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A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids

Author

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  • Marco Fagiani

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Stefano Squartini

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Leonardo Gabrielli

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Marco Severini

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Francesco Piazza

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

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.

Suggested Citation

  • Marco Fagiani & Stefano Squartini & Leonardo Gabrielli & Marco Severini & Francesco Piazza, 2016. "A Statistical Framework for Automatic Leakage Detection in Smart Water and Gas Grids," Energies, MDPI, vol. 9(9), pages 1-25, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:665-:d:76492
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    References listed on IDEAS

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    1. Wang Song Hao & Ronald Garcia, 2014. "Development of a Digital and Battery-Free Smart Flowmeter," Energies, MDPI, vol. 7(6), pages 1-15, June.
    2. Jaber Alkasseh & Mohd Adlan & Ismail Abustan & Hamidi Aziz & Abu Hanif, 2013. "Applying Minimum Night Flow to Estimate Water Loss Using Statistical Modeling: A Case Study in Kinta Valley, Malaysia," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(5), pages 1439-1455, March.
    3. Gabriele Lobaccaro & Salvatore Carlucci & Erica Löfström, 2016. "A Review of Systems and Technologies for Smart Homes and Smart Grids," Energies, MDPI, vol. 9(5), pages 1-33, May.
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    Cited by:

    1. Rejeesh Rayaroth & Sivaradje G, 2019. "Random Bagging Classifier and Shuffled Frog Leaping Based Optimal Sensor Placement for Leakage Detection in WDS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3111-3125, July.
    2. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
    3. Andrés Ortega-Ballesteros & David Muñoz-Rodríguez & Alberto-Jesus Perea-Moreno, 2022. "Advances in Leakage Control and Energy Consumption Optimization in Drinking Water Distribution Networks," Energies, MDPI, vol. 15(15), pages 1-5, July.

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