Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks

TLDR

Power quality disturbances are increasingly problematic as more loads connect to the grid, and multiple disturbances can occur simultaneously. This study introduces a dual neural‑network approach to detect and classify both single and combined power quality disturbances. The method combines an adaptive linear network that estimates harmonic indices with a feedforward neural network that uses voltage waveform histograms, enabling simultaneous detection of sags, swells, outages, harmonics, spikes, notching, flicker, and oscillatory transients, and is validated under real operating conditions.

Abstract

The detection and classification of power quality (PQ) disturbances have become a pressing concern due to the increasing number of disturbing loads connected to the power line and the susceptibility of certain loads to the presence of these disturbances; moreover, they can appear simultaneously since, in any real power system, there are multiple sources of different disturbances. In this paper, a new dual neural-network-based methodology to detect and classify single and combined PQ disturbances is proposed, consisting, on the one hand, of an adaptive linear network for harmonic and interharmonic estimation that allows computing the root-mean-square voltage and total harmonic distortion indices. With these indices, it is possible to detect and classify sags, swells, outages, and harmonics-interharmonics. On the other hand, a feedforward neural network for pattern recognition using the horizontal and vertical histograms of a specific voltage waveform can classify spikes, notching, flicker, and oscillatory transients. The combination of the aforementioned neural networks allows the detection and classification of all the aforementioned disturbances even when they appear simultaneously. An experiment under real operating conditions is carried out in order to test the proposed methodology.