Abstract
In the last few years, due to the technological improvement of advanced metering infrastructures, water and natural gas grids can be regarded as smart-grids, similarly to power ones. However, considering the number of studies related to the application of computational intelligence to distribution grids, the gap between power grids and water/gas grids is notably wide. For this purpose, in this paper, a framework for leakage identification is presented. The framework is composed of three sections aimed at the extraction and the selection of features and at the detection of leakages. A variation of the Sequential Feature Selection (SFS) algorithm is used to select the best performing features within a set, including, also, innovative temporal ones. The leakage identification is based on novelty detection and exploits the characterization of a normality model. Three statistical approaches, The Gaussian Mixture Model (GMM), Hidden Markov Model (HMM) and One-Class Support Vector Machine (OC-SVM), are adopted, under a comparative perspective. Both residential and office building environments are investigated by means of two datasets. One is the Almanac of Minutely Power dataset (AMPds), and it provides water and gas data consumption at 1, 10 and 30 min of time resolution; the other is the Department of International Development (DFID) dataset, and it provides water and gas data consumption at 30 min of time resolution. The achieved performance, computed by means of the Area Under the Curve (AUC), reaches 90 % in the office building case study, thus confirming the suitability of the proposed approach for applications in smart water and gas grids.
Highlights
From city-wide distribution grids, residential and building networks can be affected by very small leakages, which can be detected only after lengthy or complex actions, e.g., comparing winter usage or checking measured resource intake at the meter during periods of no resource usage.They may remain unnoticed for weeks or months, producing huge waste or even damage
Thereafter, since leakage detection in large-scale grids has been investigated in the literature, we focused on the detection of leakages occurring in water and natural grids of residential environments [13]
The results achieved by each approach, based on the proposed Sequential Feature Selection (SFS) procedure, with and without temporal features, are presented and discussed
Summary
From city-wide distribution grids, residential and building networks can be affected by very small leakages, which can be detected only after lengthy or complex actions, e.g., comparing winter usage or checking measured resource intake at the meter during periods of no resource usage. They may remain unnoticed for weeks or months, producing huge waste or even damage. As a matter of fact, the statistics reported in the WaterSense Project [1] show that residential water leaks, in the U.S, waste about 3.78 trillion liters annually nationwide. Many shortcomings still affect the development of proper applications for water and natural gas grids’ monitoring, as reported in the comprehensive survey by Fagiani et al [2], despite the availability of advanced metering systems [3,4,5] andsensor fault diagnosis frameworks [6,7].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
