The wind industry is looking for ways to accurately predict reliability and availability of newly installed wind turbines. Failure modes, effects and criticality analysis (FMECA) is a technique utilized to determine the critical subsystems of wind turbines. There are several studies in the literature which have applied FMECA to wind turbines, but no studies so far have used it considering different weather conditions or climatic regions. Furthermore, different wind turbine design types have been analyzed applying FMECA either distinctively or combined, but no study so far has compared the FMECA results for geared and direct-drive wind turbines. We propose to fill these gaps by using Koppen-Geiger climatic regions and two different turbine models of direct-drive and geared-drive concepts. A case study is applied on German wind farms utilizing the Wind Measurement & Evaluation Programme (WMEP) database which contains wind turbine failure data collected between 1989 and 2008. This proposed methodology increases the accuracy of reliability and availability predictions and compares different wind turbine design types and eliminates underestimation of impacts of different weather conditions.

The failure of wind turbines is a multi-faceted problem and its monetary impact is often unpredictable. In this study, we present a novel application of survival analysis on wind turbine reliability, including accounting for previous failures and the history of scheduled maintenance. We investigated the operational, climatic and geographical factors that affect wind turbine failure and modeled the risk rate of wind turbine failure based on data from 109 turbines in Germany operating for a period of 19 years. Our analysis showed that adequately scheduled maintenance can increase the survival of wind turbine systems and electric subsystems up to 2.8 and 3.8 times, respectively, compared to the systems without scheduled maintenance. Geared-drive wind turbines and their electrical systems were observed to have 1.2- and 1.4- times higher survival, respectively, compared to direct-drive turbines and their electrical systems. It was also found that the survival of frequently-failing wind turbine components, such as switches, was worse in geared-drive than in direct-drive wind turbines. We show that survival analysis is a useful tool to guide the reduction of the operating and maintenance costs of wind turbines.

Wind energy is an environmentally friendly, profitable, and renewable energy resource. Wind energy eployment is increasing in the last two decades. In this paper, Bandırma bay which has a potential for offshore wind deployment is studied to determine the most suitable area in it and eventually potential offshore power capacity of the location is estimated. This study combines applications of Geographical Information System and Multi-Criteria- Decision-Making Methodology to obtain fast results supported with visual documentation. In this scope, eight standard criteria such as wind speed, water depth, bird migration lines are applied and nvestigated in detail by using Geographical Information System. It is found that there is sufficient wind speed t the shallow level of the sea. Consequently, two locations are found to be suitable; one is on the northwest and the other one is on the north east of Bandırma, with capacities of 72 MW and 48 MW, respectively.

Operation and maintenance (O&M) costs, and associated uncertainty, for wind turbines (WTs) is a significant burden for wind farm operators. Many wind turbine failures are unpredictable while causing loss of energy production, and may also cause loss of asset. This study utilized 753 O&M event data from 21 wind turbines operating in Germany, to improve the prediction of failure frequency and associated costs. We applied Bayesian updating to predict wind turbine failure frequency and time-to-repair (TTR), in conjunction to machine learning techniques for assessing costs associated with failures. We found that time-to-failure (TTF), time-to-repair and the cost of failures depend on operational and environmental conditions. High elevation (>100 m) of the wind turbine installation was found to increase both the probability of failures and probability of delayed repairs. Furthermore, it was determined that direct-drive turbines are more favorable at locations with high capacity factor (more than 40%) whereas geared-drive turbines show lower failure costs than direct-drive ones at temperate-coastal locations with medium capacity factors (between 20% and 40%). Based on these findings, we developed a decision support tool that can guide a site-specific selection of wind turbine types, while providing a thorough estimation of O&M budgets.