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HOME Offshore

Holistic Operation and Maintenance for Energy form Offshore Wind Farms


A group of wind turbines in the water

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HOME Offshore was a research project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) which partners 5 leading UK universities. The project investigated:

·       the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort for offshore windfarms

·       predictive and diagnostic techniques allow problems to be picked up early, when easy and inexpensive maintenance allows problems to be readily fixed

·       robots and advanced sensors ed to minimise the need for human intervention in the hazardous offshore environment.

The remote inspection and asset management of offshore wind farms and their connection to shore, is an industry which will be worth billions annually in the UK alone. A substantial fraction of the cost of offshore Operation and Maintenance is generated by access requirements: such as the need to get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel, of which there is likely to be a shortage in coming years. Additionally much condition monitoring data which is presently generated is not useful or not used effectively.

The project therefore aimed to make generate more ‘actionable data’ – useful information that can reduce operation and maintenance costs and improve safety.


Position paper

M. Barnes, S. Watson, D. Flynn, S. Djurović (Editors): “Technology Drivers in Windfarm Asset Management”, doi: https://doi.org/10.17861/20180718

Journal Papers

1.     A. Mohammed, S. Djurović, “FBG Thermal Sensing Features for Hot Spot Monitoring in Random Wound Electric Machine Coils”, in IEEE Sensors Journal, vol. 17, no. 10, pp. 3058-3067, 2017. doi: 10.1109/JSEN.2017.2691137

2.     B. Hu, S. Konaklieva, S. Xu, J. O. Gonzalez, L. Ran, C. Ng, P. McKeever, and O. Alatise, “Condition monitoring for solder layer degradation in multi-device system based on neural network”, in the Journal of Engineering, doi: 10.1049/joe.2018.8025.

3.     A. Mohammed and S. Djurović, “Feasibility study of embedded FBG thermal sensing use for monitoring electrical fault induced thermal excitation in random wound coils”, in the Journal of Engineering, doi: 10.1049/joe.2018.8108

4.     R. Shah, R. Preece, M. Barnes, J. Carmona-Sanchez, “Techno-economic evaluation of power electronics assisted system frequency regulation”, in the Journal of Engineering, doi: 10.1049/joe.2018.8010

5.     J. Carmona-Sanchez, O. Marjanovic, M. Barnes, W. Wang, “Comparison of DC Linear and Nonlinear Models for Multi-terminal VSC HVDC Networks”,  in the Journal of Engineering, doi: 10.1049/joe.2018.815

6.     A. Mohammed, J. I. Melecio and S. Djurović, “Open Circuit Fault Detection in Stranded PMSM Windings Using Embedded FBG Thermal Sensors,” in IEEE Sensors Journal. doi: 10.1109/JSEN.2019.2894097

7.     A. Mohammed, J. I. Melecio and S. Djurović, “Stator Winding Fault Thermal Signature Monitoring and Analysis by in-situ FBG Sensors”, in IEEE Trans. Industrial Electronics, doi: 10.1109/TIE2018.2883260

8.     A. Stetco, F. Dinmohammadi, X. Zhao, V. Robu, D. Flynn, M. Barnes, J. Keane, G. Nenadic,  Machine learning methods for wind turbine condition monitoring: A review”, in Renewable Energy, 2018, vol. 133, pp. 620-635, doi: https://doi.org/10.1016/j.renene.2018.10.047

9.     A. Mohammed, S. Djurović, “A study of distributed embedded thermal monitoring in electric coils based on FBG sensor multiplexing,” Elsevier Microprocessors and Microsystems, Vol. 62, 2018, Pages 102-109 https://www.sciencedirect.com/science/article/pii/S0141933118301431

10.  A. Mohammed, S. Djurović, “Stator Winding Internal Thermal Stress Monitoring and Analysis Using in-situ FBG Sensing Technology”, in IEEE Trans. Energy Conversion, 2018, doi: 10.1109/TEC.2018.2826229

11.  Z. Lin, D. Cevasco, and M. Collu, A methodology to develop reduced-order models to support the operation and maintenance of offshore wind turbines. Applied Energy, doi:10.1016/j.apenergy.2019.114228

12.  F. Dinmohammadi et al, “Predicting damage and life expectancy of subsea power cables in offshore renewable energy applications”, IEEE Access, 2019, doi: 10.1109/ACCESS.2019.2911260

13.  B. Hu, S. Konaklieva, N. Kourra, M. A. Williams, L. Ran and W. Lai, “Long Term Reliability Evaluation of Power Modules with Low Amplitude Thermomechanical Stresses and Initial Defects,” in IEEE Journal of Emerging and Selected Topics in Power Electronics, doi: 10.1109/JESTPE.2019.2958737

