This section is organised as follows. Firstly, the issue of model uncertainties and
sensitivity to the leak/burst size assumed for hydraulic simulations is considered in
Sect. 3.1. Then, Sect. 3.2 focuses on the issue of uncertainties in the sensors’
measurements. Once this is done, Sect. 3.3 deals with the issues of sensor/communication failures in sensor networks. Section 3.4 examines the topic of the simultaneous use of pressure and flow sensors. Finally, Sect. 3.5 focuses on the issue of
accounting for risk when developing optimal sensor placement techniques for leak/
burst detection and localisation.
3.1 Model Uncertainties and Sensitivity to the Leak/Burst Size
Assumed for Hydraulic Simulations
Almost all the sensor placement algorithms for leak/burst detection and localisation
in a DMA rely on modelling a large number of leak/burst scenarios. A number of
reliable, readily available hydraulic solver packages exist (e.g. EPANET (see [67]);
PICCOLO (see [68]); AQUIS (see [69]); WaterGEMS (see [70]); OOPNET (see
[71]); etc.), which allow leaks/bursts to be modelled relatively easily. Many of the
sensor placement studies for leak/burst detection and localisation found in the
literature assume a perfect model (i.e. that reflects reality at all times). Such a perfect
model is assumed to contain up-to-date estimates of nodal demands, background
(i.e. not burst type) leaks, pipe friction factors, statuses and characteristics of valves,
pumps and other devices and any other model parameter/input values (e.g. heads in
service reservoirs) that may affect its predictions of network pressures and flows.
However, it is well known that a perfect model does not exist. In this context,
demand allocation in a hydraulic model, which requires a good characterisation of
consumers, is considered as one of the most critical issues. In addition to all this,
leaks and bursts in WDSs have a stochastic nature. The size, location, timing and
nature/type of a leak/burst event are generally unknown. However, a nominal leak/
burst size is assumed in many of the sensor placement methodologies that can be
found in the literature. This section presents a selection of studies that have
attempted to deal with these issues.
Blesa et al. [72] studied the robustness of the methodology introduced by Sarrate
et al. [47] against sensitivity matrix uncertainties by taking into account different
leak/burst magnitudes on the one hand and several operating points (although only
inflows variations were considered in this study) on the other hand. The authors
introduced a “robustness percentage” index, which is based on the “leak locatability”
index (see [47]), to assess the robustness of the selected sensor placement methodology. Additionally, they made use of an extended sensitivity matrix that considers
all possible leak/burst scenarios and operating point scenarios in their clustering
analysis to reduce the number of candidate sensor locations. The authors illustrated
their robustness studies by means of a simple synthetic network (note, however, that
the clustering analysis was not deemed necessary there) and the same DMA in the
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
39
sensitivity to the leak/burst size assumed for hydraulic simulations is considered in
Sect. 3.1. Then, Sect. 3.2 focuses on the issue of uncertainties in the sensors’
measurements. Once this is done, Sect. 3.3 deals with the issues of sensor/communication failures in sensor networks. Section 3.4 examines the topic of the simultaneous use of pressure and flow sensors. Finally, Sect. 3.5 focuses on the issue of
accounting for risk when developing optimal sensor placement techniques for leak/
burst detection and localisation.
3.1 Model Uncertainties and Sensitivity to the Leak/Burst Size
Assumed for Hydraulic Simulations
Almost all the sensor placement algorithms for leak/burst detection and localisation
in a DMA rely on modelling a large number of leak/burst scenarios. A number of
reliable, readily available hydraulic solver packages exist (e.g. EPANET (see [67]);
PICCOLO (see [68]); AQUIS (see [69]); WaterGEMS (see [70]); OOPNET (see
[71]); etc.), which allow leaks/bursts to be modelled relatively easily. Many of the
sensor placement studies for leak/burst detection and localisation found in the
literature assume a perfect model (i.e. that reflects reality at all times). Such a perfect
model is assumed to contain up-to-date estimates of nodal demands, background
(i.e. not burst type) leaks, pipe friction factors, statuses and characteristics of valves,
pumps and other devices and any other model parameter/input values (e.g. heads in
service reservoirs) that may affect its predictions of network pressures and flows.
However, it is well known that a perfect model does not exist. In this context,
demand allocation in a hydraulic model, which requires a good characterisation of
consumers, is considered as one of the most critical issues. In addition to all this,
leaks and bursts in WDSs have a stochastic nature. The size, location, timing and
nature/type of a leak/burst event are generally unknown. However, a nominal leak/
burst size is assumed in many of the sensor placement methodologies that can be
found in the literature. This section presents a selection of studies that have
attempted to deal with these issues.
Blesa et al. [72] studied the robustness of the methodology introduced by Sarrate
et al. [47] against sensitivity matrix uncertainties by taking into account different
leak/burst magnitudes on the one hand and several operating points (although only
inflows variations were considered in this study) on the other hand. The authors
introduced a “robustness percentage” index, which is based on the “leak locatability”
index (see [47]), to assess the robustness of the selected sensor placement methodology. Additionally, they made use of an extended sensitivity matrix that considers
all possible leak/burst scenarios and operating point scenarios in their clustering
analysis to reduce the number of candidate sensor locations. The authors illustrated
their robustness studies by means of a simple synthetic network (note, however, that
the clustering analysis was not deemed necessary there) and the same DMA in the
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
39