An automatic voltage disturbance classification system based on Clonal Selection Algorithm

B. Arruda, R. Freire, C. P. Souza
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引用次数: 1

Abstract

Classification of voltage disturbances in power systems is essential for modern society and can be very demanding according to the used method or the aimed accuracy. This paper presents a new intelligent approach aimed to automatically analyse power quality disturbances including sag, swell, outage, harmonics and normal waveform. The approach is based on Artificial Immune System and focuses on the application of a Clonal Selection Algorithm to extract features from disturbance waveforms and classify the disturbances in each 0.5 cycle of the fundamental frequency. Other important feature of the proposed approach is that it can be embedded since the resulted on-line classification tool achieves very low computational complexity. Comparisons and experimental results obtained from the application of the proposed method validate the approach and achieved a classification accuracy at least better than previous work.
基于克隆选择算法的电压干扰自动分类系统
电力系统电压扰动的分类在现代社会中是必不可少的,根据所使用的方法或目标精度的不同,对电压扰动的分类要求很高。本文提出了一种新的智能方法来自动分析电能质量扰动,包括暂降、膨胀、停电、谐波和正常波形。该方法基于人工免疫系统,重点应用克隆选择算法从干扰波形中提取特征,并对基频每0.5个周期的干扰进行分类。该方法的另一个重要特征是它可以嵌入,因为生成的在线分类工具的计算复杂度非常低。应用该方法得到的对比和实验结果验证了该方法的有效性,其分类精度至少优于以往的工作。
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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