Genetic algorithm in location identification of AEPD source: Some aspects

机译：AEPD源位置识别中的遗传算法：一些方面

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Using the experimental data obtained from an Acoustic Emission Partial Discharge (AEPD) system, efforts are made to locate the source of Partial discharge (PD) with a transformer tank. The AEPD data with 8 sensors (available in the literature) is numerically experimented with a Genetic Algorithm, although minimum of 4 sensors only are necessary for identifying the location. With eight sensors, four sensors considered at a time, form 70 (⁸C4) combination of sensors. The effect, implication and usage of superfluous sensor data in identifying the location with GA is analyzed and reported. Results are compared with Newton's method.

机译：使用从声发射局部放电（AEPD）系统获得的实验数据，人们努力寻找使用变压器油箱的局部放电（PD）的来源。使用遗传算法对具有8个传感器（文献中提供）的AEPD数据进行了数值实验，尽管最少4个传感器仅用于识别位置。具有八个传感器，一次考虑四个传感器，形成70个（^{8 C4）传感器组合。分析并报告了多余的传感器数据在利用GA识别位置中的作用，含义和用途。将结果与牛顿法进行比较。}

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《2013 IEEE 1st International Conference on Condition Assessment Techniques in Electrical Systems》|2013年|386-390|共5页
会议地点 Kolkata(IN)
作者
Punekar Gururaj S.; Antony Deepthi; Bhavanishanker T; Nagamani H N more authors;
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作者单位

Department of Electrical and Electronics Engineering, National Institute of Technology, Surathkal Karnataka, Mangalore, India;

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会议组织
原文格式 PDF
正文语种 eng
中图分类
关键词
Acoustic Emission(AE); Genetic Algorithm(GA); Partial Discharge (PD) location; Population size; Sensor location;

机译：声发射（AE）;遗传算法（GA）;局部放电（PD）位置;人口规模;传感器位置;;

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Genetic algorithm in location identification of AEPD source: Some aspects

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