基于脉冲神经网络伪量测建模的配电网三相状态估计

HUANG Manyun

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基于脉冲神经网络伪量测建模的配电网三相状态估计

更新时间：2025-08-13

- 基于脉冲神经网络伪量测建模的配电网三相状态估计
- Automation of Electric Power Systems Vol. 40, Issue 16, Pages: 38-43(2016)
- 作者机构：
  
  河海大学能源与电气学院,江苏省,南京市,210098
- 作者简介：
- 基金信息：
- DOI：
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- Published：2016
- 稿件说明：
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HUANG Manyun. [J]. Automation of electric power systems, 2016, 40(16): 38-43.
DOI：

HUANG Manyun. [J]. Automation of electric power systems, 2016, 40(16): 38-43. DOI：

摘要

为了给配电网管理系统提供全面准确的实时数据

配电网三相状态估计显得尤为重要。针对当前配电网量测信息不足

提出了基于脉冲神经网络(SNN)伪量测建模的配电网三相状态估计。该方法首先将实时和部分历史支路功率量测输入SNN进行伪量测建模

然后由高斯混合模型生成相应的量测误差

最后进行基于加权最小二乘法的配电网三相状态估计。理论分析和算例验证表明

所提模型不仅能够在正常通信时有效提高配电网状态估计精度

而且能在通信故障时保证估计精度在合理范围内

进而为配电网的运行控制提供参考依据。

Abstract

To provide comprehensive and accurate real-time data for distribution management system(DMS)

three-phase state estimation in distribution system is critically required. Lacking in real-time measurements in distribution system state estimation(DSSE)

this paper presents a spiking neural network(SNN)based method for pseudo measurement modeling. In the proposed method

the pseudo measurements are firstly derived from a few real measurements using SNN. Then

the error associated with the generated pseudo measurements is created by Gaussian mixture model(GMM). At last

the three-phase state estimation in distribution system is made based on weighted least square method. The simulation results demonstrate the accurate performance of the proposed pseudo modeling in DSSE with both regards to normal situation and communication failure. This work is supported by National Natural Science Foundation of China(No.51277052).

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