一种基于状态估计的新型窃电方法及对策研究

摘要

With the development of science and technology, electricity theft tends to be more intelligent and covert. A new kind of electricity theft based on the state estimation, namely false data injection, is analyzed. First, a standardL0-norm optimization model is proposed based on the classic false data injection model. The effects of variable factors on the solution accuracy are eliminated with this standard model. Furthermore, in order to increase the cost and difficulty of the electricity theft, the placement of PMUs based on the vulnerability index is proposed. Finally, the standard L0-norm optimization model and the countermeasure are tested on the IEEE Power Flow Test Cases. The results show that the proposed standard L0-norm optimization model can obtain more accurate false data injection vector, and setting the PMUs based on the vulnerability index can increase the electricity theft cost of illegal users effectively.%随着科学技术的不断发展，窃电手段逐渐趋向于智能化，也更加的隐蔽。研究了一种基于状态估计的新型窃电方法-虚假数据注入。首先，在传统的虚假数据注入模型基础之上，得到了标准的L0范数优化模型，消除了变量系数对虚假数据注入向量精度的影响。然后，基于脆弱性指标提出设置 PMU 的方法提高窃电成本和难度。最后在IEEE标准测试系统上对提出的标准的L0范数优化模型和保护策略进行了测试。结果表明提出的标准L0范数优化模型可以得到更精确的虚假数据注入向量，基于脆弱性指标设置PMU可以有效提高非法用户的窃电成本。