Analysis of online pressure for resilience phase characterisation of leakages/burst events

[EN] While operating a water distribution network (WDN), it is essential to prepare the system to face with intentional (e.g., cyber-physical attack) or unintentional (e.g., pipe leakage/burst) adverse events or other drivers such as the effects of climate change. Increasing the network’s preparedness to deal with anomalous events is an effective manner to improve the system’s resilience, reducing the negative impacts of events. In this paper, leakage/burst events, and ordinary network operation, are captured by both sensors and expert knowledge in a WDN in Spain. Event-driven and data-driven approaches are used to characterise the system behaviour, in particular when it is operating under the effects of an anomalous event, based on the resilience phases (i.e., absorptive, adaptive, restorative) for the collected dataset. The relationship of clustering pressure head time series based on their potential state in a particular resilience phase, in three random cases of short-term leakage events, was explored. This paper focuses on capturing the behaviour of the system, through the exploration of the hydraulic parameters of WDNs (in particular the pressure head) before, during, and after a leakage event, by means of a spatial-temporal analysis. It was observed that the network behaviour could be categorised into 1) ordinary operation and 2) during the event, which would allow to characterise the system behaviour when influenced by leakage/burst event and also explore its adaptability to resilience phases. The results show that it is possible to extract relevant patterns (i.e., feature maps) and generate an anomaly indicator from the pressure head heatmaps that facilitate the characterisation of anomalous events for WDNs.Hoseini Ghafari, S.; Francés-Chust, J.; Piller, O.; Ayala-Cabrera, D. (2024). Analysis of online pressure for resilience phase characterisation of leakages/burst events. Editorial Universitat Politècnica de València. 1-14. https://doi.org/10.4995/WDSA-CCWI2022.2022.1408211

Water distribution network disruptive events. Generation and exploration of an incident hub to increase the network preparedness

[EN] This paper seeks to develop increased knowledge about disruptive events in a water distribution network (WDN) through the experience acquired by previous anomalous events in the system. This work explores the various relationships between several parameters in an incident hub (specifically water loss events) in a Spanish real, small WDN. The incident hub consists of basic elements recorded during an incident (e.g. breakdown, maintenance activity, among others) and the corresponding causes that generated the incident (e.g. breakage due to excess pressure, breakage due to tree roots, etc.), as well as the management times of the incident (e.g. awareness time, isolation, and repair time). The utility collected and stored these data, which were completed with direct interviews with the system operator. Measurements were performed at pressure and flow sensors, which allowed evaluating the effects of both the incident itself and the actions taken to solve it. The records of the incidents are categorised depending on the nature of the data they contain to facilitate mapping their causes and effects. To characterise the disruptive events, a feature extraction process has been proposed using a temporal-spatial approach combined with a migration proposed that describes parameters’ behaviour in the spatial dimension for a certain period of time. The characteristics obtained in the previous lessons of the incidents contained in the incident hub are compared with potential causes obtained with different control parameters. The objective is to determine the potential causal relationship of the incident that allows its characterisation. The results of this characterisation are presented and analysed in this contribution. The outcomes are promising in the sense of a clear ability to provide WDNs with key parameters that foster prediction and classification processes.Ayala-Cabrera, D.; Francés-Chust, J.; Hoseini-Ghafari, S.; Stanton, G.; Izquierdo, J. (2024). Water distribution network disruptive events. Generation and exploration of an incident hub to increase the network preparedness. Editorial Universitat Politècnica de València. https://doi.org/10.4995/WDSA-CCWI2022.2022.1483

Exploring a Spatial Dynamic Approach and Landmark Detection for Leakage/Burst Event Characterisation in Water Distribution Networks

International audienceExtracting useful information from sensors that record water distribution network (WDN) data is essential to improve network performance, increase network preparedness and resilience, and advance network digitalisation. Due to the large volume of data generated, analysis of the pressure head requires advanced techniques to reduce dimensionality. While previous works were typically based on comparing hydraulic simulations and observed data, there is a lack of study on pattern recognition, a helpful method for event detection, localisation, and prevention. Since the number of metering devices and their operativity has a crucial role in the recognition of key patterns, a spatial evaluation of network behaviour (with a focus on resilience) is conducted in this study. Comparing the heatmaps leads to extracting key patterns (i.e., landmarks), which will be helpful for decision-makers to increase the preparedness by making arrangements against critical events and allow classification and prediction of the network behaviour. This paper focuses on recognising the possible landmarks in the network representing a key feature (particularly pressure) in the presence and absence of leakage through spatial analysis with the objective of dimensionality reduction. A dataset of incidents, leakage/burst events, and ordinary network operations were captured through sensors and expert knowledge in a WDN in Spain to obtain relevant information (in the form of landmarks) from them. Results were promising, recognising the patterns that characterise the network behaviour when influenced by leakage/burst events