基于决策树和支持向量机的智能变电站二次系统防误方法*

doi:10.11985/2023.01.013

电气工程学报 ›› 2023, Vol. 18 ›› Issue (1): 118-125.doi: 10.11985/2023.01.013

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基于决策树和支持向量机的智能变电站二次系统防误方法^*

周凯¹(), 曹林²(), 胡志坚², 严利雄¹, 毕如玉¹, 邓科¹, 李煜磊¹

1.国网湖北省电力有限公司检修公司武汉 430072
2.武汉大学电气与自动化学院武汉 430072

收稿日期:2021-06-10 修回日期:2021-08-22 出版日期:2023-03-25 发布日期:2023-04-19
通讯作者: 曹林，男，1998年生，硕士研究生。主要研究方向为电力系统继电保护。E-mail：lcao_whu@163.com
作者简介:周凯，男，1991年生，博士。主要研究方向为变电站远方控制及安全分析技术。E-mail：zhouktian@163.com
基金资助:
*国家自然科学基金(51977156);国网湖北电力科技(52152020003K)

Anti-error Method of Intelligent Substation Secondary System Based on Decision Tree and Support Vector Machine

ZHOU Kai¹(), CAO Lin²(), HU Zhijian², YAN Lixiong¹, BI Ruyu¹, DENG Ke¹, LI Yulei¹

1. Maintenance Company, State Grid Hubei Electric Power Co., Ltd., Wuhan 430072
2. School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072

Received:2021-06-10 Revised:2021-08-22 Online:2023-03-25 Published:2023-04-19

摘要/Abstract

摘要：

随着智能变电站的普及，电力系统的安全运行对变电站的二次检修提出了更高的要求。针对二次设备状态转换过程中易发生误操作的现问题，提出了一种新型二次系统防误方法。通过测量装置获取一二次设备状态值，构建初始样本数据集。为获取模型所需的特征向量集，采用了PCA法实现数据降维，特征向量集通过决策树实现二次操作分类。对分类后的样本集进行SVM训练和测试，实现二次设备操作的防误判别。最后构建了防误模型，用历史运行数据进行仿真测试，验证了此方法防误判别的有效性。

关键词: 智能变电站, 二次设备, 决策树, 支持向量机

Abstract:

With the popularization of intelligent substation, the safe operation of power system puts forward higher requirements for the secondary maintenance of substation. In order to solve the problem of misoperation in the process of state transition of secondary equipment, a new anti-misoperation method of secondary system is proposed. First and second equipment status values are obtained through the measuring device to construct the initial sample data set. In order to obtain the required feature vector set of the model, PCA method is adopted to achieve data dimensionality reduction, and the feature vector set is classified by decision tree for secondary operations. SVM training and testing are carried out on the classified sample set to realize the misjudgment prevention of secondary equipment operation. Finally, an anti-error model is constructed, and the simulation test is carried out with the historical operating data to verify the effectiveness of the proposed method.

Key words: Intelligent substation, secondary equipment, decision tree, support vector machine

中图分类号:

TM772

周凯, 曹林, 胡志坚, 严利雄, 毕如玉, 邓科, 李煜磊. 基于决策树和支持向量机的智能变电站二次系统防误方法^*[J]. 电气工程学报, 2023, 18(1): 118-125.

ZHOU Kai, CAO Lin, HU Zhijian, YAN Lixiong, BI Ruyu, DENG Ke, LI Yulei. Anti-error Method of Intelligent Substation Secondary System Based on Decision Tree and Support Vector Machine[J]. Journal of Electrical Engineering, 2023, 18(1): 118-125.

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参考文献 17

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设备名称	符号	状态转换	条件
合并单元	C1	运行转检修	检修压板投入“1”
合并单元	C2	检修转运行	检修压板退出“0”
保护装置	C3	运行转跳闸	满足差流跳闸条件
	C4	运行转检修	检修压板投入“1”
	C5	检修转运行	检修压板退出“0”
	C6	运行转闭锁	保护装置闭锁
	C7	闭锁转运行	保护装置正常
智能终端	C8	运行转跳闸	接收到跳闸信号
	C9	运行转检修	检修压板投入“1”
	C10	检修转运行	检修压板退出“0”
	C11	运行转闭锁	智能终端闭锁
	C12	闭锁转运行	智能终端正常

操作对象	类别	训练数量	测试数量	误判数量	准确率 (%)
合并单元	C1	660	160	0	100
合并单元	C2	500	120	0	100
保护装置	C3	420	100	0	100
	C4	780	200	2	99
	C5	320	80	0	100
	C6	640	160	1	99.4
	C7	800	200	0	100
智能终端	C8	1 200	300	2	99.3
	C9	2 000	500	1	99.8
	C10	1 780	450	2	99.6
	C11	1 520	380	2	99.5
	C12	1 600	400	1	99.8

基于决策树和支持向量机的智能变电站二次系统防误方法^*

Anti-error Method of Intelligent Substation Secondary System Based on Decision Tree and Support Vector Machine

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