Sparse Linear Models applied to Power Quality Disturbance Classification

TL;DR

This paper introduces a sparse linear modeling approach using Group Lasso for power quality disturbance classification, leveraging overcomplete dictionaries to improve accuracy and reduce complexity in pattern recognition.

Contribution

It applies sparse linear models with Group Lasso to combine multiple dictionaries for better PQ disturbance classification, a novel approach in this domain.

Findings

Sparse models reduce classification complexity.

Improved accuracy with combined dictionaries.

Effective feature selection via sparsity.

Abstract

Power quality (PQ) analysis describes the non-pure electric signals that are usually present in electric power systems. The automatic recognition of PQ disturbances can be seen as a pattern recognition problem, in which different types of waveform distortion are differentiated based on their features. Similar to other quasi-stationary signals, PQ disturbances can be decomposed into time-frequency dependent components by using time-frequency or time-scale transforms, also known as dictionaries. These dictionaries are used in the feature extraction step in pattern recognition systems. Short-time Fourier, Wavelets and Stockwell transforms are some of the most common dictionaries used in the PQ community, aiming to achieve a better signal representation. To the best of our knowledge, previous works about PQ disturbance classification have been restricted to the use of one among several available dictionaries. Taking advantage of the theory behind sparse linear models (SLM), we introduce a sparse method for PQ representation, starting from overcomplete dictionaries. In particular, we apply Group Lasso. We employ different types of time-frequency (or time-scale) dictionaries to characterize the PQ disturbances, and evaluate their performance under different pattern recognition algorithms. We show that the SLM reduce the PQ classification complexity promoting sparse basis selection, and improving the classification accuracy.