Model of Restoration of Distribution Network of Electrical Energy using Artificial Neural Networks

F. S. Avelar, P. Fritzen, M.A.A. Furucho, R. Betini
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Abstract

A computational model for self-recovery of electricity distribution network was developed to simulate it, emulated by the IEEE 123 nodes model. The electrical system considered has automatic switches capable of identifying a momentary fault in the line and finding the best reconfiguration for its reclosing. An artificial neural network (ANN), backpropagation, was used to classify the type of failure and determine the best reconfiguration of the distribution network. Initially, five power failure scenarios were simulated in certain different parts of the power grid, and power flow analysis via OpenDSS was performed. Following, the most suitable switching was observed within the shortest time interval to restore the power supply. In this way it is possible to identify the faulted segment in order to isolate it, leaving the smallest number of consumers in the shortest possible time without power supply. With the results of the simulations, tests and analyzes were performed to verify their robustness and speed, in the expectation that the model developed, be faster than an experienced Operator of a Distribution Center.
基于人工神经网络的电力配电网恢复模型
建立了配电网自恢复计算模型,采用IEEE 123节点模型进行仿真。所考虑的电气系统具有能够识别线路中的瞬时故障并为其重合闸找到最佳重新配置的自动开关。采用反向传播的人工神经网络对配电网的故障类型进行分类,确定配电网的最佳重构方案。首先,在电网的某些不同部分模拟五种停电场景,并通过OpenDSS进行潮流分析。然后,在最短的时间间隔内观察到最合适的开关以恢复供电。通过这种方式,可以识别出故障段,以便将其隔离,在最短的时间内使最少数量的消费者没有电力供应。根据仿真结果,进行了测试和分析,以验证其鲁棒性和速度,期望该模型的开发速度比有经验的配送中心操作员更快。
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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