Reliability Evaluation for Offshore Wind Farms Considering Seasonal Characteristics and Collection System Failure

Due to their advantages over onshore wind farms, such as higher average wind speeds and less land occupation, the development of large-scale offshore wind farms (OWFs) has generated a lot of attention. In order to take into consideration the effects of collecting system component failure as well as seasonal variations in wind speed, a model based on the Markov chain Monte Carlo (MCMC) method for OWF dependability evaluation is created in this study. Then, K-means clustering is used to separate the historical wind speed data into the several states. By taking seasonality into consideration and taking into account the probability distribution of wind speed specific to each wind speed level, the transition probability between various wind speed levels is computed. By taking into account the probability distribution of wind speed inside each wind speed state, MCMC is used to establish a wind speed simulation model with seasonal characteristics. The MCMC-based OWF component state simulation method is applied to generate OWF components states time series. The graph theory method is used to examine the topological connectivity of offshore wind farm systems for each state produced by the proposed model. Then the reliability evaluation procedure of OWF based on MCMC model is used to obtain the reliability index. Taking an OWF as an example, the proposed model's accuracy and efficacy are confirmed by comparison with other models, and the impact of collecting system component failure on the dependability of OWFs is examined.

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