Big data application in power systems:
Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down th...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam
Elsevier
[2024]
|
| Ausgabe: | Second edition |
| Schlagworte: | |
| Zusammenfassung: | Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes |
| Beschreibung: | Section One: Harness the Big data from Power Systems 1. A Holistic Approach to Becoming a Data-driven Utility 2. Security and Data Privacy Challenges for Data-driven Utilities 3. The Role of Big Data and Analytics in Utilities Innovation 4. Big Data integration for the digitalisation and decarbonisation of distribution grids Section Two: Put the Power of Big data into Power Systems 5. Topology Detection in Distribution Networks with Machine Learning 6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 7. Learning Stable Volt/Var Controllers in Distribution Grids 8. Grid-edge Optimization and Control with Machine Learning 9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks 10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks 11. Transient Stability Predictions in Power Systems using Transfer Learning 12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning 13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning 14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning 15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids 16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study 17. Grid resilience against wildfire with Machine Learning |
| Beschreibung: | xix, 428 Seiten Illustrationen, Diagramme 450 gr |
| ISBN: | 9780443215247 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV049776196 | ||
| 003 | DE-604 | ||
| 005 | 20240903 | ||
| 007 | t| | ||
| 008 | 240710s2024 xx a||| |||| 00||| eng d | ||
| 020 | |a 9780443215247 |9 978-0-443-21524-7 | ||
| 024 | 3 | |a 9780443215247 | |
| 035 | |a (OCoLC)1454753319 | ||
| 035 | |a (DE-599)BVBBV049776196 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-29T | ||
| 245 | 1 | 0 | |a Big data application in power systems |c edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States) |
| 250 | |a Second edition | ||
| 264 | 1 | |a Amsterdam |b Elsevier |c [2024] | |
| 300 | |a xix, 428 Seiten |b Illustrationen, Diagramme |c 450 gr | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Section One: Harness the Big data from Power Systems 1. A Holistic Approach to Becoming a Data-driven Utility 2. Security and Data Privacy Challenges for Data-driven Utilities 3. The Role of Big Data and Analytics in Utilities Innovation 4. Big Data integration for the digitalisation and decarbonisation of distribution grids Section Two: Put the Power of Big data into Power Systems 5. Topology Detection in Distribution Networks with Machine Learning 6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 7. Learning Stable Volt/Var Controllers in Distribution Grids 8. Grid-edge Optimization and Control with Machine Learning 9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks 10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks 11. Transient Stability Predictions in Power Systems using Transfer Learning 12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning 13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning 14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning 15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids 16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study 17. Grid resilience against wildfire with Machine Learning | ||
| 520 | |a Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes | ||
| 650 | 0 | 7 | |a Intelligentes Netz |0 (DE-588)4215804-7 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Big Data |0 (DE-588)4802620-7 |2 gnd |9 rswk-swf |
| 655 | 7 | |0 (DE-588)4143413-4 |a Aufsatzsammlung |2 gnd-content | |
| 689 | 0 | 0 | |a Intelligentes Netz |0 (DE-588)4215804-7 |D s |
| 689 | 0 | 1 | |a Big Data |0 (DE-588)4802620-7 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Arghandeh, Reza |0 (DE-588)1151416649 |4 edt | |
| 700 | 1 | |a Zhou, Yuxun |0 (DE-588)1151416770 |4 edt | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-035117263 | |
Datensatz im Suchindex
| _version_ | 1820889799080280064 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Arghandeh, Reza |
| author2 | Arghandeh, Reza Zhou, Yuxun |
| author2_role | edt edt |
| author2_variant | r a ra y z yz |
| author_GND | (DE-588)1151416649 (DE-588)1151416770 |
| author_facet | Arghandeh, Reza Arghandeh, Reza Zhou, Yuxun |
| author_role | aut |
| author_sort | Arghandeh, Reza |
| author_variant | r a ra |
| building | Verbundindex |
| bvnumber | BV049776196 |
| ctrlnum | (OCoLC)1454753319 (DE-599)BVBBV049776196 |
| edition | Second edition |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 c 4500</leader><controlfield tag="001">BV049776196</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20240903</controlfield><controlfield tag="007">t|</controlfield><controlfield tag="008">240710s2024 xx a||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780443215247</subfield><subfield code="9">978-0-443-21524-7</subfield></datafield><datafield tag="024" ind1="3" ind2=" "><subfield code="a">9780443215247</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1454753319</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV049776196</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-29T</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Big data application in power systems</subfield><subfield code="c">edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States)</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">Second edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam</subfield><subfield code="b">Elsevier</subfield><subfield code="c">[2024]</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xix, 