Transformer Fault Diagnosis Based on an Improved Sine Cosine Algorithm and BP Neural Network

Abstract

Background: The operation state evaluation and fault location of transformer is one of the technical bottlenecks restricting the safe operation of power grid. Methods: To improve the accuracy of transformer fault diagnosis, a hybrid intelligent method based on the improved sine cosine algorithm and BP neural network (ISCA-BP) is developed. First, the cloud model is introduced into the sine cosine algorithm (SCA) to determine the conversion parameter of each individual, so as to balance the global search and local exploitation capabilities. After that, six popular benchmark functions are used to evaluate the effectiveness of the proposed algorithm. Finally, on the basis of the dissolved gas analysis technology, the improved SCA algorithm is employed to find the optimal weight and threshold parameters of BP neural network, and the transformer fault classification model is established. Results: Simulation results indicate that the improved SCA algorithm exhibits strong competitiveness. Furthermore, compared with the BP neural network optimized by sine cosine algorithm (SCA-BP) and BP neural network, ISCA-BP method can significantly improve the diagnostic accuracy of transformer faults. Conclusion: The proposed intelligent method can provide a valuable reference idea for transformer fault classification.

Design of fault degree diagnosis algorithm for circuit breaker spring based on fuzzy clustering

“…The algorithm first performs segmented processing on the circuit breaker spring fault signal by frame [9]. Then, it selects the characteristics of the circuit breaker spring fault signal in each segment, selects appropriate parameters, and implements spatial mapping [10]. Finally, the fuzzy C-means clustering algorithm is used to implement pattern classification and recognition of the fault signal features of the circuit breaker spring, achieving the diagnosis of fault severity.…”