A Hybrid Method for Power System Fault Detection and State Estimation

电力系统故障检测与状态估计的混合方法

• 批准号: 9526341
• 负责人: Fahmida Chowdhury
• 金额: $ 1.8万
• 依托单位: Michigan Technological University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1995
• 资助国家: 美国
• 起止时间: 1995-09-15 至 1997-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9526341&HistoricalAwards=false
• 关键词: Hybrid; Method; Power; System; Fault

9521609 Chow In the previous project, the Principal Investigator has demonstrated the potential of artificial neural networks to learn the complicated motor fault detection mappings for a single-phase induction motor under constant load conditions. However, there are many uncertainty factors in the motor operations that can significantly affect the motor incipient fault detection process and which have not been addressed. In this proposed project, the Principal Investigators propose to extend the research to cover more realistic operating environments. The PIs will investigate different factors such as varying load conditions, saturation effects, temperature effects, noise effects, and their influences on the process of motor incipient fault detection for three-phase induction motors. In addition, the PIs will investigate and establish a general theory and principle for motor incipient fault detection using a set theoretic formulation. After setting up the problem in set theoretic formulation, the challenges will lie in the finding of the correct mappings from the appropriate measurements to the estimation of the actual motor faults and their severity. The PIs will investigate and demonstrate the advantages and feasibility of the use of neural network and fuzzy logic technologies to obtain the motor fault mapping from appropriate information to yield accurate motor incipient fault detection in an non-invasive, economical, and reliably manner, along with the ability to provide a qualitative and heuristic explanation of the fault detection process. ***

在之前的项目中，首席研究员已经展示了人工神经网络在恒负载条件下学习复杂电机故障检测映射的潜力。然而，电机运行中存在许多不确定因素，这些因素会对电机早期故障检测过程产生重大影响，而这些因素尚未得到解决。在这个拟议的项目中，主要研究人员建议扩大研究范围，以涵盖更实际的操作环境。pi将研究不同的因素，如不同的负载条件，饱和效应，温度效应，噪声效应，以及它们对三相感应电动机早期故障检测过程的影响。此外，pi将使用集合理论公式研究并建立电机早期故障检测的一般理论和原理。在集合理论公式中建立问题之后，挑战在于找到从适当的测量到估计实际电机故障及其严重程度的正确映射。pi将调查并展示使用神经网络和模糊逻辑技术的优势和可行性，从适当的信息中获得电机故障映射，以非侵入性，经济和可靠的方式产生准确的电机早期故障检测，以及提供故障检测过程的定性和启发式解释的能力。＊＊＊