基于两阶段自组织映射的简单故障分类方法

Edison G. Guama, Iván D. Claros
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引用次数: 0

摘要

最近,基于人工智能的算法在电力系统故障分析中的应用有了很大的增长,这主要是因为这些技术能够对具有非线性行为的系统(如电力系统)进行建模。本文提出了一种基于两阶段自组织图的输电线路故障分类方法:第一阶段通过零序电压和电流判断故障是否接地,第二阶段通过相电流识别故障相。通过COMTRADE标准格式在保护装置中提供的电流和电压信号的测量被用作模型输入。使用对称分量和R-DFT算法实现数据处理,并详细介绍了模型设置和训练。最后,通过实例分析对所提方法进行了验证,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Simple Method for Fault Classification Based on Two Stages of Self Organizing Map
Recently, the use of algorithms based on artificial intelligence for fault analysis in power systems has had a great increase mainly due to the ability of these techniques to model systems with non-linear behavior such as an electrical system. This paper presents a method for fault classification in transmission lines based on 2 stages of Self Organizing Maps: the first one determines if the fault is to ground using the voltage and current of zero sequence, and the second one identifies the failed phase through the phase current. The measures of current and voltage signals, available in the protection devices through the COMTRADE standard format, were used as model inputs. Data processing is realized using symmetrical components and the R-DFT algorithm, model settings and training are also reviewed in detail. Finally, the proposed method was evaluated through a case study under several real failure conditions, the results confirm the effectiveness of the prooosed method.
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