Beta Wavelet Neural Network Based Load-Frequency Controller for an Interconnected Reheat Power system with Hydrogen Electrolyser

Abstract

This paper investigates a renewable energy resources application to the Load-Frequency Control of interconnected power system. The Proportional plus Integral(PI) controller gains of the two area interconnected thermal power system with the fast acting energy storage devices are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network(BWNN) approaches. The energy storing device Hydrogen generative Aqua Electrolizer (HAE) with fuel cell can efficiently damp out the electromechanical oscillations in the power system because of their efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 1% and 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE designed with BWNN Controller are found to be superior than that of output response obtained using PI Controller.