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Proceedings Papers

Hyper-Parameter Optimization of Deep Learning Models for Compressor Air Leak Prediction in a Gas Turbine

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Diego I. Nogueras-Rivera, Lemuel Mojica-Vazquez, Harry Bonilla-Alvarado, Kenneth M. Bryden, David Tucker, Luis M. Traverso-Avilés, Diego A. Aponte-Roa
Proc. ASME. POWER2022, ASME 2022 Power Conference, V001T04A002, July 18–19, 2022
Publisher: American Society of Mechanical Engineers
Paper No: POWER2022-85800
https://doi.org/10.1115/POWER2022-85800
View Paper titled, Hyper-Parameter Optimization of Deep Learning Models for Compressor Air Leak Prediction in a Gas Turbine
Open the PDF for Hyper-Parameter Optimization of Deep Learning Models for Compressor Air Leak Prediction in a Gas Turbine in another window
Topics: Compressors, Failure, Gas turbines, Leakage, Optimization, Time series
Proceedings Papers

Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks

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Dorsa Ziaei, Navid Goudarzi
Proc. ASME. POWER2021, ASME 2021 Power Conference, V001T09A013, July 20–22, 2021
Publisher: American Society of Mechanical Engineers
Paper No: POWER2021-65866
https://doi.org/10.1115/POWER2021-65866
View Paper titled, Short-Term Wind Characteristics Forecasting Using Stacked LSTM Networks
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Topics: Renewable energy, Wind, Databases, Errors, Modeling, Ocean engineering, Offshore wind turbines, Physics, Pipelines, Time series
Proceedings Papers

A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin

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Weifei Hu, Yihan He, Zhenyu Liu, Jianrong Tan, Ming Yang, Jiancheng Chen
Proc. ASME. POWER2020, ASME 2020 Power Conference, V001T08A009, August 4–5, 2020
Publisher: American Society of Mechanical Engineers
Paper No: POWER2020-16500
https://doi.org/10.1115/POWER2020-16500
View Paper titled, A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin
Open the PDF for A Hybrid Wind Speed Prediction Approach Based on Ensemble Empirical Mode Decomposition and BO-LSTM Neural Networks for Digital Twin in another window
Topics: Artificial neural networks, Optimization, Time series, Wind velocity
Proceedings Papers

Use of Seismic Analyses for the Wind Energy Industry

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Weifei Hu, S. C. Pryor, F. Letson, R. J. Barthelmie
Proc. ASME. POWER2017-ICOPE-17, Volume 2: I&C, Digital Controls, and Influence of Human Factors; Plant Construction Issues and Supply Chain Management; Plant Operations, Maintenance, Aging Management, Reliability and Performance; Renewable Energy Systems: Solar, Wind, Hydro and Geothermal; Risk Management, Safety and Cyber Security; Steam Turbine-Generators, Electric Generators, Transformers, Switchgear, and Electric BOP and Auxiliaries; Student Competition; Thermal Hydraulics and Computational Fluid Dynamics, V002T09A015, June 26–30, 2017
Publisher: American Society of Mechanical Engineers
Paper No: POWER-ICOPE2017-3538
https://doi.org/10.1115/POWER-ICOPE2017-3538
View Paper titled, Use of Seismic Analyses for the Wind Energy Industry
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Topics: Seismic analysis, Wind energy, Turbines, Vibration, Wind turbines, Condition monitoring, Density, Electromagnetic spectrum, Probability, Sensors
Proceedings Papers

Performance Diagnostics of Pulverized Coal Fuelled Power Generation Plant Using Exergy-Based Specific Consumption Analysis

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Hongtao Li
Proc. ASME. POWER2007, ASME 2007 Power Conference, 103-110, July 17–19, 2007
Publisher: American Society of Mechanical Engineers
Paper No: POWER2007-22028
https://doi.org/10.1115/POWER2007-22028
View Paper titled, Performance Diagnostics of Pulverized Coal Fuelled Power Generation Plant Using Exergy-Based Specific Consumption Analysis
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Topics: Coal, Energy generation, Exergy, Energy conversion, Exergy analysis, Failure, Fuel consumption, Stress, Time series
Proceedings Papers

Spectral Analysis of Pulverized Coal Combustion Stability

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Yuefeng Yu, Jun Ma, Haojie Fan
Proc. ASME. POWER2005, ASME 2005 Power Conference, 785-789, April 5–7, 2005
Publisher: American Society of Mechanical Engineers
Paper No: PWR2005-50323
https://doi.org/10.1115/PWR2005-50323
View Paper titled, Spectral Analysis of Pulverized Coal Combustion Stability
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Topics: Coal, Combustion, Emission spectroscopy, Spectroscopy, Stability, Flames, Signals, Artificial neural networks, Radiation (Physics), Algorithms