Pattern recognition and clustering of transient pressure signals for burst location

A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization111

Multicriterial water distribution networks design

O dimensionamento de redes de distribuição de água (RDAs) é feito para que as restrições operacionais de velocidade e pressão se mantenham dentro de limites que garantam a eficiência operacional. Assim, buscam-se as tubulações de menor custo para que essas condições sejam satisfeitas. Entretanto, ao adotar essa prática, outras características da rede podem ser afetadas negativamente, como sua resiliência e capacidade de expansão. Dessa forma, este trabalho apresenta um estudo de caso em que a análise multicriterial é utilizada para realizar o dimensionamento de uma RDA. Primeiramente, um mapa cognitivo é feito para identificar os principais critérios a serem considerados na solução do problema. Em seguida, o método Delphi é usado em conjunto com o Analytic Hierarchy Process (AHP) para determinar os pesos relativos de cada critério. Por fim, três diferentes métodos de análise multicritérios são utilizados para a solução do problema: AHP, Electre e Promethee68221118130The design of water distribution networks (WDNs) is made to match velocity and pressure constraints that guarantee operational efficiency. Thus, pipes with lower cost are selected to attend these conditions. However, this procedure can harm other characteristics of the network, as for example its resilience and expansion capacity. Therefore, this paper presents a case study in which the multicriterial analysis is used to design a WDN. First, a cognitive map is built to identify the main criteria to be considered during the design process. Then, the Delphi method is used jointly with the Analytic Hierarchy Process (AHP) to define the relative weights of each criteria. Finally, three different methods for multicriterial analysis are used to solve the problem: AHP, Electre and Promethee

Selection of pumps as turbines substituting pressure reducing valves

Technical standards define operational limits for hydraulic parameters such as velocity or pressure. On this way, pressure control is a fundamental component for safe operation at water supply systems, mainly to reduce leakage, risk of disruptions and maintenance costs. The system topology and topography can define some high pressure zones and in this case the use of Pressure Reducing Valves - PRV - to maintain standards pressures on the sector is common. However, all the energy available on the fluid is dissipated trough headloss. A turbine could be used instead of the PRV to produce electrical energy and to control pressure. In general, the power available in these sites is under 100 kW, so the use of Pumps as Turbines - PAT - is recommended to reduce the investment. Due of the dynamic operation trough a day, the PAT will operate under different conditions of flow and head. This variation will affect its efficiency and head loss, which difficult the selection trough conventional methods. Therefore, this paper proposes a method for PAT selection to operate instead a PRV. The method is based in the maximization of the energy produced, constrained to the pressure limits on each node of the network. To solve this problem, the optimization technique PSO is used and the available curves in literature, on the Suter plane, are used to simulate the PAT. The method is applied on a network and the results are compared with the PRV operation18667668

Selection and location of pumps as turbines substituting pressure reducing valves

Pressure control is a fundamental component of safe operation of water supply systems, mainly to reduce leakage, risk of disruption, and maintenance costs. System topology and topography can define high-pressure zones, and the use of Pressure Reducing Valves (PRVs) to maintain standard pressures in these zones is common. However, all energy available in the fluid is dissipated trough headloss. A turbine could be used instead of PRVs to produce electrical energy and to control pressure. The use of Pumps as Turbines (PATs) is recommended to reduce investment cost. Due to dynamic operations throughout the day, PATs operate under varying conditions of flow and head. This variation affects efficiency and headloss, which makes difficult the selection of PATs to substitute PRVs through conventional methods; therefore, this paper proposes a method for such selection. The method is based on maximization of energy produced, restricted to the system pressure limits. To solve this selection problem, the optimization technique of Particle Swarm Optimization (PSO) is used, and complete pump curves are used to simulate the PATs. In addition, this method is capable of identifying the best location on the network to install the PATs10939240

Aplicação de modelo de simulação-otimização na gestão de perda de água em sistemas de abastecimento Leakage management with computational model in water supply system

Este artigo apresenta a aplicação de modelo matemático-computacional de simulação e otimização para localização de fugas. O modelo proposto é fundamentado no acoplamento de um simulador hidráulico baseado no Time Marching Approach - TMA com o algoritmo otimizador de Nelder-Mead e foi aplicado em uma rede de distribuição de água da cidade de Jundiaí-SP. Nos testes realizados ficou claro o funcionamento adequado do modelo apresentado, pois a fuga simulada foi localizada, sendo observado, entretanto, a necessidade de um aprimoramento na localização dos pontos de monitoramento durante a execução da simulação.<br>This work presents a computational model as a new tool for leak localization. The considered model was developed through the coupling of hydraulic simulator based in Time Marching Approach - TMA method with the Nelder-Mead optimization algorithm. The model was applied to a real water distribution network, in the city of Jundiaí, Brazil. In the carried through tests it was clearly the adequate functioning of the presented model, therefore the simulated escape was located, being observed, however, the necessity of an improvement in the localization of the monitor points, during the execution of the simulation

Calibration model for water distribution network using pressures estimated by artificial neural networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies314339435

Effectiveness of methods for selecting pumps as turbines to operate in water distribution networks

Pressure control is an important feature for reducing leakages in water supply systems, and the use of pressure reducing valves has been well established as an efficient option for this purpose. However, several studies have demonstrated that the energy available on such sites could be used to generate electrical energy, instead of being dissipated as head loss; therefore, a more efficient and sustainable solution could be applied for pressure control. Due to the low amount of power available, the use of pumps as turbines (PATs) is highly recommended. However, manufacturers do not provide pump curves operating as turbines, making PAT selection challenging. Different empirical methods can be found in the literature for estimating PAT performance based on the pump operating conditions. Thus, this paper presents a comparative analysis of nine different methods, using real data from 14 pumps. Furthermore, the effectiveness of these methods for PAT selection is evaluated in a hypothetical network19241742

CCWI2017: F119 'WDNs Calibration Using k-means Algorithm for Pipes Clustering and a Hybrid Model for Optimization'

The modelling of Water Distribution Networks (WDNs) is an important issue for an efficient operation of water systems. The hydraulic simulations are used to establish the optimal conditions for pumps and valves, helping stakeholders to manage the systems. A reliable model should guarantee the minimal uncertain becoming the simulations the most real as possible. The calibration processes is used to reduce the uncertainties associated to pipe roughness and nodal demand, pipe status or leakage flow. Due to the high degrees of freedom surrounding this process, the improvement of available information of the hydraulic state of the network and the reduction of parameters to be calibrated can conduct for more reliable calibrated model. In this sense, this work present a hybrid calibration method, consisting in three stages defined as grouping pipes with k- means algorithms, pressure estimation with artificial neural networks (ANN) and an optimization process using particle swarm optimization (PSO) algorithms. A comparison between the classical approaches for roughness calibration is presented, reinforcing the improvement of the calibration process through the uncertain reduction