摘要
由于海底观测网电力系统量测量冗余度低,传统的加权最小二乘(WLS)法状态估计结果精度不高,且WLS法不具有抗差性。针对该问题,引入小波分析方法,将其与WLS方法相结合,提出一种电力系统状态估计方法,该方法利用小波降噪理论提高WLS状态估计结果的精度,利用小波变换奇异性检测理论识别传感器故障,提高WLS方法抗差能力。海底观测网电力系统模型的仿真结果验证了该方法的优越性。
Because the traditional WLS（Weighted Least Squares） method has low robustness and accuracy when it is applied in the state estimation for the power system of seafloor observatory network with low measurement redundancy,a method combining the wavelet analysis with WLS is proposed,which adopts the wavelet de-noising theory to improve the accuracy detection theory to improve the WLS robustness by of WLS state estimation and the wavelet singularity identifying the senor faults. The simulative results based on the power system model for seafloor observatory network verify the superiority of the proposed method.
出处
《电力自动化设备》
EI
CSCD
北大核心
2014年第9期80-83,89,共5页
Electric Power Automation Equipment
关键词
海底观测网
状态估计
电力系统
加权最小二乘法
小波分析
降噪
奇异性检测
seafloor observatory network
state estimation
electric power systems
WLS method
wavelet analysis
de-noising
singularity detection
