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.

Publisher's copyright statement:This paper is a postprint of a paper submitted to and accepted for publication in IET renewable power generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractImproving the availability of wind turbines (WT) is critical to minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of concern to the wind industry, because gearbox downtime has a significant impact on WT availabilities. Timely detection and diagnosis of developing gear defects is essential to minimise unplanned downtime. One of the main limitations of most current CMSs is the timeconsuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on experimental evidence from the Durham condition monitoring test rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size WT gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL)programme. 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.

No abstract

Tidal stream devices are a new technology for extracting renewable energy from the sea. Various tidal stream device models have been proposed and, if they are installed at chosen high tidal stream velocity sites, they may face extreme climatic, current, and wave load conditions. As they contain complex mechanical, electrical, control, and structural systems, reliability and survivability will be a challenge. Data on their reliability have been scarce, as only a few prototypes have been built and operated. However, reliability prediction of new devices could minimize risk in prototype work. A practicable tidal stream device reliability prediction method could assist the development of cost-effective and viable future options. The present study proposes such a method and derives system reliability models for four generic-design, horizontal-axis, tidal stream devices, all rated 1-2 MW. Historical reliability data from similarly rated wind turbines and other relevant marine databases were used to populate the devised reliability models. The work shows that tidal stream devices can expect to have a lower reliability than wind turbines of comparable size and that failure rates increase with complexity. The work also shows that with these predictions, few devices can expect to survive more than a year in the water. This suggests that either predicted failure rates must be reduced dramatically or that methods for raising reliability -by the use of twin axes or improving maintenance access by unmooring or the use of a seabed pile and turbine raisingwill be needed to achieve better survivor rates. The purpose of this work is not to predict definitive individual device failure rates but to provide a comparison between the reliabilities of a number of different device concepts.

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 crosscorrelation approaches, respectively. Statistical simulations performed by the Monte Carlo method have provided, in the worst case, an expanded uncertainty of ±1.19%.

Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.

This paper presents a novel prognostic method to estimate the remaining useful life (RUL) of generators using the SCADA (Supervisory Control And Data Acquisition) systems installed in wind turbines. A data-driven wind turbine anomaly classification method is developed. The anomalies are quantified into a health indicator to measure the component degradation over time. An Autoregressive Integrated Moving Average (ARIMA) time series forecasting technique is then applied to predict the RUL of the wind turbine generator. The proposed method has been validated using industry field data showing accurate predictions of RUL with a 21 day lead time for maintenance of the turbine.

This paper presents a simplified automated fault detection scheme for wind turbine induction generators with rotor electrical asymmetries. Fault indicators developed in previous works have made use of the presence of significant spectral peaks in the upper sidebands of the supply frequency harmonics; however, the specific location of these peaks may shift depending on the wind turbine speed. As wind turbines tend to operate under variable speed conditions, it may be difficult to predict where these fault‐related peaks will occur. To accommodate for variable speeds and resulting shifting frequency peak locations, previous works have introduced methods to identify or track the relevant frequencies, which necessitates an additional set of processing algorithms to locate these fault‐related peaks prior to any fault analysis. In this work, a simplified method is proposed to instead bypass the issue of variable speed (and shifting frequency peaks) by introducing a set of bandpass filters that encompass the ranges in which the peaks are expected to occur. These filters are designed to capture the fault‐related spectral information to train a classifier for automatic fault detection, regardless of the specific location of the peaks. Initial experimental results show that this approach is robust against variable speeds and further shows good generalizability in being able to detect faults at speeds and conditions that were not presented during training. After training and tuning the proposed fault detection system, the system was tested on “unseen” data and yielded a high classification accuracy of 97.4%, demonstrating the efficacy of the proposed approach.