Abstract
Globally, Renewable Energy Resources (RER) are playing a vital role in generating the electrical energy due to the conventional fossil fuel-based power plants which are harming the environment. Also, the availability of fossil fuels is going to run out. The primary resources for RER are sun, wind, hydro, and tidal. Among energy, the harnessing rate has been rapidly increased in solar Photovoltaic (PV) and wind power plants. Since sun and wind energy are abundant in nature, nevertheless, natural resources are seasonal which are varying concerning the climatic condition. Therefore, sun and wind power generators are produced fluctuating electrical energy which causes stability issues. It can be compensated by the Maximum Power Point Tracking (MPPT) technique. At present, the MPPT technique is incorporated with RER for generating maximum electrical energy based on available resources. In this manuscript, a wind power plant with an Improved Variable Step-Radial Basis Functional Network (IVS-RBFN)-based MPPT model has been developed by using MATLAB/Simulink window to analyze the significance of MPPT. The simulation results show that wind power plants are capable of generating constant power with the help of IVS-RBFN-based MPPT technique. Furthermore, the wind power output is significantly enhanced with the accurately designed boost converter.
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Mariprasath, T., Shilaja, C., Hussaian Basha, C., Murali, M., Fathima, F., Aisha, S. (2023). Design and Analysis of an Improved Artificial Neural Network Controller for the Energy Efficiency Enhancement of Wind Power Plant. In: Asari, V.K., Singh, V., Rajasekaran, R., Patel, R.B. (eds) Computational Methods and Data Engineering. Lecture Notes on Data Engineering and Communications Technologies, vol 139. Springer, Singapore. https://doi.org/10.1007/978-981-19-3015-7_6
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DOI: https://doi.org/10.1007/978-981-19-3015-7_6
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