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 to facilitate the development of modern electric power systems, grids and devices, and smart grids and protection devices, as well as to develop tools for more accurate and efficient pow...
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Year of Publication: | 2022 |
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Physical Description: | 1 electronic resource (314 p.) |
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Leonowicz, Zbigniew edt Machine Learning and Data Mining Applications in Power Systems Basel MDPI - Multidisciplinary Digital Publishing Institute 2022 1 electronic resource (314 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier This Special Issue was intended as a forum to advance research and apply machine-learning and data-mining methods to facilitate the development of modern electric power systems, grids and devices, and 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 to solve contemporary challenges in modern power systems. The increased use of digital information and control technology can improve the grid’s reliability, security, and efficiency; the dynamic optimization of grid operations; demand response; the incorporation of demand-side resources and integration of energy-efficient resources; distribution automation; and the 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. English Technology: general issues bicssc History of engineering & technology bicssc Energy industries & utilities bicssc virtual power plant (VPP) power quality (PQ) global index distributed energy resources (DER) energy storage systems (ESS) power systems long-term assessment battery energy storage systems (BESS) smart grids conducted disturbances power quality supraharmonics 2-150 kHz Power Line Communications (PLC) intentional emission non-intentional emission mains signalling virtual power plant data mining clustering distributed energy resources energy storage systems short term conditions cluster analysis (CA) nonlinear loads harmonics, cancellation, and attenuation of harmonics waveform distortion THDi low-voltage networks optimization techniques different batteries off-grid microgrid integrated renewable energy system cluster analysis K-means agglomerative ANFIS fuzzy logic induction generator MPPT neural network renewable energy variable speed WECS wind energy conversion system wind energy frequency estimation spectrum interpolation power network disturbances COVID-19 time-varying reproduction number social distancing load profile demographic characteristic household energy consumption demand-side management energy management time series Hidden Markov Model short-term forecast sparse signal decomposition supervised dictionary learning dictionary impulsion singular value decomposition discrete cosine transform discrete Haar transform discrete wavelet transform transient stability assessment home energy management binary-coded genetic algorithms optimal power scheduling demand response Data Injection Attack machine learning critical infrastructure smart grid water treatment plant power system 3-0365-4177-2 3-0365-4178-0 Jasiński, Michał edt Leonowicz, Zbigniew oth Jasiński, Michał oth |
language |
English |
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eBook |
author2 |
Jasiński, Michał Leonowicz, Zbigniew Jasiński, Michał |
author_facet |
Jasiński, Michał Leonowicz, Zbigniew Jasiński, Michał |
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HerausgeberIn Sonstige Sonstige |
title |
Machine Learning and Data Mining Applications in Power Systems |
spellingShingle |
Machine Learning and Data Mining Applications in Power Systems |
title_full |
Machine Learning and Data Mining Applications in Power Systems |
title_fullStr |
Machine Learning and Data Mining Applications in Power Systems |
title_full_unstemmed |
Machine Learning and Data Mining Applications in Power Systems |
title_auth |
Machine Learning and Data Mining Applications in Power Systems |
title_new |
Machine Learning and Data Mining Applications in Power Systems |
title_sort |
machine learning and data mining applications in power systems |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2022 |
physical |
1 electronic resource (314 p.) |
isbn |
3-0365-4177-2 3-0365-4178-0 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT leonowiczzbigniew machinelearninganddataminingapplicationsinpowersystems AT jasinskimichał machinelearninganddataminingapplicationsinpowersystems |
status_str |
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Machine Learning and Data Mining Applications in Power Systems |
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