Special Issue "Machine Learning and Data Mining Applications in Power Systems"

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "Electrical Power and Energy System".

Deadline for manuscript submissions: 28 October 2021.

Special Issue Editors

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 and Collections in MDPI journals
Dr. Michał Jasinski
E-Mail Website
Guest Editor
Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
Interests: distributed generation; renewable energy sources; storage systems; power quality; power reliability; virtual power plants; data mining; machine learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear colleagues,

This Special Issue is intended as a forum for advancing research and for applying machine learning and data mining in order to facilitate the development of modern electric power systems, grids and devices, smart grids, and protection devices, as well as for developing tools for more accurate and efficient power system analysis.

Conventional signal processing is not more adequate for extracting all of the relevant information from distorted signals through filtering, estimation, and detection in order to facilitate decision making and to control actions. Machine learning algorithms, optimization techniques and efficient numerical algorithms, distributed signal processing, machine learning, data mining statistical signal detection, and estimation may help in solving contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; dynamic optimization of grid operations; demand response; incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and integration of smart appliances and consumer devices. Signal processing offers the tools needed to convert measurement data to information and to transform information into actionable intelligence.

The expected outcomes will be a grid with improved situation awareness, faster and more accurate control actions to detect and isolate faults, improved assurance of power quality, and higher levels of energy efficiency.

Prof. Dr. Zbigniew Leonowicz
Dr. Michał Jasinski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • data mining
  • smart grids
  • power system control
  • power system protection
  • power flow
  • energy management
  • renewable energy
  • demand-side management
  • demand response
  • load scheduling
  • uncertainty estimation
  • power balancing

Published Papers (6 papers)

