Application of CNN Based on Improved PSO in Transformer Fault Diagnosis
This paper presents a convolutional neural network(CNN)method based on improved particle swarm optimization(PSO)for fault diagnosis of transformers.The method transforms vibration signals into time-frequency images,and utilizes CNN to automatically extract features and classify faults.To enhance diagnostic performance,an improved Particle Swarm Optimization algorithm is employed to optimize the hyperparameters of the CNN,introducing an adaptive weight strategy and a relative base learning strategy to enhance the algorithm's global search capability.The results demonstrate that the proposed method can effectively improve the accuracy of transformer fault diagnosis,promoting the development of power systems towards intelligence and automation.
万方数据
维普
