Cyber-Ramping Coordinated Attack Identification Method for PV Using a Tensor Decomposition Based Personalized Federated Learning
Cui Peiran1, Cui Mingjian1, Wang Qing1, Zhang Jian2
1. School of Electrical and Information Engineering Tianjin University Tianjin 300072 China; 2. School of Electrical and Automation Engineering Hefei University of Technology Hefei 230009 China
Abstract：Photovoltaic (PV) power has emerged as one of the most promising and rapidly developing renewable energy sources in recent years, significantly facilitating the green transition of power grids. However, the uncertainty of PV ramping events imposes higher demands on the accuracy and timeliness of cyber attack detection methods. Existing studies overlook the coupling characteristics between PV ramping events and cyber attack events, making it challenging to accurately identify cyber-ramping coordinated attacks. Besides, as the scale of the power grid continues to expand, strict privacy protection requirements between different grid partitions hinder data sharing. Traditional distributed algorithms are constrained by communication conditions, resulting in low training efficiency, which further limits the development of cyber-ramping coordinated attack detection methods. To address these issues, this paper proposes a cyber-ramping coordinated attack identification method based on tensor decomposition-based personalized federated learning (TDPFed). Firstly, the attack pathways of cyber-ramping coordinated attacks are analyzed, and a corresponding attack model is constructed by integrating common cyber attack methods with the characteristics of PV ramping to elucidate the attack mechanisms. Secondly, tensor decomposition is employed to enhance traditional federated learning by compressing the communication parameters, effectively reducing communication cost while preserving nearly all information. Thirdly, a two-layer objective model is constructed, consisting of a tensorized local model and a personalized model. The personalized model is retained to enhance the identification performance on local data, while the tensorized local model is transmitted to alleviate communication load and accelerate training. Fourthly, training strategies for the personalized model and tensorized local model are formulated, and two different global aggregation strategies are introduced to improve upon the traditional federated averaging method. Finally, the characteristics of PV power output are analyzed, and a dataset generation method is proposed. To validate the effectiveness of the proposed method, a power grid-communication network model based on the IEEE 33-bus system was constructed. Cyber-ramping coordinated attack identification was performed using traditional light gradient boosting machine (LightGBM), federated averaging (FedAvg), personalized federated learning with moreau envelopes (pFedMe), an improved FedAvg method, and the proposed TDPFed method. In terms of macro-averaged precision, the traditional methods achieved 0.888 9, 0.928 8, 0.963 7, and 0.894 5, respectively. The TDPFed algorithm, utilizing the aggregating factor matrix strategy and the aggregating composed tensor strategy, achieved 0.999 9 and 0.999 5, demonstrating its superior capability in accurately identifying various cyber-ramping coordinated attacks. When the compression ratio increased from 1.5 to 2, the macro-averaged precision of the improved FedAvg method decreased by 1.53%, whereas the proposed method exhibited only minor declines of 0.02% and 0.01%. This result indicates that the proposed approach effectively reduces the required transmission parameters while preserving nearly all information. In terms of communication cost, the four traditional methods required computation times that were two orders of magnitude higher than that of the proposed method. Furthermore, under adversarial attacks, the proposed method demonstrated the highest robustness among all evaluated approaches. The following conclusions can be drawn from the above results: (1) The use of a two-layer objective model, appropriate training strategies, and aggregation strategies during model training effectively improves the accuracy of cyber-ramping coordinated attack identification and accelerates model convergence. (2) Tensor decomposition can compresses the parameters that need to be uploaded, reducing the communication resource requirements of federated learning. This improvement addresses the issue of insufficient communication resources in large-scale multi-node power grid systems, significantly shortening the training time. (3) By generating a cyber-ramping coordinated attack dataset using baseline curves and photovoltaic random components from historical data, the issue of insufficient training data is mitigated. (4) Through case analysis, the proposed TDPFed method demonstrates a significant improvement in overall identification performance compared to traditional methods such as LightGBM, FedAvg, and pFedMe. It maintains a high identification accuracy even in specific scenarios where traditional methods struggle.
崔沛然, 崔明建, 汪清, 张剑. 基于张量分解个性化联邦学习的网络-光伏爬坡协同攻击辨识[J]. 电工技术学报, 2025, 40(23): 7677-7693.
Cui Peiran, Cui Mingjian, Wang Qing, Zhang Jian. Cyber-Ramping Coordinated Attack Identification Method for PV Using a Tensor Decomposition Based Personalized Federated Learning. Transactions of China Electrotechnical Society, 2025, 40(23): 7677-7693.
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