污秽帽针绝缘子闪络电压预测神经网络模型的粒子群优化

Q3 Engineering
Diagnostyka Pub Date : 2022-09-22 DOI:10.29354/diag/154051
Belkebir Amel, Bourek Yacine, Benguesmia Hani
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引用次数: 1

摘要

本文提出用粒子群优化(PSO)技术训练人工神经网络(ANN)来预测户外绝缘子的闪络电压。该分析是在对高压绝缘子进行一系列真实世界测试之后进行的,以形成一个用于实现人工智能概念的数据库。这些测试是在不同程度的人工污染(蒸馏盐水)中进行的。每个污染水平显示隔离器每个区域的污染量,单位为毫升。采集数据库提供了与每个隔离区的电导率和不同程度的人为污染相对应的闪络电压值。结果表明,PSO训练的神经网络不仅可以提供更好的预测结果,而且可以减少计算量。它也是一个更强大的模型,因为:它不会陷入局部最优。此外,它还具有逻辑简单、实现简单、底层智能的优点。与实际试验结果相比较,表明PSO-ANN技术对高压污秽绝缘子闪络的预测是非常有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Particle swarm optimization of a neural network model for predicting the flashover voltage on polluted cap and pin insulator
This paper proposes training an artificial neural network (ANN) by a particle swarm optimization (PSO) technique to predict the flashover voltage of outdoor insulators. The analysis follows a series of real-world tests on high-voltage insulators to form a database for implementing artificial intelligence concepts. These tests are performed in various degrees of artificial contamination (distilled brine). Each contamination level shows the amount of contamination in milliliters per area of the isolator. The acquisition database provides values of flashover voltage corresponding to their electrical conductivity in each isolation zone and different degrees of artificial contamination. The results show that ANN trained by PSO can not only provide better prediction results, but also reduce the amount of computation efforts. It is also a more powerful model because: it does not get stuck in a local optimum. In addition, it also has the advantages of simple logic, simple implementation, and underlying intelligence. Compared to the results obtained by practical tests, the results obtained present that the PSO-ANN technique is very effective to predict flashover of high-voltage polluted insulators.
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来源期刊
Diagnostyka
Diagnostyka Engineering-Mechanical Engineering
CiteScore
2.20
自引率
0.00%
发文量
41
期刊介绍: Diagnostyka – is a quarterly published by the Polish Society of Technical Diagnostics (PSTD). The journal “Diagnostyka” was established by the decision of the Presidium of Main Board of the Polish Society of Technical Diagnostics on August, 21st 2000 and replaced published since 1990 reference book of the PSTD named “Diagnosta”. In the years 2000-2003 there were issued annually two numbers of the journal, since 2004 “Diagnostyka” is issued as a quarterly. Research areas covered include: -theory of the technical diagnostics, -experimental diagnostic research of processes, objects and systems, -analytical, symptom and simulation models of technical objects, -algorithms, methods and devices for diagnosing, prognosis and genesis of condition of technical objects, -methods for detection, localization and identification of damages of technical objects, -artificial intelligence in diagnostics, neural nets, fuzzy systems, genetic algorithms, expert systems, -application of technical diagnostics, -diagnostic issues in mechanical and civil engineering, -medical and biological diagnostics with signal processing application, -structural health monitoring, -machines, -noise and vibration, -analysis of technical and civil systems.
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