Multiple Model Adaptive Control And Application On The Power System

Multiple model adaptive control (MMAC) is an effective approach to resolve complicated problems such as nonlinear,uncertainties and operating points variations etc. In this paper, the ideal is applied to power system.Firstly, many fixed models are modeled at different operating points through mechanism analysis. An optimal model set based on the ideal of on-line learning is proposed, which included fixed models and adaptive models. When the operating point exceeds the initial modeling range, stability results are still obtained. Then, switching algorithms are studied and the properties of some performance indexes are analyzed. To solve the problem of ceaselessly switching, two methods are proposed: hysteretic switch and waiting period.A fuzzy-weighted control scheme is proposed for the problem of disturbance brought by controller switching. The control weights are made by fuzzy decision-making. When the model is selected through switching, the corresponding controller can be switched smoothly.Finally, a multiple model predictive control algorithm is proposed, which combines predictive control with multiple model structure. The weights are calculated by fuzzy adaptive weight algorithm and the controllers are optimized by dynamic matrix control algorithm. This algorithm is successfully applied to a main steam temperature of a supercritical Once-through 600MW boiler steam generator.