Optimization Techniques for Mining Power Quality Data and Processing Unbalanced Datasets in Machine Learning Applications

Abstract

In recent years, machine learning applications have received increasing interest from power system researchers. The successful performance of these applications is dependent on the availability of extensive and diverse datasets for the training and validation of machine learning frameworks. However, power systems operate at quasi-steady-state conditions for most of the time, and the measurements corresponding to these states provide limited novel knowledge for the development of machine learning applications. In this paper, a data mining approach based on optimization techniques is proposed for filtering root-mean-square (RMS) voltage profiles and identifying unusual measurements within triggerless power quality datasets. Then, datasets with equal representation between event and non-event observations are created so that machine learning algorithms can extract useful insights from the rare but important event observations. The proposed framework is demonstrated and validated with both synthetic signals and field data measurements.

An ALO Optimized Adaline Based Controller for an Isolated Wind Power Harnessing Unit

“…The evolution, features and validation of these three techniques are clearly mentioned in [37][38][39]. Different optimization methods for power quality and transient stability are discussed in [40][41][42]. The optimized values of controller gains, leaky factor and the step size are determined by these techniques.…”

Editorial to the Special Issue “AI Applications to Power Systems”

“…I would like to thank the staff and reviewers for their efforts and input. The task of editing and selecting papers for this collection was found to be both stimulating and rewarding [1][2][3][4][5][6].…”