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Cut-Out Strategy Adaptive Adjustment Method And Apparatus - EP3540213

The patent EP3540213 was granted to Beijing Goldwind Science & Creation Windpower Equipment on Nov 15, 2023. The application was originally filed on Jul 10, 2018 under application number EP18852761A. The patent is currently recorded with a legal status of "Patent Maintained As Amended".

EP3540213

BEIJING GOLDWIND SCIENCE & CREATION WINDPOWER EQUIPMENT
Application Number
EP18852761A
Filing Date
Jul 10, 2018
Status
Patent Maintained As Amended
Oct 13, 2023
Publication Date
Nov 15, 2023
External Links
Slate, Register, Google Patents

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ENERCONSep 9, 2021EISENFUHR SPEISERADMISSIBLE

Patent Citations (15) New

Patent citations refer to prior patents cited during different phases such as opposition or international search.

Citation PhasePublication Number
INTERNATIONAL-SEARCH-REPORTCN103244354
INTERNATIONAL-SEARCH-REPORTCN103573550
INTERNATIONAL-SEARCH-REPORTCN104806448
INTERNATIONAL-SEARCH-REPORTCN105275742
INTERNATIONAL-SEARCH-REPORTCN107061158
INTERNATIONAL-SEARCH-REPORTJP2016079965
INTERNATIONAL-SEARCH-REPORTUS2016215759
OPPOSITIONEP2840257
OPPOSITIONUS2002194113
OPPOSITIONUS2006273595
OPPOSITIONUS2011018271
OPPOSITIONUS2015003983
OPPOSITIONWO2016082838
SEARCHCN103573550
SEARCHUS2017268487

Non-Patent Literature (NPL) Citations (12) New

NPL citations refer to non-patent references such as research papers, articles, or other publications cited during examination or opposition phases.

Citation PhaseReference Text
OPPOSITION- Kariniotakis G.N. et al., "Wind power forecasting using advanced neural networks models", IEEE Transactions on Energy Conversion, December1996
OPPOSITION- Lange, Matthias et al: 'Physical Approach to Short-Term Wind Power Prediction”, Springer 2005
OPPOSITION- MATTHIAS LANGE et al., "Physical Approach to Short-Term Wind Power Prediction", Physical Approach to Short-Term Wind Power Prediction, XP055322789
OPPOSITION- Pinson, P. et al, "Wind Power Forecasting using Fuzzy Neural Networks Enhanced with On-line Prediction Risk Assessment", IEEE Bologna Power Tech Conference, June 2003
OPPOSITION- P. PINSON et al., "WIND POWER FORECASTING USING FUZZY NEURAL NETWORKS ENHANCED WITH ON-LINE PREDICTION RISK ASSESSMENT", PROCEEDINGS OF IEEE POWER TECH CONFERENCE., (20030623), XP008074043
OPPOSITION- Saleck Nada et al: "Wind power forecast error smoothing within a windfarm", Journal of Physics 2007
OPPOSITION- V. Petrovic and C. Bottasso: „Wind turbine optimal control during storms”, Journal of Physics 2014
OPPOSITION- V. PETROVIC et al., "Wind turbine optimal control during storms", Journal of Physics: Conference Series, (2014), vol. 524, XP055845088
OPPOSITION- V. Petrovic et al.: „Wmd turbine control beyond the cut-out wind speed’,Croatian scientific bibliography 2013
OPPOSITION- NADJA SALECK et al., "Wind power forecast error smoothing within a wind farm", JOURNAL OF PHYSICS: CONFERENCE SERIES, (20070701), vol. 75, no. 1, XP020125124
OPPOSITION- G.N. KARINIOTAKIS et al., "WIND POWER FORECASTING USING ADVANCED NEURAL NETWORKS MODELS", IEEE TRANSACTIONS ON ENERGY CONVERSION., (19961201), vol. 11, no. 4, XP001019746
SEARCH- PETROVI&CACUTE ET AL, "Wind turbine optimal control during storms", JOURNAL OF PHYSICS: CONFERENCE SERIES, INSTITUTE OF PHYSICS PUBLISHING, BRISTOL, GB, vol. 524, no. 1, doi:10.1088/1742-6596/524/1/012052, ISSN 1742-6596, (20140616), page 12052, (20140616), XP020266216 [A] 1-15 * abstract * * paragraphs [0002] , [0004] *

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