Clustering and Dimensionality-reduction Techniques Applied on Power Quality Measurement Data

Abstract

The power system is changing rapidly, and new tools for predicting unwanted events are needed to keep a high level of security of supply. Large volumes of data from the Norwegian power grid have been collected over several years, and unwanted events as interruptions, earth faults, voltage dips and rapid voltage changes have been logged. This paper demonstrates the application of clustering and dimensionality-reduction techniques for the purpose of predicting unwanted events. Several techniques have been applied to reduce the dimensionality of the datasets and to cluster events based on analytical features, to separate events containing faults from a normal situation. The paper shows that the developed predictive model has some predictive capability when using balanced datasets containing similar muber of fault events and non-fault events. One of the main findings, however, is that this predictive capability is significantly reduced when using unbalanced datasets. Thus, the development of an accurate predictive model based on normal power system conditions, i.e. an unbalanced dataset of events and non-events, is a topic for further research.

Lessons for Data-Driven Modelling from Harmonics in the Norwegian Grid

“…[55] include anomaly prediction and-by using random forests-obtains inherently explainable models. Similarly, our own recent works have also focused on predicting PQ disturbances using a variety of data sources, methods, and features [56][57][58][59][60][61]. Unfortunately, most works (including our own) omit describing the underlying data, and instead jump straight to feature engineering and machine learning.…”