This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

Wind turbine pitch system condition monitoring is an active area of research, and this paper investigates the use of the Isolation Forest Machine Learning model and Supervisory Control and Data Acquisition system data for this task. This paper examines two case studies, turbines with hydraulic or electric pitch systems, and uses an Isolation Forest to predict failure ahead of time. This novel technique compared several models per turbine, each trained on a different number of months of data. An anomaly proportion for three different time-series window lengths was compared, to observe trends and peaks before failure. The two cases were compared, and it was found that this technique could detect abnormal activity roughly 12 to 18 months before failure for both the hydraulic and electric pitch systems for all unhealthy turbines, and a trend upwards in anomalies could be found in the immediate run up to failure. These peaks in anomalous behaviour could indicate a future failure and this would allow for on-site maintenance to be scheduled. Therefore, this method could improve scheduling planned maintenance activity for pitch systems, regardless of the pitch system employed.

Offshore wind turbine drive train technology is evolving as developers increase size, aim to maximise availability and adapt to changing electricity grid requirements. This work first of all explores offshore technology market trends observed in Europe, providing a comprehensive overview of installed and planned capacity, showing a clear shift from smaller high-speed geared machines to larger direct-drive machines. To examine the implications of this shift in technology on reliability, stop rates for direct-drive and gear-driven turbines are compared between 39 farms across Europe and South America. This showed several key similarities between configurations, with the electrical system contributing to largest amount of turbine downtime in either case. When considering overall downtime across all components, the direct-drive machine had the highest value, which could be mainly attributed to comparatively higher downtime associated with the electrical, generator and control systems. For this study, downtime related to the gearbox and generator of the gear-driven turbine was calculated at approximately half of that of the direct-drive generator downtime. Finally, from a perspective of both reliability and fault diagnostics at component level, it is important to understand the key similarities and differences that would allow lessons learned on older technology to be adapted and transferred to newer models. This work presents a framework for assessing diagnostic models published in the literature, more specifically whether a particular failure mode and required input data will transfer well between geared and direct-drive machines. Results from 35 models found in the literature shows that the most transferable diagnostic models relate to the hydraulic, pitch and yaw systems, while the least transferable models relate to the gearbox. Faults associated with the generator, shafts and bearings are failure mode specific, but generally require some level of modification to adapt features to available data.

Data-driven normal behaviour models have gained traction over the last few years as a convenient way of modelling turbine operational health to detect anomalies. By leveraging high-dimensional operational relationships, temperature thresholds can be automatically calculated based on each individual turbine unique operating envelope, in theory minimising false alarms and providing more reliable diagnostics. The aim of this work is to provide further insight into practical uses and limitations of implementing normal behaviour temperature models in practice, to inform practitioners, as well as assist in improving wind turbine generator fault detection systems. Results suggest that, on average, as little as two months of data are adequate to produce stable temperature alarm thresholds, with the worst case example requiring approximately 200–290 days of data depending on the component and desired convergence criteria.

Increasing the number of offshore wind farms and installing larger wind turbines, are just two ways to meet the Net Zero targets set in both the UK and EU. The offshore environment is harsh and there are additional challenges such as accessibility, so it is important to have reliable equipment installed within these wind turbines. Geared drivetrains have been observed to lack the sufficient level of reliability required in an offshore environment, so the direct-drive generator designs without any gearbox, aim to increase the reliability. Due to the increased level of torque the direct-drive generators tend to be larger and heavier, they require more permanent magnets and accordingly more rare earth material, as well as more demanding mechanical structures for the generator and drives and these all cause issues with design, supply chain, manufacturing and installation for original equipment manufacturers (OEMs). This paper has reviewed the state-of-the-art design, manufacturing and assembly of direct-drive permanent magnet generators. The key OEMs that supply the current state-of-the-art direct-drive turbines have been identified and some interviews with experts from industry have been conducted. These efforts aimed to understand the challenges with direct-drive turbines, that is a significant contribution to the growth of offshore wind, to address Net Zero’s growing demand. These challenges are found to be primarily imposed on the manufacturing side, to the scaling up in numbers and size to catch up with the market demands. Finally, this work proposes recommendations to overcome these challenges, with regards to the design and manufacturing respectively, which includes, reducing the amount of permanent magnet material, optimizing the design to reduce the structural mass, automating as many of the manufacturing/assembly processes as possible and practicable, and using alternative processing such as additive manufacturing.

The mass of a direct-drive generator is often defined by the requirements for structural stiffness to meet the magnetic stiffness between the rotor and stator surfaces. This paper analyses this magnetic stiffness and estimates the structural stiffness of direct-drive generators for different modes of deflection. The magnetic stiffness modelling is based on an analytical model of the airgap closing forces. The final models are verified using finite element analysis and developed for both permanent magnet and wound rotor generators. It shows that wound rotor machines have higher stiffness requirements than permanent magnet machines. The structural stiffness of the generator rotor and stator is evaluated for different modes by applying spatially varying forces and finding the associated deflections. Structural stiffnesses for the rotor, stator and bearing are then combined. Finally, the magnetic and structural stiffnesses are combined and a stiffness margin can be found. This method is applied to a relatively stiff and a relatively compliant set of generator structures in a case study. The analytical model presented in this paper is useful for structural optimisation purposes or as part of an online structural health monitoring system as it could assess the integrity of the machines in real time.