Machine Learning and Data Mining Applications in Power Systems

Edited by
May 2022
314 pages
  • ISBN978-3-0365-4177-8 (Hardback)
  • ISBN978-3-0365-4178-5 (PDF)

This book is a reprint of the Special Issue Machine Learning and Data Mining Applications in Power Systems that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Physical Sciences

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.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
virtual power plant (VPP); power quality (PQ); global index; distributed energy resources (DER); energy storage systems (ESS); power systems; long-term assessment; virtual power plant (VPP); distributed energy resources (DER); battery energy storage systems (BESS); power systems; smart grids; conducted disturbances; power quality; supraharmonics; 2–150 kHz; Power Line Communications (PLC); intentional emission; non-intentional emission; mains signalling; virtual power plant; power quality; data mining; clustering; distributed energy resources; energy storage systems; short term conditions; virtual power plant (VPP); cluster analysis (CA); power quality (PQ); global index; distributed energy resources (DER); energy storage systems (ESS); power systems; smart grids; nonlinear loads; harmonics, cancellation, and attenuation of harmonics; waveform distortion; THDi; power quality; low-voltage networks; optimization techniques; different batteries; off-grid microgrid; integrated renewable energy system; power quality; cluster analysis; K-means; agglomerative; virtual power plant; 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; power quality; 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; n/a

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