Power Quality Improvement of a Hybrid Renewable Energy Sources Based Standalone System Using Neuro-Fuzzy Controllers

Wind and Photovoltaic (PV) based hybrid standalone systems for supplying electricity to remote consumers are becoming increasingly popular around the world. In general, power generation from both PV and wind is always variable due to irradiance and speed fluctuations. Furthermore, the load is unpredictably variable and changes at random. As a result, one of the main challenges in such standalone micro grids is maintaining power quality. To maintain power quality in such standalone systems, a novel controller should be developed. Furthermore, due to the unpredictable random changes in wind speed, solar irradiance, and load, a battery bank must be integrated into the standalone system via a bidirectional converter. Furthermore, the interconnection of multiple wind turbines and PV panels can create a powerful hybrid system. This paper examines a 1 MW stand-alone power generation system using numerous PV and wind systems. Maximum power points tracking devices with perturbed and observe algorithms are used to optimize the utilization of individual PV modules and wind turbines. Because of its superior priority over conventional PI controllers, a Neuro-Fuzzy integrated controller-based novel control scheme for both inverter and bidirectional converter is proposed to achieve precise response while maintaining power quality. This paper discusses extensive results obtained by establishing hardware-in-the-loop on the OPAL-RT platform to evaluate the responses of proposed controllers. The experimental results also included to provide strength to the paper.