Presidential Faculty Fellows

9453422刘开发智能诊断方法，利用自适应识别技术和人工神经网络检测电力系统设备的早期故障。继续开展人工神经网络在电力系统设备诊断中的应用。之前开发的方法和经验将得到广泛测试。故障诊断的一项重要任务是确定设备状态的最具代表性的参数，以及最有可能导致特定故障或性能恶化的原因。这将通过对神经网络中的单个或一组参数进行敏感性测试来实现。虽然基于人工神经网络的诊断系统是依赖于问题的，但一些关于输入特征选择、神经网络体系结构和训练算法的一般规则将被开发出来，作为类似应用的指导方针。利用星基相量测量技术开发动态事故分析和控制算法。开发实时动态模型，以研究实时跟踪电力系统动态的可能性。提出了一种综合概率谐波流算法，该算法考虑了谐波电压和电流的非线性相关性，以及运行方式和谐波源的开关状态的随机变化，从而建立了电力系统谐波噪声特征的研究计划。更多的努力将致力于开发基于多个电路节点的有限测量来识别谐波源的算法。将扩展用于教授困难概念的计算机动画包，并将用动画来说明更复杂的领域问题和概念。多媒体工具将逐步加入到传统的课堂教学中。电力工程核心课程教材将进行更新和修订，以建立一个理想的知识组合，为当今的电气工程毕业生。电能质量教学与研究实验室正在规划中，实验室设备正在研究中。按照计划，将增加新的实验室设备用于谐波测量。这将使我们能够开始收集和分析数据。此外，还将开发实验室实验，供学生在新的电能质量课程中使用。***

9453422 Liu Develop intelligent diagnostic methodologies to detect incipient failures in power system equipment using adaptive recognition techniques and artificial neural networks. We will continue the work of power system equipment diagnosis using artificial neural networks. The previously developed methods and experiences will be widely tested. An important task for failure diagnosis is to determine the most representative parameters of equipment status, and the most likely cause of a particular failure or performance deterioration. These will be achieved through sensitivity tests of individual or a group of parameters in ANNs. Although ANN- based diagnosis systems are problem-dependent, some general rules on input feature selection, ANN architecture, and training algorithms will be developed for use as guidelines in similar applications. Develop dynamic contingency analysis and control algorithms using Satellite-Based Phasor Measurement Technology. Develop real-time dynamic models in order to investigate the possibility of tracking power system dynamics in real-time. Establish research programs in characterizing harmonic noise in power systems by developing an integrated probabilistic harmonic flow algorithm that considers the nonlinear dependence of harmonic voltages and currents, and random variations of operation modes and switching status of harmonic sources. More effort will be devoted to develop algorithms for identifying harmonic sources based on limited measurements for multiple circuit nodes. The computer animation packages for teaching difficult concepts will be expanded and more complex field problems and concepts will be illustrated with animation. Multimedia tools will be gradually added to the conventional classroom teaching. The power engineering core course materials will be updated and revised in order to establish an ideal knowledge mix for today's Electrical Engineering graduates. The Power Quality Teaching and Research Lab are being planned and lab equipment are being researched. It is planned that new lab equipment will be added for harmonic measurements. This will allow us to begin data gathering and analysis. Also, lab experiments will be developed for students to use in the new power quality course. ***