scholarly journals 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

Energies ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 2050 ◽  
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Paweł Kostyła ◽  
Zbigniew Leonowicz ◽  
Klaudiusz Borkowski
Keyword(s):  
Cluster Analysis ◽  
Power Quality ◽  
Distributed Generation ◽  
Working Conditions ◽  
Electrical Power ◽  
Quality Data ◽  
Power Network ◽  
Quality Indices ◽  
Aggregated Data ◽  
Global Power

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.

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

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2407 ◽  
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Zbigniew Leonowicz ◽  
Klaudiusz Borkowski ◽  
Elżbieta Jasińska
Keyword(s):  
Power Quality ◽  
Distributed Generation ◽  
Electrical Power ◽  
High Harmonic ◽  
Measurement Data ◽  
Mining Industry ◽  
Power Network ◽  
Initial Stage ◽  
Related Approach ◽  
Point Data

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.

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

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 907
Author(s):  
Michał Jasiński ◽  
Tomasz Sikorski ◽  
Dominika Kaczorowska ◽  
Jacek Rezmer ◽  
Vishnu Suresh ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Power Quality ◽  
Hierarchical Clustering ◽  
Working Conditions ◽  
Power Plants ◽  
General Information ◽  
Quality Data ◽  
Global Index ◽  
Virtual Power ◽  
Virtual Power Plants

The integration of virtual power plants (VPP) has become more popular. Thus, research on VPP for different issues is highly desirable. This article addresses power quality issues. The presented investigation is based on multipoint, synchronic measurements obtained from five points that are related to the VPP. This article provides a proposition and discussion of using one global index in place of the classical power quality (PQ) parameters. Furthermore, in the article, one new global power quality index was proposed. Then the PQ measurements, as well as global indexes, were used to prepare input databases for cluster analysis. The mentioned cluster analysis aimed to detect the short-term working conditions of VPP that were specific from the point of view of power quality. To realize this the hierarchical clustering using the Ward algorithm was realized. The article also presents the application of the cubic clustering criterion to support cluster analysis. Then the assessment of the obtained condition was realized using the global index to assure the general information of the cause of its occurrence. Furthermore, the article noticed that the application of the global index, assured reduction of database size to around 74%, without losing the features of the data.

scholarly journals Combined Correlation and Cluster Analysis for Long-Term Power Quality Data from Virtual Power Plant

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 641
Author(s):  
Michał Jasiński
Keyword(s):  
Cluster Analysis ◽  
Power Plant ◽  
Power Quality ◽  
Quality Data ◽  
Virtual Power ◽  
Distributed Resources ◽  
Virtual Power Plant ◽  
And Cluster Analysis ◽  
Global Indicators

Analysis of the connection between different units that operate in the same area assures always interesting results. During this investigation, the concerned area was a virtual power plant (VPP) that operates in Poland. The main distributed resources included in the VPP are a 1.25 MW hydropower plant and an associated 0.5 MW energy storage system. The mentioned VPP was a source of synchronic, long-term, multipoint power quality (PQ) data. Then, for five related measurement points, the conclusion about the relation in point of PQ was performed using correlation analysis, the global index approach, and cluster analysis. Global indicators were applied in place of PQ parameters to reduce the amount of analyzed data and to check the correlation between phase values. For such a big dataset, the occurrence of outliers is certain, and outliers may affect the correlation results. Thus, to find and exclude them, cluster analysis (k-means algorithm, Chebyshev distance) was applied. Finally, the correlation between PQ global indicators of different measurement points was performed. It assured general information about VPP units’ relation in point of PQ. Under the investigation, both Pearson’s and Spearman’s rank correlation coefficients were considered.

scholarly journals Prospective Smart Distribution Substation in Bangladesh: Modeling and Analysis

2021 ◽  
Vol 13 (19) ◽  
pp. 10904
Author(s):  
Abdul Hasib Siddique ◽  
Mehedi Hasan ◽  
Sharnali Islam ◽  
Khalid Rashid
Keyword(s):  
Distributed Generation ◽  
Fossil Fuel ◽  
New Technology ◽  
Electrical Power ◽  
Electrical Network ◽  
Power Network ◽  
Major Focus ◽  
Modeling And Analysis ◽  
Generation Structure ◽  
Smart Substation

Being one of the fastest-growing economies in the world, Bangladesh needs to upgrade its electrical network and aim to reduce dependency on fossil fuel-based energy. For the aging and ever-expanding power network, it is necessary to have a smart substation in order to provide reliable, affordable, and sustainable electrical power. As Bangladesh is looking to integrate Distributed Generation (DG) in the power system, it is high time to think about integrating a smart distribution substation into its power network. In this paper, an investigation of the current power generation structure of Bangladesh was conducted and is described. The major focus was given to the upgradation of the existing substation and distribution setup of Bangladesh by providing suitable architectures, technologies, and communication protocols. Detailed studies of Bangladesh’s prospects to incorporate the new technology and renewable energy into its power network are discussed. ETAP was used to simulate the prospective system to show the feasibility of the prospective smart distribution substation in Bangladesh’s power network.

Intelligent Techniques for the Analysis of Power Quality Data in Electrical Power Distribution System

2012 ◽  
pp. 563-575
Author(s):  
Zahir Javed Paracha ◽  
Akhtar Kalam
Keyword(s):  
Neural Network ◽  
Power Quality ◽  
Power Distribution ◽  
Distribution System ◽  
Electrical Power ◽  
Power Industry ◽  
Quality Data ◽  
Power Utility ◽  
Power Distribution System ◽  
The Real

This chapter is about the intelligent techniques for the analysis of power quality problems in electrical power distribution system. The problems related with electrical power industry are becoming more widespread, complex, and diversified. The behaviour of power distribution systems can be monitored effectively using artificial intelligence techniques and methodologies. There is a need of understanding the power system operations from power utility perspectives and application of computational intelligence methods to solve the problems of the power industry. The real power quality (PQ) data is taken from a power utility in Victoria Australia. Principal Component Analysis Technique (PCAT) is used to reduce the large number of PQ data attributes of the power distribution system. After the pre-processing of PQ data using PCAT, intelligent computational techniques will be used for the analysis of power quality data. Neural network techniques will be employed to estimate the values of PQ parameters of the power distribution system. The Feed Forward Back Propagation (FFBP) neural network and Recurrent Neural Networks (RNN) are used for intelligent estimation of PQ data. The results obtained through these intelligent techniques are compared with the real data of power utility in Victoria, Australia for stability, reliability and enhanced power systems performance.

Sign in / Sign up

Export Citation Format

Share Document