Ensemble methods of classification for power systems security assessment
| Autor: | Denis Sidorov, Aleksei Zhukov, Daniil Panasetsky, V. G. Kurbatsky, Aoife Foley, Nikita Tomin |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
random forests Boosting (machine learning) Computer Science - Artificial Intelligence Computer science boosting 020209 energy Feature vector security assessment Decision tree 02 engineering and technology heuristics computer.software_genre Machine Learning (cs.LG) Electric power system 0202 electrical engineering electronic engineering information engineering SDG 7 - Affordable and Clean Energy lcsh:T58.5-58.64 lcsh:Information technology ensemble methods Decision rule Grid Classification Ensemble learning Computer Science Applications Random forest 68T05 Computer Science - Learning Artificial Intelligence (cs.AI) power system 020201 artificial intelligence & image processing Data mining computer Software Information Systems |
| Zdroj: | Zhukov, A, Tomin, N, Kurbatsky, V, Sidorov, D, Panasetsky, D & Foley, A 2019, ' Ensemble methods of classification for power systems security assessment ', Applied Computing and Informatics, vol. 15, pp. 45-53 . https://doi.org/10.1016/j.aci.2017.09.007 Applied Computing and Informatics, Vol 15, Iss 1, Pp 45-53 (2019) |
| DOI: | 10.1016/j.aci.2017.09.007 |
| Popis: | One of the most promising approaches for complex technical systems analysis employs ensemble methods of classification. Ensemble methods enable to build a reliable decision rules for feature space classification in the presence of many possible states of the system. In this paper, novel techniques based on decision trees are used for evaluation of the reliability of the regime of electric power systems. We proposed hybrid approach based on random forests models and boosting models. Such techniques can be applied to predict the interaction of increasing renewable power, storage devices and swiching of smart loads from intelligent domestic appliances, heaters and air-conditioning units and electric vehicles with grid for enhanced decision making. The ensemble classification methods were tested on the modified 118-bus IEEE power system showing that proposed technique can be employed to examine whether the power system is secured under steady-state operating conditions. Comment: 6 pages, 4 figures, 4 tables. Submitted to PSSC |
| Databáze: | OpenAIRE |
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