| 引用本文: | 张明,嵇文路,潘小辉,等.基于矩阵变换的双线性WLAV状态估计[J].电力系统保护与控制,2019,47(19):1-6.[点击复制] |
| ZHANG Ming,JI Wenlu,PAN Xiaohui,et al.Bilinear WLAV state estimation based on matrix transformation[J].Power System Protection and Control,2019,47(19):1-6[点击复制] |
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| 摘要: |
| 当电力系统中的杠杆点存在不良数据时会严重影响状态估计的坏数据辨识和估计效果。现有的抗差估计方法也难以有效处理杠杆量测坏数据，而剔除杠杆点量测可能会影响系统的可观性。在双线性WLAV状态估计的基础上，通过对雅克比矩阵进行矩阵变换来实现对杠杆点不良数据的抗差。这种方法对算法本身进行改进，不需要剔除杠杆量测，既能提高对杠杆点不良数据的抗差能力，又不会影响系统的可观测性，是一种比较理想的处理杠杆点不良数据的方法。基于IEEE标准系统以及国内某实际省网的仿真结果验证了该方法在提高计算精度和计算效率方面的有效性。 |
| 关键词: 状态估计 矩阵变换 内点法 双线性 |
| DOI：10.19783/j.cnki.pspc.181286 |
| 投稿时间：2018-10-17修订日期：2019-04-22 |
| 基金项目:国家自然科学基金项目(51277052) |
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| Bilinear WLAV state estimation based on matrix transformation |
| ZHANG Ming,JI Wenlu,PAN Xiaohui,QIAN Qiang,WEI Zhinong,ZANG Haixiang |
| (Nanjing Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210005, China;College of Energy and Electrical Engineering, Hohai University, Nanjing 210098, China) |
| Abstract: |
| If bad data exists in the leverage point of the power system, it will seriously affect the bad data identification and state estimation results. It is difficult for the existing robust estimation methods to effectively deal with the leverage measurement. The removal of the leverage point measurement may affect the observability of the system. This method can improve the bad data of the leverage point by transformation of the Jacobian matrix based on the bilinear WLAV state estimation. This method improves the algorithm itself. It does not need to eliminate the leverage measurements, which can not only improve the ability to resist the leverage point bad data, but also can not affect the observability of the system. It’s an ideal way to deal with bad data at leverage points. Simulation results based on the IEEE standard system and a real provincial network verify the effectiveness of the proposed method in improving the computation accuracy and computing efficiency. This work is supported by National Natural Science Foundation of China (No.51277052). |
| Key words: state estimation matrix transformation interior point method bilinear |
