Partial shading conditions for photovoltaic system using artificial neural networks technique

Abstract

Partial shading condition in solar photovoltaic (PV) systems is an inevitable problem due to the behavior of high nonlinear and unpredictable characteristics due to different shading states. However, several scientific works and research aimed to find approximate and expressive models of this nonlinear behavior using modern methods and techniques to allow researchers to find effective solutions to these critical situations. This paper aims to obtain the appropriate model for partial shading cases using artificial intelligence techniques through machine learning of neural network technology, based on experimental data of PV characteristics for different cases. This model allows for diagnosing the state of faults of Partial shading (PV) systems. Moreover, it allows the development of appropriate algorithms in order to maintain, perform, and prevent the complete shutdown of the systems. All results of the model photovoltaic partial shading characteristics for different situations based on the machine learning process confirm the effectiveness of the adopted technique after comparing it with the real data with a very acceptable margin of error.