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。 预计需要智能控制系统的绿色能源技术的数量将出现爆炸式增长。这种需求是全球激励计划、命令和印度等增长中经济体投资绿色能源的州政府指令的直接结果。因此，在众多的实际应用中，燃料电池和风车等绿色能源技术将是这项研究的首要应用领域。 未来五年，该研究计划将：目标1。目标2：为不确定的非线性系统开发直接投资技术，并使用燃料电池堆的模型验证这些结果；目标3：开发能够使用无电池无线传感器等新型自供电传感器的不连续传感器信号的外国直接投资系统，并在监测风力发电叶片结构健康的情况下进行验证。申请人目前与绿色能源行业的合作，例如与Ballard Power Systems(加拿大)和RRB Energy(印度)的合作，将用于将FDI和PAI方面的预期进展和创新推广到绿色能源系统。 这项工作将在培训HQP以及帮助加拿大在高科技和绿色能源领域提高全球竞争力方面发挥重要作用。

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. An explosion is anticipated in the number of green energy technologies requiring smart control systems. This demand is a direct result of global incentive programs, mandates and state government directives in growing economies like India to invest in green energy. Hence, amongst the many real-world applications, green energy technology such as fuel-cells and windmills will be the initial primary application area for this research. In the next five years, the research program will: Obj1. Develop new nonlinear observers to broaden the scope of control theory and validate these techniques using models of fuel-cell stacks and windmill systems; Obj 2: Develop FDI techniques for uncertain nonlinear systems and validate these results using models of fuel-cell stacks; Obj 3: Develop FDI systems capable of using discontinuous sensor signals from new self-powered sensors such as battery-less wireless sensors and validate for the case of monitoring the structural health of wind turbines blades. The applicant's current collaborations with the green energy industry, such as with Ballard Power Systems (Canada) and RRB Energy (India), will be used to extend the anticipated advances and innovation in FDI and PAI to green energy systems. This work will play a major role in training of HQP and in aiding the global competitiveness of Canada in the high-tech and green energy sectors.