| SCADA - based wind turbine anomaly detection using Gaussian Process models for wind turbine condition monitoring purposes R Pandit, D Infield IET Renewable Power Generation, 2018 | 110 | 2018 |
| Gaussian process power curve models incorporating wind turbine operational variables RK Pandit, D Infield, A Kolios Energy Reports 6, 1658-1669, 2020 | 67 | 2020 |
| Incorporating air density into a Gaussian process wind turbine power curve model for improving fitting accuracy RK Pandit, D Infield, J Carroll Wind Energy 22 (2), 302-315, 2019 | 56 | 2019 |
| Comparison of advanced non-parametric models for Wind Turbine Power Curves R Pandit, D Infield, A Kolios IET Renewable Power Generation, 2019 | 52 | 2019 |
| Comparative assessments of binned and support vector regression-based blade pitch curve of a wind turbine for the purpose of condition monitoring RK Pandit, D Infield International Journal of Energy and Environmental Engineering 10 (2), 181-188, 2019 | 46 | 2019 |
| Gaussian process operational curves for wind turbine condition monitoring R Pandit, D Infield Energies 11 (7), 1631, 2018 | 46 | 2018 |
| SCADA data for wind turbine data-driven condition/performance monitoring: A review on state-of-art, challenges and future trends SM Pandit R, Astolfi D, Hong J, Infield D Wind Engineering, 2022 | 45* | 2022 |
| SCADA Data-Based Support Vector Machine Wind Turbine Power Curve Uncertainty Estimation and Its Comparative Studies R Pandit, A Kolios Applied Sciences 10 (23), 8685, 2020 | 41 | 2020 |
| Wind turbine pitch reinforcement learning control improved by PID regulator and learning observer JE Sierra-Garcia, M Santos, R Pandit Engineering Applications of Artificial Intelligence 111, 104769, 2022 | 40 | 2022 |
| Operational Variables for improving industrial wind turbine Yaw Misalignment early fault detection capabilities using data-driven techniques R Pandit, D Infield, T Dodwell IEEE Transactions on Instrumentation and Measurement, 2021 | 40 | 2021 |
| Comparative analysis of Gaussian Process power curve models based on different stationary covariance functions for the purpose of improving model accuracy RK Pandit, D Infield Renewable Energy, 2019 | 36 | 2019 |
| Data‐driven weather forecasting models performance comparison for improving offshore wind turbine availability and maintenance RK Pandit, A Kolios, D Infield IET Renewable Power Generation 14 (13), 2386-2394, 2020 | 32 | 2020 |
| Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning M Richmond, A Sobey, R Pandit, A Kolios Renewable energy 161, 650-661, 2020 | 31 | 2020 |
| Accounting for environmental conditions in data-driven wind turbine power models R Pandit, D Infield, M Santos IEEE Transactions on Sustainable Energy 14 (1), 168-177, 2022 | 25 | 2022 |
| Discussion of wind turbine performance based on SCADA data and multiple test case analysis D Astolfi, R Pandit, L Terzi, A Lombardi Energies 15 (15), 5343, 2022 | 21 | 2022 |
| Open O&M: Robust O&M Open Access Tool for Improving Operation and Maintenance of Offshore wind Turbines JCR Athanasios Kolios, Julia Walgern, Sofia Koukoura,Ravi Pandit Proceedings of the 29th European Safety and Reliability Conference (ESREL), 7, 2019 | 19* | 2019 |
| Multivariate wind turbine power curve model based on data clustering and polynomial LASSO regression D Astolfi, R Pandit Applied Sciences 12 (1), 72, 2021 | 18 | 2021 |
| Data-Driven assessment of wind turbine performance decline with age and interpretation based on comparative test case analysis D Astolfi, R Pandit, L Celesti, M Vedovelli, A Lombardi, L Terzi Sensors 22 (9), 3180, 2022 | 16 | 2022 |
| Comparative analysis of binning and Gaussian Process based blade pitch angle curve of a wind turbine for the purpose of condition monitoring RK Pandit, D Infield Journal of Physics: Conference Series 1102 (1), 012037, 2018 | 15 | 2018 |
| Performance Assessment of a Wind Turbine Using SCADA based Gaussian Process Model R Pandit, D Infield The International Journal of Prognostics and Health Management (IJPHM) 9 (2), 6, 2018 | 15 | 2018 |
David InfieldProfessor of Renewable Energy Technologies, University of Strathclyde在 eee.strath.ac.uk 的电子邮件经过验证
