Power Quality Event Recognition and Classification Using an Online Sequential Extreme Learning Machine Network based on Wavelets

TL;DR

This paper presents a real-time power quality event classifier combining wavelet-based feature extraction with an online sequential extreme learning machine, improving detection speed and accuracy for various transient disturbances.

Contribution

It introduces a novel online classifier that integrates wavelet analysis with OS-ELM for efficient power disturbance detection and classification.

Findings

Effective detection of multiple transient power disturbances

Reduced memory and processing requirements

High classification accuracy demonstrated

Abstract

Reduced system dependability and higher maintenance costs may be the consequence of poor electric power quality, which can disturb normal equipment performance, speed up aging, and even cause outright failures. This study implements and tests a prototype of an Online Sequential Extreme Learning Machine (OS-ELM) classifier based on wavelets for detecting power quality problems under transient conditions. In order to create the classifier, the OSELM-network model and the discrete wavelet transform (DWT) method are combined. First, discrete wavelet transform (DWT) multi-resolution analysis (MRA) was used to extract characteristics of the distorted signal at various resolutions. The OSELM then sorts the retrieved data by transient duration and energy features to determine the kind of disturbance. The suggested approach requires less memory space and processing time since it can minimize a large quantity of the distorted signal's characteristics without changing the signal's original quality. Several types of transient events were used to demonstrate the classifier's ability to detect and categorize various types of power disturbances, including sags, swells, momentary interruptions, oscillatory transients, harmonics, notches, spikes, flickers, sag swell, sag mi, sag harm, swell trans, sag spike, and swell spike.