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Paper details
Number 2 - June 2018
Volume 28 - 2018
An unsupervised approach to leak detection and location in water distribution networks
Marcos Quiñones-Grueiro, Cristina Verde, Alberto Prieto-Moreno, Orestes Llanes-Santiago
Abstract
The water loss detection and location problem has received great attention in recent years. In particular, data-driven methods
have shown very promising results mainly because they can deal with uncertain data and the variability of models better
than model-based methods. The main contribution of this work is an unsupervised approach to leak detection and location
in water distribution networks. This approach is based on a zone division of the network, and it only requires data from
a normal operation scenario of the pipe network. The proposition combines a periodic transformation and a data vector
extension together with principal component analysis of leak detection. A reconstruction-based contribution index is used
for determining the leak zone location. The Hanoi distribution network is employed as the case study for illustrating the
feasibility of the proposal. Single leaks are emulated with varying outflow magnitudes at all nodes that represent less than
2.5% of the total demand of the network and between 3% and 25% of the node’s demand. All leaks can be detected within
the time interval of a day, and the average classification rate obtained is 85.28% by using only data from three pressure
sensors.
Keywords
water distribution networks, leak location, unsupervised methods, principal component analysis, demand model