Fault Detection in Nonlinear Systems using New Observers and Self-powered Sensors

在实际系统中，由于非线性和参数的不确定性，故障检测是很困难的。在风车和燃料电池等绿色能源系统中，由于其潜在的动态特性，这种非线性会出现。智能控制系统可以在这些系统中执行故障检测和识别(FDI)和参数识别(PAI)，方法是使用“观测器”估计系统状态(内部变量的值)，并将期望的系统响应与传感器测量的真实系统响应进行比较。外国直接投资面临的一个新挑战是使用来自较新的自供电传感器的信号，这些传感器通过提供不连续的数据来节省电力。在偏远的风车安装中，使用这种传感器的外国直接投资特别有吸引力。

Fault detection is difficult in real systems because of nonlinearities and parameter uncertainties. Such nonlinearities arise in green energy systems such as windmills and fuel-cells due to their underlying dynamics. Smart control systems can perform fault detection and identification (FDI), and parameter identification (PAI) in these systems by using an "observer" to estimate system states (values of internal variables) and comparing the expected system response with the true system response as measured by sensors. An emerging challenge for FDI lies in using signals from newer self-powered sensors that conserve power by providing discontinuous data. FDI using such sensors is particularly attractive in remote windmill installations.