Fault Diagnosis of Protection Systems in Intelligent Substations Based on Machine Learning Algorithms

This paper presents a fault diagnosis model for protection systems in intelligent substations using advanced intelligent algorithms. Specifically, the model integrates Convolutional Neural Networks (CNNs) for feature extraction from waveform data and Long Short-Term Memory (LSTM) networks for real-time analysis of sequential event logs. The mathematical formulation of the CNN and LSTM algorithms is included to clarify the diagnostic process. Simulation experiments demonstrate that the proposed model achieves a detection accuracy of up to 97% and reduces fault response time by 50 milliseconds compared to traditional methods. The results highlight the effectiveness of these intelligent algorithms in enhancing the reliability, accuracy, and real-time performance of modern power system protection.