In the production work of the State Grid, the power equipment is often in a high and low temperature environment. The timely detection of the fault of the power equipment is an important part of the stable development of the State Grid. This paper proposes a method to detect the quality of power equipment based on the measurement data of power equipment at room temperature. Based on the LSTM model, the working data of power equipment is predicted in the form of serialized time nodes during the experiment. Then, the feature data is extracted and dimensionality reduction according to the operating environment, and finally the anomaly detection is carried out according to the Local Outlier Factor (LOF) algorithm. The experimental results show that the prediction effect of the model prediction diagnosis results is ideal, and the LSTM-LOF combined with the model can be used to assist engineers in the quality diagnosis of power equipment.
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