Intelligent Fault Diagnosis System of Electrical Equipment Based on Neural Network Algorithm - Zhang, Chuanjun; Zhang, Chunfang (Procedia computer science, 2024)
With the progress of electrical equipment, various faults often occur during its operation, which adversely affect the efficiency and quality of power supply. In this paper, the neural network algorithm is used to detect the system parameters. This paper first discusses the shortcomings and defects of traditional diagnosis methods, and then proposes a new method combining with existing technologies to build fault state identification criteria and classifiers based on neural network model, and also uses a variety of intelligent algorithms such as fuzzy reasoning and support vector machine to solve the common short-circuit problem of electrical equipment on the power line, so as to provide reliable security for actual operation and help the power sector. The performance of the diagnostic system is tested in this paper. The test results show that in the process of real-time monitoring, analysis and feedback processing of the fault identification system, the fault detection time is 5.2, 5.1, 4.9, 4.7, 4.6, 4.5, 4.4, 4.3, 4.2, 4.1 seconds by applying the neural network algorithm. This data represents a significant improvement in fault detection speed, from 5.2 seconds to 4.1 seconds.