Abstract
Many studies on pressure sensor (PS) placement and pressure reducing valve (PRV) localization in water distribution systems (WDSs) have been made with the objective of improving water leakage detection and pressure reduction, respectively. However, due to varying operation conditions, it is expected to realize pressure control using a number of PSs and PRVs to keep minimum operating pressure in real-time. This study aims to investigate the PS placement and PRV localization for the purpose of pressure control system design for WDSs. For such a control system, a PS should be positioned to represent the pressure patterns of a region of the WDS. Correspondingly, a PRV should be located to achieve a maximum pressure reduction between two neighboring regions. According to these considerations, an approach based on the k-means++ method for simultaneously determining the numbers and positions of both PSs and PRVs is proposed. Results from three case studies are presented to demonstrate the effectiveness of the suggested approach. It is shown that the sensors positioned have a high accuracy of pressure representation and the valves localized lead to a significant pressure reduction.
Highlights
Water distribution systems (WDSs) are aimed at supplying consumers’ water demands
Due to varying operating conditions, it is necessary to design a closed-loop control system to keep a low pressure of a WDS in real-time
This study proposes a method that is able to simultaneously determine both the numbers and positions of pressure sensor (PS) as well as pressure reducing valve (PRV)
Summary
Water distribution systems (WDSs) are aimed at supplying consumers’ water demands. the water pressure of the system must be stabilized. In the study of Soldevila et al [19], the sensor placement task was formulated as an optimization problem with binary decision variables solved by a genetic algorithm (GA) Since these methods for sensor placement are mainly for the localization of leakage, they cannot be used for monitoring the real-time pressure aiming at closed-loop pressure control. This non-smooth model was smoothed using an interior-point approximation method, so that a continuous optimization problem can be solved by an NLP solver These existing methods can help to optimally place PSs and PRVs separately, as of today, no method is available to simultaneously optimize both, especially for the purpose of designing a closed-loop pressure control system. A weighted gap statistic was proposed to improve the accuracy of the clustering [35] Based on these methods, a simple approach is developed to simultaneously localize PSs and PRVs in this study.
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