基于数字孪生的在运安控系统故障诊断方法

doi:10.11930/j.issn.1004-9649.202306047

摘要/Abstract

摘要：

数字孪生技术旨在打破虚实界墙，通过虚-实信息链接实现物理实体与虚拟空间的有机融合，模拟出物理实体的实时状态。安全稳定控制系统（简称安控系统）作为保证电网安全稳定运行的第二道防线，一旦发生故障将会给电网的运行造成严重危害。为此，提出一种基于数字孪生的在运安控系统故障诊断方法。首先，通过分析安控系统的一般运行策略和可能存在的故障环节，提出安控系统的数字孪生体系；其次，建立基于海洋捕食者算法优化支持向量机的安控系统故障诊断模型；最后，利用安控系统的运行数据构建样本数据集，验证了所提方法的有效性和正确性。

关键词: 安控系统, 数字孪生, 故障诊断, 支持向量机, 海洋捕食者算法

Abstract:

The digital twin technology aims to break through the boundary between virtual and real, achieve organic integration of physical entities and virtual space through virtual real information links, and simulate the real-time state of physical entities. The security and stability control system (security control system) is the second line of defense to ensure the safe and stable operation of the power grid. If it refuses to act or malfunction due to a fault, it will cause serious harm to the operation of the power grid. Therefore, this paper proposes a fault diagnosis method for operational security control system based on digital twins. By analyzing the general operation strategies and possible failure links of the security control system, the digital twin system of the security control system is proposed. And a fault diagnosis model for security control system based on MPA-SVM is established. Finally, the validity and correctness of the proposed method is verified by using the operational data of the security control system to construct the sample datasets.

Key words: security control system, digital twin, fault diagnosis, support vector machine, marine predator algorithm

韩伟, 段文岩, 杜兴伟, 姚峰, 马伟东, 刘磊. 基于数字孪生的在运安控系统故障诊断方法[J]. 中国电力, 2023, 56(11): 121-127.

Wei HAN, Wenyan DUAN, Xingwei DU, Feng YAO, Weidong MA, Lei LIU. Fault Diagnosis Method for Operational Security Control System Based on Digital Twins[J]. Electric Power, 2023, 56(11): 121-127.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202306047

https://www.electricpower.com.cn/CN/Y2023/V56/I11/121

图/表 10

图 1 基于策略表的安控系统

Fig.1 Security control system based on strategy table

图 2 安控系统数字孪生体系

Fig.2 The digital twin system of security control system

图 3 安控系统故障诊断的工作原理

Fig.3 Working principle of fault diagnosis in safety control system

图 4 基于MPA-SVM的安控系统故障诊断模型

Fig.4 Fault diagnosis model of safety control system based on MPA-SVM

表 1 训练及测试样本分布

Table 1 Distribution of training and test samples

故障类型	类型标签	训练样本数	测试样本数
正常	1	32	10
控制策略不匹配	2	46	15
通信异常	3	36	10
采样异常	4	33	10
定值越限	5	36	12

表 2 样本数据

Table 2 Sample data

样本号	逻辑值	误码率/10^–8	方差/%	安全裕度	故障类型
1	0	0.173	3.23	1.012	1
2	0	0.325	1.52	1.006	1
3	1	0.216	2.18	0.992	2
4	1	0.154	1.75	1.027	2
5	0	1.523	2.61	1.135	3
6	0	0.245	6.94	1.037	4
7	0	0.312	3.17	1.722	5

图 5 MPA优化SVM参数的适应度曲线

Fig.5 The fitness curve for MPA optimizing SVM parameters

图 6 MPA-SVM测试样本诊断结果

Fig.6 Diagnostic results of MPA-SVM test samples

图 7 PSO-SVM测试样本诊断结果

Fig.7 Diagnostic results of PSO-SVM test samples

表 3 不同模型的分类准确率

Table 3 Classification accuracy of different models

故障诊断模型	准确率/%
故障诊断模型	类型1	类型2	类型3	类型4	类型5	综合
PSO-SVM	100	100	100	90	88.33	94.74
ABC-SVM	100	100	100	90	75.00	92.98
SSA-SVM	100	100	100	100	88.33	96.49
MPA-SVM	100	100	100	100	91.67	98.24

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