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Fast voltage contingency selection using fuzzy parallel self-organizing hierarchical neural network

机译:基于模糊并行自组织层次神经网络的快速电压应急选择

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摘要

A fuzzy neural network comprising of a screening module and ranking module is proposed for online voltage contingency screening and ranking. A four-stage multioutput parallel self-organizing hierarchical neural network (PSHNN) has been presented in this paper to serve as the ranking module to rank the screened critical contingencies online based on a static fuzzy performance index formulated by combining voltage violations and voltage stability margin. Compared to the deterministic crisp ranking, the proposed approach provides a more informative and flexible ranking and is very effective in handling contingencies lying on the boundary between two severity classes. Angular distance-based clustering has been employed to reduce the dimension of the fuzzy PSHNN. The potential of the fuzzy PSHNN to provide insight into the ranking process, without having to go through the complicated task of rule framing is demonstrated on IEEE 30-bus system and a practical 75-bus Indian system.
机译:提出了一种由筛选模块和排序模块组成的模糊神经网络,用于在线电压应急筛选和排序。本文提出了一种四阶段多输出并行自组织层次神经网络(PSHNN)作为排序模块,基于结合了电压违规和电压稳定裕度的静态模糊性能指标,对筛选出的紧急事件进行在线排序。与确定性明晰排序相比,所提出的方法提供了更具信息性和灵活性的排序,并且在处理两个严重性级别之间的边界上的突发事件方面非常有效。基于角距离的聚类已被用于减少模糊PSHNN的维数。在IEEE 30总线系统和实用的75总线印度系统上,演示了模糊PSHNN无需深入研究规则框架就可以提供对排名过程的深入了解的潜力。

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