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Proceedings Papers

Online Adaptive Control Tuning in a Gas Turbine Hybrid System

Harry Bonilla-Alvarado, Bernardo Restrepo, Paolo Pezzini, Lawrence Shadle, David Tucker, Kenneth Mark Bryden

Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T06A007, August 4–5, 2020

Paper No: POWER2020-16534

DOI: https://doi.org/10.1115/POWER2020-16534

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Topics: Adaptive control, Control equipment, Gas turbines

Proceedings Papers

Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection

Selorme Agbleze, Fernando V. Lima, Natarianto Indrawan, Rupendranath Panday, Paolo Pezzini, Harry Bonilla-Alvarado, Kenneth M. Bryden, David Tucker, Lawrence J. Shadle

Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A002, August 4–5, 2020

Paper No: POWER2020-16571

DOI: https://doi.org/10.1115/POWER2020-16571

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Topics: Algorithms, Boilers, Coal, Control modeling, Flaw detection, Leakage, Power stations, Steam, Dynamics (Mechanics), Stress

Proceedings Papers

Using Machine Learning to Increase Model Performance for a Gas Turbine System

Samuel M. Hipple, Zachary T. Reinhart, Harry Bonilla-Alvarado, Paolo Pezzini, Kenneth Mark Bryden

Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T12A003, August 4–5, 2020

Paper No: POWER2020-16580

DOI: https://doi.org/10.1115/POWER2020-16580

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Topics: Artificial neural networks, Gas turbines, Machine learning, Modeling, Turbomachinery

Journal Articles

Using Machine Learning Tools to Predict Compressor Stall

Samuel M. Hipple, Harry Bonilla-Alvarado, Paolo Pezzini, Lawrence Shadle, Kenneth M. Bryden

J. Energy Resour. Technol. July 2020, 142(7): 070915.

Paper No: JERT-19-1798

DOI: https://doi.org/10.1115/1.4046458

Published Online: April 8, 2020

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Topics: Compressors, Machine learning, Surges, Turbines, Turbomachinery, Stall inception, Regression models, Gas turbines, Artificial neural networks

Journal Articles

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Harry Bonilla-Alvarado, Kenneth M. Bryden, Lawrence Shadle, David Tucker, Paolo Pezzini

J. Energy Resour. Technol. July 2020, 142(7): 070903.

Paper No: JERT-19-1712

DOI: https://doi.org/10.1115/1.4046144

Published Online: February 28, 2020

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Topics: Algorithms, Fuels, Gas turbines, System identification, Transfer functions, Turbines, Valves, Electric load, Leakage, Parameter estimation

Journal citation

NARROW

Online Adaptive Control Tuning in a Gas Turbine Hybrid System

Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection

Using Machine Learning to Increase Model Performance for a Gas Turbine System

Using Machine Learning Tools to Predict Compressor Stall

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Journals

Conference Proceedings

eBooks

Resources

Opportunities

Online Adaptive Control Tuning in a Gas Turbine Hybrid System

Modeling and Control of Subcritical Coal-Fired Power Plant Components for Fault Detection

Using Machine Learning to Increase Model Performance for a Gas Turbine System

Using Machine Learning Tools to Predict Compressor Stall

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Journals

Conference Proceedings

eBooks

Resources

Opportunities

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