controllability in power systems by means of power electronic devices. FACTS devices can effectively control the load flow distribution, improve the usage of existing system installations by increasing transmission capability, compensate reactive power, improve power quality, and improve stabilities of the power network. However, the location and settings of these devices in the system plays a significant role to achieve such benefits. This work presents the application of Real Coded Genetic Algorithm (RGA) and Particle Swarm Optimisation (PSO) for finding out the optimal locations, and the optimal parameter settings of multi type FACTS devices to achieve maximum system loadability (MSL) in the power system. The FACTS devices used are Thyristor Controlled Series Capacitor (TCSC) and Unified Power Flow Controller (UPFC). The reactance model of TCSC and the decoupled model of UPFC are considered in this work. The thermal limits of the line and voltage limits of the buses are taken as constraints during the optimization. Simulated Binary Crossover (SBX) and Non-uniform polynomial mutation are employed to improve the performance of the Genetic Algorithm used. Simulations are performed on IEEE 6 bus and 30 bus power systems. The obtained results are encouraging and show the effectiveness of RGA over PSO algorithm