14.  A. Mohammed and S. Djurović, “FBG Thermal Sensing Ring Scheme for Stator Winding Condition Monitoring in PMSMs,” in IEEE Transactions on Transportation Electrification. doi: 10.1109/TTE.2019.2945523

15.  M. Heggo, K. Kababbe, V. Peesapati, R. Gardner, S. Watson & W. Crowther, “Operation of Aerial Inspections Vehicles in HVDC Environments Part A: Evaluation and Mitigation of High Electrostatic Fields on Operation of Aerial Inspections Vehicles in HVDC Environments”, Journal of Physics: Conference Series (formerly paper at EERA DeepWind’19, 16 – 18 Jan 2019, Trondheim) https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012009

16.  M. Heggo, A. Mohammed, J. Melecio Ramirez, K. Kababbe, P. Tuohy, S. Watson & S. Durovic, “Operation of Aerial Inspections Vehicles in HVDC Environments Part B: Evaluation and Mitigation of Magnetic Field Impact ” , Journal of Physics: Conference Series (formerly paper at EERA DeepWind’19, 16 – 18 Jan 2019, Trondheim) https://iopscience.iop.org/article/10.1088/1742-6596/1356/1/012010

17.  C. Dao, B.  Kazemtabrizi, C.J. Crabtree, “Wind Turbine reliability data review and impacts on levelised cost of energy”, Wind Energy. 2019; 1-24 Link to the PDF version of the article: http://dro.dur.ac.uk/28711/, doi: 10.1002/we.2404

18.  Hu, Z. Hu, L. Ran, C. Ng, C. Jia, P. Mckeever, P. Tavner, C. Zhang, H. Jiang, and P. Mawby, "Heat-Flux Based Condition Monitoring of Multi-chip Power Modules Using a Two-Stage Neural Network," IEEE Transactions on Power Electronics, pp. 1-1, 2020,  doi: 10.1109/TPEL.2020.3045604.

19.  A. Mohammed, S. Djurovic ‘Electric Machine Bearing Health Monitoring and Ball Fault Detection by Simultaneous Thermo-Mechanical Fibre Optic Sensing’, June 2020, IEEE Trans. Energy Converesion, doi: 10.1109/TEC.2020.3003793

20.  J. Carmona, O. Marjanovic, M. Barnes, Senior and P. R. Green. “Secondary Model Predictive Control Architecture for VSC-HVDC Networks Interfacing Wind Power”, in IEEE Transactions on Power Delivery, Jan. 2020, doi: 10.1109/TPWRD.2020.2966325

21.  A. Stetco, J. M. Ramirez, A. Mohammed, S. Djurović, G. Nenadic, and J. Keane. "An End-to-End, Real-Time Solution for Condition Monitoring of Wind Turbine Generators." Energies 13, no. 18 (2020): 4817.       https://www.mdpi.com/1996-1073/13/18/4817

22.  A. Mohammed, J.I. Melecio, and S. Djurović. "Electrical machine permanent magnets health monitoring and diagnosis using an air-gap magnetic sensor." IEEE Sensors Journal 20, no. 10 (2020): 5251-5259. https://ieeexplore.ieee.org/abstract/document/8970344

23.  A. Mohammed, and S. Durović. "Design, Instrumentation and Usage Protocols for Distributed In Situ Thermal Hot Spots Monitoring in Electric Coils using FBG Sensor Multiplexing." JoVE (Journal of Visualized Experiments) 157 (2020): e59923, doi: 10.3791/59923 , https://www.jove.com/t/59923/design-instrumentation-usage-protocols-for-distributed-situ-thermal

24.  A. Mohammed, B. Hu, Z. Hu, S. Djurović, L. Ran, M. Barnes, and P.A. Mawby. "Distributed Thermal Monitoring of Wind Turbine Power Electronic Modules Using FBG Sensing Technology." IEEE Sensors Journal 20, no. 17 (2020): 9886-9894. https://ieeexplore.ieee.org/abstract/document/9087895

25.  Y. Wang, A. Mohammed, N. Sarma, and S. Djurović. "Double Fed Induction Generator Shaft Misalignment Monitoring by FBG Frame Strain Sensing." IEEE Sensors Journal 20, no. 15 (2020): 8541-8551. https://ieeexplore.ieee.org/abstract/document/9050770

