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

使用新型观测器和自供电传感器进行非线性系统故障检测

• 批准号: 418375-2013
• 负责人: Vijayaraghavan, Krishna
• 金额: $ 1.75万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=573621
• 关键词: Fault; Detection; Nonlinear; Systems; using

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.

由于非线性和参数不确定性，实际系统中的故障检测很困难。由于其潜在的动态，这种非线性是在绿色能源系统（例如风车和燃料电池）中出现的。智能控制系统可以通过使用“观察者”来估计系统状态（内部变量值），并将预期系统响应与传感器测量的真实系统响应进行比较，可以在这些系统中执行故障检测和识别（FDI）和参数识别（PAI）。 FDI的新兴挑战在于使用来自较新的自动传感器的信号，这些传感器通过提供不连续的数据来保护功率。 FDI在远程风车安装中使用此类传感器特别有吸引力。 在需要智能控制系统的绿色能源技术的数量中预计会爆炸。这种需求是全球激励计划，授权和州政府指令在不断发展的经济体中投资绿色能源的直接结果。因此，在许多现实世界中，绿色能源技术（例如燃料电池和风车）将是这项研究的最初主要应用领域。 在接下来的五年中，研究计划将：OBJ1。开发新的非线性观察者，以扩大控制理论的范围，并使用燃料电池堆栈和风车系统模型来验证这些技术； OBJ 2：开发用于不确定非线性系统的FDI技术，并使用燃料电池堆栈模型验证这些结果； OBJ 3：开发能够使用新的自动传感器（例如无电池无线传感器）的不连续传感器信号的FDI系统，并验证监测风力涡轮机叶片结构健康的情况。申请人目前与绿色能源行业的合作，例如Ballard Power Systems（加拿大）和RRB Energy（印度），将用于将FDI和PAI的预期进步和创新扩展到绿色能源系统。 这项工作将在HQP培训以及协助加拿大在高科技和绿色能源领域的全球竞争中发挥重要作用。