Data insights from an offshore wind turbine gearbox replacement

This is the final version. Available from IOP Publishing via the DOI in this record.Gearboxes are a complex, yet vital assembly for non-direct-drive offshore wind turbines, which are designed to last for the lifetime of the asset. However, recent studies indicate that they may have to be replaced as early as 6.5 years. Moreover, their contribution to offshore wind farm failures and downtime has been shown to be amongst the three most critical assemblies with the highest material cost required. An improved understanding of these premature failures and the ability to predict them in advance could reduce inspection and maintenance costs, as well as to help overcome many logistical and planning challenges. The objective of this paper is to present the lessons learnt from a gearbox exchange performed in one of the offshore wind turbines at Teesside offshore wind farm, comprising 27 2.3MW wind turbines. The paper takes a condition monitoring perspective and uses the identified spalling at the inner part of the planetary bearing as the governing failure mode. A data management system has been setup, incorporating all the operational data received, including maintenance log information and sensor data. A period of up to 2.5 years, prior to the the gearbox exchange, is examined for this study. SCADA and CMS data of the faulty turbine are compared against the wind farm, using statistical methods and machine learning techniques. Supervised learning models are built, which will help predict similar failures in the future. Results show how different data sources can contribute in gearbox failure diagnosis and help to expedite failure detection for Teesside offshore wind farm and similar wind turbine and gearbox types. This paper will be of interest to wind farm developers and operators to build predictive models from monitoring data that can forecast potential gearbox failures.Energy Technology InstituteResearch Council Energy ProgrammeEuropean Unions Horizon 202

Towards automated and integrated data collection - standardising workflow processes for the offshore wind industry

Conference paper abstractA significant amount of operation and maintenance (O&M) data are being generated daily from offshore wind farms. Most of them are coming from a variety of monitoring systems, maintenance reports and environmental sources. The challenge with having a wide diversity of data in inhomogeneous types and formats, is the considerable human effort involved in the initial extraction, transformation and loading (ETL) stages for these data to be processed and analysed. Although several commercial solutions are available, aiming to improve data management to support O&M decision making, the initial ETL phase is still a work-intensive process. One of the main reasons is that the organization and structure of the data flow does not allow easy access to the data. Due to the rapid growth of the offshore wind industry, there is a need to automate and integrate some of these processes in order to reduce the human effort and the associated costs. The aim is to facilitate a responsive, data driven decision making for O&M. This paper and presentation show the results of re-structuring and automation of the daily maintenance procedures that achieve a more efficient data analysis. These early results also indicate that less man-hours and a smaller number of people need to work on data collection. The framework and the steps followed will be of interest to offshore wind farm developers and operators to automate their data collection workflow

Offshore Wind Turbine Fault Alarm Prediction

This is the final version. Available on open access from Wiley via the DOI in this recordOffshore wind operation and maintenance (O&M) costs could reach up to 1/3 of the overall project costs. In order to accelerate the deployment of offshore wind farms, costs need to come down. A key contributor to the O&M costs are the component failures and the downtime caused by them. Thus, an understanding is needed on the root cause of these failures. Previous research has indicated the relationship between wind turbine failures and environmental conditions. These studies are using work order data from onshore and offshore assets. A limitation of using work orders is that the time of the failure is not known and consequently the exact environmental conditions cannot be identified. However, if turbine alarms are used to make this correlation, more accurate results can be derived. This paper quantifies this relationship and proposes a novel tool for predicting wind turbine 1 fault alarms for a range of subassemblies, using wind speed statistics. A large variation of the failures between the different subassemblies against the wind speed is shown. The tool uses five years of operational data from an offshore wind farm to create a data-driven predictive model. It is tested under low and high wind conditions, showing very promising results of more than 86% accuracy on seven different scenarios. This study is of interest to wind farm operators seeking to utilize the operational data of their assets to predict future faults, which will allow them to better plan their maintenance activities and have a more efficient spare part management system.Energy Technology InstituteRCUK Energy ProgrammeEDF Energ

Data-Informed Lifetime Reliability Prediction for Offshore Wind Farms

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordOffshore wind operation and maintenance (O&M) costs can reach up to 1/3 of the overall project costs. In order to accelerate the deployment of these clean energy assets, costs need to come down. This requires, a good understanding of the different operations along with a robust planning, maintenance and monitoring strategy. Asset management tools have been developed, which require reliability inputs, able to estimate the lifetime operational expenditure (OPEX) and optimize the maintenance strategies for the assets. The lack of large datasets with offshore wind failure rate data in the literature increases the uncertainty in the estimations made by those tools. This paper aims to compare whether the publicly available data could provide an accurate information of the lifetime reliability predictions of the assets. It initially uses a generic average failure rate, taken from literature to model the wind farm; as most wind farm developers will not have any detailed understanding of the reliability of the asset prior to construction. It then uses a more detailed, turbine-specific model, taking into account reliability data from an operational wind farm. Results show a small overall difference when the model uses the data-informed parameters, by up to 0.4% in the overall availability. Moreover, it is shown that the use of generic values can create more pessimistic results compared to the data-informed data. The results of the paper are of interest to offshore wind farm developers and operators aiming to improve their lifetime reliability estimations and reduce the O&M costs of the offshore wind farms.Energy Technology InstituteEngineering and Physical Sciences Research Council (EPSRC)EDF Energ

