| 引用本文: | 杨 杰,吴 浩,董星星,陈佳豪,刘益岑.基于电流故障分量特征和随机森林的输电线路故障类型识别[J].电力系统保护与控制,2021,49(13):53-63.[点击复制] |
| YANG Jie,WU Hao,DONG Xingxing,CHEN Jiahao,LIU Yicen.Transmission line fault type identification based on the characteristics of current fault components and random forest[J].Power System Protection and Control,2021,49(13):53-63[点击复制] |
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| 摘要: |
| 为提高输电线路故障选相的精确性，提出了一种基于电流故障分量特征结合随机森林的输电线路故障类型识别新方法。分别在多个短时序列下计算三相电流故障分量能量相对熵与零序电流故障分量能量和，并按特定顺序把在各短时序列下计算得到的结果组成特征样本向量，以表征输电线路故障类型特征。建立随机森林智能故障类型识别模型，并利用故障特征样本集对其进行训练和测试，识别具体故障类型。仿真结果表明，所提算法在不同故障初始角、不同过渡电阻以及不同故障距离情况下均能准确识别具体故障类型，在数据丢失和噪声影响下也能较好地对故障类型进行识别。 |
| 关键词: 输电线路 电流故障分量 随机森林 故障类型识别 |
| DOI：DOI: 10.19783/j.cnki.pspc.201051 |
| 投稿时间：2020-08-26修订日期：2020-10-14 |
| 基金项目:四川省科技厅项目资助(2017JY0338，2019YJ0477，2018GZDZX0043，2018JY0386)；人工智能四川省重点实验室项目资助(2019RYY01) |
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| Transmission line fault type identification based on the characteristics of current fault components and random forest |
| YANG Jie1,WU Hao1，2,DONG Xingxing1,CHEN Jiahao1,LIU Yicen3 |
| (1. School of Automation and Information Engineering, Sichuan University of Science & Engineering, Zigong 643000,
China; 2. Artificial Intelligence Key Laboratory of Sichuan Province, Zigong 643000, China; 3. Electric Power
Research Institute, State Grid Sichuan Electric Power Company, Chengdu 610000, China) |
| Abstract: |
| In order to improve the accuracy of fault phase selection of transmission lines, a new method of fault type identification of transmission lines based on the characteristics of current fault components and random forest is proposed. This paper calculates the energy relative entropy of the three-phase current fault component and the sum of energy of the zero-sequence current fault component under multiple short-time sequences, and combines the results calculated under each short-time sequence into a characteristic sample vector in a specific order to characterize the fault type characteristics of transmission lines. It establishes a random forest intelligent fault type identification model, and uses the fault characteristic sample set to train and test it to identify specific fault types. Simulation results show that the proposed algorithm can accurately identify specific fault types under different initial fault angles, different transitional resistances and different fault distances, and can accurately identify fault types even with data loss and noise interference.
This work is supported by the Project of Sichuan Provincial Science and Technology Department (No. 2017JY0338, No. 2019YJ0477, No. 2018GZDZX0043 and No. 2018JY0386) and the Project of Artificial Intelligence Key Laboratory of Sichuan Province (No. 2019RYY01). |
| Key words: transmission line current fault component random forest fault type identification |
