TY - BOOK ID - 133968754 TI - Machine Learning and Data Mining Applications in Power Systems AU - Leonowicz, Zbigniew AU - Jasiński, Michał PY - 2022 PB - Basel MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Technology: general issues KW - History of engineering & technology KW - Energy industries & utilities KW - virtual power plant (VPP) KW - power quality (PQ) KW - global index KW - distributed energy resources (DER) KW - energy storage systems (ESS) KW - power systems KW - long-term assessment KW - battery energy storage systems (BESS) KW - smart grids KW - conducted disturbances KW - power quality KW - supraharmonics KW - 2–150 kHz KW - Power Line Communications (PLC) KW - intentional emission KW - non-intentional emission KW - mains signalling KW - virtual power plant KW - data mining KW - clustering KW - distributed energy resources KW - energy storage systems KW - short term conditions KW - cluster analysis (CA) KW - nonlinear loads KW - harmonics, cancellation, and attenuation of harmonics KW - waveform distortion KW - THDi KW - low-voltage networks KW - optimization techniques KW - different batteries KW - off-grid microgrid KW - integrated renewable energy system KW - cluster analysis KW - K-means KW - agglomerative KW - ANFIS KW - fuzzy logic KW - induction generator KW - MPPT KW - neural network KW - renewable energy KW - variable speed WECS KW - wind energy conversion system KW - wind energy KW - frequency estimation KW - spectrum interpolation KW - power network disturbances KW - COVID-19 KW - time-varying reproduction number KW - social distancing KW - load profile KW - demographic characteristic KW - household energy consumption KW - demand-side management KW - energy management KW - time series KW - Hidden Markov Model KW - short-term forecast KW - sparse signal decomposition KW - supervised dictionary learning KW - dictionary impulsion KW - singular value decomposition KW - discrete cosine transform KW - discrete Haar transform KW - discrete wavelet transform KW - transient stability assessment KW - home energy management KW - binary-coded genetic algorithms KW - optimal power scheduling KW - demand response KW - Data Injection Attack KW - machine learning KW - critical infrastructure KW - smart grid KW - water treatment plant KW - power system KW - n/a KW - 2-150 kHz UR - https://www.unicat.be/uniCat?func=search&query=sysid:133968754 AB - 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. ER -