一种用于序贯蒙特卡罗仿真的风电机组多状态可靠性模型

摘要

A multistage reliability model of wind turbine is built utilizing a systematic method based on Markov chain approach, considering the drawback of the traditional wind turbine reliability model in sequential Monte Carlo Simulation. The probability of occurrence and duration of each state can be obtained using the state transition rate between each output power state of wind turbine calculated out with the regional wind regime of wind farm and operation historical data of wind turbine. On this basis, the double sampling method for the sequential Monte Carlo simulation is proposed. The simulation program for multistage reliability model of wind turbine is compiled. Then it is compared with the commonly used two-state model based on a single sampling method. Simulation results verify the feasibility of the proposed model based on Markov method. It can reflect accurately the output power of the wind turbine of any duration under fault conditions, improve the accuracy and expand the application range of the simulation model.%　　针对传统风电机组可靠性模型不适合序贯蒙特卡罗仿真的不足，利用基于马尔可夫链的解析方法建立了风电机组的多状态可靠性模型。通过对整个风电场的风况和风机的历史运行数据的统计，得出风机有功输出状态之间的转移率，利用基于马尔可夫链的解析方法求出每个风机状态出现的概率和该状态的平均持续时间。在此基础上，提出了用于序贯蒙特卡罗仿真的双重抽样方法。在 Matlab 中编制了风电机组多状态可靠性模型的仿真程序，并与常用的基于单重抽样方法的两状态模型进行比较分析。仿真结果表明了所建多状态可靠性模型和所提双重抽样方法的有效性，该模型能反映故障情况下任意持续时间的风机出力，从而提高了模型的准确性和应用范围。