基于最小二乘估计融合的分布式电力系统动态状态估计

CAI Yongzhi

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基于最小二乘估计融合的分布式电力系统动态状态估计

更新时间：2025-08-13

- 基于最小二乘估计融合的分布式电力系统动态状态估计
- Automation of Electric Power Systems Vol. 40, Issue 8, Pages: 29-35(2016)
- 作者机构：
  
  华南理工大学电力学院,广东省,广州市,510640
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2016
- 稿件说明：
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CAI Yongzhi. [J]. Automation of electric power systems, 2016, 40(8): 29-35.
DOI：

CAI Yongzhi. [J]. Automation of electric power systems, 2016, 40(8): 29-35. DOI：

摘要

随着电力系统规模不断扩大，分布式状态估计是解决集中式状态估计计算维数过高和量测大数据处理难等问题的可行策略之一。文中以容积卡尔曼滤波算法作为区域本地估计算法，在协调中心侧采用最小二乘估计融合技术协调估计区间边界状态。根据边界状态协调估计值，各区域通过带等式约束的卡尔曼滤波算法进一步修正区域内部状态的本地估计值。算例仿真表明，所提方法估计精度与集中式估计相当，相比集中式估计具有更好的实时性，且所需数据通信量少，易于实现。

Abstract

With the expanding scale of the power system

the distributed state estimation is one of the effective ways to overcome the difficulties of the centralized state estimation

such as too high calculation dimensions and large measurement data processing. This paper adopts the Cabuture Kalman filter to do the local estimation

and employs the least square estimation fusion to coordinate the estimates of the boundary bus states in the coordination center. According to the coordinated estimates of the boundary bus states

each subarea can further correct the local estimates of the internal bus states through the Kalman filter algorithm. Finally

the simulation results show that the estimated precision of the proposed method is close to the centralized method

and the proposed method has a better real-time property. Moreover

the proposed method only need less communication data

which is easy to be implemented.This work is supported by National Natural Science Foundation of China(No. 51177049)and National Science Funds for Excellent Young Scholars of China(No. 51322702).

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