Title :
Identification of static distribution load parameters using general regression neural networks
Author :
J.B. Patton;J. Ilic
Author_Institution :
Dept. of Electr. & Comput. Eng., Maine Univ., Orono, ME, USA
Abstract :
This paper explains the motivation for and use of a general regression neural network to map temporal load class distribution data into static LOADSYN load parameters. Simulated data generated by LOADSYN is used as a training set. A general regression neural network (GRNN) is trained to achieve LOADSYN functionality, and a method is outlined for further associating the load parameters with temperature, time of day, day of week, and customer type.
Keywords :
"Neural networks","Load modeling","Power system modeling","Load flow","Power system analysis computing","Power system stability","Voltage","Frequency","Load management","Senior members"
Conference_Titel :
Circuits and Systems, 1993., Proceedings of the 36th Midwest Symposium on
Print_ISBN :
0-7803-1760-2
DOI :
10.1109/MWSCAS.1993.343245