输电线路距离保护功率摆动闭锁的深度序列到序列模型

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-05 DOI:10.1016/j.engappai.2024.109538
Amin Mehdipour Birgani , Mohammadreza Shams , Mohsen Jannati , Farhad Hatami Aloghareh
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引用次数: 0

摘要

作为输电线路的主要保护方式,距离继电器很容易在功率波动时发生故障。事实上,距离继电器无法区分功率波动和短路故障,这给电力系统的稳定性带来了巨大风险,可能导致停电。近年来,人们越来越关注利用机器学习技术来识别电力系统中的各类故障和功率波动。然而,以往的工作主要集中在故障分类上,而故障分类大多是在故障发生后的很长一段时间内完成的。这就是诊断需要大量故障后数据的原因。为了应对这一挑战,本研究提出了一种预测性保护策略,利用深度学习方法,特别是序列到序列模型,对电力系统进行持续监控。目标是在尽量不依赖故障后数据的情况下,有效检测短路故障引起的功率波动,并在功率波动期间准确识别短路故障。在所提出的方法中,使用希尔伯特变换和经验模式分解算法从电网电流信号中提取特征。然后将这些特征输入序列到序列模型,该模型在确认存在功率摆动或功率摆动期间出现故障时发出闭锁/解闭锁指令。在 DIgSILENT 和 MATLAB 环境中对 IEEE 39 总线电网进行的各种仿真结果表明,在检测短路故障、功率摆动和功率摆动期间发生的短路故障方面,所提出的方案优于基准方法。拟议保护方案的及时正确运行有助于提高输电线路和电力系统的稳定性。
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A deep sequence-to-sequence model for power swing blocking of distance protection in power transmission lines
As the primary protection method for transmission lines, distance relays are prone to malfunction during power swings. In fact, the inability of distance relays to differentiate between power swings and short-circuit faults imposes a significant risk to power system stability that can result in blackouts. In recent years, there has been increasing interest in leveraging machine learning techniques to identify various types of faults and power swings in electrical systems. However, previous works mainly focus on fault classification, which is mostly done after a long period from the moment of fault initiation. This is the reason for requiring extensive post-fault data for diagnosis. To address this challenge, this study proposes a predictive protection strategy utilizing deep learning methodologies, specifically a sequence-to-sequence model, to monitor electrical power systems continuously. The objective is to effectively detect power swings from short-circuit faults with minimal reliance on post-fault data and accurately identify short-circuit faults during power swings. In the proposed approach, features are extracted from grid current signals using the Hilbert transform and empirical mode decomposition algorithms. These features are then fed into the sequence-to-sequence model, which issues block/unblock commands upon confirming the presence of a power swing or fault during the power swing. Results from various simulations conducted on an IEEE 39-bus grid in DIgSILENT and MATLAB environments demonstrate that the proposed scheme outperforms baseline methods in the detection of short-circuit faults, power swings, and short-circuit faults occurring during the power swings. The timely and correct operation of the proposed protection scheme contributes to the stability of transmission lines and power systems.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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