Application of Signal Processing and Pattern Recognition Theory in Fault Diagnosis of Electrical System
Yutao Guo · Transactions on Computer Science and Intelligent Systems Research · 2025
The application of signal processing and pattern recognition theory in fault diagnosis of electrical system is discussed in this paper. Due to the continuous expansion of the scale and complexity of the power system, the traditional fault diagnosis methods have been unable to meet the modern needs. This study first introduces the common types and characteristics of electrical system faults, and then focuses on the analysis of the role of signal processing technology in fault signal extraction and feature analysis, covering Fourier transform, wavelet transform and empirical mode decomposition. Then, the application of pattern recognition theory in fault classification and identification is discussed, with emphasis on algorithms such as support vector machine, artificial neural network and fuzzy logic. After comparing the advantages and disadvantages of different signal processing and pattern recognition methods in electrical system fault diagnosis, a comprehensive fault diagnosis method based on multi-source information fusion is proposed. The effectiveness of the proposed method is verified by a case study and future research directions, such as the potential application of deep learning and big data analysis in fault diagnosis of electrical systems, are discussed. This study provides theoretical basis and technical support for improving the accuracy and real-time performance of electrical system fault diagnosis, which is of great significance to ensure the safe and stable operation of power system.