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...
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| Định dạng: | Online |
| Ngôn ngữ: | Tiếng Anh |
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MDPI - Multidisciplinary Digital Publishing Institute
2021
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| Những chủ đề: | |
| Truy cập trực tuyến: | 38846 |
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| _version_ | 1869529602085355520 |
|---|---|
| author | Kodogiannis, Vassilis S. |
| author_browse | Kodogiannis, Vassilis S. |
| author_facet | Kodogiannis, Vassilis S. |
| author_sort | Kodogiannis, Vassilis S. |
| collection | Directory of Open Access Books |
| description | 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. |
| format | Online |
| id | doab-20.500.12854ir-41063 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-410632024-04-11T15:10:36Z Applications of Computational Intelligence to Power Systems Kodogiannis, Vassilis S. TA1-2040 T1-995 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology 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. 2021-02-11T08:19:35Z 2021-02-11T08:19:35Z 2019-11-08 11:31:56 2019 book 38846 9783039217618 9783039217601 https://directory.doabooks.org/handle/20.500.12854/41063 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/1781 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-03921-761-8 10.3390/books978-3-03921-761-8 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783039217618 9783039217601 116 open access |
| spellingShingle | TA1-2040 T1-995 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Kodogiannis, Vassilis S. Applications of Computational Intelligence to Power Systems |
| 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_short | Applications of Computational Intelligence to Power Systems |
| title_sort | applications of computational intelligence to power systems |
| topic | TA1-2040 T1-995 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | TA1-2040 T1-995 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 thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | 38846 |
| work_keys_str_mv | AT kodogiannisvassiliss applicationsofcomputationalintelligencetopowersystems |