华南理工大学学报(自然科学版) ›› 2022, Vol. 50 ›› Issue (2): 102-110.doi: 10.12141/j.issn.1000-565X.210198

所属专题: 2022年能源、动力与电气工程

• 能源、动力与电气工程 • 上一篇    下一篇

输电线路微气象跳闸预警及成因量化分析

欧阳森陈义森杨墨缘张真苏浩辉王奇2   

  1. 1.华南理工大学 电力学院,广东 广州 510640   2.中国南方电网超高压输电公司 检修试验中心,广东 广州 510700
  • 收稿日期:2021-04-09 修回日期:2021-06-08 出版日期:2022-02-25 发布日期:2022-02-01
  • 通信作者: 陈义森(1996-),男,硕士生,主要从事电力系统故障诊断研究. E-mail:962659930@qq.com
  • 作者简介:欧阳森(1974-),男,博士,副研究员,主要从事电能质量、配电网规划与智能电器研究。
  • 基金资助:
    国家自然科学基金资助项目(51677073);中央高校基本科研业务费重大产学研合作扶持专项(x2dlD2201280)

Transmission Line Trip Warning Based on Microclimate and Quantitative Analysis of Causes

OUYANG Sen1 CHEN Yisen1 YANG Moyuan1 ZHANG Zhen1 SU Haohui2 WANG Qi2   

  1. 1.School of Electric Power,South China University of Technology,Guangzhou 510640,Guangdong,China. 2.Maintenance and Test Center of Extral High Voltage Company of China Southern Power Grid,Guangzhou 510700,Guangdong,China
  • Received:2021-04-09 Revised:2021-06-08 Online:2022-02-25 Published:2022-02-01
  • Contact: 陈义森(1996-),男,硕士生,主要从事电力系统故障诊断研究. E-mail:962659930@qq.com
  • About author:欧阳森(1974-),男,博士,副研究员,主要从事电能质量、配电网规划与智能电器研究。
  • Supported by:
    Supported by the National Natural Science Foundation of China(51677073)

摘要: 为充分挖掘微气象与线路跳闸的关联规律,文中提出一种输电线路微气象跳闸预警及成因量化分析方法。以微气象历史数据为基础,在跳闸前基于轻梯度提升机(LightGBM)构建微气象与输电线路跳闸概率预测模型,从线路层面为LightGBM损失函数中的失稳样本赋予更高的权重以应对数据集不平衡的问题,并添加线路抵御能力差异惩罚项以削减对薄弱线路的学习|在跳闸后基于Sigmoid云模型量化微气象对跳闸事件的置信度,并从区段层面构造微气象耦合系数实现跳闸成因的量化分析。文中方法可在给定微气象条件下对跳闸事件进行实时预警,并进一步量化各个微气象对引发跳闸事件的贡献程度,为检修维护工作提供决策支持。

关键词: 微气象, 输电线路, 跳闸预警, 置信度, 成因分析

Abstract: In order to fully explore the correlation between microclimate and transmission line tripping, this paper proposed a method of early warning for microclimate-based tripping and cause quantitative analysis for transmission line. On the basis of meteorological history data, the probability prediction model of microclimate and transmission line tripping was constructed before the tripping based on Light Gradient Boosting Machine (LightGBM). From the circuit level, higher weights were given to the instable samples in LightGBM loss function to cope with the unba-lanced data sets, and the line resistance difference punishment item was added to cut thin lines for learning. After the tripping, the confidence degree of the micrometeorology to the tripping event was quantified based on the Sigmoid cloud model, and the micrometeorology coupling coefficient was constructed at the sectional level to achieve the quantitative analysis of the cause of the tripping. The proposed method can provide real-time early warning for tripping events under given micrometeorology conditions and further quantify the contribution of each micrometeoro-logy to trigger tripping events, thus providing decision support for maintenance work.


Key words: microclimate, transmission line, tripping warning, confidence, causes analysis

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