Research on Fault Prognostic of Photovoltaic System Based on LSTM-SA

Y.J Huang, S.N Zhang, X. Xu
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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.
基于LSTM-SA的光伏系统故障预测研究
在光伏系统故障预测中,建立复杂部件或系统的数学或物理模型比较困难。因此,本文基于自注意(SA)机制和长短期记忆(LSTM)神经网络的原理,提出了一种LSTM-SA混合模型,将自注意和LSTM的思想结合起来,对时序问题处理能力进行预测,以预测不同设备的故障。分别使用#102、#110和#519设备数据对LSTM、LSTM- sa、BPNN和RNN模型进行实验验证,结果表明,晴天条件下基于LSTM- sa模型的均方根误差(RMSE)低于其他三种模型,表明具有自注意机制的LSTM模型得到了优化。最后,利用基于LSTM-SA的混合模型对不同设备的故障进行预测。结果为:#611设备在第136个时间点故障,#513设备在第187个时间点故障。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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