An evolutionary algorithm for Volt/Var control in an active distribution network with a deep learning surrogate mode

被引：0

作者：

Pan S. ^{[1
]}

Liu Y. ^{[1
]}

Tang Z. ^{[1
]}

Zhang X. ^{[1
]}

Qi H. ^{[1
]}

Liu J. ^{[1
]}

机构：

[1] College of Electrical Engineering, Sichuan University, Chengdu

来源：

Dianli Xitong Baohu yu Kongzhi/Power System Protection and Control | 2022年 / 50卷 / 17期

基金：

中国国家自然科学基金;

关键词：

active distribution network; highway neural networks; non-dominated sequencing genetic algorithm; surrogate-assisted model; three-phase unbalance; Volt/Var control;

D O I：

10.19783/j.cnki.pspc.211509

中图分类号：

学科分类号：

摘要：

The integration of large-scale distributed renewable energy sources brings new challenges to the active distribution network (ADN), including the three-phase imbalance problem, unexpected voltage violations and increased line losses. However, due to the incomplete installation of measurement equipment in the current distribution network, it is difficult to accurately obtain the load data of some nodes. Therefore, the traditional ADN voltage control method based on global observation is difficult to meet the actual control requirements. To solve these problems, a fast Volt/Var control (VVC) evolutionary algorithm with a deep learning surrogate model is proposed. In the development of the algorithm, a highway neural network is first designed as the surrogate model to accurately fit the mapping relationship between limited measured load information, voltage regulation control strategy and system performance indices. Then, the trained surrogate model is embedded into the iterative optimization process of the non-dominated sorting genetic algorithm, and the voltage deviation rate, three-phase unbalance degree and line losses indicators are directly calculated, and the data-driven distribution network VVC strategy can be quickly obtained. A modified IEEE 123-node three-phase distribution network is employed to verify the advantages and efficiency of the proposed algorithm. © 2022 Power System Protection and Control Press. All rights reserved.

引用

页码：97 / 106

页数：9

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