Hidden Markov Models for Wind Farm Power Output
| Autor: | Stella Kapodistria, Daan Crommelin, Bert Zwart, Debarati Bhaumik |
|---|---|
| Přispěvatelé: | Stochastic Operations Research, Analysis (KDV, FNWI), Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Mathematical optimization
Computer science 020209 energy Power system reliability 02 engineering and technology Markov model Turbine Wind speed Stochastic processes Wind turbines 0202 electrical engineering electronic engineering information engineering Wind farms Wind farm power generation SDG 7 - Affordable and Clean Energy Time series Hidden Markov model Solar power power system modeling Wind power Renewable Energy Sustainability and the Environment business.industry hidden Markov models 020208 electrical & electronic engineering Renewable energy data models time series analysis Wind power generation business SDG 7 – Betaalbare en schone energie |
| Zdroj: | IEEE Transactions on Sustainable Energy, 10(2):8356139, 533-539. Institute of Electrical and Electronics Engineers IEEE Transactions on Sustainable Energy, 10(2), 533-539. Institute of Electrical and Electronics Engineers Inc. IEEE Transactions on Sustainable Energy, 10(2), 533-539 |
| ISSN: | 1949-3037 1949-3029 |
| DOI: | 10.1109/TSTE.2018.2834475 |
| Popis: | The reliability of the transmission grid is challenged by the integration of intermittent renewable energy sources into the grid. For model-based reliability studies, it is important to have suitable models available of renewable energy sources like wind and solar power. In this study, we investigate to what extent the power output of wind farms can be modeled with discrete Hidden Markov Models (HMMs). The parameters of the HMMs are inferred from measurement data from multiple turbines in a wind farm. We use these models both for individual turbine output and for total aggregated power output of multiple turbines. When modeling individual turbine output, the hidden process in the HMM is instrumental in capturing the dependencies between the output of the different turbines. It is important to account for these dependencies in order to correctly capture the upper quantiles (90%, 95%, 99%) of the distribution of the wind farm aggregated power output. We show that despite their simple structure, HMMs are able to reproduce important features of the power output of wind farms. This opens up possibilities to model and analyze these features with methods and techniques stemming from the field of Markov models and stochastic processes. |
| Databáze: | OpenAIRE |
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