One important function of Power System State Estimation is identification and suppression of bad data.
不良数据检测和辩识是电力系统状态估计中的一个重要环节。
It can overcome the effect of bad data on parameter estimation, and has strong robustness, and can track time-varying parameters because the discontinuous recursive computation was applied.
该方法由于采用了间断迭代，因此可以克服坏数据对参数估计的影响，且具有较强的鲁棒性，又可以跟踪时变参数。
Present status of the distribution system state estimation, and methods of bad data detection and identification are surveyed here.
并对配电网状态估计的研究现状和常用不良数据的检测与辨识方法进行了描述。
Based on this, it is presented to a new method on bad data of distribution systems monitored and modified and a distribution state estimation based on matching power flow.
由此提出了一种新的配电系统不良数据检测及校正的方法，并给出了一种基于配电网匹配潮流状态估计方法。
In terms of state estimation, there are great differences between the innovation graph technique and the conventional methods on obtaining the topology configuration and identifying bad data.
新息图状态估计在拓扑结构获取和不良数据辨识方面与一般状态估计差异较大。
The power system state estimation involves network topology, network observability analysis, state estimation and detection and identification of bad data.
电力系统状态估计的内容包括：网络拓扑分析、网络可观测性分析、状态估计、状态估计潮流、不良数据检测和辨识等。
The problem about bad data detection and identification in distribution system state estimation is focused in this thesis.
本文着重分析了配电网状态估计中的不良数据检测与辨识的问题。
The results show that the state estimation method based on improved particle swarm algorithm can be the best value, but a longer time, and the bad data can be detected by J(x) detection method.
结果表明基于改进粒子群算法的状态估计可以得到最优值，但时间较长，检测法可以检测出不良数据。
The results show that the state estimation method based on improved particle swarm algorithm can be the best value, but a longer time, and the bad data can be detected by J(x) detection method.
结果表明基于改进粒子群算法的状态估计可以得到最优值，但时间较长，检测法可以检测出不良数据。
应用推荐