Intelligent Detection and Diagnosis of Power Failure Relying on BP Neural Network Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 10
• Main Author: Liu, Linna
• Format: Journal Article
• Language: English
• Published: United States Hindawi 21.09.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Analysis; Back propagation networks; Competition; Computer Simulation; Consumption; Data analysis; Economic impact; Electric Power Supplies; Electricity distribution; Failure; Fault detection; Fault diagnosis; Feature extraction; Feedback; Generators; Hydroelectric power; Neural networks; Neural Networks, Computer; Optimization; Power failure; Power failures; Power supply circuits; System effectiveness; Urban planning; Wind power
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/3758660

The development of economy and the needs of urban planning have led to the rapid growth of power applications and the corresponding frequent occurrence of power failures, which many times lead to a series of economic losses due to failure to repair in time. To address these needs and shortcomings, this paper introduces a BP neural network algorithm to determine the neural network structure and parameters for fault diagnosis of power electronic inverter circuits with improved hazard. By optimizing the weights and thresholds of neural networks, the learning and generalization ability of neural network fault diagnosis systems can be improved. It can effectively extract fault features for training, sort out the business logic of power supply intelligent detection, analyze the potential hazards of power supply, and effectively perform circuit intelligent control to achieve effective fault detection of power supply circuits. It can provide timely feedback and hints to improve the fault identification ability and the corresponding diagnosis accuracy. Simulation results show that the method can eventually determine the threshold value for intelligent power fault detection and diagnosis by analyzing the convergence of long-term relevant indicators, avoiding the blindness of subjective experience and providing a theoretical basis for intelligent detection and diagnosis.