Performance Analysis of Student Psychology-Based Optimization for the Frequency Control of Hybrid-Power System

Abstract

Access to reliable electricity is crucial for rural development and improving the quality of life in remote areas. Standalone photovoltaic PV systems and hybrid power systems HPS are promising solutions for rural electrification. However, frequency stabilization is a critical challenge in such systems, and conventional control techniques are inadequate. This work proposes a novel approach to optimize the PID controller gains for the standalone PV-based isolated HPS (IHPS). The student psychology-based optimization algorithm (SPBOA) and quasi-oppositional-based whale optimization algorithm (QOWOA) are employed to enhance the performance of the IHPS models separately. The objective function considered is the integral of time absolute error (ITAE), and the frequency response profile is studied in the presence of the proposed SPBOA and QOWOA-based PID controllers. The results show that the proposed SPBOA outperforms the QOWOA in terms of reducing the ITAE by nearly 5% and minimizing the peak and settling time of the frequency response by 5%-7% in different scenarios. The transient responses of the systems to different input conditions are also analyzed, indicating that both the IHPS-I and IHPS-II models are feasible for remote rural electrification. Overall, the proposed approach offers an efficient solution to optimize the PID controller gains for frequency stabilization in standalone PV and HPS. The results demonstrate the benefits of using the SPBOA and QOWOA algorithms to enhance the performance of the systems, making them suitable for remote rural electrification.INDEX TERMS Diesel engine generator; Isolated hybrid power system; Photovoltaic; Proportionalintegral-derivative; Wind turbine generator; quasi-oppositional-based whale optimization algorithm; student psychology-based optimization algorithm.

An efficient high-gain bidirectional interleaved boost converter for PV integration to DC microgrid

“…The decentralized DC microgrid voltage control structures were reported in [ 6 , 44 ]. Optimal control for energy storage charge / discharge methods [ 45 , 46 ], artificial intelligence such as fuzzy inference [ 47 ], ANN-based island control [ 48 , 49 ] were developed to find the reliability study for microgrids [ 50 ].…”