428 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield><subfield code="c">450 gr</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Section One: Harness the Big data from Power Systems 1. A Holistic Approach to Becoming a Data-driven Utility 2. Security and Data Privacy Challenges for Data-driven Utilities 3. The Role of Big Data and Analytics in Utilities Innovation 4. Big Data integration for the digitalisation and decarbonisation of distribution grids Section Two: Put the Power of Big data into Power Systems 5. Topology Detection in Distribution Networks with Machine Learning 6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 7. Learning Stable Volt/Var Controllers in Distribution Grids 8. Grid-edge Optimization and Control with Machine Learning 9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks 10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks 11. Transient Stability Predictions in Power Systems using Transfer Learning 12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning 13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning 14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning 15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids 16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study 17. Grid resilience against wildfire with Machine Learning</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Intelligentes Netz</subfield><subfield code="0">(DE-588)4215804-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="655" ind1=" " ind2="7"><subfield code="0">(DE-588)4143413-4</subfield><subfield code="a">Aufsatzsammlung</subfield><subfield code="2">gnd-content</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Intelligentes Netz</subfield><subfield code="0">(DE-588)4215804-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Big Data</subfield><subfield code="0">(DE-588)4802620-7</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arghandeh, Reza</subfield><subfield code="0">(DE-588)1151416649</subfield><subfield code="4">edt</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Zhou, Yuxun</subfield><subfield code="0">(DE-588)1151416770</subfield><subfield code="4">edt</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035117263</subfield></datafield></record></collection> |
| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV049776196 |
| illustrated | Illustrated |
| indexdate | 2025-01-10T19:03:54Z |
| institution | BVB |
| isbn | 9780443215247 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035117263 |
| oclc_num | 1454753319 |
| open_access_boolean | |
| owner | DE-29T |
| owner_facet | DE-29T |
| physical | xix, 428 Seiten Illustrationen, Diagramme 450 gr |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| record_format | marc |
| spelling | Big data application in power systems edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States) Second edition Amsterdam Elsevier [2024] xix, 428 Seiten Illustrationen, Diagramme 450 gr txt rdacontent n rdamedia nc rdacarrier Section One: Harness the Big data from Power Systems 1. A Holistic Approach to Becoming a Data-driven Utility 2. Security and Data Privacy Challenges for Data-driven Utilities 3. The Role of Big Data and Analytics in Utilities Innovation 4. Big Data integration for the digitalisation and decarbonisation of distribution grids Section Two: Put the Power of Big data into Power Systems 5. Topology Detection in Distribution Networks with Machine Learning 6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 7. Learning Stable Volt/Var Controllers in Distribution Grids 8. Grid-edge Optimization and Control with Machine Learning 9. Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks 10. Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks 11. Transient Stability Predictions in Power Systems using Transfer Learning 12. Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning 13. Virtual Inertia Provision from Distribution Power Systems using Machine Learning 14. Electricity Demand Flexibility Estimation in Warehouses using Machine Learning 15. Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids 16. Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study 17. Grid resilience against wildfire with Machine Learning Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes Intelligentes Netz (DE-588)4215804-7 gnd rswk-swf Big Data (DE-588)4802620-7 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Intelligentes Netz (DE-588)4215804-7 s Big Data (DE-588)4802620-7 s DE-604 Arghandeh, Reza (DE-588)1151416649 edt Zhou, Yuxun (DE-588)1151416770 edt |
| spellingShingle | Arghandeh, Reza Big data application in power systems Intelligentes Netz (DE-588)4215804-7 gnd Big Data (DE-588)4802620-7 gnd |
| subject_GND | (DE-588)4215804-7 (DE-588)4802620-7 (DE-588)4143413-4 |
| title | Big data application in power systems |
| title_auth | Big data application in power systems |
| title_exact_search | Big data application in power systems |
| title_full | Big data application in power systems edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States) |
| title_fullStr | Big data application in power systems edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States) |
| title_full_unstemmed | Big data application in power systems edited by Reza Arghandeh (Western Norway University of Applied Sciences, Bergen, Norway), Yuxun Zhou (Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States) |
| title_short | Big data application in power systems |
| title_sort | big data application in power systems |
| topic | Intelligentes Netz (DE-588)4215804-7 gnd Big Data (DE-588)4802620-7 gnd |
| topic_facet | Intelligentes Netz Big Data Aufsatzsammlung |
| work_keys_str_mv | AT arghandehreza bigdataapplicationinpowersystems AT zhouyuxun bigdataapplicationinpowersystems |