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方法研究

doi:10.19595/j.cnki.1000-6753.tces.240896

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摘要

光伏爬坡事件的不确定性对网络攻击检测方法的准确度和时效性提出更高要求,现有研究忽视光伏爬坡事件与网络攻击事件的耦合特性,导致网络-光伏爬坡协同攻击难以精准辨识。本文提出一种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方法,首先,分析网络-光伏爬坡协同攻击路径,构建网络-光伏爬坡协同攻击模型并分析攻击原理;然后,提出基于张量分解的个性化联邦学习（TDPFed）方法进行网络-光伏爬坡协同攻击辨识,保证不同电网分区数据隐私安全的同时进行快速低成本通信,实现本地数据的隐私保护和全局共享模型的高效训练;最后,通过算例分析证明本文所提方法相较于轻梯度提升机方法（LightGBM）、传统联邦平均方法（FedAvg）和基于Moreau envelopes策略的个性化联邦学习方法（pFedMe）,在辨识准确度、收敛速度等指标均有较大提升,能够突破辨识方法因通信条件制约导致联邦学习训练难的瓶颈。

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	崔沛然
	崔明建
	汪清
	张剑

关键词 ：光伏爬坡事件, 网络-光伏爬坡协同攻击, 虚假数据注入攻击, 张量分解, 联邦学习

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 0.8889, 0.9288, 0.9637, and 0.8945, respectively. The TDPFed algorithm, utilizing the aggregating factor matrix strategy and the aggregating composed tensor strategy, achieved 0.9999 and 0.9995, 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.

Key words： PV ramping cyber-ramping coordinated attack false data injection attack tensor decomposition federated learning

收稿日期: 2024-10-24

PACS:	TM73
	TM769

基金资助:

国家自然科学基金资助项目（52207130）

通讯作者: 崔明建男,1987年生,教授,博士生导师,研究方向为电力系统优化运行等。E-mail：mj_cui@tju.edu.cn

作者简介: 崔沛然男,2001年生,硕士,研究方向为电力系统网络安全、联邦学习、配电网规划。E-mail：peirancui@tju.edu.cn

引用本文:

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方法研究[J]. 电工技术学报, 0, (): 250428-. 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, 0, (): 250428-.

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