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信息物理系统（cyber-physical systems,CPS）融合视角下主动配电网状态估计（state estimation,SE）易遭受黑客发起的恶意攻击，这将影响对系统运行状态的准确感知。为有效应对配电网中的虚假信息注入攻击（false data injection attack,FDIA），提出了一种面向配电网FDIA检测的预测辅助SE方法。首先，提出了一般不完美FDIA模型，通过引入随机扰动模拟实际的攻击行为，并为后续的检测方法提供理论支持。其次，采用量测变换技术对分散的量测数据进行集中处理，以解决配电网中量测精度不足的问题。在此基础上，提出了一种融入多源量测数据的配电网预测辅助状态估计算法，该算法结合自适应无迹卡尔曼滤波（adaptive unscented Kalman filter,AUKF）进行状态修正，并利用投影统计（projection statistics,PS）有效检测和抑制破坏性异常值，从而提升配电网对FDIA的适应性和抗干扰能力。最后，在改进的IEEE 33节点测试系统中进行了仿真验证，结果表明，所提出的方法显著提高了配电网状态估计的精度，并增强了FDIA检测的准确性，为配电网的安全稳定运行提供了重要的技术支持。
Abstract：The distribution network state estimation(SE)combined with cyber-physical systems(CPS)faces challenges such as data explosion and malicious attacks from hackers,affecting the accurate perception of the system operation state. A dynamic SE method for false data injection attack(FDIA)detection is proposed to effectively address FDIAs in distribution networks.Firstly,a general imperfect FDIA model is introduced,which simulates real-world attack behavior through random disturbances,providing a theoretical foundation for subsequent detection methods. Secondly,a measurement transformation technique is employed to centralize dispersed measurement data,addressing the issue of inadequate measurement accuracy in distribution networks.Building on this foundation,a dynamic state estimation algorithm for distribution networks is proposed,which integrates multi-source measurement data and leverages anadaptive unscented Kalman filter(AUKF)for state correction.Additionally,the projection statistics(PS)method effectively detects and suppresses disruptive outliers,enhancing the network's adaptability and robustness to FDIA. Finally,simulations conducted on an improved IEEE 33-node test system demonstrate that the proposed method significantly improves state estimation accuracy and enhances FDIA detection accuracy,providing essential technical support for the secure and stable operation of distribution networks.
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基本信息:
DOI：10.20097/j.cnki.issn1007-9904.240498
中图分类号:TM73
引用信息:
[1]张晟,姜宇森,林曈,等.计及虚假信息注入攻击检测的主动配电网预测辅助状态估计[J].山东电力技术,2025,52(11):42-51.DOI:10.20097/j.cnki.issn1007-9904.240498.
基金信息:
国家自然科学基金项目（52107101）~~