首页|变电设备温度态势感知及辅助决策系统方案研究

变电设备温度态势感知及辅助决策系统方案研究

Research on Temperature Situation Awareness and Auxiliary Decision-Making System Scheme of Substation Equipment

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
[目的]为了提升变电设备运维管理的智能化水平,及时发现并预防因设备过热导致的故障风险,保障电网安全稳定运行,提出了变电设备温度态势感知及辅助决策方案.[方法]从感知层、理解层、预测层和辅助决策层4个方面展开研究.在感知层,利用K近邻(K-nearest neighbor,KNN)分类算法分析多类温度数据的关联性.在理解层,通过BP神经网络构建历史数据传递模型,以处理历史数据缺失问题.在预测层,为应对非线性数据和噪声,设计了自回归积分滑动平均(autoregressive integrated moving average,ARIMA)模型与支持向量机(support vector machine,SVM)组合的温度预测模型.在辅助决策层,应用灰色关联度分析设备温度变化与故障风险之间的关系.[结果]基于所提方案的算例验证结果表明,该方案实现了对设备未来温度变化趋势的有效感知,并为设备缺陷判断提供了依据.[结论]所提方案通过多维度、深层次的温度数据分析,揭示了设备温度与故障风险之间潜在的关联关系,实现了对变电设备运行趋势的预判,为变电设备运行方式优化以及制定设备检修计划提供参考.
[Objectives]To enhance the intelligent management of substation equipment maintenance,timely identify and mitigate the risks of failures caused by device overheating,and ensure the safe and stable operation of the power grid,the temperature situation awareness and auxiliary decision-making scheme of substation equipment were proposed.[Methods]The research was carried out from four aspects:the perception layer,the understanding layer,the prediction layer,and the auxiliary decision-making layer.In the perception layer,the K-nearest neighbor(KNN)classification algorithm was used to analyze the correlation of multi-class temperature data.In the understanding layer,a BP neural network was employed to construct a historical data transmission model to address missing historical data issues.In the prediction layer,a temperature prediction model combining autoregressive integrated moving average(ARIMA)and support vector machine(SVM)was designed to handle nonlinear data and noise.In the auxiliary decision-making layer,the grey relational analysis was applied to analyze the relationship between equipment temperature changes and fault risks.[Results]The verification results of numerical examples based on the proposed scheme show that the scheme realizes the effective perception of the future temperature variation trends of the equipment and provides a basis for the identification of equipment defects.[Conclusions]Through multi-dimensional and deep-level temperature data analysis,the proposed scheme reveals the potential correlation between equipment temperature and fault risk,realizes the prediction of the operational trend of substation equipment,and provides a reference for the optimization of operational mode and the formulation of equipment maintenance plan.

power systemsubstationtemperature state awarenessauxiliary decision-makingautoregressive integrated moving average(ARIMA)modelBP neural networksupport vector machine(SVM)

陈昱、丁鸿、崔勇、朱里、陈士俊、凌秋阳、徐勇生、郑建

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国网浙江省电力有限公司湖州供电公司,浙江省 湖州市 313000

安徽科技学院管理学院,安徽省 蚌埠市 233030

三峡大学电气与新能源学院,湖北省 宜昌市 443002

浙江泰仑电力集团有限责任公司,浙江省 湖州市 313000

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电力系统 变电站 温度态势感知 辅助决策 自回归积分滑动平均(ARIMA)模型 BP神经网络 支持向量机(SVM)

安徽省高校协同创新项目浙江泰仑电力集团有限责任公司科技项目安徽科技学院人才引进项目

GXXT-2023-065TLGZ2212001-012GLYJ202202

2024

10.12096/j.2096-4528.pgt.23118

发电技术
华电电力科学研究院

发电技术

CSTPCD
影响因子：0.388
ISSN：2096-4528
年,卷(期)：2024.45(4)
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