arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2003.02422eess.SYcs.SY
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DER丰富的配电网中的深层钢筋Learning-BasedRobust保护

Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids

Dongqi Wu, Dileep Kalathil, Miroslav Begovic, Le Xie

摘要:

本文介绍了基于深度强化学习的架构的概念,用于具有许多分布式能源(DER)的配电系统中的保护继电器设计。分布式电源、电力电子接口器件和故障阻抗的存在阻碍了广泛使用的过电流保护方案的性能。在本文中,提出了一种基于强化学习的方法来设计和实现配电网中的保护继电器。所使用的特定算法是一种长短期记忆(LSTM)增强的深度神经网络,具有高度准确性,无通信性和易于实现。在IEEE 34节点测试馈线上进行OpenDSS仿真测试,结果表明该保护在故障率、鲁棒性和响应速度等方面均优于传统过流保护。

英文摘要:

This paper introduces the concept of Deep Reinforcement Learning based architecture for protective relay design in power distribution systems with many distributed energy resources (DERs). The performance of widely-used overcurrent protection scheme is hindered by the presence of distributed generation, power electronic interfaced devices and fault impedance. In this paper, a reinforcement learning-based approach is proposed to design and implement protective relays in the distribution grid. The particular algorithm used is an Long Short-Term Memory (LSTM) enhanced deep neural network that is highly accurate, communication-free and easy to implement. The proposed relay design is tested in OpenDSS simulation on the IEEE 34-node test feeder and demonstrated much more superior performance over traditional overcurrent protection from the aspect of failure rate, robustness and response speed.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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