DocumentCode :
3261852
Title :
A power quality forecasting model as an integrate part of active demand side management using Artificial Intelligence Technique - Multilayer Neural Network with Backpropagation Learning Algorithm
Author :
Stuchly, Jindrich ; Misak, Stanislav ; Vantuch, Tomas ; Burianek, Tomas
Author_Institution :
Dept. of Electr. Power Eng., VSB - Tech. Univ. of Ostrava, Ostrava, Czech Republic
fYear :
2015
fDate :
10-13 June 2015
Firstpage :
611
Lastpage :
616
Abstract :
This paper presents a power quality forecasting model with using Artificial Intelligence Technique, more precisely the Multilayer Neural Network with Backpropagation Learning Algorithm. This forecasting model is used as a supporting tool for a keeping of power quality parameters within the limits in the Off-Grid systems with renewables sources connected via AC By-Pass topology. Results of the most important power quality parameters forecasting are introduced in this paper. The developed algorithm of this model will be implemented into system for controlling the power flows inside the Off-Grid systems operated under Active Demand Side Management.
Keywords :
artificial intelligence; backpropagation; demand side management; load flow control; neural nets; power grids; power supply quality; renewable energy sources; AC by-pass topology; active demand side management; artificial intelligence technique; backpropagation learning algorithm; multilayer neural network; off-grid systems; power flows; power quality forecasting model; power quality parameter forecasting; renewable sources; Artificial neural networks; Batteries; Correlation; Home appliances; Inverters; Neurons; Power quality; Active Demand Side Management; Artificial Intelligence; Neural Network; Power Quality;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Environment and Electrical Engineering (EEEIC), 2015 IEEE 15th International Conference on
Conference_Location :
Rome
Print_ISBN :
978-1-4799-7992-9
Type :
conf
DOI :
10.1109/EEEIC.2015.7165233
Filename :
7165233
Link To Document :
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