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Research

Article
Impact of Harmonic Currents of Nonlinear Loads on Power Quality of a Low Voltage Network–Review and Case Study
Energies 2021, 14(12), 3665; https://doi.org/10.3390/en14123665 - 19 Jun 2021
Viewed by 522
Abstract
The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the [...] Read more.
The paper presents a power-quality analysis in the utility low-voltage network focusing on harmonic currents’ pollution. Usually, to forecast the modern electrical and electronic devices’ contribution to increasing the current total harmonic distortion factor (THDI) and exceeding the regulation limit, analyses based on tests and models of individual devices are conducted. In this article, a composite approach was applied. The performance of harmonic currents produced by sets of devices commonly used in commercial and residential facilities’ nonlinear loads was investigated. The measurements were conducted with the class A PQ analyzer (FLUKE 435) and dedicated to the specialized PC software. The experimental tests show that the harmonic currents produced by multiple types of nonlinear loads tend to reduce the current total harmonic distortion factor (THDI). The changes of harmonic content caused by summation and/or cancellation effects in total current drawn from the grid by nonlinear loads should be a key factor in harmonic currents’ pollution study. Proper forecasting of the level of harmonic currents injected into the utility grid helps to maintain the quality of electricity at an appropriate level and reduce active power losses, which have a direct impact on the price of electricity generation. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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Article
A Case Study on Data Mining Application in a Virtual Power Plant: Cluster Analysis of Power Quality Measurements
Energies 2021, 14(4), 974; https://doi.org/10.3390/en14040974 - 12 Feb 2021
Cited by 1 | Viewed by 597
Abstract
One of the recent trends that concern renewable energy sources and energy storage systems is the concept of virtual power plants (VPP). The majority of research now focuses on analyzing case studies of VPP in different issues. This article presents the investigation that [...] Read more.
One of the recent trends that concern renewable energy sources and energy storage systems is the concept of virtual power plants (VPP). The majority of research now focuses on analyzing case studies of VPP in different issues. This article presents the investigation that is based on a real VPP. That VPP operates in Poland and consists of hydropower plants (HPP), as well as energy storage systems (ESS). For specific analysis, cluster analysis, as a representative technique of data mining, was selected for power quality (PQ) issues. The used data represents 26 weeks of PQ multipoint synchronic measurements for 5 related to VPP points. The investigation discusses different input databases for cluster analysis. Moreover, as an extension to using classical PQ parameters as an input, the application of the global index was proposed. This enables the reduction of the size of the input database with maintaining the data features for cluster analysis. Moreover, the problem of the optimal number of cluster selection is discussed. Finally, the assessment of clustering results was performed to assess the VPP impact on PQ level. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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Article
A Case Study on a Hierarchical Clustering Application in a Virtual Power Plant: Detection of Specific Working Conditions from Power Quality Data
Energies 2021, 14(4), 907; https://doi.org/10.3390/en14040907 - 09 Feb 2021
Cited by 1 | Viewed by 848
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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Article
The Impact of Supply Voltage Waveform Distortion on Non-Intentional Emission in the Frequency Range 2–150 kHz: An Experimental Study with Power-Line Communication and Selected End-User Equipment
Energies 2021, 14(3), 777; https://doi.org/10.3390/en14030777 - 02 Feb 2021
Viewed by 597
Abstract
Knowledge of the conducted emissions in the frequency range 2–150 kHz contains some gaps related to the impact of the harmonics in the supply voltage on the nature of these emissions. It can be noticed that the conducted emissions from non-sinusoidal power supplies [...] Read more.
Knowledge of the conducted emissions in the frequency range 2–150 kHz contains some gaps related to the impact of the harmonics in the supply voltage on the nature of these emissions. It can be noticed that the conducted emissions from non-sinusoidal power supplies have not been studied sufficiently, and that the impact of this distortion may be greater than the generally known results of emission tests carried out under standardized test conditions. This paper is aimed at investigating experimental cases of the influence of supply voltage waveform distortion on non-intentional emission in the range 2–150 kHz and the efficiency of power line communication based on selected PRIME (PoweRline Intelligent Metering Evolution) power line communication (PLC) technology. A series of experimental laboratory studies were investigated, representing the operation of the investigated PLC system with different types of end-user equipment (LED—Light Emitting Diode, CFL—Compact Fluorescent Lamp, induction motor with frequency converter) working under a distorted supply voltage condition obtained by the programmable power supply for different scenarios of the admissible harmonics contribution in the range 0–2 kHz. The scenarios included limits defined in standards EN 50160 and IEC 61000-4-13. The researchers used spectral analysis with a notation to emission limits, compatibility levels, and mains signalling, as well as statistics of the PLC communication. The obtained results provide important conclusions, which may be applied both in the development of the design of the appliances in question and the higher frequency emission testing methods. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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Article
A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics
Energies 2020, 13(24), 6670; https://doi.org/10.3390/en13246670 - 17 Dec 2020
Cited by 1 | Viewed by 483
Abstract
A virtual power plant (VPP) can be defined as the integration of decentralized units into one centralized control system. A VPP consists of generation sources and energy storage units. In this article, based on real measurements, the charging and discharging characteristics of the [...] Read more.
A virtual power plant (VPP) can be defined as the integration of decentralized units into one centralized control system. A VPP consists of generation sources and energy storage units. In this article, based on real measurements, the charging and discharging characteristics of the battery energy storage system (BESS) were determined, which represents a key element of the experimental virtual power plant operating in the power system in Poland. The characteristics were determined using synchronous measurements of the power of charge and discharge of the storage and the state of charge (SoC). The analyzed private network also includes a hydroelectric power plant (HPP) and loads. The article also examines the impact of charging and discharging characteristics of the BESS on its operation, analyzing the behavior of the storage unit for the given operation plans. The last element of the analysis is to control the power flow in the private network. The operation of the VPP for the given scenario of power flow control was examined. The aim of the scenario is to adjust the load of the private network to the level set by the function. The tests of power flow are carried out on the day on which the maximum power demand occurred. The analysis was performed for four cases: a constant value limitation when the HPP is in operation and when it is not, and two limits set by function during normal operation of the HPP. Thus, the article deals not only with the issue of determining the actual characteristics of charging and discharging the storage unit, but also their impact on the operation of the entire VPP. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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Article
A Case Study on Power Quality in a Virtual Power Plant: Long Term Assessment and Global Index Application
Energies 2020, 13(24), 6578; https://doi.org/10.3390/en13246578 - 14 Dec 2020
Cited by 3 | Viewed by 453
Abstract
The concept of virtual power plants (VPP) was introduced over 20 years ago but is still actively researched. The majority of research now focuses on analyzing case studies of such installations. In this article, the investigation is based on a VPP in Poland, [...] Read more.
The concept of virtual power plants (VPP) was introduced over 20 years ago but is still actively researched. The majority of research now focuses on analyzing case studies of such installations. In this article, the investigation is based on a VPP in Poland, which contains hydropower plants (HPP) and energy storage systems (ESS). For specific analysis, the power quality (PQ) issues were selected. The used data contain 26 weeks of multipoint, synchronic measurements of power quality levels in four related points. The investigation is concerned with the application of a global index to a single-point assessment as well as an area-related assessment approach. Moreover, the problem of flagged data is discussed. Finally, the assessment of VPP’s impact on PQ level is conducted. Full article
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
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