Wind turbine technologies are evolving rapidly in terms of complexity and size and there is an urgent need for cost-effective Operations and Maintenance (O&M) strategies to increase the profitability of wind power assets.

Component reliability as well as maintenance strategies have the greatest effect on O&M planning and costs, especially in offshore applications.

This Chapter provides an overview of the state of the art in the field of wind turbine and wind farm reliability and maintenance, discussing the main points, lessons and opportunities learnt from both industry and academic experience.@en

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.

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Wind turbine condition monitoring is gaining in importance as operators and developers move towards larger, further offshore, less accessible wind farms. However, condition monitoring is made highly challenging by the variable speed, variable load nature of wind turbines. Electrical faults, in the form of brush gear or slip ring damage contribute significantly to downtime yet operators have little experience of detecting these faults in the field. This paper subjects a physical test rig to rotor electrical unbalances and applies a frequency tracking algorithm to mechanical and electrical monitoring signals to compare the sensitivity of the various signals. The results shows that the total electrical power signal gives the clearest response to rotor electrical unbalance, as expected, but that torque measurements could be a viable alternative. Speed signal analysis showed much lower sensitivity to unbalance than torque or power measurements.@en

Improving the availability of wind turbines is critical for minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of concern to the wind industry, because the gearbox downtime has a significant effect on the wind turbine availabilities. Timely detection and diagnosis of developing gear defects is essential for minimising an unplanned downtime. One of the main limitations of most current CMSs is the time consuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This study 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 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 wind turbine gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL) programme. The results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into the wind turbine CMSs, this algorithm can automate the data interpretation, thus reducing the quantity of the information that the wind turbine operators must handle.

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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.

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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 raising-will 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.@en

As wind power is growing towards a major utility source of electricity, reducing the cost of energy becomes a critical issue in order to make wind power competitive to conventional sources. The current trend in wind turbine (WT) development is for larger, more complex machines located in remote offshore locations which present operation and maintenance (O&M) challenges and high costs. Significant savings can be made by establishing technically and economically viable condition-based maintenance (CBM) through the implementation of automated condition monitoring system (CMS) data interpretation. Gearbox faults, with high replacement costs, complex repair procedures and revenue loss due to long downtime, are widely considered as the leading issue for WT drive train condition monitoring. The purpose of this paper is to design a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. To this aim, experimental work was carried out on a condition monitoring test rig equipped with a commercial WT CMS. Gear tooth damage has been investigated by establishing pattern feature parameters from gearbox vibration signatures. The results demonstrate that the presence of meshing frequency harmonic sidebands and their amplitudes can prove to be very valuable when diagnosing gear defects. A gear fault diagnostic algorithm, able to track the overall power of the spectra sideband frequency window, was proposed in order to reduce each spectrum to only one parameter and automate the data interpretation. The algorithm has proved successful to detect the progression of a gear tooth defect even at the early development stages.@en