Localization of Leaks in Water Distribution Networks using Flow Readings

This paper presents a novel approach to localize single and sequential leaks based on the lumped model of a water distribution network (WDN). The principal features of such a model are: a new friction term expressed as a power-law and a suitable representation expressed only in terms of the flow rate. From the response of this model and flow rate measurements at junctions of the pipelines composing the WDN, a set of residuals1 is proposed for each pipeline. The residuals closest to zero will indicate the leak positions in the faulty pipelines. We present some simulation tests based on data from PipelineStudio® from Energy Solutions to illustrate the suitability of our method

Sensor placement for classifier-based leak localization in water distribution networks using hybrid feature selection

This paper presents a sensor placement approach for classifier-based leak localization in water distribution networks. The proposed method is based on a hybrid feature selection algorithm that combines the use of a filter based on relevancy and redundancy with a wrapper based on genetic algorithms. This algorithm is applied to data generated by hydraulic simulation of the considered water distribution network and it determines the optimal location of a prespecified number of pressure sensors to be used by a leak localization method based on pressure models and classifiers proposed in previous works by the authors. The method is applied to a small-size simplified network (Hanoi) to better analyze its computational performance and to a medium-size network (Limassol) to demonstrate its applicability to larger real-size networks.Peer ReviewedPostprint (author's final draft

Localization Techniques for Water Pipeline Leakages: A Review

Pipeline leakages in water distribution network (WDN) is one of the prominent issues that has gain an interest among researchers in the past few years. Time and accuracy play an important role in leak localization as it has huge impact to the human population and economic point of view. The complexity of WDN has prompt numerous techniques and methods been introduced focusing on the accuracy and efficacy. In general, localization techniques can be divided into two broad categories; external and internal systems. This paper reviews some of the techniques that has been explored and proposed including the limitations of each techniques. Â

Design and Implementation of Google Cloud Framework for Monitoring Water Distribution Networks

With urbanization and growing human population, water demand is constantly on the rise. Due to limited water resources, providing access to fresh potable water to the rising needs is challenging. Water distribution systems are the main arteries that supply fresh water to all the house-holds, offices and industries. Various factors such as excessive water pressure, aging or environmental disturbances, e.g., from road traffic, can all contribute to damage of water distribution pipelines and can result in leaks in water distribution networks (WDN). This could in turn result in financial loss and could pose additional challenges in providing potable water to the entire community, sometimes even leading to contaminant intrusion. Traditional leak detection methods such as visual inspections can detect leaks; however, this method is reactive in nature and can result in potentially losing large amounts of water before intervention strategies can be employed. On the other hand, hardware-based inspection techniques can accurately detect leaks, but are labor intensive, time consuming, expensive and effective only for short distances. Some existing software techniques are less expensive; however, their effectiveness depends on the accuracy of data collected and operating conditions. Modern existing leak detection techniques based on Internet of Things (IoT)—consisting of data collection sensor sub system, internet connectivity and a decision making sub system—alleviate many issues associated with hardware and software methods, however they are considered to scale poorly and face security issues, fault tolerance issues, inter-operability issues, insufficient storage and processing abilities to store and process large quantities of real time data captured by the sensor sub systems. As a potential solution to these issues, this thesis deals with the application of a cloud-based leak detection system within the overarching concept of IoT. A detailed design and implementation of Google Cloud Platform (GCP) which can provide scalable, secure data processing system to analyze both real-time and batch data collected from IoT devices monitoring a WDN is presented. To circumvent the issue of access to a live WDN, the proposed system uses emulators, python Hyper Text Transport Protocol (HTTP) client running on a computer and a python HTTP client running on an IoT device (Raspberry Pi 3) to simulate live streams of acoustic pressure data from hydrophone sensors. Since the data itself was collected from a live WDN, the decision-making subsystem mimics results expected from live WDN data. The data ingestion layer on GCP incorporates two types of authentication: OAuth2.0 authentication and Application Program Interface (API) key authentication along with other GCP components using service account features to ensure end-to-end secure data processing. Decision support sub-system includes simple, yet powerful algorithm, namely the one class support vector machine (OCSVM) with non-linear radial basis function (RBF) kernel. It is shown in this thesis that GCP provides a scalable and fault tolerant infrastructure at every stage of data life cycle such as data ingestion, storage, processing and results visualization. The implementation in this thesis demonstrates the applicability of the leak detection IoT framework and the concept of a cloud based IoT solution for leak detection in WDN, which is the first demonstration of its kind to the author’s knowledge

Integrated optimal pressure sensor placement and localisation of leak/burst events using interpolation and a genetic algorithm

Leak/burst events are a serious problem because they disturb customer supplies, lead to water loss and managing them consumes vast resources. Water companies are continually seeking solutions to improve the situation. Presented in this thesis is the development, verification and validation of an integrated framework of methods for determining the optimal configurations of pressure sensors in a DMA and for localising new leak/burst events using a data-driven leak/burst localisation technique. The integration of the leak/burst localisation technique with the sensor placement technique is a novel feature of this framework of methods. A data-driven leak/burst localisation technique, featuring a novel spatially constrained inverse-distance weighted interpolation technique, was developed which quantifies the change in pressure due to a new leak/burst event using pressure sensors deployed in a DMA, without using a hydraulic model. The leak/burst localisation technique combines data from multiple pressure sensors to localise a leak/burst event by interpolating using the distance travelled along pipes. The leak/burst localisation technique was combined with the GALAXY multi-objective evolutionary algorithm to identify the optimal sensor configurations and parameters for the leak/burst localisation technique efficiently. The sensor placement technique automatically determines the leak/burst event sizes for each DMA and groups them to minimise the number of leak/burst event scenarios which are considered. The framework of methods was developed and verified iteratively using data from hydraulic models and a real DMA and validated using data from 20 engineered events conducted in two real DMAs in the UK. During validation, the sensor placement technique identified the optimal sensor configurations from a constrained subset of hydrants in each DMA. The agreement between the leak/burst localisation performance for the real and modelled engineered events demonstrated that the sensor placement technique can accurately predict the expected level of performance which will be achieved in a real DMA, particularly as the number of optimal sensors increases. Engineered events as small as 3.5% of the peak daily flow (6% of the average daily flow) were correctly localised with search areas containing as few as 12% of the pipes in a DMA