Free-Model Based Intelligent Control of Power Plants and Power Systems

ECS-9705105 Lee 本计画旨在发展一个以自由模型为基础之智慧型控制系统，应用于发电厂及电力系统。 目前，发电厂正常运行的自动化是以简单直接的方式实现的，这种方式是基于发电厂子系统的线性化模型，使用单回路反馈和前馈控制。 在这种环境下，当设备或控制器发生故障或操作条件发生显著变化时，控制系统非常困难。 所提出的基于自由模型的智能控制方法为实现高度自治的发电厂和电力系统提供了一种替代方案。 目前智能控制的主流是模糊逻辑和人工神经网络。 两者都试图通过结合人类的决策和学习能力来模仿人类的控制方案。 然而，它们本身并不能完美地模仿人类。 因此，提出了一种基于“自由模型”概念的新方法，这将有助于更紧密地建模人类行为。 自由模型仅用从系统获得的输入和输出数据来定义，因此，不需要用于控制目的的数学模型。 因此，它可以用于任何复杂的，高度非线性的，易受干扰的系统，如发电厂和电力系统。 通过用自由模型来表示系统，人的控制方案将在五个不同的方面被建模：（1）人的学习能力将在自由模型的前提下使用神经网络和模糊系统来模拟：（2）监督控制系统将被开发来模拟人的监督能力使用一组并行的控制器;（3）通过将复杂系统划分为多个由相互作用变量耦合的较小子系统，将开发一种分散控制方案：（4）通过增加优化前馈控制，将开发一种预测控制方案;（5）采用基于自由模型的神经元或模糊辨识器，建立故障自适应控制系统。 本研究及自由模型基智能控制（FMBIC）将在宾州州立大学作为一个常规项目进行。 为了丰富该项目并促进研究的进展，还建议通过美国与韩国首尔的首尔国立大学（SNU）合作，增加国际层面。韩国合作研究计划。 这将使首席研究员和一名研究生能够访问汉城大学，并与韩国同行交流研究成果和经验。 这种合作的理论和实施方面将有利于宾夕法尼亚州立大学和首尔国立大学，以及美国和韩国的电力工业，一般。 预期的科学和实际效益是：（1）共享基于自由模型的智能控制（FMBIC）系统的实验室设施和理论发展;（2）互补技能的结合：宾州州立大学在发电厂智能分布式控制方面的经验和SNU在电力系统智能控制方面的经验;（3）为美国和韩国的电力工程教育提供新的见解和改进;以及（4）智能控制技术的发展提供了改进的效率、经济性在两国未来放松管制和工厂建设困难的情况下，发电厂和电力系统的安全。

ECS-9705105 Lee This project is to develop a Free-Model Based Intelligent Control (FMBIC) system for power plants and power systems. Automation of normal power plant operation is currently achieved in a simple and direct manner using single-loop feedback and feedforward controls based on linearized models of plant subsystems. In this environment, controlling a system is very difficult when there are plant or controller failures or a significant variation in operating conditions. The proposed free-model based intelligent control approach offers an alternative in achieving highly autonomous power plants and power systems. Current main streams for intelligent control are fuzzy logic and artificial neural networks. Both attempt to emulate human control schemes by incorporating human decision making and learning ability. However, they alone can not emulate humans perfectly. Therefore, it is proposed to develop a new approach based on the "free model" concept, which will help in modeling human behavior more closely. The free model is defined only with input and output data obtained from the system and consequently, no mathematical model is required for control purposes. Thus, it can be used for any system which is complex, highly nonlinear, and susceptible to disturbances, such as power plants and power systems. By representing a system in terms of the free model, human control schemes will be modeled in five different areas: (1) human learning ability will be emulated on the premise of the free model using neural networks and fuzzy systems; (2) a supervisory control system will be developed to emulate human supervisory ability using a bank of controllers in parallel; (3) a decentralized control scheme will be developed by partitioning a complex system into a number of smaller subsystems which are coupled by interaction variables; (4) a predictive control scheme will be developed by adding an optimizing feedforward control; and (5) a fault-accommodating control system will be devel oped by using the free-model based neuro or fuzzy identifier. This research an the Free-Model Based Intelligent Control (FMBIC) will be conducted at Penn State University as a regular project. In order to enrich the project and enhance the progress of the research, it is also proposed to add an international dimension by collaborating with Seoul National University (SNU), Seoul, Korea through the U.S.-Korea Cooperative Research Program. This will enable the Principal Investigator and a graduate student to visit SNU and exchange research results and experiences with the Korean counterpart. The theoretical and implementation aspects of this collaboration will be of benefit both to Penn State University and Seoul National University, as well as to the U.S. and Korea power industries, in general. The anticipated scientific and practical benefits are: (1) sharing of laboratory facilities and theoretical developments in the Free-Model Based Intelligent Control (FMBIC) system; (2) joining of complementary skills: Penn State's experience in power plant intelligent distributed control and SNU's experience in power system intelligent control; (3) providing new insights and improvements to power engineering education in the U.S. and Korea; and (4) development of intelligent control technology providing improved efficiency, economy, and safety of power plants and power systems in the face of future deregulation and plant construction difficulties in both countries.