Different configurations of gearbox, generator and power converter exist for offshore wind turbines. This paper investigated the performance of four prominent drive train configurations over a range of sites distinguished by their distance to shore. Failure rate data from onshore and offshore wind turbine populations was used where available or systematically estimated where no data was available. This was inputted along with repair resource requirements to an offshore accessibility and operation and maintenance model to calculate availability and operation and maintenance costs for a baseline wind farm consisting of 100 turbines. The results predicted that turbines with a permanent magnet generator and a fully rated power converter will have a higher availability and lower operation and maintenance costs than turbines with doubly fed induction generators. This held true for all sites in this analysis. It was also predicted that in turbines with a permanent magnet generator, the direct drive configuration has the highest availability and lowest operation and maintenance costs followed by the turbines with two-stage and three-stage gearboxes.
Offshore wind turbine technology is moving forward as a cleaner alternative to the fossil fuelled power production. However, there are a number of challenges in offshore; wind turbines are subject to different loads that are not often experienced onshore and more importantly challenging wind and wave conditions limit the operability of the vessels needed to access offshore wind farms. As the power generation capacity improves constantly, advanced planning of Operation and Maintenance (O&M)activities, which supports the developers in achieving reduced downtime, optimised availability and maximised revenue, has gained vital importance. In this context, the focus of this research is the investigation of the most cost-effective approach to allocate O&M resources which may include helicopter, crew transfer vessels, offshore access vessels, and jack-up vessels. This target is achieved through the implementation of a time domain Monte-Carlo simulation approach which includes analysis of environmental conditions (wind speed, wave height, and wave period), operational analysis of transportation systems, investigation of failures (type and frequency), and simulation of repairs. The developed methodology highlights how the O&M fleets can be operated in a cost-effective manner in order to support associated day-to-day O&M activities and sustain continuous power production
Due to lack of operating experience in the field of offshore wind energy and large costs associated with maintaining offshore wind farms, there is a need to develop accurate operation and maintenance models for strategic planning purposes. This paper provides an approach for verifying such simulation models and demonstrates it by describing the verification process for four models. A reference offshore wind farm is defined and simulated using these models to provide test cases and benchmark results for verification for wind farm availability and O&M costs. This paper also identifies key modelling assumptions that impact the results. The calculated availabilities for the four models show good agreement apart from cases where maintenance resources are heavily constrained. There are also larger discrepancies between the cost results. All the differences in the results can be explained by different modelling assumptions. Therefore, the models can be regarded as verified based on the presented approach. INTRODUCTION MotivationOffshore wind energy is a new area for operation and maintenance (O&M) research, and the operational legacy of the industry is only just over a decade. Operation and maintenance cost modelling software tools are being developed to support activities in this field. Because of the novelty of offshore wind energy generation and lack of real data, there are limited options for validation and verification of these models. Verification and validation of a simulation model is essential if the model is to say something useful about the system it is meant to represent. We define verification as ensuring that the simulation model is implemented according to the specifications of the conceptual model of the system; validation is defined as ensuring that this conceptual model is in fact a faithful representation of the real system for the purposes of the model [1]. It may prove difficult for researchers to acquire suitable data to perform model validation. For full operational validation [1], necessary historical data would include repair and logistical costs, statistical information on component reliability and performance indicators such as total operations costs or availability. This type of information is possessed by the farm owner/operator, turbine manufacturer or non-existent for new generation wind turbines. BackgroundSeveral O&M simulation models for offshore wind farms have been developed, of which Hofmann [2] provides a thorough overview. Often, the intended applications of the models differ slightly. For example, one model will focus on assessing heavy-lift vessels, whereas another will be used for maintenance strategy optimisation. [3]. One position is that models are never entirely validated because it is not practicable to assess correspondence between the system and the model for its entire domain of applicability [1]. Even if the system is observable and a comparison of model output and system output is possible, one is often interested in predicting system behaviour under circumst...
This paper presents a novel approach to repair modeling using a time domain Auto-Regressive model to represent meteo-ocean site conditions. The short term hourly correlations, medium term access windows of periods up to days and the annual distribution of site data are captured. In addition, seasonality is included. Correlation observed between wind and wave site can be incorporated if simultaneous data exists. Using this approach a time series for both significant wave height and mean wind speed is described. This allows MTTR to be implemented within the reliability simulation as a variable process, dependent on significant wave height. This approach automatically captures site characteristics including seasonality and allows for complex analysis using time dependent constraints such as working patterns to be implemented. A simple cost model for lost revenue determined by the concurrent simulated wind speed is also presented. A preliminary investigation of the influence of component reliability and access thresholds at various existing sites on availability is presented demonstrating the ability of the modeling approach to offer new insights into offshore wind turbine operation and maintenance.
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