Optimization of Hybrid Power Systems Performance Based on Adaptive Neuro-Fuzzy Inference System

Abstract

Hybrid Power Systems (HPSs) is a promising solution for the shortages of electricity in several situations. However, HPSs are still facing several problems. These problems are the cost of electrical kilowatt-hour and repetitive breaking in the utility grid with existence varying loads. Besides the problem of non-optimal utilization of available renewable energy resources and the problems associated with the operation of large generators along small loads, which are the high cost of generation and the minimize in lifetime of the generator. This paper presents study and analyze the load profile and power system generation for a selected case. A fuzzy control system based on ANFIS has been proposed to optimize the performance of the HPS. The proposed system has ten ANFIS models, which linked to the outputs of the proposed control system. All models have been trained to achieve the minimum root mean square error (RMSE). The proposed system has been built and simulated using MATLAB.