关键词: 极限梯度提升, 长短期记忆, 卷积神经网络, 注意力机制, 非周期巡视, 电力系统

Abstract:

With the increasing scale of the power system， the power grid is facing the risk of uncertain faults that can affect people’s daily life and can even lead to major accidents. Therefore， it is very important to predict the operating status of power facilities in advance and make inspection and repair decisions. However， commonly used decision-making methods， such as SVM （Support Vector Machine） model， have problems of low accuracy， low recall， in these practical application scenarios. Aming at the issue， a non-periodic inspection decision-making method LSTM-CNN-Attention was proposed for the non-periodic inspection of power facility by combining Long Short-Term Memory （LSTM）， Convolutional Neural Network （CNN） and Attention mechanism. The data was input into the decision model after XGBoost （eXtreme Gradient Boosting） feature selection and normalization， and attention mechanism was used to weignt the temporal and spatial information extracted by the LSTM and CNN layers， so as to differentitate the importance of the information that was most influential to the result， thus ensuring that the more important inforamtion could receive greater attention and contribution during the prediction process， and the accruracy and reliability of the prediction results could be improved. Through comparative experiments on power facility operation datasets， it is verified that the proposed method is superior to the learning algorithms of CNN-LSTM， XGBoost， CNN， random forest， SVM and logistic regression models in accuracy， precision， recall and F1 score.

Key words: eXtreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), attention mechanism, non-periodic inspection, power system