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Optimized Ensemble Extreme Learning Machine for Classification of Electrical Insulators Conditions

机译：用于电气绝缘子条件分类的优化集合极限学习机

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摘要

The classification of distinct problems of insulators in the distribution networks is a task that requires operator's experience. The applications of techniques to automate the inspection of electrical systems with the objective of detecting faults in insulators have shown to be reasonable alternatives to improve reliability in power grid. In this paper, based on the development of an experimental setup, signals are acquired considering three distinct faults in insulators. In this case, 13.8 kV (rms) is applied in drilled, contaminated, and good insulators considering an ultrasound detector connected to a computer. In the sequence, a multiclass classification method is proposed considering the ensemble of classifiers. The method considers the association of five distinct techniques, Bottom-Up segmentation, wavelet energy coefficient, principal component analysis, and particle swarm optimization associated with ensemble extreme learning machine (EN-ELM). Named optimized ensemble extreme learning machine, the present approach outperforms the original EN-ELM method. Finally, results show significant increase in robustness and faster training procedure when compared to classical approaches.
机译：分销网络中绝缘体不同问题的分类是需要运营商经验的任务。技术来自动化电气系统的应用，其目的是检测绝缘体中的故障的目的是具有提高电网可靠性的合理替代方案。本文基于实验设置的发展，考虑了绝缘体中的三个不同故障的信号。在这种情况下，考虑连接到计算机的超声检测器，将13.8kV（RMS）应用于钻孔，污染和良好的绝缘体中。在序列中，考虑到分类器的集合，提出了一种多字符分类方法。该方法考虑了五种不同技术，自下而上分割，小波能量系数，主成分分析和与集合极限学习机（EN-ELM）相关的粒子群优化的关联。命名优化集合极端学习机，本方法优于原始的en-elm方法。最后，与古典方法相比，结果显示稳健性和更快的培训程序显着增加。

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