Malicious data identification in smart grid based on residual error method

Zongshuai Hu, Yong Wang, C. Gu, Dejun Mengm, Xiaoli Yang, Shuai Chen
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引用次数: 1

Abstract

Most of methods on malicious data identification are based on the residual in power system applications. Residual error method, which is an effective method to identify a single malicious data can be basically divided into weighted residual error method and normalized residual error method. In this paper the states and measurement estimated value can be calculated firstly by the traditional weighted least squares state estimation algorithm. Then the measurement residual and the objective function value can be also calculated. The algorithm of weighted residual error method is tested on IEEE5 bus system by MATLAB and the analysis on the results of calculation example shows that this method is an effective one which a single malicious data can be effectively dealt with, and it can be applied to malicious data identification. In this paper the largest weighted residues in the case of single malicious data are 8.361 and correspond to real power injection at bus2, which are far above the threshold to improve the efficiency of malicious data identification.
基于残差法的智能电网恶意数据识别
在电力系统中,大多数恶意数据识别方法都是基于残差的。残差法是识别单个恶意数据的有效方法,基本可分为加权残差法和归一化残差法。本文首先用传统的加权最小二乘状态估计算法计算出状态和测量估计值。然后计算出测量残差和目标函数值。利用MATLAB在IEEE5总线系统上对加权残差法算法进行了测试,算例结果分析表明,该方法是一种有效的方法,可以有效地处理单个恶意数据,可以应用于恶意数据识别。本文中单个恶意数据情况下的最大加权残数为8.361,对应于bus2的实际功率注入,远远超过了提高恶意数据识别效率的阈值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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