Abstract
In photovoltaic (PV) modules the manufacturer provides rating under standard test conditions (STC). But STC hardly occur under outdoor conditions so it is important to investigate PV power by experimental analysis. It is found that experimental analysis of PV modules’ maximum power under outdoor conditions remains a major research area. For this measurement of 74 Wp, PV module is performed under outdoor conditions at National Institute of Technology, Sikkim, India. To find the most influencing variables for PV power prediction relief attribute evaluator is implemented and cascade ANN models are used to predict maximum power under sunny outdoor condition. It is found by Relief algorithm that Temperature, Solar Radiation, Short-Circuit Current, Open-Circuit Voltage are relevant variables and humidity is a less influencing variable. Cascade ANN model which utilizes open-circuit voltage and short-circuit current has least root mean square error of 0.17 and SVRM utilizes solar radiation that has least RMSE of 0.40, showing these variables can be used for prediction of maximum power. This study is useful for PV installation for providing prior knowledge of maximum power.
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Reddy Chimmula, V.K., Yadav, A.K., Malik, H. (2020). Novel Application of Relief Algorithm in Cascade ANN Model for Prognosis of Photovoltaic Maximum Power Under Sunny Outdoor Condition of Sikkim India: A Case Study. In: Malik, H., Iqbal, A., Yadav, A. (eds) Soft Computing in Condition Monitoring and Diagnostics of Electrical and Mechanical Systems. Advances in Intelligent Systems and Computing, vol 1096. Springer, Singapore. https://doi.org/10.1007/978-981-15-1532-3_17
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DOI: https://doi.org/10.1007/978-981-15-1532-3_17
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