Fault Classification of Photovoltaic Power Plants using Convolutional Neural Networks

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  • 畳み込みニューラルネットワークを用いた太陽電池システムの異常種類の判定
  • タタミコミ ニューラルネットワーク オ モチイタ タイヨウ デンチ システム ノ イジョウ シュルイ ノ ハンテイ

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Abstract

<p>Recently, faults of photovoltaic power plants are becoming a serious issue because it may prolong the payout time for power plant installation. Under this situation, not only detection of the photovoltaic module itself but also classification of the kind of faults has been attracting much attention. This study applies supervised the machine learning algorithm using neural networks to fault classification, which is able to lower maintenance costs. However, a large amount of input and output data are required to obtain enough estimation accuracy for machine learning models. In fact, in a single photovoltaic power plant, anomalies are not frequent and it is difficult to collect data to withstand practical use.</p><p>In this study, a numerical simulation to generate a large amount of voltage data when the photovoltaic power plant has fault has been developed and the voltage data was generated to train machine learning models. The generated voltage data reproduce measured data from an actual photovoltaic power plant. For faults such as cell fault and shadow, the proposed method functions properly and classified the anomaly at high accuracies.</p>

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