Applications of Computational Intelligence to Power Systems
Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation...
Saved in:
: | |
---|---|
Year of Publication: | 2019 |
Language: | English |
Physical Description: | 1 electronic resource (116 p.) |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
993547914804498 |
---|---|
ctrlnum |
(CKB)4100000010106246 (oapen)https://directory.doabooks.org/handle/20.500.12854/41063 (EXLCZ)994100000010106246 |
collection |
bib_alma |
record_format |
marc |
spelling |
Kodogiannis, Vassilis S. auth Applications of Computational Intelligence to Power Systems MDPI - Multidisciplinary Digital Publishing Institute 2019 1 electronic resource (116 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier Open access Unrestricted online access star Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field. English localization reactive power optimization model predictive control CNN long short term memory (LSTM) meter allocation particle update mode combined economic emission/environmental dispatch glass insulator emission dispatch genetic algorithm grid observability defect detection feature extraction parameter estimation incipient cable failure active distribution system boiler load constraints multivariate time series particle swarm optimization inertia weight VMD NOx emissions constraints spatial features penalty factor approach self-shattering differential evolution algorithm short term load forecasting (STLF) genetic algorithm (GA) economic load dispatch least square support vector machine Combustion efficiency electricity load forecasting 3-03921-760-7 |
language |
English |
format |
eBook |
author |
Kodogiannis, Vassilis S. |
spellingShingle |
Kodogiannis, Vassilis S. Applications of Computational Intelligence to Power Systems |
author_facet |
Kodogiannis, Vassilis S. |
author_variant |
v s k vs vsk |
author_sort |
Kodogiannis, Vassilis S. |
title |
Applications of Computational Intelligence to Power Systems |
title_full |
Applications of Computational Intelligence to Power Systems |
title_fullStr |
Applications of Computational Intelligence to Power Systems |
title_full_unstemmed |
Applications of Computational Intelligence to Power Systems |
title_auth |
Applications of Computational Intelligence to Power Systems |
title_new |
Applications of Computational Intelligence to Power Systems |
title_sort |
applications of computational intelligence to power systems |
publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
publishDate |
2019 |
physical |
1 electronic resource (116 p.) |
isbn |
3-03921-761-5 3-03921-760-7 |
illustrated |
Not Illustrated |
work_keys_str_mv |
AT kodogiannisvassiliss applicationsofcomputationalintelligencetopowersystems |
status_str |
n |
ids_txt_mv |
(CKB)4100000010106246 (oapen)https://directory.doabooks.org/handle/20.500.12854/41063 (EXLCZ)994100000010106246 |
carrierType_str_mv |
cr |
is_hierarchy_title |
Applications of Computational Intelligence to Power Systems |
_version_ |
1789884009210183681 |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>03630nam-a2200673z--4500</leader><controlfield tag="001">993547914804498</controlfield><controlfield tag="005">20240131171805.0</controlfield><controlfield tag="006">m o d </controlfield><controlfield tag="007">cr|mn|---annan</controlfield><controlfield tag="008">202102s2019 xx |||||o ||| 0|eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">3-03921-761-5</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(CKB)4100000010106246</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(oapen)https://directory.doabooks.org/handle/20.500.12854/41063</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(EXLCZ)994100000010106246</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kodogiannis, Vassilis S.</subfield><subfield code="4">auth</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Applications of Computational Intelligence to Power Systems</subfield></datafield><datafield tag="260" ind1=" " ind2=" "><subfield code="b">MDPI - Multidisciplinary Digital Publishing Institute</subfield><subfield code="c">2019</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 electronic resource (116 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="506" ind1=" " ind2=" "><subfield code="a">Open access</subfield><subfield code="f">Unrestricted online access</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Electric power systems around the world are changing in terms of structure, operation, management and ownership due to technical, financial, and ideological reasons. Power systems keep on expanding in terms of geographical areas, asset additions, and the penetration of new technologies in generation, transmission, and distribution. The conventional methods for solving the power system design, planning, operation, and control problems have been extensively used for different applications, but these methods suffer from several difficulties, thus providing suboptimal solutions. Computationally intelligent methods can offer better solutions for several conditions and are being widely applied in electrical engineering applications. This Special Issue represents a thorough treatment of computational intelligence from an electrical power system engineer’s perspective. Thorough, well-organised, and up-to-date, it examines in detail some of the important aspects of this very exciting and rapidly emerging technology, including machine learning, particle swarm optimization, genetic algorithms, and deep learning systems. Written in a concise and flowing manner by experts in the area of electrical power systems who have experience in the application of computational intelligence for solving many complex and difficult power system problems, this Special Issue is ideal for professional engineers and postgraduate students entering this exciting field.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">English</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">localization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">reactive power optimization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">model predictive control</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">CNN</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">long short term memory (LSTM)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">meter allocation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">particle update mode</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">combined economic emission/environmental dispatch</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">glass insulator</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">emission dispatch</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">genetic algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">grid observability</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">defect detection</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">feature extraction</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">parameter estimation</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">incipient cable failure</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">active distribution system</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">boiler load constraints</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">multivariate time series</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">particle swarm optimization</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">inertia weight</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">VMD</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">NOx emissions constraints</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">spatial features</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">penalty factor approach</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">self-shattering</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">differential evolution algorithm</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">short term load forecasting (STLF)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">genetic algorithm (GA)</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">economic load dispatch</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">least square support vector machine</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Combustion efficiency</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">electricity load forecasting</subfield></datafield><datafield tag="776" ind1=" " ind2=" "><subfield code="z">3-03921-760-7</subfield></datafield><datafield tag="906" ind1=" " ind2=" "><subfield code="a">BOOK</subfield></datafield><datafield tag="ADM" ind1=" " ind2=" "><subfield code="b">2024-02-03 11:20:58 Europe/Vienna</subfield><subfield code="f">system</subfield><subfield code="c">marc21</subfield><subfield code="a">2020-02-01 22:26:53 Europe/Vienna</subfield><subfield code="g">false</subfield></datafield><datafield tag="AVE" ind1=" " ind2=" "><subfield code="i">DOAB Directory of Open Access Books</subfield><subfield code="P">DOAB Directory of Open Access Books</subfield><subfield code="x">https://eu02.alma.exlibrisgroup.com/view/uresolver/43ACC_OEAW/openurl?u.ignore_date_coverage=true&portfolio_pid=5338707000004498&Force_direct=true</subfield><subfield code="Z">5338707000004498</subfield><subfield code="b">Available</subfield><subfield code="8">5338707000004498</subfield></datafield></record></collection> |