Publication

Flow meter data validation and reconstruction using neural networks: Application to the Barcelona water network

Conference Article

Conference

European Control Conference (ECC)

Edition

2016

Pages

1746-1751

Doc link

http://dx.doi.org/10.1109/ECC.2016.7810543

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Authors

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Abstract

The use of false or erroneous data can lead to wrong decisions when operating a system. In case of a water distribution network, the use of incorrect data could lead to errors in the billing system, waste of energy, incorrect management of control elements, etc. This paper is focused on detecting ow meters reading abnormalities by exploiting the temporal redundancy of the demand time series by means of articial neural networks (ANN). Communication problems with the sensor generate missing data and bad maintenance service in the ow meters produce false data. In this work, a methodology to detect the false data (validate) and replace the missing or false data (reconstruct) is proposed. As a core methodology, ANNs are used to model the time series generated from the water demand ow meters, and use the condence intervals to validate the information. To illustrate the proposed methodology, the application to ow meters in the water distribution network of Barcelona is used.

Categories

control theory.

Scientific reference

H. Rodriguez, V. Puig, J.J. Flores and R. Lopez. Flow meter data validation and reconstruction using neural networks: Application to the Barcelona water network, 2016 European Control Conference, 2016, Aalborg, Denmark, pp. 1746-1751.