利用极端学习机分析双回路输电线路故障情况的智能方法

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-07-26 DOI:10.1007/s00202-024-02621-3
Hongmei Gu, Qingqing Zhang, Lei Wang
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引用次数: 0

摘要

现有的故障情况框架通常针对不同的故障类型使用不同的 ABC 域或序列网络等效电路。环境条件会导致双回路输电线路的参数发生变化,而这些不正确的参数会导致故障情况框架出现错误。双回路输电线路故障情况和保护的最佳工具是使用独立于线路参数的框架。在本文中,双回路输电线路的故障情况是基于每条线路的电压和电流测量值,利用能够识别测量值和故障情况之间非线性方程的极限学习机来实现的。首先,模拟了双回路输电线路电网中不同距离的各类故障。然后,利用获得的信息训练智能工具。最后,对不同距离和电阻的故障情况进行估计,以评估所建议的方法。为了评估所建议的框架相对于其他智能框架的优越性,本文将其结果与人工神经网络和支持向量机这两种智能工具的结果进行了比较,结果表明极限学习机比其他工具更精确、更可靠。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An intelligent method for fault situation in double-circuit transmission lines utilizing extreme learning machine

Existing fault situation frameworks conventionally use different ABC-domain or sequence network equivalent circuits for different fault types. The environmental conditions lead to changes in the parameters of the double-circuit transmission lines, and these incorrect parameters cause errors in the fault situation frameworks. The best tool for fault situation and protection of double-circuit transmission lines is the use of frameworks that work independently of the line parameters. In this article, fault situation for double-circuit transmission lines is implemented based on the measured voltage and current of each line, utilizing an Extreme Learning Machine capable of identifying nonlinear equations between measured values and fault situation. First, all types of faults were simulated at different distances in a power grid with a double-circuit transmission line. Then, the information obtained is utilized to train intelligent tools. Finally, the fault situations for different distances and resistances are estimated to assess the suggested method. To assess the superiority of the suggested framework over other intelligent frameworks, the outcomes of this article are compared with the outcomes obtained from two intelligent tools, artificial neural networks and support vector machines, which show more precision and reliability of the Extreme Learning Machine than other tools.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
期刊介绍: The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed. Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).
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