26.  N. Sarma, P.M. Tuohy, A. Mohammed, and S. Djurović. "Rotor Electrical Fault Detection in DFIGs Using Wide-Band Controller Signals." IEEE Transactions on Sustainable Energy 12, no. 1 (2020): 623-633.  https://ieeexplore.ieee.org/abstract/document/9159939

27.  C.D. Dao, B. Kazemtabrizi and C.J. Crabtree, “Offshore wind turbine reliability and operational simulation under uncertainties”, June 2020, Wind Energy,  https://doi.org/10.1002/we.2526

28.  K. Kabbabe Poleo, B. Crowther, and M. Barnes, “Estimating the Impact of Drone-based Inspection on the Levelised Cost of Electricity for Offshore Wind Farms”, Results in Engineering, no. 9, 2021, https://doi.org/10.1016/j.rineng.2021.100201

29.  C. D. Dao, B. Kazemtabrizi, C. J. Crabtree and P.J. Tavner, “Integrated condition-based maintainenace modelling and optimisation for offshore wind turbines”, Wind Energy, Feb 2021, https://doi.org/10.1002/we.2625

30.  Al-Ajmi, A.; Wang, Y.; Djurović, S. Wind Turbine Generator Controller Signals Supervised Machine Learning for Shaft Misalignment Fault Detection: A Doubly Fed Induction Generator Practical Case Study. Energies 2021, 14, 1601. https://doi.org/10.3390/en14061601

31.  H. Ren et al., "Quasi-distributed Temperature Detection of Press Pack IGBT Power Module Using FBG Sensing," in IEEE Journal of Emerging and Selected Topics in Power Electronics, doi: 10.1109/JESTPE.2021.3109395.

32.  Heggo, M.; Mohammed, A.; Melecio, J.; Kabbabe, K.; Tuohy, P.; Watson, S.; Durovic, S. The Operation of UAV Propulsion Motors in the Presence of High External Magnetic Fields. Robotics 202110, 79. https://doi.org/10.3390/robotics10020079

33.  H. Ren et al., "Quasi-Distributed Temperature Detection of Press-Pack IGBT Power Module Using FBG Sensing," in IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 5, pp. 4981-4992, Oct. 2022, doi: 10.1109/JESTPE.2021.3109395.

34.  H. Ren et al., "In Situ Contact Pressure Monitoring of Press Pack Power Module Using FBG Sensors," in IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-11, 2022, Art no. 7006211, doi: 10.1109/TIM.2022.3193950.

35.  Daniel Mitchell, Jamie Blanche, Sam Harper, Theodore Lim, Ranjeetkumar Gupta, Osama Zaki, Wenshuo Tang, Valentin Robu, Simon Watson, David Flynn, “A review: Challenges and opportunities for artificial intelligence and robotics in the offshore wind sector”, Energy and AI, Volume 8, 2022, https://doi.org/10.1016/j.egyai.2022.100146.


Conference Papers

1.     Mohammed, N. Sarma, S. Djurović, “Fibre optic monitoring of induction machine frame strain as a diagnostic tool,” 2017 IEEE International Electric Machines and Drives Conference (IEMDC), Miami, FL, 2017, pp. 1-7. doi: 10.1109/IEMDC.2017.8002208

2.     Mohammed, S. Djurović, “FBG array sensor use for distributed internal thermal monitoring in low voltage random wound coils”, 2017 6th Mediterranean Conference on Embedded Computing (MECO), Bar, 2017, pp. 1-4. doi: 10.1109/MECO.2017.7977124

3.     D. Cevasco, M. Collu, and Z. Lin, “O&M cost-based FMECA: identification and ranking of the most critical components for 2-4 MW geared offshore wind turbines” in IOP Conference Series: Journal of Physics, 2018, vol. 1102, pp. 1-12. Global Wind Summit 2018, Hamburg, Germany doi: 10.1088/1742-6596/1102/1/012039

4.     W.Tang, K.Brown, D.Flynn, H.Pellae, “Integrity Analysis Inspection and Lifecycle Prediction of Subsea Power Cables”, Prognostics and System Health Management Conference, Chongqing, 25-28 Oct, 2018

5.     U Mupambireyi, A Crane, L Ran, P Mawby, “A Multiphase Machine and Converter Topology for Renewable Energy Generation”, in 2018 Energy Conversion Congress and Exposition (ECCE), Portland, OR, Sept, 2018.

6.     B Hu, S Konaklieva, L Ran, N Kourra, M A Williams, W Lai, P Mawby, “Long Term Reliability of Power Modules with Low Amplitude Thermomechanical Stresses and Initial Defects”, in 2018 Energy Conversion Congress and Exposition (ECCE), Portland, OR, Sept, 2018.

