Affected by environmental factors, the performance of fiber optic current transformer (FOCT) will deteriorate over a long period of time. Intelligent fault diagnosis algorithm of Long-Short Term Memory (LSTM) combing with Support Vector Machine (SVM) is an effective way to deal with FOCT failures.According to the characteristics of LSTM, a signal prediction model in FOCT based on LSTM is proposed by analyzing the historical data. The residual signal can be obtained by the prediction signal and the observed signal. Set the residual threshold to determine whether the FOCT has fault. With the residual signal characteristics, a fault diagnosis model based on SVM is established. By analyzing the residual signal and extracting features, the diagnostic network can realize the pattern recognition and system fault diagnosis. Experiments demonstrate that the drift deviation fault, the ratio deviation fault and the fixed deviation fault can be diagnosed with an accuracy of 94.5%.

中文翻译：

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光纤电流互感器的智能故障诊断算法

受环境因素的影响，光纤电流互感器（FOCT）的性能将在很长一段时间内下降。结合支持向量机（SVM）的长期记忆（LSTM）智能故障诊断算法是一种有效的解决FOCT故障的方法。针对LSTM的特点，提出了基于LSTM的FOCT信号预测模型。分析历史数据。残差信号可以通过预测信号和观察信号来获得。设置剩余阈值以确定FOCT是否存在故障。利用剩余信号特征，建立了基于支持向量机的故障诊断模型。通过分析残差信号并提取特征，诊断网络可以实现模式识别和系统故障诊断。