An Artificial Neural Network based approach for Harmonic Component Prediction in a Distribution Line

TL;DR

This paper presents an ANN-based method for real-time prediction of harmonic components in power distribution lines, aiming to improve harmonic filtering accuracy and response time amidst increasing nonlinear power device usage.

Contribution

It introduces a novel neural network approach trained on real-time harmonic data to predict harmonic components quickly and accurately for effective harmonic filtering.

Findings

Neural networks can accurately predict harmonic components in real-time.

The best model achieved minimal prediction loss in simulations.

ANN-based harmonic prediction enhances filtering effectiveness.

Abstract

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.