Application of Expert Systems to Power System Dynamic Analysis and Control

9520456斯塔雷特当前电力系统调控和规划的趋势正在导致复杂的、不可预测的、非线性的现象，限制了电力系统的经济运行。确保系统在其可靠性标准范围内运行所需的分析和计算正变得越来越困难和耗时。由此产生的数据通常很难解释。这项研究的目标是开发基于计算机的专家系统，以处理数据，识别模式，根据预期的问题对系统状况进行分类，建议调度或控制变化，并控制系统。人工神经网络(ANN)在模式识别和数据分类中是很有用的。根据使用的网络类型，可以训练ANN在特定条件下提供所需的输出，或者允许它们在没有预定义输出规范的情况下识别数据中的模式。提出了一个人工神经网络咨询系统，它将监控系统数据，处理模式，并警告操作员危险的系统状况。模糊逻辑系统基于在系统条件范围内应用的规则集来制定输出决策。因此，它们可以有效地处理不确定的条件和一系列工作点。神经网络的分类结果可以利用模糊逻辑控制来形成灵活的、可预测的控制系统。人工神经网络/模糊逻辑系统的设计将允许根据电力系统条件的变化进行调整。***

9520456 Starrett Current trends in power system regulation and planning are causing complex, unpredictable, nonlinear phenomenon to limit the economic operation of power systems. The analysis and computations required to ensure that the system will operate within its reliability criteria are becoming increasingly difficult and time consuming. The resulting data is often hard to interpret. The goal of this research is to develop computer- based expert systems to: process data, identify patterns, classify system conditions according to expected problems, suggest scheduling or control changes, and control the system. Artificial neural networks (ANN's) are useful for pattern recognition and data classification. Depending on the type of network used, ANN's can be trained to give a desired output under certain conditions, or they can be allowed to recognize patterns in data without pre-defined output specifications. An ANN advisory system is proposed that will monitor system data, process patterns and warn the operator of dangerous system conditions. Fuzzy logic systems formulate output decisions based on sets of rules that apply within ranges of system conditions. Thus they can efficiently deal with uncertain conditions and a range of operating points. The classification results of the neural networks can be used to form a flexible, yet predictable control system using fuzzy logic control. The ANN/fuzzy logic system will be designed to allow adjustments in response to changing power system conditions. ***