虚假数据攻击面临掌握的电气参数存在误差,甚至不完整及量测数据中存在不良数据的问题,提出一种基于拉格朗日乘子法的虚假数据攻击策略。首先通过拉格朗日乘子法和增广状态估计法辨识不良数据和估计未知支路电抗,然后在凸松弛技术框架内,将传统的攻击单个量测点的次优虚假数据攻击向量模型转化为基追踪(BP)模型,最后采用交替方向乘子法(ADMM)快速求解次优攻击向量。以典型的IEEE节点测试系统为例进行仿真测试,仿真结果表明:与传统的线性规划算法相比,将攻击单个量测点的次优攻击向量模型转化为BP模型后,采用ADMM求解次优攻击向量具有更高的计算效率;电抗未知支路数量较少时,攻击成功率较高,但是状态变量的误差向量的标准差较小时,电抗未知支路数量对攻击成功率影响减弱;该方法不会显著增加攻击成本。 False data attacks face the problem that the electrical parameters are inaccurate, even incomplete and there are some bad data in the measurement data. A fake data attack strategy based on Lagrange multiplier method is proposed. Firstly, the bad data and the unknown branch reactance are identified by Lagrange multiplier method and augmented state estimator. Then, the traditional hypothetical data attack vector model attacking a single measurement point is transformed within the framework of convex relaxation technique (BP) model. Finally, the alternate direction multiplier method (ADMM) is used to solve the suboptimal attack vector. A typical IEEE node test system is taken as an example to simulate the test. The simulation results show that, compared with the traditional linear programming algorithm, the sub-optimal attack vector model attacking a single measurement point is transformed into BP model, Vector has a higher computational efficiency. When the number of reactance unknown branches is small, the attack success rate is high, but when the standard deviation of the error vector of the state variables is small, the influence of the number of branches with unknown reactance on attack success rate is weakened. Will significantly increase the cost of attack.