Economic Based Neural Control Switching of TCR and TSC for Optimal Reactive Power Flow and Harmonic Minimization with Fuzzy-Genetic

Abstract

Optimal Reactive Power Flow (ORPF) for improving voltage profile and power loss reduction is very important in power system planning; though its method, constraints, and quality of compensation are very effective. Value of compensator, transformer tap ratio, and generator voltages are assumed as controlling variables. Usually this optimization is accompanied by harmonic production. The most important parameter of reactive power compensators is minimum production of harmonics. Nowadays by considering the improvement of power systems in power quality and the importance of harmonics in power quality, compensators by minimum harmonic distortion should be designed. In this paper, ORPF is executed in two stages. At First stage, a genetic algorithm with a fuzzy fitness model employed to solve this multi objective optimization problem. The entire discrete controlling variable is assumed discretely as their natures in all steps of this stage. Outputs of this stage are values of controlling variable that include compensations values. In Second stage, compensation considering the minimum harmonic production is applied. The issue of harmonic reduction in determining the fire angle of TCR and TSC, that are very important in FACTs, is proposed. Determination of optimum angles for minimizing the total harmonic distortion (THD) is investigated and finally for faster control and decision, Artificial Neural Network (ANN) has been used and satisfactory results have been obtained and to have minimum THD, existence of maximum possible capacitors, if bank of capacitors are employed, for both negative and positive reactive power is calculated