Application of Expert Systems to Power System Dynamic Analysis and Control

专家系统在电力系统动态分析与控制中的应用

• 批准号: 9520456
• 负责人: Shelli Starrett
• 金额: $ 1.8万
• 依托单位: Kansas State 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: 1995-07-15 至 1996-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9520456&HistoricalAwards=false
• 关键词: Application; Expert; Systems; Power; System

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. ***

9520456斯塔雷特 当前电力系统的调节和规划趋势导致了复杂的、不可预测的、非线性的现象，限制了电力系统的经济运行。 为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。 由此产生的数据往往很难解释。 本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条件进行分类，建议调度或控制更改，并控制系统。 人工神经网络（ANN）对于模式识别和数据分类是有用的。 根据所使用的网络类型，可以训练ANN在某些条件下给出所需的输出，或者可以允许它们在没有预定义的输出规范的情况下识别数据中的模式。 提出了一种人工神经网络咨询系统，该系统将监测系统数据、过程模式并警告操作员危险的系统条件。 模糊逻辑系统根据适用于系统条件范围内的规则集制定输出决策。 因此，它们可以有效地处理不确定的条件和一系列的操作点。 神经网络的分类结果可用于形成使用模糊逻辑控制的灵活的、但可预测的控制系统。 人工神经网络/模糊逻辑系统的设计将允许调整，以响应不断变化的电力系统条件。 ***