FDIA Detection in Power Grid Based on Opposition-Based Whale Optimization Algorithm and Multi-layer Extreme Learning Machine

doi:10.11930/j.issn.1004-9649.202309029

Abstract

Abstract:

At present, the existing false data injection attack (FDIA) detection methods for cyber-physical power system can not precisely obtain the location of the attack due to its limited ability of feature expression. Therefore, this paper proposes a FDIA location detection method based on opposition-based learning whale optimization algorithm and multi-layer extreme learning machine (OWOA-ELM^ML). The proposed method not only extends the extreme learning machine into a multi-layer neural network to solve the problem of its limited ability of feature expression, but also introduces the whale optimization algorithm to optimize the number of neurons of the multi-layer extreme learning machine, and uses the opposition-based learning strategy to improve its convergence speed and detection accuracy so as to prevent the influence of randomly determining the number of neurons in each hidden layer on the generalization performance and location detection results of the detection method. Through a large number of simulation tests on IEEE-14 and 57-bus test systems under different scenarios, it is verified that the proposed method can automatically identify the exact position of the attacked system state through the historical data. Compared with other comparative methods, the proposed method has better precision, recall rate and F1 value.

Key words: cyber-physical power system, false data injection attack, multi-layer extreme learning machine, whale optimization algorithm, opposition-based learning

Lei XI, Yixiao WANG, Miao HE, Chen CHENG, Xilong TIAN. FDIA Detection in Power Grid Based on Opposition-Based Whale Optimization Algorithm and Multi-layer Extreme Learning Machine[J]. Electric Power, 2024, 57(9): 20-31.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202309029

https://www.electricpower.com.cn/EN/Y2024/V57/I9/20

Figures/Tables 16

Fig.1 The structure of CPPS

Fig.2 The mathematical model of FDIA location detection based on ELMML

Fig.3 Location detection result of the test set

Fig.4 The flow chart of OWOA-ELMML detection method

Fig.5 The topology of IEEE-14 and 57-bus test system

Fig.6 The largest normalized residual before and after the attack on the IEEE-14 and 57-bus test system

Fig.7 Measurement data before and after the attack on the IEEE-14 and 57-bus test system

Fig.8 State data before and after the attack on the IEEE-14 and 57-bus test system

Table 1 Confusion matrix

	预测值正类	预测值负类
真实值正类	真正类（ture positive，T_P）	假负类（false negative，F_n）
真实值负类	假正类（false positive，F_P）	真负类（ture negative，T_n）

Fig.9 Location detection accuracy for each bus of the IEEE-14 bus test system

Table 2 The location detection index results of the IEEE-14 bus test system

指标	精度	召回率	F1值
OWOA-ELM^ML	0.9836	0.9684	0.9759
WOA-ELM^ML	0.9765	0.9628	0.9696
ELM^ML	0.9774	0.9571	0.9671
DBN-ELM	0.9707	0.9402	0.9552
CNN	0.9709	0.9295	0.9497
ELM	0.9499	0.9020	0.9253
SVM	0.9595	0.8461	0.8992

Fig.10 ROC curves of the IEEE-14 bus test system

Fig.11 Location detection accuracy for each bus of the IEEE-57 bus test system

Table 3 The location detection index results of the IEEE-57 bus test system

指标	精度	召回率	F1值
OWOA-ELM^ML	0.9527	0.9299	0.9411
WOA-ELM^ML	0.9302	0.9020	0.9159
ELM^ML	0.9314	0.8946	0.9126
DBN-ELM	0.8991	0.8173	0.8563
CNN	0.8875	0.8123	0.8482
ELM	0.8857	0.8068	0.8444
SVM	0.9028	0.7544	0.8220

Fig.12 ROC curves of the IEEE-57 bus test system

Table 4 The training and testing time of each testing method 单位：s

检测方法	IEEE-14节点测试系统		IEEE-57节点测试系统
检测方法	训练用时	测试用时	训练用时	测试用时
ELM^ML	0.20576	0.00621	0.25172	0.01067
DBN-ELM	1.04498	0.00901	1.27239	0.01726
CNN	13.97473	0.01049	21.00797	0.02125
SVM	2.27905	0.00863	18.71482	0.02322

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