Archived project

HOME Offshore Project

Goal: HOME Offshore is a research project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) which partners 5 leading UK universities. The project is investigating 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 will allow problems to be picked up early, when easy and inexpensive maintenance will allow problems to be readily fixed. Robots and advanced sensors will be used 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 up to £2 billion annually by 2025 in the UK alone. As much as 80% to 90% of the cost of offshore Operation and Maintenance may be 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 aims to make generate more ‘actionable data’ – useful information that can reduce operation and maintenance costs and improve safety.

Publications

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 2021, 10, 79. https://doi.org/10.3390/robotics10020079
33. Z. Hu et al., "Monitoring Power Module Solder Degradation from Heat Dissipation in Two Opposite Directions," in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2022.3157464.

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 Exposition. Volume 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.

Date: 11 April 2017 - 10 November 2020

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Khristopher Kabbabe Poleo
added a research item
Using drones for infrastructure inspection is becoming routine, driven by the benefit of reducing risk and costs. In this paper, the business case for drone-based inspection is examined from the perspective of the wind farm operator and the Drone Service Provider (DSP). A physical and financial model of an offshore wind farm is built using techno-economic analysis and activity-based costing, and data from the open literature. Drone operational models are developed based on domain specific knowledge of operation practices and the predicted physical environment. Rope-access inspection is used as a baseline and accounts for 0.7% of the wind farm operational expenditure. Replacing rope-access inspection with drones reduces costs by up to 70% and decreases revenue lost due to down-time by up to 90%. Increasing autonomy of drones increases the speed at which inspections can be performed but increases costs and complexity. For wind farm operator there is marginal economic benefit (2% reduction in inspection costs) in moving towards a fully autonomous drone-based inspection system from the current visual line of sight operation of single drone. However, from the point of view of the DSP, fully autonomous operations allow greater scalability of the business and enables higher utilisation of the fleet.
Mike Barnes
added a project goal
HOME Offshore is a research project funded by the UK Engineering and Physical Sciences Research Council (EPSRC) which partners 5 leading UK universities. The project is investigating 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 will allow problems to be picked up early, when easy and inexpensive maintenance will allow problems to be readily fixed. Robots and advanced sensors will be used 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 up to £2 billion annually by 2025 in the UK alone. As much as 80% to 90% of the cost of offshore Operation and Maintenance may be 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 aims to make generate more ‘actionable data’ – useful information that can reduce operation and maintenance costs and improve safety.
Publications
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 2021, 10, 79. https://doi.org/10.3390/robotics10020079
33. Z. Hu et al., "Monitoring Power Module Solder Degradation from Heat Dissipation in Two Opposite Directions," in IEEE Transactions on Power Electronics, doi: 10.1109/TPEL.2022.3157464.
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 Exposition. Volume 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.
 
Mohammad Heggo
added a research item
Offshore high voltage DC (HVDC) wind farms offer a promising solution to growing energy demands in the future. The fundamental process in an HVDC electric power transmission system is the power conversion from AC to DC form, that takes place inside HVDC substation building known as valve hall. Condition monitoring of substation assets is essential for reliable and resilient operation of electrical networks. Existing condition monitoring techniques for onshore substation and electrical network assets include regular and scheduled on-site visits by trained personnel to substation sites. However, such routine inspection is not a cost-effective solution for offshore sites. In cases where the accessibility to network assets becomes difficult, robotic and autonomous condition monitoring provides a solution. Such a robotic/autonomous solution would need to interact with the surrounding substation environment, without increasing the risk of electrical breakdown and flashovers between different energised components. The solution would also need to withstand the high electric fields seen within indoor substation environments. High electric fields could damage both the vehicle and also increase the risk of a flashover event within an indoor substation environment. In this paper, the effect of high electrostatic fields on an inspection UAV are analyzed, with respect to 1) AC corona emissions interference on the UAV, and 2) Air breakdown clearance characteristics. The paper presents both simulation and experimental results regarding the impact of high electric fields on a UAV. Moreover, the impact of navigating a shielded UAV inside a HVDC valve hall, and its impact on the clearances is evaluated. Simulation and experimental results show that UAV autopilot and motor sections are highly vulnerable to corona emission currents. Also, the results prove the limited UAV shield impact on reducing the air breakdown voltage between valve halls racks. HVDC offshore platforms are normally rated to high voltages up to 500 kV , which creates high electrostatic fields that can influence UAV nominal operation. A typical UAV mainly consists of four subsystems: 1) Autopilot, 2) Communication, 3) Sensors Payload, 4) Actuation. The main paper aim is identifying corona discharge current interference effect on each UAV subsystem . To test the corona discharge current interference on a UAV, a test rig was developed which used two spherical electrodes to generate an electrostatic field. Two metal spheres with equal diameters (10 cm) and smooth curvatures were used and mounted 102 cm apart. One sphere was connected to an AC voltage source (0 – 800 kV) through a nonconductive resistor, while the other sphere was connected to the ground. The UAV was mounted between them on a wooden stand, midway between the two spheres. The UAV was secured to the mount so that it could not fly. The performance of the UAV subsystems was monitored remotely using QGroundControl software. Experiment results show a significant interference from the corona discharge current to both the UAV control/communication signals at the autopilot and motor sections. To validate the experiment results, a simulation model using COMSOL software was developed. A CAD model of the UAV was imported which took into consideration the different materials used in construction. Simulation results (Fig. 2) show the electric field magnitude at the autopilot section as 1200 kV/m at a voltage difference of 200 kV between the two spheres. This simulation result validates our experiment result, regarding the vulnerability of UAV autopilot to interference from corona discharge current. In this paper, corona discharge current interference is investigated to different UAV sections for inspection of offshore HVDC valve halls. Initial results show a significant interference to both control and communication signals, which is validated using simulation model. In the full paper version, further analysis for UAV onboard signals will be presented in presence of high electrostatic field. Also, UAV shield effect on changing the air breakdown voltage clearance inside the valve hall will be investigated. The presented methodology in this paper can be used as a testing criterion for offshore HVDC valve hall inspection UAVs.
Mohammad Heggo
added a research item
For HVDC links, the substation is a major cost factor. 3D visualization using virtual reality has the potential to play a significant part in cost reduction. This paper outlines the problem and gives two examples: the use of visualization for planning internal drone inspection; and the application of visualization to the design of a superconducting fault-current limiter breaker.