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

Abstract

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.

Digital twin-based 5G+ longitudinal differential protection method

“…BP neural network is also known as back propagation neural network [18]. It is trained by training on samples.…”

Personalized Recommendation Method of “Carbohydrate-Protein” Supplement Based on Machine Learning and Enumeration Method

“…Compared with traditional statistical methods, machine learning techniques such as artificial neural networks can identify nonlinear relationships between complex indicators and facilitate the construction of regression or classification models. BP neural networks are an essential type of neural network [46,47]. As shown in the FIGURE 3, the network consisted of three parts: input, hidden, and output layers.…”

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”