Machine Learning and Data Mining Applications in Power Systems
Faculty of Electrical Engineering, Wroclaw University of Science and Technology, Wyb. Wyspianskiego 27, 50370 Wroclaw, Poland
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Author to whom correspondence should be addressed.
Energies 2022, 15(5), 1676; https://doi.org/10.3390/en15051676
Submission received: 18 February 2022
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Revised: 20 February 2022
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Accepted: 21 February 2022
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Published: 24 February 2022
(This article belongs to the Special Issue Machine Learning and Data Mining Applications in Power Systems)
This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods in order to facilitate the development of modern electric power systems, grids and devices, smart grids and protection devices, as well as to develop tools for more accurate and efficient power system analysis.
Conventional signal processing is no longer adequate to extract all the relevant information from distorted signals through filtering, estimation, and detection to facilitate decision making and 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.
This Special Issue includes fifteen articles, authored by international research teams from several countries. For a straightforward browsing of the volume content, the articles can be grouped into the following subjects:
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- Minority oversampling techniques applied to the detection of injection of false data and commands into communication [1].
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- Binary-coded genetic algorithms applied to the intelligent scheduling of smart home appliances [2].
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- Adaptive supervised dictionary learning (SDL) for wide-area stability assessment [3].
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- Forecasting of consumption in isolated areas using data sequencing, sequential mining, and pattern mining to infer the results into a Hidden Markov Model (MAESHA H2020 project) [4].
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- Impact of social distancing, implemented as a result of COVID-19, on residential energy consumption [5].
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- Application of the IpDFT spectrum interpolation method to estimate the fundamental frequency of a power waveform [6].
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- Application of an adaptive neuro-fuzzy inference system (ANFIS) maximum power point-tracking (MPPT) controller for DFIG-based wind-energy conversion systems (WECS) [7].
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- Application of Dynamic Differential Annealed Optimization to design of off-grid rural electrification in India using renewable energy resources and battery technologies [11].
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To conclude, with reference to presented papers, we have seen a broad spectrum of data-mining and modern machine-learning techniques applied to recent problems of operation of power systems.
Data mining is a powerful new technology with great potential to help researchers focus on the most important information in their large databases. Machine learning aims to build computer systems that can learn how to solve complex problems by themselves. Deep learning builds a complex mathematical structure (a neural network) based on vast quantities of data. The two group of methods will eventually merge to provide more powerful tools for the unsupervised analysis of “big data” sets.
Author Contributions
All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Acknowledgments
The editors of this Special Issue are grateful to the MDPI Publisher for the invitation to act as guest editors of this Special Issue. All authors are indebted to the editorial staff of “Energies” for their kind co-operation, patience and committed engagement.
Conflicts of Interest
The authors declare no conflict of interest.
References
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- Kaczorowska, D.; Rezmer, J.; Jasinski, M.; Sikorski, T.; Suresh, V.; Leonowicz, Z.; Kostyla, P.; Szymanda, J.; Janik, P. A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics. Energies 2020, 13, 6670. [Google Scholar] [CrossRef]
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MDPI and ACS Style
Leonowicz, Z.; Jasinski, M. Machine Learning and Data Mining Applications in Power Systems. Energies 2022, 15, 1676. https://doi.org/10.3390/en15051676
AMA Style
Leonowicz Z, Jasinski M. Machine Learning and Data Mining Applications in Power Systems. Energies. 2022; 15(5):1676. https://doi.org/10.3390/en15051676
Chicago/Turabian StyleLeonowicz, Zbigniew, and Michal Jasinski. 2022. "Machine Learning and Data Mining Applications in Power Systems" Energies 15, no. 5: 1676. https://doi.org/10.3390/en15051676
APA StyleLeonowicz, Z., & Jasinski, M. (2022). Machine Learning and Data Mining Applications in Power Systems. Energies, 15(5), 1676. https://doi.org/10.3390/en15051676
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