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
Language:English
Physical Description:1 electronic resource (314 p.)
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520 |a 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. 
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653 |a supraharmonics 
653 |a 2-150 kHz 
653 |a Power Line Communications (PLC) 
653 |a intentional emission 
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653 |a mains signalling 
653 |a virtual power plant 
653 |a data mining 
653 |a clustering 
653 |a distributed energy resources 
653 |a energy storage systems 
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653 |a cluster analysis (CA) 
653 |a nonlinear loads 
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653 |a THDi 
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653 |a optimization techniques 
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653 |a wind energy 
653 |a frequency estimation 
653 |a spectrum interpolation 
653 |a power network disturbances 
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653 |a social distancing 
653 |a load profile 
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653 |a household energy consumption 
653 |a demand-side management 
653 |a energy management 
653 |a time series 
653 |a Hidden Markov Model 
653 |a short-term forecast 
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653 |a supervised dictionary learning 
653 |a dictionary impulsion 
653 |a singular value decomposition 
653 |a discrete cosine transform 
653 |a discrete Haar transform 
653 |a discrete wavelet transform 
653 |a transient stability assessment 
653 |a home energy management 
653 |a binary-coded genetic algorithms 
653 |a optimal power scheduling 
653 |a demand response 
653 |a Data Injection Attack 
653 |a machine learning 
653 |a critical infrastructure 
653 |a smart grid 
653 |a water treatment plant 
653 |a power system 
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700 1 |a Jasiński, Michał  |4 edt 
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