Investigation of scaling effect on power factor of permanent magnet Vernier machines for wind power application

This study investigates the scaling effect on power factor of surface mounted permanent magnet Vernier (SPM-V) machines with power ratings ranging from 3 kW, 500 kW, 3 MW to 10 MW. For each power rating, different slot/pole number combinations have been considered to study the influence of key parameters including inter-pole magnet leakage and stator slot leakage on power factor. A detailed analytical modelling, incorporating these key parameters, is presented and validated with two-dimensional finite-element analysis for different power ratings and slot/pole number combinations. The study has revealed that with scaling (increasing power level), significant increase in electrical loading combined with the increased leakage fluxes, i.e. (i) magnet leakage flux due to large coil pitch to rotor pole pitch ratio, (ii) magnet inter-pole leakage flux and (iii) stator slot leakage flux, reduces the ratio of armature flux linkage to permanent magnet flux linkage and thereby has a detrimental effect on the power factor. Therefore, unlike conventional SPM machines, the power factor of SPM-V machines is found to be significantly reduced at high power ratings

Data-driven Operations & Maintenance for Offshore Wind Farms: Tools and Methodologies

Offshore wind assets have reached multi-GW scale and additional capacity is being installed and developed. To achieve demanding cost of energy targets, awarded by competitive auctions, the operations and maintenance (O&M) of these assets have to become increasingly efficient, whilst ensuring compliance and effectiveness. Existing offshore wind farm assets generate a significant amount of inhomogeneous operational data. These data contain rich information about the condition of the assets, which are rarely fully utilized by the operators and service providers. This thesis provides useful methodologies and tools that can help wind farm owners, operators and service providers to reduce their O&M costs by better managing their data, integrating processes and providing data-driven decision making. The developed methodologies and tools are being presented through several case studies, showing the effectiveness of the solutions and their potential cost reduction opportunities. These are split into the following four themes: (i) Data management techniques, methodologies and case studies, aiming to improve data collection and data integration strategies for a data informed decision making. (ii) Processes and best practices for workflow improvements and automated datacollection and standardization. (iii) Data analytics including reliability, diagnostic and prognostic methodologies and case studies. (iv) Maintenance planning including enhanced planning strategies, decision support frameworks and optimized maintenance operations. All of the above frameworks, methodologies and case studies are linked together as they provide insights for data-driven decision making, which results in better informed and thus less costly maintenance strategies. The methodologies and case studies presented will assist in creating data-driven O&M processes and allowing the full utilization of the produced offshore wind farm data

Cost-effective risk-based inspection planning for offshore wind farms

This is the final version. Available from British Institute of Non-destructive Testing via the DOI in this record.Offshore wind farm assets require regular inspections. Studies and industry experience have highlighted the importance of selecting the appropriate inspection and maintenance (I&M) plan, as it directly impacts the reliability of the components and the associated maintenance cost. If inspections are carried out too frequently, the associated risks will be low and reliability will be high, but the cost will also be high. On the contrary, if no or very few inspections are carried out, unexpected failures of the structures could occur. This paper presents a risk-based inspection (RBI) framework for offshore wind farms, building on existing knowledge from other industries including nuclear, oil & gas, chemical and aerospace. This approach considers the probability, the consequences and the cost of the operational or maintenance activity via a criticality analysis that allows optimal selection and prioritisation of I&M activities. A case study is presented where this framework is implemented on the transition pieces (TPs) of the wind turbines, by investigating information received from design, operation and inspection reports as well as monitoring equipment. Guidelines are also proposed on how to utilise novel monitoring and visual inspection techniques to further improve the implementation of RBI. The results of this paper suggest a less frequent I&M strategy, which could reduce the associated TP inspection costs by up to £0.7 million/MW installed and increase the safety of personnel. The study will be of interest to offshore wind farm developers, operators and maintenance providers, to better prioritise I&M activities and increase the operating revenue of their assets.Energy Technology InstituteRCUK Energy ProgrammeEDF Energ

An integrated data management approach for offshore wind turbine failure root cause analysis

This is the author accepted manuscript.A significant amount of operation and maintenance (O&M) data are being generated daily from offshore wind farms, including a variety of monitoring systems, maintenance reports and environmental sources. The challenge with having a wide variety of data sources with different temporal and format characteristics, is that a significant effort is required to identify evidence that supports a root cause analysis (RCA) of a turbine fault. In addition, the organization of the O&M data flow does not lend itself to support easy reporting of the O&M key performance indicators. Since the offshore wind industry is growing rapidly, there is a need to better understand and manage the O&M data generated. This paper demonstrates a novel integrated data management system that combines all the O&M data from an offshore wind farm and proves that the proposed RCA framework, based on this integrated platform, can lower O&M costs, by reducing the number of visits to the turbines. It also provides failure rates for subassemblies and looks at the failure distribution within the wind farm. The results of the paper will be of interest to offshore wind farm developers and operators to streamline and optimize O&M planning and activities for their assets.This work was funded by the Energy Technology Institute and the RCUK Energy Programme as part of the IDCORE programme (Grant EP/J500847/1) and EDF Energy