Data-driven and model-based framework for smart water grid anomaly detection and localization

Abstract

With increasing adoption of advanced meter infrastructure, smart sensors together with SCADA systems, it is imperative to develop novel data analytics and couple the results with hydraulic modeling to improve the quality and efficiency of water services. One important task is to timely detect and localize anomaly events, which may include, but not be limited to, pipe bursts and unauthorized water usages. In this paper, a comprehensive solution framework has been developed for anomaly detection and localization by formulating and integrating data-driven analytics with hydraulic model calibration. Data analysis for anomaly detection proceeds in multiple steps including the following: (1) data pre-processing to eliminate and correct erroneous data records, (2) outlier detection by statistical process control methods and deep machine learning, and (3) system anomaly classification by correlation analysis of multiple sensor events. Classified system anomaly events are subsequently localized via hydraulic model calibration. The integrated solution framework is developed as a user-friendly and effective software tool, tested, and validated on the selected target areas in Singapore.

Hyperparameter Sensitivity Analysis of Deep Learning-Based Pipe Burst Detection Model for Multiregional Water Supply Networks

“…Wang et al [5] and Lee and Yoo [6] proposed a leak accident detection model using an LSTM (long short-term memory) technique in a recurrent neural network (RNN) and conducted a model validation study through random accident data generation. Quiñones-Grueiro et al [7] and Wu et al [8] used deep learning techniques to propose a leak detection methodology combining a data-based method and a model-based method. Quiñones-Grueiro et al [7] used a deep neural network model to detect leaks and proposed a model to identify leak location based on the inverse problem solution.…”

“…Quiñones-Grueiro et al [7] used a deep neural network model to detect leaks and proposed a model to identify leak location based on the inverse problem solution. Wu et al [8] developed a leak detection methodology in the form of a system that learns using data generated from well-calibrated pipe network analysis data and deep learning techniques.…”

Development of the Methodology for Pipe Burst Detection in Multi-Regional Water Supply Networks Using Sensor Network Maps and Deep Neural Networks

“…Lee and Yoo [18] used a LSTM to generate a flow rate (demand) prediction model, established the threshold conditions using the Shewhart Control Chart, a univariate statistical process management technique, and proposed a methodology where the cases of predicted error-exceeding threshold conditions were determined to indicate pipe burst. Some recent studies [19][20][21][22] proposed leak detection methodologies combining data-based and model-based methods. Quiñones-Grueiro et al [19] proposed a combined model using a deep neural network model to detect the occurrence of leaks, and locate leaks based on inverse problem solutions, while Ares-Milián et al [20] used a support vector machine and inverse solution-based procedures to accurately determine the leak location.…”

“…Quiñones-Grueiro et al [19] proposed a combined model using a deep neural network model to detect the occurrence of leaks, and locate leaks based on inverse problem solutions, while Ares-Milián et al [20] used a support vector machine and inverse solution-based procedures to accurately determine the leak location. Wu et al [21] proposed a leak detection methodology using well-calibrated pipe network analysis data and deep machine learning techniques, and applied the same to a small-scale network in Singapore. Daniel et al [22] proposed a methodology where the linear regression analysis of data from two pressure gauges is used to identify potential leaks, and leaking pipes are identified and located using the mixed-integer programming method.…”

“…Daniel et al [22] proposed a methodology where the linear regression analysis of data from two pressure gauges is used to identify potential leaks, and leaking pipes are identified and located using the mixed-integer programming method. While these deep learning and combined model methodologies are advantageous in situations where measurement data is readily available and the amount of data is large, most of the latest studies are limited in that the model congruence is tested using a simulated leak accident, and in that the real-world networks adopting these methods are rather limited in size [21,22]. Recently, Wan et al [23] performed an in-depth review of various data-based methods to detect leaks in water supply network systems.…”