Clustering as a tool to support the assessment of power quality in electrical power networks with distributed generation in the mining industry

Abstract

This paper presents a case study of using cluster analysis (CA) as one of the data mining (DM) techniques applied in the analysis of long-term power quality (PQ) data that is recorded in electrical power networks of the mining industry. The aim of the clustering is to highlight the impact of distributed generation (DG) on the level of power quality parameters. The carried out investigations concern the application of the K-mean clustering algorithm with Euclidean and Chebyshev distance with a different number of clusters for standardised and non-standardised data. The obtained results show the possibility to obtain automatic classification of data into distinguishable clusters that represent the period of time when local DG is active, switched-off or when a different power consumption level is denoted. It leads to the possibility of using CA as a suitable tool for assessing the impact of local generation on the working conditions of electrical power networks that depend on a DG contribution or power consumption. Additionally, the obtained results allow CA to be indicated as a proper method for the automatic identification of the PQ data which are affected by voltage events that can be treated as alternative way for present flagging concept.