7.     Z. Lin, D. Cevasco, M. Collu, “Progress on the development of a holistic coupled model of dynamics for offshore wind farms, phase I: aero-hydro-servo-elastic model, with drive train model, for a single wind turbine”, in the 37th International Conference on Ocean, Offshore and Arctic Engineering, Madrid, Spain, 17-22 June, 2018

8.     Stetco, A. Mohammed, S. Djurović, G. Nenadic and J. Keane, “Wind Turbine operational state prediction: towards featureless, end-to-end predictive maintenance,” 2019 IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA, 2019, pp. 4422-4430, doi: 10.1109/BigData47090.2019.9005584

9.     Wenshuo Tang, David Flynn, Keith Brown, Xinyu Zhao and Robu Valentin, “The Application of Machine Learning and Low Frequency Sonar for Subsea Power Cable Integrity Evaluation”,  IEEE Oceans 2019, Seattle,doi: 10.23919/OCEANS40490.2019.8962840

10.  Wenshuo Tang, David Flynn, Keith Brown, Xinyu Zhao and Robu Valentin, “The Design of a Fusion Prognostic Model and Health Management System for Subsea Power Cables”,  IEEE Oceans 2019, Seattle, doi: 10.23919/OCEANS40490.2019.8962816

11.  A. Mohammed and S. Djurović, “Multiplexing FBG Thermal Sensing for Uniform/Uneven Thermal Variation Monitoring in In-service Electric Machines,” 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED), Toulouse, France, 2019, pp. 316-322.  doi: 10.1109/DEMPED.2019.8864832

12.  E. Welburn, H. Khalili, A Gupta, S. Watson and J. Carrasco, “A navigational system for quadcopter remote inspection of offshore substation”, 15th Conf. on Autonomic and Autonomous Systems, 2019

13.  A. Mohammed and S. Djurovic, “In-Situ Thermal and Mechanical Fibre Optic Sensing for In-Service Electric Machinery Bearing Condition Monitoring,” 2019 IEEE International Electric Machines & Drives Conference (IEMDC), San Diego, CA, USA, 2019, pp. 37-43.   doi: 10.1109/IEMDC.2019.8785203

14.  Dao C, Kazemtabrizi, B., Crabtree, C. (2019), “Impacts of Reliability on Operational Performance and Cost of Energy Evaluation of Multimegawatt, Far-Offshore Wind Turbines”, ASME 38th International Conference on Ocean, Offshore & Arctic Engineering (OMAE) 2019, Glasgow, UK, American Society of Mechanical Engineers, doi: 10.1115/OMAE2019-9556

15.  Dao C. D., Kazemtabrizi. B., Crabtree C.J., and Li X. (2019), “Impacts of Reliability and Cost Uncertainties on Offshore Wind Turbine Operational Simulation and Cost of Energy Estimation”, WindEurope Offshore conference 2019, Copenhagen, Denmark, November 2019

16.  Li X., Dao C. D., Kazemtabrizi. B., and Crabtree C.J. (2019), “Availability Analysis for Different Offshore Wind Farm Electrical Connection Topologies”, Wind Europe Offshore conference 2019, Copenhagen, Denmark, November 2019

17.  C. Dao, B. Kazemtabrizi. C.J. Crabtree and X. Li, “Modelling and Optimising Offshore Wind Levelised Cost of Energy Based on Reliability and Maintenance Improvements”, WindEurope Offshore conference 2019, Copenhagen, Denmark, November 2019.

18.  J. I. Melecio, A. Mohammed, N. Schofield and S. Djurović, “3D-Printed rapid prototype rigs for surface mounted PM rotor controlled segment magnetisation and assembly,” 2019 IEEE International Electric Machines & Drives Conference (IEMDC), San Diego, CA, USA, 2019, pp. 1830-1836.      doi: 10.1109/IEMDC.2019.8785121

19.  Hu, Z. Hu, L. Ran, P. Mawby, C. Jia, C. Ng, and P. McKeever, “Deep Learning Neural Networks for Heat-Flux Health Condition Monitoring Method of Multi-Device Power,” IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, 29 Sept-3 Oct, 2019, doi: 10.1109/ECCE.2019.8912666.

20.  Hu, X. Guo, S. Konaklieva, L. Ran, H. Li, C. Jia, C. Ng, and P. McKeever, “Lifetime Consumption of Wind Turbine Power Converter in the Whole Wind Speed Range,” The 9th International Energy Conference REMOO, Hong Kong, 16-18 Apr, 2019.

