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基于多数据源融合的电网故障判别与告警技术研究

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针对国家电网故障判别误差率较高的问题,设计一种基于多数据源融合的电网故障判别与告警方案.利用最大离散小波变换技术和长短期记忆网络算法结合的方法提高电网故障判别与告警能力;利用最大重叠离散小波变换技术具有的扩充冗余自成正交特性对故障类型进行划分;将长短期记忆网络算法由单向进程转为双向网络,避免了反馈传输过程中的网络层无法得到合适的偏导数等梯度消失情况.试验结果表明,通过所提算法进行数据质量核查的准确度高达九成以上,表明所提研究系统对解决提升故障判别准确度的提升具有较强的实用性、优越性.
Research on Power Grid Fault Identification and Warning Technology Based on Multiple Data Source Fusion
To address the issue of high error rates in fault identification of the state grid of China,a power grid fault identification and alarm scheme based on multi data source fusion was designed.The method combined with maximum overlap discrete wavelet transform(MODWT)technology and long short-term memory(LSTM)network algorithm to improve the power grid fault identification and alarm capabilities;MODWT technology was used with expanded redundancy and self-orthogonal characteristics to classify fault types;transforming the LSTM network algorithm from a unidirectional to a bi-directional network avoided the situation where the network layer can not obtain appropriate partial derivatives and other gradients during feedback transmission.The experimental results show that the accuracy of data quality verification through the proposed algorithm is over 90％,indicating that the research system has strong practicality and superiority in improving the accuracy of fault discrimination.

fault identificationmaximum overlap discrete wavelet transform(MODWT)technologylong short-term memory(LSTM)network algorithmtype classificationbi-directional network

朱轶伦、俞一峰、虞明智、杜晟炜、姚高、许杰

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浙江华云信息科技有限公司,浙江杭州 310007

国网浙江省电力有限公司,浙江杭州 310007

国网金华供电公司,浙江金华 321000

故障判别 最大重叠离散小波变换技术 长短期记忆网络算法 类型划分 双向网络

国家电网科技项目国家电网浙江省电力公司科技项目

5500-202219275A-2-0-XG5211DS220003

2024

10.3969/j.issn.1000-3886.2024.02.009

电气自动化
上海电气自动化设计研究所有限公司 上海市自动化学会

电气自动化

CSTPCD
影响因子：0.377
ISSN：1000-3886
年,卷(期)：2024.46(2)
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