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Predicting The Oil Temperature Of A Transformer - EP2941674

The patent EP2941674 was granted to ABB Power Grids Switzerland on Sep 12, 2018. The application was originally filed on Nov 19, 2013 under application number EP13802783A. The patent is currently recorded with a legal status of "Revoked".

EP2941674

ABB POWER GRIDS SWITZERLAND
Application Number
EP13802783A
Filing Date
Nov 19, 2013
Status
Revoked
Apr 15, 2022
Grant Date
Sep 12, 2018
External Links
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MASCHINENFABRIK REINHAUSENJun 5, 2019REICHERT & LINDNER PARTNERSCHAFT PATENTANWALTEADMISSIBLE

Patent Citations (1) New

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

Citation PhasePublication NumberPublication Link
OPPOSITIONWO2012142355

Non-Patent Literature (NPL) Citations (16) New

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

Citation PhaseReference TextLink
INTERNATIONAL-SEARCH-REPORT- QING HE ET AL, "Prediction of Top-Oil Temperature for Transformers Using Neural Networks", IEEE TRANSACTIONS ON POWER DELIVERY, IEEE SERVICE CENTER, NEW YORK, NY, US, (20001001), vol. 15, no. 4, ISSN 0885-8977, XP011049941 [X] 1-10,18-20 * the whole document *-
OPPOSITION- Assuncao T. C. B. N., Et Al., "Transformer Top-Oil Temperature Modeling and Simulation", International Scholarly and Scientific Research & Innovation, (20080000), vol. 2, no. 10, pages 3580 - 3585, XP055609801-
OPPOSITION- H P SCHMIDT et al., "ANN-based Real-Time Short-Term Load Forecasting in Distribution Substations Proceedings", IEEE /PES Transmission and Distribution Conference Latin America, Brazil, (20020000), pages 1 - 6, XP055609794-
OPPOSITION- J YASUOKA et al., "Artificial Neural Network-Based Distribution Substation And Feeder Load Forecast CIRED200I", Conference Publication No. 482, IEE, (20010000), XP055609791-
OPPOSITION- QING HE et al., "Prediction of Top-Oil Temperature for Transformers Using Neural Networks", IEEE Transactions on Power Delivery, (20001000), vol. I5, no. 4, XP011049941-
OPPOSITION- V TELRANDHE et al., "Simulation of Electrical Load Forecasting", Substation Transformers Using ANFIS Proceedings NCIPET, (20120000), pages 1 - 5, XP055609809-
OPPOSITION- W CHEN et al., "Combination of Support Vector Regression with Particle Swarm Optimization for Hot-spot temperature prediction of oil-immersed power transformer", Przeglad Elektrotechniczny, (20120000), XP055609796-
OPPOSITION- WEIHUI FU et al., "Risk Assessment for Transformer Loading", IEEE Transactions on Power Systems, (20010800), vol. I6, no. 3, XP011051135-
OPPOSITION- V GALDI et al., "Application of local memory-based techniques for power transformer thermal overload protection", IEE Proc.-Electr. Power Appl., (20010300), vol. 148, no. 2, doi:10.1049/ip-epa:20010086, XP006016159
OPPOSITION- B C LESIEUTRE et al., "An Improved Transformer Top Oil Temperature Model for Use in An On-Line Monitoring and Diagnostic System", IEEE TRANSACTIONS ON POWER DELIVERY, (19970100), vol. 12, no. 1, pages 249 - 256, XP011049307
OPPOSITION- Pradhan M K et al, "On-line Monitoring of Temperature in Power Transformers using Optimal Linear Combination of ANNs Conference Record", IEEE International Symposium on Electrical Insulation M K Pradhan, (20040000), pages 70 - 73, XP010758999
OPPOSITION- HUI MA et al., "Predictive learning and information fusion for condition assessment of power transformer", Power Transformer, (20110000), pages 1 - 8, XP032055139
OPPOSITION- S TENBOHLEN et al., "Assessment of Overload Capacity of Power Transformers by On-line Monitoring Systems", IEEE Power Engineering Society Winter Meeting, (20010000), pages 329 - 334, XP001042841
OPPOSITION- Villacci D ET AL., "The Role of Learning Methods in the Dynamic Assessment of Power February Components Loading Capability", IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, (20050200), vol. 52, no. 1, pages 280 - 290, XP011126568
OPPOSITION- M HELL et al., "Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers", IEEE Transactions on Power Delivery, (20070400), vol. 22, no. 2, doi:10.1109/TPWRD.2006.874613, XP011176069
OPPOSITION- J K PYLVANAINEN et al., "Studies to Utilize Loading Guides and ANN for Oil-Immersed Distribution Transformer Condition Monitoring", IEEE Transactions on Power Delivery, vol. 22, no. I, pages 201 - 207, XP011147352

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