Fault detection method for power communication network based on machine learning

To enhance the accuracy of fault diagnosis in power communication networks, a machine learning-based fault detection approach is proposed, incorporating advanced algorithms and analytical techniques. Through the ant lion algorithm, the parameters of the support vector machine are optimized, with alarm data serving as input and fault types of power communication networks as output. To optimize the support vector machine (SVM), an ant lion optimization (ALO)-based fault diagnosis model for power communication networks is subsequently established. Simulated fault data from power communication networks is analyzed, with performance compared to that of the Bayesian algorithm and the convolutional neural network algorithm. The experimental results demonstrate that the proposed ALO-SVM method reaches a fault diagnosis accuracy rate of 98%, which exceeds the performance metrics of the other two algorithms, thus verifying both the validity and practical applicability of this approach in real-world scenarios.