Effective and timely health monitoring of wind turbine gearboxes and generators is essential to reduce the costs of operations and maintenance activities, especially offshore. This paper presents a scalable and lightweight convolutional neural network (CNN) framework using high-dimensional raw condition monitoring data for the automatic detection of multiple wind turbine electromechanical faults. The proposed approach leverages the potential of combining information from a variety of signals to learn features and to discriminate the types of fault and their severity. As a result of the CNN layers used to extract features from the signals, this architecture works in the time domain and can digest high-resolution multi-sensor data streams in real-time. To overcome the inherent black-box nature of AI models, this research proposes two interpretability techniques, multidimensional scaling and layer-wise relevance propagation, to analyse the proposed model's inner-working and identify the signal features relevant for fault classification. Experimental results show high performance and classification accuracies above 99.9% for all fault cases tested, demonstrating the efficacy of the proposed fault-detection system.

In MW-sized wind turbines, the most widely-used generator is the wound rotor induction machine, with a partially-rated voltage source converter connected to the rotor. This generator is a significant cause of wind turbine fault modes. In this paper, a harmonic time-stepped generator model is applied to derive wound rotor induction generator electrical & mechanical signals for fault measurement, and propose simple closed-form analytical expressions to describe them. Predictions are then validated with tests on a 30 kW induction generator test rig. Results show that generator rotor unbalance produces substantial increases in the side-bands of supply frequency and slotting harmonic frequencies in the spectra of current, power, speed, mechanical torque and vibration measurements. It is believed that this is the first occasion in which such comprehensive approach has been presented for this type of machine, with healthy & faulty conditions at varying loads and rotor faults. Clear recommendations of the relative merits of various electrical & mechanical signals for detecting rotor faults are given, and reliable fault indicators are identified for incorporation into wind turbine condition monitoring systems. Finally, the paper proposes that fault detectability and reliability could be improved by data fusion of some of these electrical & mechanical signals.

Non-intrusive, reliable and precise torque measurement is critical to dynamic performance monitoring, control and condition monitoring of rotating mechanical systems. This paper presents a novel, contactless torque measurement system consisting of two shaft-mounted zebra tapes and two optical sensors mounted on stationary rigid supports. Unlike conventional torque measurement methods, the proposed system does not require costly embedded sensors or shaft-mounted electronics. Moreover, its non-intrusive nature, adaptable design, simple installation and low cost make it suitable for a large variety of advanced engineering applications. Torque measurement is achieved by estimating the shaft twist angle through analysis of zebra tape pulse train time shifts. This paper presents and compares two signal processing methods for torque measurement: rising edge detection and cross-correlation. The performance of the proposed system has been proven experimentally under both static and variable conditions and both processing approaches show good agreement with reference measurements from an in-line, invasive torque transducer. Measurement uncertainty has been estimated according to the ISO GUM (Guide to the expression of uncertainty in measurement). Type A analysis of experimental data has provided an expanded uncertainty relative to the system full-scale torque of ±0.30% and ±0.86% for the rising edge and cross-correlation approaches, respectively. Statistical simulations performed by the Monte Carlo method have provided, in the worst case, an expanded uncertainty of ±1.19%.

This paper presents a discussion of the development of wind energy generation in the United Kingdom and the challenges faced by the wind industry including reliability, performance and condition monitoring, particularly in the offshore environment. The worldwide installed capacity of offshore wind has now risen to over 7 GW, with an ever increasing deployment rate of new assets. About 90% of the global currently installed capacity is in Northern Europe, with the United Kingdom having the world's largest share at 4 GW. Capacity factor data from UK offshore wind farms is presented, providing an insight into the current performance of large Round 2 offshore wind farms compared to the earlier Round 1 farms and to onshore farms. The data reveal that the United Kingdom's Round 2 offshore farms are achieving an average monthly capacity factor of 38.3% with a peak value of 75.8%. The older Round 1 farms have a lower average capacity factor of 33.6% while large onshore farms with capacities above 100 MW have achieved 25.6%. Offshore wind turbine performance has improved over time, and the industry is applying the learning from early experiences to achieve better performances at the more recently installed farms. Despite these improvements in turbine availability, the cost of energy from wind, particularly offshore, remains too high for it to be a commercially viable form of generation without subsidies. Reducing the cost of energy from wind to economically sustainable levels is the most important challenge facing the industry today. Operation and maintenance costs constitute up to 30 % of the total cost of energy from wind in large farms. The industry must overcome the challenges associated with improving component reliability and the development and adoption by operators of appropriate condition monitoring systems and maintenance strategies, in order to reduce costs to sustainable levels. Research and development work carried out with these goals in mind is also reviewed in the paper.

Improving the availability of wind turbines (WT) is critical to minimise the cost of wind energy, especially for offshore installations. Because gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary because turbine load and speed vary continuously with time. Manual handling of large amounts of monitoring data is time-consuming and costly, and is one of the main limitations of most current CMSs. Therefore, automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham condition monitoring test rig, a gear condition indicator has been proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The algorithm allows the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. The performance of the proposed technique has then been successfully tested on signals from a field test of a full-size wind turbine gearbox that has sustained gear damage. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation, reducing the quantity of information that WT operators must handle.