Offshore Wind Farms Reliability Evaluation And Repair/Maintenance Resources Optimization Considering Severe Weather Conditions

In recent years,due to the increasing environmental pollution and global warming caused by fossil energy power generation,countries around the world are competing to increase the use of renewable energy.Among them,wind energy has been rapidly developed and utilized due to its rich resources and pollution-free characteristic.Since the offshore wind farms(OWFs)have the advantages of being located close to the load center,higher average wind speed and less land occupancy compared with the onshore wind farms,the development of large-scale OWF has attracted considerable attention.However,OWFs are susceptible to offshore severe weather,and require high maintenance costs and long repair time.Therefore,it is important to accurately quantify the impact of severe weather on component failure rate and repair time to evaluate the OWF reliability and optimize the allocation of repair/maintenance resources of OWFs.Supported in part by the National Natural Science Foundation of China(No.51677011),this thesis focus on the reliability evaluation and repair/maintenance resources optimization of OWFs considering severe weather conditions and carry out the following research work:Considering the seasonal characteristics of wind speed and the influence of components failure of the collection system,a Markov Chain Monte Carlo(MCMC)model for reliability evaluation of OWFs without considering the severe weather conditions is established.First,the historical wind speed data is divided into different states based on K-means clustering.The transition probability between different wind speed states are calculated by considering the seasonality,and the wind speed probability distribution corresponding to each wind speed state is accounted for.Based on MCMC,a wind speed simulation model with seasonal characteristics is established by considering the wind speed probability distribution within each wind speed state.Then,taking into account the component temporal failure and repair process,the MCMC-based OWF component state simulation method is studied.For each system state generated by the MCMC model,a breadth-first search(BFS)method is applied to analyze the connectivity of the WTs and the sink node.Finally,the output of the wind farm is determined based on the wind speed data at this state.Taking an OWF as an example,the correctness and effectiveness of the proposed model are verified by comparison with existing models and the influence of component failure of the collection system on the reliability of OWFs is analyzed.A MCMC model for reliability evaluation of OWFs considering the failure rate and repair time affected by severe weather is studied.First,main factors affecting the wind turbine(WT)failure rate are analyzed.Second,Three types of WT failure rates are considered: the failure rate under normal weather,that under strong wind,and that affected by lightning.Time-varying analytical models for the WT failure rate affected by wind speed and lightning are established.Moreover,a time-varying analytical model for the repair time of main components of OWF is established by considering the influence of severe weather on offshore transportation time and maintenance efficiency after component failure.Then,a MCMC model that can simulate both the weather intensity and the component state is proposed,so that the temporal correlation between the weather and the repair process of failed component can be considered.Case study shows: The impact of severe weather cannot be ignored in the reliability evaluation of OWF,which is mainly reflected on the repair process of components.Considering the impact of severe weather on the repair/maintenance process,an optimal allocation model for repair/maintenance resources of OWFs is established.Firstly,based on the queuing theory and other methods,the influence of repair /maintenance resources,such as transportations,repair/maintenance teams and spare parts,on the downtime of the WT is studied.The evaluation method of total cost including economic loss caused by WT failure and the cost of repair/maintenance resources is studied.Then the optimal number of repair/maintenance teams and spare parts in OWFs are determined by minimizing the total cost.By comparing different plans,the allocation of logistical support resources such as vehicle selection,repair/maintenance team's accommodation location and work shift arrangement is optimized.Case study shows: the repair/maintenance support resource allocation scheme corresponding to the ship transportation,the onshore accommodation and the 12-hour/7-day work shift is superior.In addition,the effects of failure rate and electricity price variations on the optimal allocation of repair/maintenance resources are analyzed.