Water Distribution Networks Model Identification using Artificial Neural Networks

2019 ◽  
Author(s):  
Thapelo Mosetlhe ◽  
Yskandar Hamam ◽  
Shengzhi Du ◽  
Eric Monacelli ◽  
Yasser Alayli
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Distribution ◽  
Model Identification ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
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

scholarly journals Metamodel for nodal pressure estimation at near real-time in water distribution systems using artificial neural networks

2017 ◽  
Vol 20 (2) ◽  
pp. 486-496 ◽  
Author(s):  
Gustavo Meirelles Lima ◽  
Bruno Melo Brentan ◽  
Daniel Manzi ◽  
Edevar Luvizotto
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Distribution Systems ◽  
Water Distribution ◽  
Computational Models ◽  
Pressure Sensors ◽  
Water Supply Systems ◽  
Pressure Data ◽  
Proposed Model ◽  
Artificial Neural

Abstract The development of computational models for analysis of the operation of water supply systems requires the calibration of pipes' roughness, among other parameters. Inadequate values of this parameter can result in inaccurate solutions, compromising the applicability of the model as a decision-making tool. This paper presents a metamodel to estimate the pressure at all nodes of a distribution network based on artificial neural networks (ANNs), using a set of field data obtained from strategically located pressure sensors. This approach aims to increase the available pressure data, reducing the degree of freedom of the calibration problem. The proposed model uses the inlet flow of the district metering area and pressure data monitored in some nodes, as input data to the ANN, obtaining as output, the pressure values for nodes that were not monitored. Two case studies of real networks are presented to validate the efficiency and accuracy of the method. The results ratify the efficiency of ANN as state forecaster, showing the high applicability of the metamodel tool to increase a database or to identify abnormal events during an operation.

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