Power Production Fault Prediction and Diagnosis System based on Deep Learning Algorithm

The increasing complexity of power production systems due to equipment aging, operational errors, and environmental factors requires effective fault prediction and diagnosis to prevent significant economic losses and safety risks. In this paper, a CNN-LSTM-based system is proposed to analyze real-time monitoring data and predict potential faults. The method of the system includes collecting data from key nodes of the power system, preprocessing to deal with outliers, missing data, and noise, and building CNN and LSTM models. CNN extracts spatial features of power signals, while LSTM processes time series data to capture long-term correlations. Compared with systems using CNN or LSTM alone, the integrated CNN-LSTM model shows better accuracy and response time in fault prediction, with an average response time of 277 milliseconds and an average error rate of only 0.326%. These findings highlight the potential of the system in real-time fault diagnosis of power systems. Although the proposed system shows good prospects, it also has the problem of high computational resource requirements and needs to be further verified in different power system environments. Future work will focus on model optimization to reduce resource consumption and improve operational efficiency, and enhance the generalization ability of the model through data expansion and regularization techniques.