Title :
Ensemble of genetic programming models for designing reactive power controllers
Author :
Grosan, Crina ; Abraham, Ajith
Author_Institution :
Dept. of Comput. Sci., Babes-Bolyai Univ., Cluj-Napoca, Romania
Abstract :
In this paper, we present an ensemble combination of two genetic programming models namely linear genetic programming (LGP) and multi expression programming (MEP). The proposed model is designed to assist the conventional power control systems with added intelligence. For on-line control, voltage and current are fed into the network after preprocessing and standardization. The model was trained with a 24-hour load demand pattern and performance of the proposed method is evaluated by comparing the test results with the actual expected values. For performance comparison purposes, we also used an artificial neural network trained by a backpropagation algorithm. Test results reveal that the proposed ensemble method performed better than the individual GP approaches and artificial neural network in terms of accuracy and computational requirements.
Keywords :
backpropagation; control system synthesis; genetic algorithms; power engineering computing; reactive power control; intelligent power control systems; linear genetic programming; multi expression programming; online control; reactive power controllers; Artificial neural networks; Genetic programming; Intelligent systems; Linear programming; Power control; Power system modeling; Reactive power control; Standardization; Testing; Voltage control;
Conference_Titel :
Hybrid Intelligent Systems, 2005. HIS '05. Fifth International Conference on
Print_ISBN :
0-7695-2457-5
DOI :
10.1109/ICHIS.2005.36