The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation

Abstract

This article presents the application of data mining (DM) to long-term power quality (PQ) measurements. The Ward algorithm was selected as the cluster analysis (CA) technique to achieve an automatic division of the PQ measurement data. The measurements were conducted in an electrical power network (EPN) of the mining industry with distributed generation (DG). The obtained results indicate that the application of the Ward algorithm to PQ data assures the division with regards to the work of the distributed generation, and also to other important working conditions (e.g., reconfiguration or high harmonic pollution). The presented analysis is conducted for the area-related approach—all measurement point data are connected at an initial stage. The importance rate was proposed in order to indicate the parameters that have a high impact on the classification of the data. Another element of the article was the reduction of the size of the input database. The reduction of input data by 57% assured the classification with a 95% agreement when compared to the complete database classification.

A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data

“…Thus 25,069 10-min data points were accessible [24]. However, as a preprocessing of PQ data, the voltage events connecting data were also excluded as suggested in [36]. Thus, the final number of 10-min data points was 24,612.…”

Clustering Methods for Power Quality Measurements in Virtual Power Plant

“…On the other hand, with the hierarchical approach, reference [34] proposed the application of the Ward algorithm to detect short-term working conditions of a VPP by analyzing the classical PQ parameters as the input data, and the qualitative assessment of the clustering process was realized by using the cubic clustering criterion. In comparison, reference [35] involves the same approach but using the dendrogram to select the final number of clusters, which was the unquestionable disadvantage of the hierarchical approach. Reference [36] uses the Ward algorithm for a prosumer fair load sharing and surplus trading approach based on the micro-grid concept of the transactive energy concept.…”

Metric of Hierarchical Choices and Possibilities of its Application in Computational Linguistics

“…характеристики -более воспринимаемыми являются результаты проведённой по ним категоризации. Кроме того, иерархическая категоризация применяется к данным атмосферных наук, в частности -климатических, метеорологических и загрязнения воздуха [5] 2 ; измерениям качества мощности [7] и генерированию распределения энергии [9] 3 ; региональным описательным данным [6] 4 и даже аэрофотосьёмке [24] 5 ; текстовым данным и документам [10]; биометрическим данным [18] 6 ; данным микрочипов в биостатистике [3] 7 ; данным социального поведения, например, производительности студентов [15]; и т.д.…”