Feature dimension reduction and partitioning based deep learning method for probabilistic optimal power flow of transmission network

被引：0

作者：

Wu X. ^{[1
]}

Guo S. ^{[1
]}

Dai W. ^{[1
]}

Wang Z. ^{[1
]}

Zhao Z. ^{[1
]}

Shi B. ^{[1
]}

Zhang D. ^{[1
]}

机构：

[1] College of Electrical Engineering, Guangxi University, Nanning

来源：

Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 08期

基金：

中国国家自然科学基金;

关键词：

association analysis; cluster analysis; deep neural network; optimal power flow; power flow correction; probabilistic optimal power flow;

D O I：

10.16081/j.epae.202302004

中图分类号：

学科分类号：

摘要：

Probabilistic optimal power flow(POPF) requires repeated solving of the nonlinear optimal power flow(OPF) problem,thus its application is limited by large calculation amount. A two-stage solution method for OPF is proposed based on feature dimension reduction,partitioning and auxiliary forecasting of deep neural network(DNN). In the first stage,a DNN-based priority identification strategy for partial key decision variables of OPF is proposed,which solves the problem of numerical annihilation caused by too high feature dimension in the deep learning,further,guiding by the result characteristics of OPF,the correlation matching degree between input and output characteristics of OPF is extracted based on the correlation analysis and cluster analysis,and the block feature database of sample data is constructed to reduce the learning difficulty. In the second stage,DNN is used to complete the block mapping of partial key decision variables,the remaining state variables are recovered based on the power flow model,and the conditions that the calculation results do not converge or do not satisfy the constraints are corrected to restore the feasibility. A solution method of POPF is constructed according to the two-stage solution method of OPF. The simulative results show that the proposed method has good performance in the solving speed and accuracy of OPF and POPF. © 2023 Electric Power Automation Equipment Press. All rights reserved.

引用

页码：174 / 180

页数：6

共 20 条

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