Artificial Intelligence based Nonlinear Integral Back-stepping Control Approach for MPPT of Photovoltaic System

Abstract

The energy demand of the world has been intensively increased since last two decades. The need of energy is forcing the think tanks of the developed countries to move towards the alternative energy resources. Solar energy is the most suitable solution to overcome the energy crises. In this regard, this article presents the nonlinear integral back-stepping (IB) control approach for maximum power extraction of stand-alone photovoltaic (PV) system. The proposed control strategy gives robustness against constantly varying conditions of environment. Non-inverting case of buck-boost DC-DC converter is used as interface between load and PV array. Radial basis function neural network (RBFNN) is generated the reference (V <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ref</sub> ) under different climatic conditions for the tracking of the developed control scheme. IB control technique is also checked under faulty conditions. The Simulations are preformed in the environment of MATLAB/Simulink. Moreover, the proposed technique results are compared with perturb and observe (P&O) maximum power point tracking (MPPT) technique.