Abstract
In this article, a case study is presented on applying cluster analysis techniques to evaluate the level of power quality (PQ) parameters of a virtual power plant. The conducted research concerns the application of the K-means algorithm in comparison with the agglomerative algorithm for PQ data, which have different sizes of features. The object of the study deals with the standardized datasets containing classical PQ parameters from two sub-studies. Moreover, the optimal number of clusters for both algorithms is discussed using the elbow method and a dendrogram. The experimental results show that the dendrogram method requires a long processing time but gives a consistent result of the optimal number of clusters when there are additional parameters. In comparison, the elbow method is easy to compute but gives inconsistent results. According to the Calinski–Harabasz index and silhouette coefficient, the K-means algorithm performs better than the agglomerative algorithm in clustering the data points when there are no additional features of PQ data. Finally, based on the standard EN 50160, the result of the cluster analysis from both algorithms shows that all PQ parameters for each cluster in the two study objects are still below the limit level and work under normal operating conditions.
Highlights
The electrical power system aims to generate electrical power and deliver it through the transmission and distribution system to customers’ devices in a stable, secure, reliable, and sustainable manner [1]
The object of this study focuses on the analysis of the measurement data of three power quality (PQ) recorders of the virtual power plant (VPP)
The experimental results of this study show that the execution of the dendrogram method provided by the SciPy library [62] required a long processing time of about 1350.28 s for dataset I, which belongs to the first study object, and for dataset II, which belongs to the second study object, the execution time was about 1421.64 s
Summary
The electrical power system aims to generate electrical power and deliver it through the transmission and distribution system to customers’ devices in a stable, secure, reliable, and sustainable manner [1]. Nowadays, various electronic devices such as AC/DC converters, switching power supplies, and industrial non-linear load are becoming the factors responsible for the increasing PQ disturbances{XE “PQ”\t “Power Quality”} [2,3]. These devices tend to significantly distort the waveform of the supply and voltage [4,5]. PQ disturbances are defined into two types based on the characteristics: voltage variations [7,8] and voltage events [9,10]. The analysis of PQ can be used to monitor the characteristic disturbances to capture PQ events that potentially detect faults associated with power quality problems in electrical power systems [11,12,13]
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