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Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model

Liu F., Li R., Dreglea A. Wind speed and power ultra short-term robust forecasting based on Takagi–Sugeno fuzzy model // Energies. Vol.12. No.18. ID: 3551. 2019. DOI: 10.3390/en12183551 Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can...

Теги: linearization , machine learning , wind power: wind speed: t–s fuzzy model: forecasting , backpropagation , clustering algorithms , fuzzy clustering , learning systems , least squares approximations , neural networks , support vector machines , wind , wind power , b
Раздел: ИСЭМ СО РАН
Short-term wind power forecasting based on T-S fuzzy model

Liu F., Li R., Li Y. et al. Short-term wind power forecasting based on T-S fuzzy model // Asia-Pacific Power and Energy Engineering Conference, APPEEC. Vol.Decem. 2016. P.414-418. ISBN 9781509054183. DOI: 10.1109/APPEEC.2016.7779537. Due to the impacts of wind speed, wind direction, temperature and pressure, it is uncertain and nonlinear for the wind power forecasting. To address these problems, this paper proposes a wind power short-time forecasting method based on the T-S fuzzy model, which does...

Теги: fuzzy c-means (fcm) , recursive least squares method (rls) , t-s fuzzy model , wind power forecasting , clustering algorithms , forecasting , fuzzy systems , least squares approximations , signal processing , support vector machines , wind , wind effects , wind power
Раздел: ИСЭМ СО РАН
An intelligent security alert system for power system pre-emergency control

... large-scale blackouts have demonstrated that secure operation of large interconnected power systems cannot be achieved without full understanding of the system behavior during abnormal and emergency conditions. This paper is focused on applying learning clustering algorithms for identifying critical states in power systems. The authors propose an intelligent security alert system for early detection of alarm states using the clustering ensemble concept. The security assessment clustering ensemble is ...

Теги: alert systems , blackout , clustering ensemble , power system , pre-emergency state , security assessment , electric power system interconnection , electrical engineering , clustering algorithms
Раздел: ИСЭМ СО РАН


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