Talebi Nasser, Sadrnia Mohammad Ali, Darabi Ahmad
School of Electrical and Robotic Engineering, University of Shahrood, P.O. Box 3619995161, Shahrood, Iran.
Comput Intell Neurosci. 2014;2014:580972. doi: 10.1155/2014/580972. Epub 2014 Mar 11.
Occurrence of faults in wind energy conversion systems (WECSs) is inevitable. In order to detect the occurred faults at the appropriate time, avoid heavy economic losses, ensure safe system operation, prevent damage to adjacent relevant systems, and facilitate timely repair of failed components; a fault detection system (FDS) is required. Recurrent neural networks (RNNs) have gained a noticeable position in FDSs and they have been widely used for modeling of complex dynamical systems. One method for designing an FDS is to prepare a dynamic neural model emulating the normal system behavior. By comparing the outputs of the real system and neural model, incidence of the faults can be identified. In this paper, by utilizing a comprehensive dynamic model which contains both mechanical and electrical components of the WECS, an FDS is suggested using dynamic RNNs. The presented FDS detects faults of the generator's angular velocity sensor, pitch angle sensors, and pitch actuators. Robustness of the FDS is achieved by employing an adaptive threshold. Simulation results show that the proposed scheme is capable to detect the faults shortly and it has very low false and missed alarms rate.
风力能量转换系统（WECS）中出现故障是不可避免的。为了在适当的时候检测出已发生的故障，避免重大经济损失，确保系统安全运行，防止相邻相关系统受损，并便于及时修复故障部件，需要一个故障检测系统（FDS）。递归神经网络（RNN）在故障检测系统中占据了显著地位，并且已被广泛用于复杂动态系统的建模。设计故障检测系统的一种方法是准备一个模拟正常系统行为的动态神经模型。通过比较实际系统和神经模型的输出，可以识别故障的发生。在本文中，通过利用一个包含风力能量转换系统机械和电气部件的综合动态模型，提出了一种使用动态递归神经网络的故障检测系统。所提出的故障检测系统可检测发电机角速度传感器、桨距角传感器和桨距执行器的故障。通过采用自适应阈值实现了故障检测系统的鲁棒性。仿真结果表明，该方案能够在短时间内检测出故障，并且误报和漏报率非常低。
