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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spelling 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
format eBook
author2 Jasiński, Michał
Leonowicz, Zbigniew
Jasiński, Michał
author_facet Jasiński, Michał
Leonowicz, Zbigniew
Jasiński, Michał
author2_variant z l zl
m j mj
author2_role 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
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