Power quality disturbance detection and classification using wavelets and artificial neural networks

被引：0

作者：

Perunicic, B ^{[1
]}

Mallini, M ^{[1
]}

Wang, Z ^{[1
]}

Liu, Y ^{[1
]}

机构：

[1] Lamar Univ, Dept Elect Engn, Beaumont, TX 77710 USA

来源：

8TH INTERNATIONAL CONFERENCE ON HARMONICS AND QUALITY OF POWER, PROCEEDINGS, VOLS 1 AND 2 | 1998年

关键词：

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This article develops a method to detect and classify power quality problems using a novel combination of digital filtering, wavelets, and artificial neural networks. The method is developed for voltage waveforms of arbitrary sampling rate and number of cycles, using a large variety of power quality events:simulated with MATLAB(R) software, in addition to sampled waveforms from utility monitoring and EMTP(R) simulations. Power system monitoring, augmented by the ability to automatically characterize disturbed signals, is a powerful tool for the power system engineer to use in addressing power quality issues. This is a step toward the goal of automating the real-time monitoring, detection and classification of power signals.

引用

页码：77 / 82

页数：6

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