| 引用本文: | 席 磊,白芳岩,王文卓,等.基于海马优化深层极限学习机的电力信息物理系统FDIA检测[J].电力系统保护与控制,2025,53(4):14-26.[点击复制] |
| XI Lei,BAI Fangyan,WANG Wenzhuo,et al.Cyber-physical power system FDIA detection based on seahorse optimized deep extreme learning machine[J].Power System Protection and Control,2025,53(4):14-26[点击复制] |
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| 摘要: |
| 虚假数据注入攻击(false data injection attack, FDIA)严重威胁电力信息物理系统的安全稳定。针对已有FDIA检测算法无法精确定位受攻击位置的局限性，提出了一种基于精英余弦变异融合的海马优化算法优化深层极限学习机(deep extreme learning machine, DELM)的FDIA检测定位算法。首先，该算法将极限学习机和极限学习机自编码器相结合得到了具备强特征表达能力的DELM。然后，通过海马优化算法对DELM的偏置和输入权重进行择优，用于改善算法指标不稳定的问题。同时在捕食阶段引入精英余弦变异算法以提升海马的收敛速度与DELM的精度。最后，将系统量测数据作为输入特征，利用DELM得到节点状态标签，从而实现污染状态量的定位。通过在IEEE 14节点系统和IEEE 57节点系统进行大量仿真对比分析，验证了所提算法在准确率、精确率、召回率及F1值等检测定位性能方面均具有明显优势，能够实现FDIA的精确定位。 |
| 关键词: 电力信息物理系统 虚假数据注入攻击 海马优化算法 深层极限学习机 |
| DOI：10.19783/j.cnki.pspc.246133 |
| 投稿时间：2024-03-31修订日期：2024-09-10 |
| 基金项目:国家自然科学基金项目资助(52277108，52477104) |
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| Cyber-physical power system FDIA detection based on seahorse optimized deep extreme learning machine |
| XI Lei1,2,BAI Fangyan1,WANG Wenzhuo1,PENG Dianming1,CHEN Hongjun1,LI Zongze1 |
| (1. College of Electrical Engineering and New Energy, China Three Gorges University, Yichang 443002, China; 2. Hubei
Provincial Key Laboratory for Operation and Control of Cascaded Hydropower Station, Yichang 443002, China) |
| Abstract: |
| False data injection attack (FDIA) poses a serious threat to the security and stability of cyber-physical power systems. To address the limitations of the existing FDIA detection algorithms, ones that fail to precisely locate the attacked positions, an FDIA detection and localization algorithm based on elite cosine variation fusion of the seahorse optimization algorithm optimized deep extreme learning machine (DELM) is proposed. First, the algorithm combines the extreme learning machine and an auto-encoder to obtain the DELM with strong feature expression ability. Then, the bias and input weight of the DELM are optimized by the seahorse optimization, to improve the algorithmic index instability. Meanwhile, the elite cosine variation algorithm is introduced in the predation stage to further improve the convergence and accuracy of the DELM. Finally, system measurement data are used as input features to obtain the bus state labels using DELM, to realize the localization of the contaminated state variables. Through a large number of simulation comparative analyses in the IEEE 14-bus and 57-bus systems, it is verified that the proposed algorithm has obvious advantages in the detection and localization performance, such as accuracy, precision, recall, and F1 score, and it can achieve the precise localization of an FDIA. |
| Key words: cyber-physical power system false data injection attack sea-horse optimization deep extreme learning machine |
