On-line Optimization Method for Phase Sequence in Station Area Based on Improved Support Vector Machine and Non-dominated Sorting Genetic Algorithm-Ⅲ

被引：0

作者：

Tang J. ^{[1
]}

Yang Y. ^{[2
]}

Liu S. ^{[2
]}

Zhang Y. ^{[2
]}

Li Q. ^{[2
]}

Yi Y. ^{[2
]}

机构：

[1] Guangdong Power Grid Co., Ltd., Guangzhou

[2] Research Center of Smart Energy Technology (School of Electric Power, South China University of Technology), Guangzhou

来源：

Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2022年 / 46卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Data-driven; Genetic algorithm; Load forecasting; Phase sequence; Support vector machine;

D O I：

10.7500/AEPS20210318006

中图分类号：

学科分类号：

摘要：

In view of the insufficient adjustment of the phase sequence in station area based on historical load modeling and the inability to guarantee the duration of the power supply state after phase sequence adjustment, from the data-driven perspective, an on-line optimization method for the phase sequence in station areas based on improved support vector machine for ultra-short-term load forecasting is proposed. First, the variational mode decomposition is used to decompose the historical load of the forecasting object into multiple sub-sequences, and an improved support vector machine is used to forecast each sub-sequence. An adaptive weight mechanism is introduced in the forecasting model to improve the performance of least squares support vector machines, which can filter the impact of data noise on the results and improve the forecasting accuracy. Then, a multi-objective optimization model for the load branch of the intelligent commutation switch is established with the smallest three-phase unbalance, the smallest number of commutations, and the longest phase sequence duration. A set of satisfactory solutions is selected from the Pareto optimal solution set solved by the non-dominated sorting genetic algorithm-Ⅲ as the automatic phase sequence adjustment scheme for decision-makers. Finally, a certain station area in Guangdong power grid of China is used as an example to analyze and compared with other methods to verify the effectiveness of the proposed method. © 2022 Automation of Electric Power Systems Press.

引用

页码：50 / 58

页数：8

共 29 条

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