21.  Juan I. Melecio, Anees Mohammed and Siniša Djurović, “Characterisation of FBG based Magnetic Field Sensor Response Sensitivity to Excitation Orientation for Rotating Electric Machine Applications”, 8th MECO Conf,  10-14 JUNE 2019, Montenegro, doi: 10.1109/MECO.2019.8760181

22.  C. Dao, B. Kazemtabrizi, and C. J. Crabtree, “Modelling the Effects of Reliability and Maintenance on Levelised Cost of Wind Energy”, presented at ASME Turbo Expo 2019, Phoenix, AZ, June 2019. doi: 10.1115/GT2019-90015

23.  Z. Lin, A. Stetco, J, Carmona-Sanchez, D. Cevasco, M. Collu, G. Nenadic, O. Marjanovic, M. Barnes, “Progress on the development of a holistic coupled model of dynamics for offshore wind farms, phase II: study on a data-driven based reduced-order model for a single wind turbine”,  Proceedings of the ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering, OMAE 2019, Glasgow, UK 9-14 June, 2019, doi: 10.1115/OMAE2019-95542

24.  E. Welburn, T. Wright, C.  Marsh, S. Lim, A. Gupta, W. Crowther & S. Watson , “A mixed reality approach to robotic inspection of remote environments”, UK-RAS19 Conference, 24 Jan 2019, Loughborough

25.  A. Thompson, B. Kazemtabrizi, C. J. Crabtree, C. D. Dao, F. Dinmohamadi, and D. Flynn, “Reliability and economic evaluation of High Voltage Direct Current interconnectors for large-scale renewable energy integration and transmission.,” IET AC/DC Conference, 6-7 Feb 2019, Coventry.

26.  J Carmona Sanchez, M Barnes, O Marjanovic, Z Lin, M Collu, D Cevasco, “An analysis of the impact of an advanced aero-hydro-servo-elastic model of dynamics on the generator-converter dynamics, for an offshore fixed 5MW PMSG wind turbine“, IET AC/DC Conference, 6-7 Feb 2019, Coventry

27.  C Marsh, M Barnes, W Crowther, S Watson, D Vilchis-Rodriguez, J Carmona-Sanchez, R Shuttleworth, K Kabbabe, M Heggo, A Smith, X Pei, “Virtual reality interface for HVDC substation and DC breaker design and maintenance” , IET AC/DC Conference, 6-7 Feb 2019, Coventry

28.  R Shah, M Barnes, R Preece, “Impact of MTDC grid reconfiguration and control on the dynamics of the GB System” , IET AC/DC Conference, 6-7 Feb 2019, Coventry

29.  J Carmona Sanchez, P Green, M Barnes, O Marjanovic, “A realistic telecommunication model for electromagnetic transient simulations and control assessment of multi-terminal VSC-HVDC networks in PSCAD/EMTDC”, IET AC/DC Conference, 6-7 Feb 2019, Coventry

30.  A. Stetco, R. Mosincat, G. Nenadic and J. Keane, "Towards a framework for incorporating data acquisition cost in predictive time series models", 6th Workshop on Mining and Learning from Time Series, (MiLeTS), KDD 2020

31.  Li, X, Dao, CD, Kazemtabrizi, B, & Crabtree, CJ. "Optimization of Large Offshore Wind Farm Layout Considering Reliability and Wake Effect." Proceedings of the ASME Turbo Expo 2020: Turbomachinery Technical Conference and ExpositionVolume 12: Wind Energy. Virtual, Online. September 21–25, 2020. V012T42A011. ASME. https://doi.org/10.1115/GT2020-15495

32.  Anees Mohammed and Siniša Djurović, “Rotor Condition Monitoring Using Fibre Optic Sensing Technology”, IET PEMD, 2020

33.  Ignacio Melecio, Anees Mohammed, Nigel Schofield, and Siniša Djurović, “Manifestation of Partial Demagnetisation Fault Induced Unbalanced Magnetic Pull Effects in the Stator Current and Torque of Surface-Mounted PM Machines”, IET PEMD 2020

34.  K. Kabbabe and W. Crowther, “Estimating the economic cost of beyond visual line of sight drone operations for offshore energy asset inspection”, 1st Int’l Conf. on Unmanned Aerial Vehicles, Remote Control Vehicles and Remotely Operated Vehicles for Onshore, Offshore and Subsea Asset and System Integrity, 2020

35.  W. Tang, D. Flynn and V. Robu, "Sensing Technologies and Artificial Intelligence for Subsea Power Cable Asset Management," 2021 IEEE International Conference on Prognostics and Health Management (ICPHM), 2021, pp. 1-6, doi: 10.1109/ICPHM51084.2021.9486586.