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Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using each fault detection method, and second, a diagnosis matrix, representing the individual outcome of each fault detection method. Once the confidence and diagnosis matrices of a component are defined, the individual diagnoses of each fault detection method are combined into a final verdict on the fault state of that component. Furthermore, this paper introduces a Bayesian updating model based on observations collected by inspections to decrease the uncertainty of initial confidence matrix. The framework and implementation of the presented diagnostic model are further explained within a case study for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions.
In this paper the state of the art in O&M models for O&M cost estimation of offshore wind farms is discussed and then, a case study for O&M cost estimation of an 800 MW reference offshore wind farm is given. Moreover, a framework for an ideal O&M strategy optimizer to achieve the maximum possible O&M costs reduction during operational years of an offshore wind farm is described and recommendations are given. MOTIVATIONOffshore wind energy is growing rapidly in Europe. By the end of 2014 about 2500 offshore wind turbines were installed in Europe, making a cumulative installed capacity of 8 GW. Moreover, there are European and government plans to install turbines amounting to another 32 GW of offshore wind energy by 2020, making a cumulative installed capacity of 40 GW. Moreover, Asian countries and the US are slowly planning their first offshore wind farms. Despite the rapid growth of offshore wind installations, the offshore wind industry is not yet a mature industry and several enhancements can be done to reduce the cost of energy. In Equations (1) and (2) cost of energy (CoE) and levelized cost of energy (LCoE) calculated using the discount rate are shown.(1)(2) If the discount rate is set to zero, then the calculated CoE and LCoE values will be equal. Based on the Equation (1), the cost of energy can be reduced by decreasing capital (CAPEX) and operational (OPEX) expenditures and/or increasing the energy yield or Captured power. Reducing CAPEX, while maintaining the reliability level, and increasing the energy yield, while maintaining the CAPEX, are studied in several European and international projects. The focus of this paper is reduction of OPEX by means of advanced O&M models. STATE OF THE ARTDue to the harsh offshore climate, limited accessibility to the wind farms during cold seasons and expensive offshore vessels, O&M costs of offshore wind farms are much higher than onshore ones. During the past two decades, several O&M models for offshore wind farms have been developed. The two main applications of offshore wind O&M models are O&M cost estimation and O&M strategy optimization, which are discussed further in the following sections. O&M cost estimatorsDuring the tendering phase of an offshore wind farm, the asset owner should estimate the LCoE produced by the planned offshore wind farm. Therefore, a good estimation of the total OPEX during the lifetime of the planned offshore wind farm, as well as the total CAPEX and energy yield is required. Typically there are two types of O&M cost estimators available, deterministic models and stochastic models. Deterministic O&M cost estimatorsDeterministic O&M models calculate long-term averaged O&M costs and downtime over the lifetime of an offshore wind farm. In other words, it is not particularly important when exactly in the lifetime of the wind farm a failure happens.The ECN O&M Tool [1] is a deterministic Excel based O&M cost estimator developed by Energy research Centre of the Netherlands (ECN) in 2003. The ECN O&M Tool known as the industry standard for O&M...
The operation and maintenance costs of offshore wind farms can be significantly reduced if existing corrective actions are performed as efficient as possible and if future corrective actions are avoided by performing sufficient preventive actions. In this paper a prognostic model for degradation monitoring, fault prediction and predictive maintenance of offshore wind components is defined.The diagnostic model defined in this paper is based on degradation, remaining useful lifetime and hybrid inspection threshold models. The defined degradation model is based on an exponential distribution with stochastic scale factor modelled by a normal distribution. Once based on failures, inspection or condition monitoring data sufficient observations on the degradation level of a component are available, using Bayes’ rule and Normal-Normal model prior exponential parameters of the degradation model can be updated. The components of the diagnostic model defined in this paper are further explained within several illustrative examples. At the end, conclusions are given and recommendations for future studies on this topic are discussed.
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