{"title":"Research on Fault Prognostic of Photovoltaic System Based on LSTM-SA","authors":"Y.J Huang, S.N Zhang, X. Xu","doi":"10.1109/ICRMS55680.2022.9944602","DOIUrl":null,"url":null,"abstract":"In the fault prognostic of photovoltaic systems, it is difficult to establish mathematical or physical models of complex components or systems. Therefore, this paper proposes a hybrid model of LSTM-SA, based on the principle of self-attention(SA) mechanism and long short-term memory (LSTM) neural network, combining the idea of self-attention and LSTM for timing problems processing capability to prognosticate faults of different equipment. Experimental verification of LSTM, LSTM-SA, BPNN and RNN models using the data of#102, #110 and #519 equipment respectively shows that the root mean square error (RMSE) of the model based on LSTM-SA is lower than that of the other three models in sunny days, indicating that the LSTM model with self-attention mechanism is optimized. Finally, the mixed model based on LSTM-SA is used to prognosticate the fault of different devices. The results are as follows: the fault of the #611 device at the 136th time point, and the fault of the #513 device at the 187th time point.","PeriodicalId":421500,"journal":{"name":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 13th International Conference on Reliability, Maintainability, and Safety (ICRMS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICRMS55680.2022.9944602","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
In the fault prognostic of photovoltaic systems, it is difficult to establish mathematical or physical models of complex components or systems. Therefore, this paper proposes a hybrid model of LSTM-SA, based on the principle of self-attention(SA) mechanism and long short-term memory (LSTM) neural network, combining the idea of self-attention and LSTM for timing problems processing capability to prognosticate faults of different equipment. Experimental verification of LSTM, LSTM-SA, BPNN and RNN models using the data of#102, #110 and #519 equipment respectively shows that the root mean square error (RMSE) of the model based on LSTM-SA is lower than that of the other three models in sunny days, indicating that the LSTM model with self-attention mechanism is optimized. Finally, the mixed model based on LSTM-SA is used to prognosticate the fault of different devices. The results are as follows: the fault of the #611 device at the 136th time point, and the fault of the #513 device at the 187th time point.