IMECE2002-DCS-33445 DEVELOPMENT OF AN INTELLIGENT AUTOMATIC GENERATION CONTROL SYSTEM FOR ELECTRICAL POWER PLANTS

ABSTRACT The United States electric grid is a complex structure that requires high precision control of frequency and tieline power flows among different generation areas. Highly varying loads introduce a major challenge for the present automatic generation control systems. Arc furnaces, rolling mills and other large motors can create large demands on the system which result in an unsatisfactory area control error (ACE). Recent studies have shown that very-short term load prediction can be incorporated into control schemes which are then able to compensate for the highly varying demand. Using a neural network prediction of the area load a new fuzzy logic controller has been developed that adjusts the set point of the area generation to attempt to match the upcoming changes on the system. Performance of the neural-fuzzy controller in a two-area tie-line model with actual load data from a collaborating utility is demonstrated and compared with the present AGC system through simulations

Thermal modelling using mixed energy and pseudo bond graphs

Pseudo and true Bond Graphs have been seen as competing approaches to modelling thermodynamic systems. This paper provides a simple mechanism for mixing the two approaches to obtain the best features of each. In so doing, an alternative and more accessible approach to thermodynamic modelling than that provided by classical texts is given