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深度学习辅助的区域交直流配电网区间状态估计
作者:
作者单位:

河海大学能源与电气学院,江苏省南京市 211100

摘要:

针对区域交直流混合配电网中实时量测覆盖率低、量测误差分布具有不确定性的问题,提出了基于深度神经网络(DNN)伪量测建模的交直流配电网区间状态估计方法。该方法首先对DNN进行离线训练,然后将实时量测数据和电压源换流器控制的变量值作为DNN的输入特征,建立伪量测模型;接着,在实时量测更新时,利用已训练好的DNN快速生成伪量测;最后,对伪量测和实时量测的不确定性采用区间形式建模并进行区间状态估计,进而准确监测交直流系统状态。算例仿真结果表明,所提方法能够避免对量测误差的概率分布进行假设,并且能够在低冗余量测配置或量测缺失时,准确获得交直流配电网状态变量的上下界信息。

关键词:

基金项目:

国家自然科学基金资助项目(U1966205);中央高校基本科研业务费专项资金资助项目(B200201067)。

通信作者:

作者简介:

费有蝶(1997—),女,硕士研究生,主要研究方向:电力系统状态估计。E-mail:2286520005@qq.com
黄蔓云(1991—),女,博士,讲师,主要研究方向:互联大电网状态估计、中低压配电网态势感知。E-mail:hmy_hhu@yeah.net
卫志农(1962—),男,通信作者,博士,教授,博士生导师,主要研究方向:电力系统运行分析与控制、输配电系统自动化。E-mail:wzn_nj@263.net


Deep-learning-assisting Interval State Estimation of Regional AC/DC Distribution Network
Author:
Affiliation:

College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Abstract:

Aiming at the problem of low coverage of real-time measurements and uncertainty of probability distribution of measurement errors, an interval state estimation method based on a pseudo measurement modeling method using deep neural networks (DNN) is proposed for regional AC/DC distribution network. Firstly, DNN is trained offline in this method. Then the real-time measurement data and the variable values controlled by VSC are used as the input features of DNN to establish a pseudo-measurement model. Secondly, the trained DNN is used to generate the pseudo measurements quickly when the real-time measurements are updated. Finally, the uncertainty of the pseudo-measurement and the real-time measurement is modeled in the interval form and the interval state estimation is carried out in order to accurately monitor the states of the AC/DC distribution system. The simulation results of the calculation example show that the proposed method can avoid assumptions about the probability distribution of the measurement errors, and it can obtain the accurate upper and lower bounds of the state variables in the case of low real-time measurement redundancy or insufficient configuration.

Keywords:

Foundation:
This work is supported by National Natural Science Foundation of China (No. U1966205) and Fundamental Research Funds for the Central Universities (No. B200201067).
引用本文
[1]费有蝶,黄蔓云,卫志农,等.深度学习辅助的区域交直流配电网区间状态估计[J].电力系统自动化,2022,46(1):101-109. DOI:10.7500/AEPS20210616006.
FEI Youdie, HUANG Manyun, WEI Zhinong, et al. Deep-learning-assisting Interval State Estimation of Regional AC/DC Distribution Network[J]. Automation of Electric Power Systems, 2022, 46(1):101-109. DOI:10.7500/AEPS20210616006.
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  • 收稿日期:2021-06-16
  • 最后修改日期:2021-08-20
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  • 在线发布日期: 2022-01-05
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