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Signal Analysis in Power Systems
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Signal Analysis in Power Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A1: Smart Grids and Microgrids".

Deadline for manuscript submissions: closed (19 June 2020) | Viewed by 24333

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editor

Prof. Dr. Zbigniew Leonowicz

E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: signal analysis; advanced signal processing methods; renewable energy; ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The analysis of power systems under various conditions represents one of the most important and complex task in electrical power engineering. Studies in this area are necessary to ensure that the reliability, efficiency, and stability of the power system is not adversely affected. This issue is devoted to reviews and applications of modern methods of signal processing used to analyze the operation of a power system and evaluate the performance of the system in all aspects. Smart Grid as an emerging research field of the current decade is the focus of this issue. Monitoring capability with data integration, advanced analysis of support system control, enhanced power security and effective communication to meet the power demand, efficient energy consumption and minimum costs, as well as intelligent interaction between power-generating and -consuming devices depends on the selection and implementation of advanced signal analysis and processing techniques.

Prof. Dr. Zbigniew Leonowicz
Guest Editor

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Keywords

  • smart grid
  • power system control
  • power system protection
  • stochastic methods
  • signal processing
  • computational intelligence
  • artificial intelligence

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Published Papers (6 papers)

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Editorial

Jump to: Research

3 pages, 153 KiB  
Editorial
Signal Analysis in Power Systems
by Zbigniew Leonowicz and Michał Jasiński
Energies 2021, 14(23), 7850; https://doi.org/10.3390/en14237850 - 23 Nov 2021
Viewed by 1547
Abstract
The idea of the call for the Special Issue “Signal Analysis in Power Systems” came from scholarly discussions about ever increasing complexity of the management and operation of today’s power system [...] Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)

Research

Jump to: Editorial

30 pages, 4241 KiB  
Article
A Case Study on Distributed Energy Resources and Energy-Storage Systems in a Virtual Power Plant Concept: Technical Aspects
by Tomasz Sikorski, Michal Jasiński, Edyta Ropuszyńska-Surma, Magdalena Węglarz, Dominika Kaczorowska, Paweł Kostyla, Zbigniew Leonowicz, Robert Lis, Jacek Rezmer, Wilhelm Rojewski, Marian Sobierajski, Jarosław Szymańda, Daniel Bejmert, Przemysław Janik and Beata Solak
Energies 2020, 13(12), 3086; https://doi.org/10.3390/en13123086 - 15 Jun 2020
Cited by 44 | Viewed by 5113
Abstract
The article presents calculations and power flow of a real virtual power plant (VPP), containing a fragment of low and medium voltage distribution network. The VPP contains a hydropower plant (HPP), a photovoltaic system (PV) and energy storage system (ESS). The purpose of [...] Read more.
The article presents calculations and power flow of a real virtual power plant (VPP), containing a fragment of low and medium voltage distribution network. The VPP contains a hydropower plant (HPP), a photovoltaic system (PV) and energy storage system (ESS). The purpose of this article is to summarize the requirements for connection of generating units to the grid. Paper discusses the impact of the requirements on the maximum installed capacity of distributed energy resource (DER) systems and on the parameters of the energy storage unit. Firstly, a comprehensive review of VPP definitions, aims, as well as the characteristics of the investigated case study of the VPP project is presented. Then, requirements related to the regulation, protection and integration of DER and ESS with power systems are discussed. Finally, investigations related to influence of DER and ESS on power network condition are presented. One of the outcomes of the paper is the method of identifying the maximum power capacity of DER and ESS in accordance with technical network requirements. The applied method uses analytic calculations, as well as simulations using Matlab environment, combined with real measurement data. The obtained results allow the influence of the operating conditions of particular DER and ESS on power flow and voltage condition to be identified, the maximum power capacity of ESS intended for the planed VPP to be determined, as well as the influence of power control strategies implemented in a PV power plant on resources available for the planning and control of a VPP to be specified. Technical limitations of the DER and ESS are used as input conditions for the economic simulations presented in the accompanying paper, which is focused on investigations of economic efficiency. Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)
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18 pages, 1944 KiB  
Article
Analysis of the Power Supply Restoration Time after Failures in Power Transmission Lines
by Alexander Vinogradov, Vadim Bolshev, Alina Vinogradova, Michał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Radomir Goňo and Elżbieta Jasińska
Energies 2020, 13(11), 2736; https://doi.org/10.3390/en13112736 - 29 May 2020
Cited by 20 | Viewed by 3978
Abstract
This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the [...] Read more.
This paper presents the analysis of power supply restoration time after failures occurring in power lines. It found that the power supply restoration time depends on several constituents, such as the time for obtaining information on failures, the time for information recognition, the time to repair failures, and the time for connection harmonization. All these constituents have been considered more specifically. The main constituents’ results values of the power supply restoration time were analyzed for the electrical networks of regional power supply company “Oreolenergo”, a branch of Interregional Distribution Grid Company (IDGC) of Center. The Delphi method was used for determining the time for obtaining information on failures as well as the time for information recognition. The method of mathematical statistics was used to determine the repair time. The determined power supply restoration time (5.28 h) is similar to statistical values of the examined power supply company (the deviation was equal to 9.9%). The technical means of electrical network automation capable of the reduction of the power supply restoration time have also been found. These means were classified according to the time intervals they shorten. Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)
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19 pages, 5463 KiB  
Article
The Application of Hierarchical Clustering to Power Quality Measurements in an Electrical Power Network with Distributed Generation
by Michał Jasiński, Tomasz Sikorski, Zbigniew Leonowicz, Klaudiusz Borkowski and Elżbieta Jasińska
Energies 2020, 13(9), 2407; https://doi.org/10.3390/en13092407 - 11 May 2020
Cited by 16 | Viewed by 3510
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)
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21 pages, 2856 KiB  
Article
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
by Michał Jasiński, Tomasz Sikorski, Paweł Kostyła, Zbigniew Leonowicz and Klaudiusz Borkowski
Energies 2020, 13(8), 2050; https://doi.org/10.3390/en13082050 - 20 Apr 2020
Cited by 23 | Viewed by 2761
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)
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15 pages, 5776 KiB  
Article
Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm
by Vishnu Suresh, Przemyslaw Janik, Jacek Rezmer and Zbigniew Leonowicz
Energies 2020, 13(3), 723; https://doi.org/10.3390/en13030723 - 7 Feb 2020
Cited by 98 | Viewed by 6085
Abstract
The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural [...] Read more.
The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts. Full article
(This article belongs to the Special Issue Signal Analysis in Power Systems)
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