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基于小波包分解的电力系统运行故障自动诊断方法

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为提高电力系统运行的安全性,本文设计了基于小波包分解的电力系统运行故障自动诊断方法。采集并预处理电力系统实时监测数据,并对预处理后的电力系统数据进行小波包分解,将信号分解为一系列不同尺度和频率的子频带。从每个子频带中提取与故障相关的特征,根据特征提取的结果,利用事先建立的故障模型和规则库进行故障诊断,判断故障的类型、位置和原因。实验表明,该方法可以将诊断错误率控制在 0。5%以下,具有较高的可行性。
Automatic method for the fault diagnosis of power system operation based on wavelet packet decomposition
In order to improve the operation safety of power system,the automatic fault diagnosis method of power system based on wavelet packet decomposition is designed.The real-time monitoring data of the power system is collected and pre-processed,and the pretreatment power system data is decomposed into a series of subfrequency bands of different scales and frequencies.The features related to the fault are extracted from each sub-band,and according to the results of the feature extraction,the previously established fault model and rule base are used to determine the type,location and cause of the fault.The experiment shows that this method can control the diagnostic error rate below 0.5%,which has higher feasibility.

power systemoperation faultfault diagnosiswavelet packet decompositionfeature extraction

周君

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青岛特锐德电气股份有限公司,山东 青岛 266100

电力系统 运行故障 故障诊断 小波包分解 特征提取

2024

中国高新科技
中华预防医学会,国家食品安全风险评估中心

中国高新科技

ISSN:
年,卷(期):2024.(11)