A Neural Network Approach for Modeling Water Distribution System

2017 ◽  
Author(s):  
Andrei S. Popa ◽  
Conor O'Toole ◽  
Juan Munoz ◽  
Steve Cassidy ◽  
Dallas Tubbs ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Network Approach ◽  
Neural Network Approach

A neural network approach to burst detection

2002 ◽  
Vol 45 (4-5) ◽  
pp. 237-246 ◽  
Author(s):  
S.R. Mounce ◽  
A.J. Day ◽  
A.S. Wood ◽  
A. Khan ◽  
P.D. Widdop ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Time Series Data ◽  
Quality Data ◽  
Series Data ◽  
Neural Network Approach ◽  
Water Quality Data ◽  
Artificial Neural

This paper describes how hydraulic and water quality data from a distribution network may be used to provide a more efficient leakage management capability for the water industry. The research presented concerns the application of artificial neural networks to the issue of detection and location of leakage in treated water distribution systems. An architecture for an Artificial Neural Network (ANN) based system is outlined. The neural network uses time series data produced by sensors to directly construct an empirical model for predication and classification of leaks. Results are presented using data from an experimental site in Yorkshire Water's Keighley distribution system.

Risk Analysis Model for Water Pipeline Leakage Based on FAHP and BPNN

2013 ◽  
Vol 441 ◽  
pp. 1093-1096
Author(s):  
Wen Zeng ◽  
Yong Ting Pan ◽  
Hong Mei Huang
Keyword(s):  
Neural Network ◽  
Risk Analysis ◽  
Bp Neural Network ◽  
Distribution System ◽  
Water Distribution ◽  
Water Distribution System ◽  
Economic Loss ◽  
Analysis Model ◽  
Initial State ◽  
Water Pipeline

Scientific analysis of the leakage of the water distribution system in city is very helpful to water supply network’s maintenance and renovation, and hence reduces negative social effect and economic loss. A leakage risk nalysis model for water distribution system was established based on fuzzy analytical hierarchy process (FAHP) and BP neural network (BPNN). This model introduces FAHP to reasonably ensure initial state of BP neural network, and uses weighted superposition to mend learning sample set of BP neural network. The water distribution system of a city in Zhejiang province P. R. China was selected to test the proposed risk analysis model, which verifise its feasibility and effectivity.

Reliability Improvement of Transformer Using Neural Network Approach

2019 ◽  
Vol 27 (02) ◽  
pp. 2040005
Author(s):  
Neelam Chantola ◽  
S. B. Singh ◽  
Ekata
Keyword(s):  
Neural Network ◽  
Distribution System ◽  
System State ◽  
Network Approach ◽  
Variable Technique ◽  
Neural Network Approach ◽  
Reliability Improvement ◽  
Insulating Paper ◽  
Proposed Model ◽  
Insulation Oil

This paper deals with reliability analysis of transformer using stochastic or random process through supplementary variable technique (SVT) and neural network approach. The failure of the considered complex system can occur due to the failure of breakdown voltage (BDV) and moisture content (MC) of insulating paper and oil. The repair rates for both insulation oil and insulation paper are presumed to be general, whereas for insulation, oil repair rates are exponentially distributed. Further, whenever there are two possibilities of repairs, it has been coupled using Gumbel–Hougaard copula. All types of failures and repair rates are treated as weights while analyzing with the help of neural network approach which are governed by exponential distribution. System state probabilities, up and down states probabilities and reliability are evaluated for the proposed model by using Markovian process with the help of Laplace transforms. Numerical examples are included to illustrate the results.

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