HOTSPOT REVELATION IN SOLAR PANEL USING SPARSE RECONSTRUCTION AND EXTREME LEARNING MACHINE

In today’s world, solar panel is one of the major sources for generating powerdirectly from the sunlight by using electronic processes and there is no greenhouseemission in photovoltaic cell as it does not require any other source of fuel like coal,natural gas, oil, nuclear power systems. Hotspot is one of the main causes ofphotovoltaic cell which occurs due to the dissipation of power in shaded cells. In theexisting literature, the hotspot in solar panel is detected by using various algorithmsand techniques but it does not improve accuracy, performance, temperaturedistribution, problem like overfitting and underfitting also exists. To overcome that,the proposed work deals with capturing the hotspot as thermal image through aninfrared camera which is mainly used for temperature distribution. For identifyinghotspot, the features like shade, correlation, contrast, energy, entropy, homogeneity,prominence, sparse are extracted using sparse reconstruction and GLCM algorithms.The features are given to the classification algorithm named as Extreme LearningMachine which gives the good generalization performance and improves accuracyhigher when compared to other algorithms. The overfitting and underfitting problemcan also be rectified by using these algorithms. Finally using extreme learningmachine, the percentage of hotspot in photovoltaic cell can be identified.