| 引用本文: | 冯迎宾,李智刚,王晓辉.海底观测网电力系统状态估计[J].电力自动化设备,2014,34(9): |
| FENG Yingbin,LI Zhigang,WANG Xiaohui.State estimation for power system of seafloor observatory network[J].Electric Power Automation Equipment,2014,34(9): |
|
| 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。 |
| 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 |
| DOI： |
| 分类号: |
| 基金项目: |
|
| State estimation for power system of seafloor observatory network |
|
FENG Yingbin1,2, LI Zhigang2, WANG Xiaohui2
|
|
1.University of Chinese Academy of Sciences，Beijing 100049，China;2.State Key Laboratory of Robotics，Shenyang Institute of Automation，Chinese Academy of Sciences，Shenyang 110016，China
|
| Abstract: |
| 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 of WLS state estimation and the wavelet singularity detection theory to improve the WLS robustness by identifying the senor faults. The simulative results based on the power system model for seafloor observatory network verify the superiority of the proposed method. |
| Key words: seafloor observatory network state estimation electric power systems WLS method wavelet analysis de-noising singularity detection |
