基于FIR神经网络的风电场虚拟风速传感器

风电机组风速传感器易发故障，故障可能导致机组安全风险和发电量损失。针对现行的故障处理方法因与机组控制策略紧密耦合而日益面临挑战，提出了一种基于数据处理的虚拟风速传感器原理与方法：由风电场上风向测量风速计算下风向推算风速，用推算风速取代故障传感器。着重讨论了基于FIR神经网络的推算风速计算方法和计算模型，探讨了系统实现的关键技术。实验证明了虚拟传感器的误差在机组控制系统可接受的程度内。提出的方法独立于机组自身属性，具有普遍适用性，可部署在任意类型的场，在物理传感器故障时向机组提供风速信号，支撑风电机组持续安全运行。

Common virtual wind speed sensors for wind farm based on finite impulse response neural network

Wind speed sensors of wind turbines has high fault rate,those faults may lead wind turbines into safety risks and energy production loss.However,many current methods of improving reliability of wind speed information face the challenges of high cost or high error.A virtual wind speed sensor based on spatial correlation was presented in this paper.Its key character is generating a downwind turbine’s logic wind speed based on a special upwind turbine’s real wind speed.The calculation mode of logic wind speed just need the outputs of the existed wind speed and direction sensors.A FIR(Finite Impulse Response) neural network computing model was proposed for deal with the complexity of logic wind speed calculating.Moreover,key technologies were discussed for building the virtual wind speed sensor system.The logic wind speed generated by virtual sensor can be used for providing reliable wind speed information input to a turbine controller.The approach presented in this paper is applicable to wind turbines of any type in wind farms.