Sliding Mode Neural Network Controller for PV
A. Ambikapathy, Ezhilan Thirunavukkarasu, Arunprasad Govindharaj, Shiva Yadav, Sunil Kumar, Suraj Kumar Yadav, Ujjwal Kumar Gupta, Parthasarathi Jayaraman
Abstract
This article presents a sliding mode neural network controller to extract the maximum electricity from a photovoltaic (PV) panel and to acquire the required voltage level from both the PV panel and the battery system. The control law is based on the Lyapunov control function to produce an asymptotically stable system. Changes in irradiance and temperature inject variables into the system, leading in skewed transient and steady-state responses. As a result, a Chebyshev Neural Network is employed in the suggested controller to estimate the solar current, which will fluctuate owing to irradiance and temperature uncertainty. In MATLAB Simulink, the suggested controller is simulated for changes in irradiance and temperature and compared to the conventional PID controller. Based on the outcomes. The data show that the suggested controller has superior transient and steady-state responses than a conventional PID controller.