Modeling And Classification Of Photovoltaic Hotspot Fault With Shaded And Un-Shaded Conditions Using Mamdani-type Fuzzy Inference

Photovoltaic power generation as the main content of the energy revolution has attracted more and more attention and research all over the world.Fault detection techniques play a decisive role in reliability as well as boost the output power and overall performance of a photovoltaic system(PVS).Faults that are shading and hotspots can be found in the PV module(PVM).Shading faults can occur due to continuous changes in the irradiance level of a module,soiling,dust or other shadows can cause such faults.Hotspot(HS)occurs when a cell in a module considerably heats up instead of generating power,and starts consuming power from adjacent cells or dissipates power which further results in reducing optimum power and leading to cell cracking and other degradation faults.HS fault can be further categorized as shaded and unshaded HS.Shaded HS is an HS fault with shading PV cells while unshaded HS is due to the cell’s internal defects such as low resistance.In this work,the photovoltaic system explores techniques to enhance the overall performance of the PVS by detection and classification of faults.Different faults can occur in a module of a PVS which can degrade the system’s reliability as well as reduce the optimal power.To mitigate these PV faults,investigation of detection and classification techniques is of significance.A hotspot and shading faults are sometimes confusing and are of significance in identifying these faults.In this thesis,an algorithm has been proposed to identify these faults.The confusion causes such as environments,shading,and HS are analyzed.And the main objection is to classify the HS faults under shaded and un-shaded conditions.The Impacts of HS on the overall performance of a PV system are also analyzed.In this thesis,HS fault is categorized as shaded and un-shaded HS,and hotspot in both shaded and un-shaded conditions has been analyzed,and then a technique called Mamdani-type Fuzzy logic system for detection and classification of Shading and HS both shaded and unshaded HS faults is proposed.The process and design to detect these faults are given out,and the tested result is given out by simulating three cases,which shows that the presented technique not only detects HS faults but can also classify various shading and HS faults under shaded and un-shaded conditions.In addition,a dynamic and low resistance model has been modeled and used to simulate on the MATLAB/Simulink platform to evaluate the performance and accuracy of the presented technique under various faulty conditions.