scholarly journals Modelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection technique

2016 ◽  
Vol 7 (1) ◽  
pp. 33 ◽  
Author(s):  
Eugine Makaya ◽  
Oliver Hensel
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Distribution ◽  
Distribution Networks ◽  
Flow Dynamics ◽  
Detection Technique ◽  
Water Distribution Networks ◽  
Leakage Detection ◽  
Artificial Neural

scholarly journals Leakage detection in water distribution networks using hybrid feedforward artificial neural networks

2021 ◽  
Author(s):  
Hamideh Fallahi ◽  
Mohammadreza Jalili Ghazizadeh ◽  
Babak Aminnejad ◽  
Jafar Yazdi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Demand ◽  
Water Distribution ◽  
Distribution Networks ◽  
Simulation Software ◽  
Water Distribution Networks ◽  
Flow Rates ◽  
Artificial Neural ◽  
Matlab Programming

Abstract Water leakage control in water distribution networks (WDNs) is one of the main challenges of water utilities. The present study proposes a new method to locate a leakage in WDNs using feedforward artificial neural networks (ANNs). For this purpose, two ANNs training cases are considered. For case1, the ANNs are trained by average daily water demand, including small to large hypothetical leakages. In case 2, the ANNs are trained by hourly water demand and variable hourly nodal leakages over 24 hours. The training parameters are determined by EPANET2.0 hydraulic simulation software using MATLAB programming language. In both cases, first, ANNs are trained using flow rates of total pipes number. Then, sensitivity analysis is performed by hybrid ANNs for the flow rates of pipes number less than the number of the total pipes. The results of proposed hybrid ANNs indicate that if at least the flow rates of 10% of the total pipes are known (using flowmeters), then the leakage locations in both cases can be determined. Despite the complexity of case 2, because of the variations of demand and leakage over the 24-hour, the proposed method could detect the leakage location with high accuracy.

scholarly journals Optimal architecture for artificial neural networks as pressure estimator

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
Rui Gabriel Modesto de Souza ◽  
Bruno Melo Brentan ◽  
Gustavo Meirelles Lima
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Distribution ◽  
Distribution Networks ◽  
Hydraulic Parameter ◽  
Lack Of Information ◽  
Accurate Indication ◽  
Pipe Roughness ◽  
Artificial Neural

ABSTRACT The knowledge of hydraulic parameters in water distribution networks can indicate problems in real time, such as pipe bursts, small leakages, increase in pipe roughness and illegal connections. However, an accurate indication relies on the quantity and quality of the data acquired, i.e., the number of sensors used to monitor the network and their location. It is not economic feasible have a great number of sensors, thus, the use of artificial intelligence, such as Artificial Neural Networks (ANNs) can reduce the lack of information necessary to identify problems, estimating hydraulic parameter through the few information collected. The reliability of ANNs depends on its architecture, so, in this paper, different conditions are tested for ANN training to identify which are the most relevant parameters to be adjusted when the ANN is used for pressure estimation.

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

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