Abstract
This paper presents the idea of a combined analysis of long-term power quality data using cluster analysis (CA) and global power quality indices (GPQIs). The aim of the proposed method is to obtain a solution for the automatic identification and assessment of different power quality condition levels that may be caused by different working conditions of an observed electrical power network (EPN). CA is used for identifying the period when the power quality data represents a different level. GPQIs are proposed to calculate a simplified assessment of the power quality condition of the data collected using CA. Two proposed global power quality indices have been introduced for this purpose, one for 10-min aggregated data and the other for events—the aggregated data index (ADI) and the flagged data index (FDI), respectively. In order to investigate the advantages and disadvantages of the proposed method, several investigations were performed, using real measurements in an electrical power network with distributed generation (DG) supplying the copper mining industry. The investigations assessed the proposed method, examining whether it could identify the impact of DG and other network working conditions on power quality level conditions. The obtained results indicate that the proposed method is a suitable tool for quick comparison between data collected in the identified clusters. Additionally, the proposed method is implemented for the data collected from many measurement points belonging to the observed area of an EPN in a simultaneous and synchronous way. Thus, the proposed method can also be considered for power quality assessment and is an alternative approach to the classic multiparameter analysis of power quality data addressed to particular measurement points.
Highlights
Over the years, global electric energy consumption has increased from 440 Mtoe in 1973 to1737 Mtoe in 2015 [1]
A novelty of this work is the implementation of global power quality indices (GPQIs) for the group of power quality (PQ) data identified by cluster analysis (CA)
We propose using the levels of GPQIs that characterize particular clusters for the comparative analysis
Summary
Global electric energy consumption has increased from 440 Mtoe in 1973 to. 1737 Mtoe in 2015 [1] This has resulted in electricity becoming a specific product that is subject to market regulation in both quantitative and qualitative terms. The issues related to power quality (PQ) include definitions of the parameters, and the methods of measurements and assessment, which are already standardized, among others [6,7,8]. The parameters which characterize power quality include: frequency variation, voltage variation, voltage fluctuation, voltage asymmetry, and voltage waveform distortion. These parameters are collected during the period of observation, with the aggregation time interval usually equal to
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