Title :
Load profile synthesis and wind power generation prediction for an isolated power system
Author :
Kang, M.S. ; Chen, C.S. ; Ke, Y.-L. ; Lin, C.H. ; Huang, C.W.
Author_Institution :
Dept. of Electr. Eng., Kao Yuan Inst. of Technol., Kaohsiung
Abstract :
This paper investigates the load composition by load survey study and predicts the wind power generation with a probabilistic network for an isolated power system. The power consumption by each customer-class with the application of load patterns and the total power consumption of all customers within the same class can be obtained and calculated. Probabilistic neural network (PNN) solves the wind power generation based on the wind speed for an offshore island in Taiwan. With the hourly wind speed and load composition, the power generation of diesel generators has been obtained. Results of this study demonstrate that wind power generation can economically and effectively replace the generation of the diesel power plant and provide partial power supply capability for the net peak load requirement
Keywords :
diesel-electric generators; diesel-electric power stations; load forecasting; neural nets; power consumption; wind power plants; diesel generators; diesel power plant; isolated power system; load profile synthesis; load survey; partial power supply; power consumption; probabilistic neural network; wind power generation; wind speed; Energy consumption; Load management; Network synthesis; Neural networks; Power generation; Power generation economics; Power systems; Wind energy generation; Wind power generation; Wind speed;
Conference_Titel :
Industrial and Commercial Power Systems Technical Conference, 2005 IEEE
Conference_Location :
Saratoga Springs, NY
Print_ISBN :
0-7803-9021-0
DOI :
10.1109/ICPS.2005.1436366
