Fast dynamic voltage security margin estimation: concept and development (English)
Free access
- New search for: Hagmar, Hannes
- New search for: Eriksson, Robert
- New search for: Tuan, Le Anh
- New search for: Hagmar, Hannes
- New search for: Eriksson, Robert
- New search for: Tuan, Le Anh
In:
IET Smart Grid
;
3
, 4
;
470-478
;
2020
-
ISSN:
- Article (Journal) / Electronic Resource
-
Title:Fast dynamic voltage security margin estimation: concept and development
-
Contributors:
-
Published in:IET Smart Grid ; 3, 4 ; 470-478
-
Publisher:
- New search for: The Institution of Engineering and Technology
-
Publication date:2020-05-29
-
Size:9 pages
-
ISSN:
-
DOI:
-
Type of media:Article (Journal)
-
Type of material:Electronic Resource
-
Language:English
-
Keywords:machine learning-based method , power system stability , DVSM , dynamic system response , learning (artificial intelligence) , fast dynamic voltage security margin estimation , power system security , power system faults , trained neural networks , static VSM , neural nets , time-domain simulations , Nordic32 test system , static voltage security margin , power system control , power engineering computing
-
Source:
Metadata by IET is licensed under CC BY 3.0
Table of contents – Volume 3, Issue 4
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 419
-
Guest Editorial: Machine Learning in Power Systems| 2020
- 421
-
Application technique for model-based approach to estimate fault locationNavalpakkam Ananthan, Sundaravaradan / Santoso, Surya et al. | 2020
- 435
-
AI in arcing‐HIF detection: a brief reviewHao, Bai et al. | 2020
- 445
-
Hybrid data‐driven physics model‐based framework for enhanced cyber‐physical smart grid securityRuben, Cody / Dhulipala, Surya / Nagaraj, Keerthiraj / Zou, Sheng / Starke, Allen / Bretas, Arturo / Zare, Alina / McNair, Janise et al. | 2020
- 454
-
Real-time stability assessment in smart cyber-physical grids: a deep learning approachDarbandi, Farzad / Jafari, Amirreza / Karimipour, Hadis / Dehghantanha, Ali / Derakhshan, Farnaz / Raymond Choo, Kim-Kwang et al. | 2020
- 462
-
Deep learning for day‐ahead electricity price forecastingZhang, Chi / Li, Ran / Shi, Heng / Li, Furong et al. | 2020
- 470
-
Fast dynamic voltage security margin estimation: concept and developmentHagmar, Hannes / Eriksson, Robert / Tuan, Le Anh et al. | 2020
- 479
-
Research on hierarchical control and optimisation learning method of multi‐energy microgrid considering multi‐agent gameLiu, Hong / Li, Jifeng / Ge, Shaoyun et al. | 2020
- 490
-
Phase identification using co‐association matrix ensemble clusteringBlakely, Logan / Reno, Matthew J. et al. | 2020
- 500
-
Improving primary frequency response in networked microgrid operations using multilayer perceptron-driven reinforcement learningRadhakrishnan, Nikitha / Chakraborty, Indrasis / Xie, Jing / Thekkumparambath Mana, Priya / Tuffner, Francis K. / Bhattarai, Bishnu P. / Schneider, Kevin P. et al. | 2020
- 508
-
Adapting big data standards, maturity models to smart grid distributed generation: critical reviewSundararajan, Aditya / Hernandez, Alexander S. / Sarwat, Arif I. et al. | 2020
- 520
-
Stacking battery energy storage revenues with enhanced service provisionBrogan, Paul Vincent / Best, Robert / Morrow, John / Duncan, Robin / Kubik, Marek et al. | 2020
- 530
-
Power system virtual inertia implemented by thermostatically controlled loadsWang, Zhe / Bao, Yu-Qing / Di, Hui-Fang et al. | 2020
- 538
-
Distributed PV generation estimation using multi-rate and event-driven Kalman kriging filterAlam, SM Shafiul / Florita, Anthony R. / Hodge, Bri-Mathias et al. | 2020
- 547
-
Corrigendum: Design and techno‐economic analysis of plug‐in electric vehicle‐integrated solar PV charging system for India| 2020