Author(s): Ahmed M. Abdelrazek; Engy M. Khalil; Farouk A El-Fitiany; Mohammad; A. Abourohiem
Linked Author(s):
Keywords: Neural Networks; Clustering; Energy saving; Irrigation Network
Abstract: With continuously increasing cost of energy, conservation of energy in pressurized irrigation networks has become an important goal. Such networks are usually designed to irrigate sectors of approximately equal areas in turns. Pumps are often operated to guarant ee maximum head required for all sectors. This design criterion, however, does not guarantee minimum energy consumption. In this study, Neural Networks, Self-Organizing Maps (SOMs) clustering t echnique is used for grouping (sectoring) hydrants with the same characteristics in order to minimize the energy consumption. To identi fy the hydrant characteristics, three dimensionless parameters are proposed; the relative elevation (z*), the relative distance from pump station (l*), and the relative head at hydrant (h*). MATLAB - EPANET Toolkit is used to implement the suggested clust ering technique and evaluate the impact of proposed management on energy consumption. The proposed methodology is applied to a drip irrigation network at Kostol area, Egypt. Results show that energy savings up to 16.42% can be achieved for the whole irrigation season.
Year: 2020