Self-adaptive protection strategies for distribution system with DGs and FCLs based on data mining and neural network

2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe) Pub Date : 2017-07-12 DOI:10.1109/EEEIC.2017.7977738

Wen-jun Tang, Hong-Tzer Yang

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引用次数: 7

Abstract

Owing to development of renewable energy and environmental protection issues, distributed generations (DGs) have become a trend. In addition, fault current limiters (FCLs) may be installed to prevent the short circuit current from exceeding the capacity of the power apparatus. Nevertheless, some issues appear, simultaneously, the most important among which is the mis-coordination of the protection system. This paper proposes overcurrent protection strategies for distribution systems with DGs and FCLs. Via the proposed approach, the relays with communication ability can determine their own operating states from the operation setting decision tree and topology-adaptive Neural Network model based on the data processed by Fast Fourier Transformer (FFT). The performance and effectiveness of the proposed protection strategies are verified via the simulation results obtained from different system topologies with/without DGs, FCLs, and load variation.

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基于数据挖掘和神经网络的配电系统dg和fcl自适应保护策略

由于可再生能源的发展和环境保护问题，分布式发电已成为一种趋势。此外，可安装故障限流器(fcl)，以防止短路电流超过电力设备的容量。但同时也出现了一些问题，其中最重要的是保护制度的不协调。本文提出了具有直流断路器和fcl的配电系统的过流保护策略。通过该方法，具有通信能力的中继可以根据快速傅里叶变换(FFT)处理的数据，通过运行设置决策树和拓扑自适应神经网络模型确定自己的运行状态。通过不同系统拓扑的仿真结果验证了所提出的保护策略的性能和有效性，包括有/没有dg、fcl和负载变化。

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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2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe)

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