Optimal reactive power dispatch using teaching learning based optimization algorithm with consideration of FACTS device “STATCOM”

This paper shows solution of optimal reactive power dispatch (ORPD) problem using a Teaching Learning Based Optimization Algorithm (TLBO) with consideration of flexible alternating current transmission systems (FACTS) device “STATCOM”. The target is to minimize the transmission losses, enhance the voltage profile, determine the optimal value of control variables such as generator voltage magnitudes, tap setting of the transformer and number of compensation devices and also maintain a reasonable system performance in terms of limits on generator real power and reactive power outputs, bus voltages and power flow of transmission lines. In order to reduce the total active power loss, improve power system voltage, enhance reliability and increase power transfer limits. We propose also the optimization of the placement of FACTS devices in the power system (STATCOM). The proposed method is examined on IEEE 14-bus and modified IEEE 30-bus power systems. The results of this technique is compared with previous results obtained by particle swarm optimization, Differential evolution (DE), Modified Hybrid PSO (MHPSO), Mutated PSO (MPSO), Self adaptive real coded genetic algorithm (SARGA), Genetic Search (GS), Comprehensive learning PSO, Control schemes of the strategy parameters (CSSPs), Evolutionary programming (EP), Sequential quadratic programming (SQP), Particle swarm optimization-Cauchy mutation (PSO-CM), Particle swarm optimization-Adaptive mutation (PSO-AM), Hybrid algorithm of differential evolutionary programming (DEEP).