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• W2921796023 abstract "The main challenge associated with solar Photovoltaic power generation is its intermittent nature which highly dictates its performance. Since PV generation is mainly dependent on climatic parameters, it is necessary to have a mechanism for understanding and diagnosing the state of performance of the system at any given instance. To address this challenge, a deep neural network architecture is presented for instantaneous performance diagnosis. The proposed model enables to model and diagnose soiling and partial shade conditions prevalent in any PV generation system with an accuracy of 98.3%. The output of this model is used as input to another neural network model for modeling the state of performance of the system. The novelty of this work lies in creating a solely data- driven model for diagnosing PV power performance" @default.
• W2921796023 created "2019-03-22" @default.
• W2921796023 creator A5045221683 @default.
• W2921796023 date "2018-11-01" @default.
• W2921796023 modified "2023-09-24" @default.
• W2921796023 title "Automatic PV Performance Diagnosis through Inverter Data using Dual Neural Network Architecture" @default.
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• W2921796023 doi "https://doi.org/10.1109/ropec.2018.8661450" @default.
• W2921796023 hasPublicationYear "2018" @default.
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