NSF Young Investigator

最初的研究项目是通过开发一种综合概率谐波流算法来表征电力系统中的谐波，该算法考虑了谐波电压和电流的非线性依赖关系，以及非线性负载的运行模式和开关状态的随机变化。另一个主题是开发智能诊断方法，以检测早期故障，并使用自适应模式识别技术和人工神经网络评估电力设备的剩余寿命。最后，将努力开发和模拟不同的失步中继算法，以便在检测到潜在不稳定条件时，在佛罗里达和佐治亚接口上实施实时联络线操作控制。要实现这一目标，准确地表示电力系统的不同部分是一个关键问题。本研究将继续寻找适合系统失步识别的最佳等效策略。＊＊＊

9358351 Liu The initial research project is to characterize harmonics in power systems by developing an integrated probabilistic harmonic flow algorithm that considers the nonlinear dependency of harmonic voltages and currents, and random variations of operation modes and switching status of nonlinear loads. An additional topic is to develop intelligent diagnostic methodologies to detect incipient failures and assess the remaining life of power equipment using adaptive pattern recognition techniques and artificial neural networks. Finally an effort will be made to develop and simulate different out-of-step relaying algorithms to be implemented at the Florida and Georgia interface for real time tie-line operation control upon the detection of potentially unstable conditions. The accurate representation of the different sections of a power system is a critical issue in achieving such a goal. This study will continue to look for an optimum equivalence strategy tailored for system out-of-step identification. ***