文献类型：期刊文献

英文题名：Cyber-Attack on Charge Pump Phase-Locked Loops in Distributed Energy Systems

作者：Wu, Xing[1];Liu, Chensheng[2,3,4];Tang, Yang[1];Dong, Zhao Yang[5]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Initiat Postdocs Supporting Program, Shanghai 200237, Peoples R China;[3]Qilu Univ Technol, Shandong Acad Sci, Key Lab Comp Power Network & Informat Secur, Minist Educ,Shandong Comp Sci Ctr, Jinan 250014, Peoples R China;[4]Shandong Fundamental Res Ctr Comp Sci, Shandong Prov Key Lab Ind Network & Informat Syst, Jinan 250014, Peoples R China;[5]City Univ Hong Kong, Dept Elect Engn, Hong Kong, Peoples R China

年份：2026

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS

收录：;EI(收录号：20262020727441);WOS:【SCI-EXPANDED(收录号:WOS:001760491800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62293502 and Grant U2441245. The work of Chensheng Liu was supported in part by Taishan Scholars Program under Grant tsqn202408241, in part by the Qilu University of Technology (Shandong Academy of Sciences) "Outstanding Young Talents" Project under Grant 2025QZJH01, and in part by the General Program of Natural Science Foundation of Shandong Province under Grant ZR2025MS1020.

语种：英文

外文关键词：Cyber-attack; charge pump phase-locked loop; inverter; vulnerability; multi-objective optimization; reinforcement learning

摘要：This article analyzes the vulnerability of the circuit system in the charge pump phase-locked loop (CPPLL) in inverter-based distributed energy systems by designing an attack strategy that can exploit the tracking characteristics and closed-loop bandwidth of the CPPLL at non-nominal grid frequencies. Meanwhile, considering attack resource constraints, the CPPLL-based attack is analyzed across different regions to study the selection of attack targets. A multi-objective optimization problem is formulated to maximize the attack effect, such as renewable energy revenue loss, power dispatch error and voltage deviation rate. To solve this dynamic and high-dimensional optimization problem, an improved Deep Deterministic Policy Gradient (DDPG) combined with robust optimization is proposed to integrate the multi-objective into a scalarized reward function and learn the optimal policy with an Actor-Critic network. Simulation studies, conducted on a modified IEEE-33 bus system, reveal that the CPPLL-based attack with the proposed attack strategy can result in: 1) larger loss to renewable energy revenue, power dispatch, and voltage deviation rate at the low-nominal grid frequency and 2) maximized comprehensive loss in the distributed energy system by attacking cluster inverters compared to distributed inverters.