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

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.