Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation

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.

A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements

“…The qualitative comparison was realized for the number of clusters equal to 3, as indicated by the v-fold cross-validation test. The assessment was realized using the global index ADI [55,70]. The standard used to apply limits to the global value was EN 50160 [73].…”

“…Thus, in this article, there is a proposition of using the synthetic (global) index. The selected index was called the aggregated data index (ADI) [55,70]. The selected index consists of five classic 10 min PQ parameters (frequency, voltage, flicker severity, asymmetry, harmonic distortion in voltage), and two extremal (envelope of voltage deviation and maximum harmonic distortion).…”

“…Finally, the cluster analysis results are analyzed to establish an evaluation system. Also, the concept of "global/synthetic/unified/total" power quality indices are indicated in the literature, such as total power quality index [49,50], synthetic power quality index [51,52], unified power quality index [53,54], or global power quality index [55,56]. All of these approaches are aimed toward reducing the number of analyzing power quality data.…”

Instantaneous Disturbance Index for Power Distribution Networks

“…Traditionally, DWT [ 18 , 19 , 20 , 21 , 22 , 23 , 24 ], the generalized S-transform (GST) [ 25 , 26 , 27 , 28 , 29 , 30 ], the Time-Frequency distributions, and the Short-DFT (SDFT) [ 31 ] have been used for the analysis of the transient and time-varying nature of disturbance signals in electric power systems. In particular, power quality indices [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] have been defined using these transforms. Thus, the estimation of standard time-varying PQIs is done based on a proper JTFA [ 32 ], adaptive window-based fast generalized S-transform [ 33 ], empirical wavelet transform (EWT)-based time-frequency technique [ 34 ], cluster analysis of long-term power quality (PQ) data [ 35 ], wavelet packet transform [ 36 ], load composition rate and Euclidean norm of total harmonic distortions [ 37 ], and global harmonic parameters for phasor measurement units [ 38 ].…”

“…In particular, power quality indices [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] have been defined using these transforms. Thus, the estimation of standard time-varying PQIs is done based on a proper JTFA [ 32 ], adaptive window-based fast generalized S-transform [ 33 ], empirical wavelet transform (EWT)-based time-frequency technique [ 34 ], cluster analysis of long-term power quality (PQ) data [ 35 ], wavelet packet transform [ 36 ], load composition rate and Euclidean norm of total harmonic distortions [ 37 ], and global harmonic parameters for phasor measurement units [ 38 ]. Most of these works use many PQIs in their proposals, for example, the global PQIs introduced in [ 35 ] contain up to seven PQI factors.…”