基于改进支持向量机的数字化变电站安全措施生成技术

doi:10.11930/j.issn.1004-9649.202303067

摘要/Abstract

摘要：

针对数字化变电站二次检修安全措施工作效率低的问题，提出一种基于改进支持向量机的数字化变电站安全措施自动生成技术。首先，构建基于邻接矩阵的二次回路模型及设备模型，进一步综合二次安全措施规则库形成样本数据集；其次，利用支持向量机对二次安全措施进行分类，并引入细菌觅食算法对惩罚因子和核参数进行优化，有效提高了安全措施自动生成模型的训练效果；最后，通过算例验证了所提方法的有效性。

关键词: 数字化变电站, 二次回路模型, 二次设备模型, 改进支持向量机

Abstract:

A digital substation safety measure automatic generation technology based on improved support vector machine is proposed to address the low efficiency of secondary maintenance safety measures in digital substations. Firstly, construct a secondary circuit model and equipment model based on adjacency matrix, and further integrate the secondary security measure rule library to form a sample dataset; Secondly, support vector machines were used to classify secondary security measures, and bacterial foraging algorithms were introduced to optimize penalty factors and kernel parameters, effectively improving the training effectiveness of the automatic generation model for security measures; Finally, the effectiveness of the proposed method was verified through numerical examples.

Key words: digital substation, secondary circuit model, secondary device model, improved support vector machine

严亚兵, 褚旭, 肖豪龙, 梁文武, 李辉, 夏振兴. 基于改进支持向量机的数字化变电站安全措施生成技术[J]. 中国电力, 2023, 56(10): 194-201.

Yabing YAN, Xu CHU, Haolong XIAO, Wenwu LIANG, Hui LI, Zhenxing XIA. Automatic Generation Technology of Safety Measures for Digital Substation Based on Improved Support Vector Machine[J]. Electric Power, 2023, 56(10): 194-201.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303067

https://www.electricpower.com.cn/CN/Y2023/V56/I10/194

图/表 11

图 1 二次安全措施自动生成系统

Fig.1 The system of automatic generation of secondarysafety measures

图 2 二次回路连接

Fig.2 Secondary circuit connection

图 3 二次设备模型结构

Fig.3 The structure of secondary equipment model

表 1 隔离方式编码

Table 1 Code of safety measures

隔离方式		编码
无须操作		1
退出GOOSE软压板		2
退出SV软压板		3
投入检修压板		4
退出出口硬压板		5
断开光纤		6

图 4 基于BFA-SVM的二次安全措施自动生成流程图

Fig.4 The flow chart of automatic generation of safety measures based on BFA-SVM

图 5 BFA-SVM参数寻优过程

Fig.5 The process of BFA-SVM parameter optimization

图 6 BFA-SVM测试结果

Fig.6 The results of BFA-SVM testing

表 2 不同模型的分类准确率

Table 2 Classification accuracy of different models 单位：%

隔离方式	PSO-SVM	ABC-SVM	BFA-SVM
无须操作	100.0	100.0	100
退出GOOSE软压板	78.6	85.7	100
退出SV软压板	85.7	92.8	100
投入检修压板	80.0	90.0	100
退出出口硬压板	100.0	100.0	100
断开光纤	100.0	100.0	100
综合准确率	88.3	93.3	100

图 7 线路保护装置关联模型连接图

Fig.7 The connection diagram of associated model of line protection device

图 8 系统导航界面

Fig.8 The interface of system navigation

图 9 二次安全措施票

Fig.9 Secondary safety measure ticket

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