NSF Young Investigator

9358351刘最初的研究项目是通过开发一个综合的概率谐波流算法，考虑谐波电压和电流的非线性依赖性，以及运行模式和非线性负载的开关状态的随机变化，以表征电力系统中的谐波。 另一个主题是开发智能诊断方法，利用自适应模式识别技术和人工神经网络检测早期故障并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继电保护算法，以实现在佛罗里达和格鲁吉亚接口的真实的时间联络线操作控制后，检测到潜在的不稳定条件。 电力系统各部分的准确表示是实现这一目标的关键问题。 本研究将继续寻找适合系统失步辨识的最佳等值策略。 ***

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. ***