IEEE Transactions on Energy Conversion · 2009 · 15 citations · 11 references
AgricultureFinancial ManagementProject ManagementEnergy EconomicsMaintenance EngineeringWind EngineeringPower System ReliabilityReliability ModelingRenewable EnergyWind Energy SystemsWind Turbine TechnologyMaintenance Planning CostsWarranty Coverage PeriodWarranty Period LengthWind Observation Data AnalysisWind Turbine Reliability Analysis
The study proposes a novel analytical method to estimate reliability, average failure rate, and O&M costs of wind turbine generators from site wind data, aiming to aid investors and equity holders in better financial assessment of wind power projects at the planning stage. The method uses a failure rate function derived from the Renewable Energy Information System’s wind turbine failure frequency distribution, is implemented on an Indian wind site, and computes O&M costs over a 20‑year horizon. The method is computationally fast, requires little wind data, yields annually increasing O&M costs that improve financial assessment, and its results match field data over the survey period except during warranty periods.
A novel analytical method is proposed to find the reliability, the average failure rate, and the operation and maintenance costs (OMCs) of wind turbine generator based on the site wind data. The failure rate function is developed using wind turbine failure frequency distribution, published by Renewable Energy Information System on Internet, Germany. The presented method works computationally fast and does not require large wind data input, which is a requirement of many existing, commercially available simulation methods. The annually increasing OMCs, computed by this method, give better financial assessment of the wind power projects. The method presented is implemented on an Indian wind site. OMCs are obtained for 20 years. Results match the actual field data, for the survey period, barring their warranty period. It is hoped that the proposed method helps financial investors and equity holders for a better financial assessment of the wind power projects at planning stage.
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