of all possible pairings of locations. They tested their method on two UK DMAs with
different geometries assuming a perfect model and no measurements noise. Through
comparison with the leak/burst detection performance of already installed instrumentation (according to UK standard practices), they demonstrated that the optimal
location(s) identified using their method enable detecting a higher percentage of
simulated leak/burst events. The authors noted however that the threshold selection
is cause of concern as, if obtained from the simulation of large leaks/bursts, smaller
events may not be detected. Of particular importance is the fact that, in a later study
[33], the authors conducted a set of field trials to evaluate their approach. These field
trials simulated five different leak/burst events through the opening of fire hydrants
within a selected DMA. By installing pressure instrumentation at different locations
in the DMA, an understanding of how accurately the model methodology can
determine sensitivity of instrument location was obtained. Indeed, the results showed
that pressure instrumentation location is crucial to sensitivity and that their modelling methodology was able to predict instrument location sensitivity to leak/burst
events reasonably well.
Farley et al. [11, 34] built on the work carried out in [30, 33] and proposed to
search the sensitivity matrix to achieve selective sensitivity to events in different
network areas. By doing this, their approach enabled providing useful leak/burst
localisation information by subdividing a DMA in smaller detection zones. The main
differences from the work presented in Farley et al. [30, 33] are that a genetic
algorithm (GA – see, e.g. [35]) is used to improve the search efficiency when
identifying the best sensor locations and an uncertainty band is applied to either
side of the threshold to (somehow) account for a certain degree of model/measurements uncertainty. The single objective function of the GA search aims to identify
combinations of instruments that provide an even division of the DMA and to
minimise the number of nodes within the penalty zone (i.e. a zone whereby a
response within the uncertainty band is produced at one or more instruments). The
authors presented results from field tests using hydrant flushing to simulate leak/
burst events in real DMAs. The field tests’ results demonstrated the practical
applicability of the method, showing that by combining quantification of differential
sensitivities with event detection techniques for data analysis (i.e. [12, 13]), events
can effectively be localised using a small number of instruments (i.e. taking into
account existing instrumentation and one or two additional pressure sensors). However, it was noted that the effectiveness of the localisation method was dependent on
where in a DMA a leak/burst event occurs (e.g. event near a DMA inlet are likely to
be missed) and, most importantly, that the method only works if all the considered
instruments are working and the event detections from all the sensors’ data agree
with what the model expects to happen (i.e. an incorrect or uncertain detection even
at a single sensor location would cause the method to indicate an incorrect leak/burst
search area).
Pérez et al. [31] proposed a sensor placement method conceptually similar to the
one presented in Farley et al. [30]. This method is based on computing and analysing
the differences (i.e. residuals) between the pressure measurements at the sensor
locations following a leak/burst and their estimations obtained using a hydraulic
model. The basic idea is that the values of these residuals for a particular leak/burst
32
M. Romano
different geometries assuming a perfect model and no measurements noise. Through
comparison with the leak/burst detection performance of already installed instrumentation (according to UK standard practices), they demonstrated that the optimal
location(s) identified using their method enable detecting a higher percentage of
simulated leak/burst events. The authors noted however that the threshold selection
is cause of concern as, if obtained from the simulation of large leaks/bursts, smaller
events may not be detected. Of particular importance is the fact that, in a later study
[33], the authors conducted a set of field trials to evaluate their approach. These field
trials simulated five different leak/burst events through the opening of fire hydrants
within a selected DMA. By installing pressure instrumentation at different locations
in the DMA, an understanding of how accurately the model methodology can
determine sensitivity of instrument location was obtained. Indeed, the results showed
that pressure instrumentation location is crucial to sensitivity and that their modelling methodology was able to predict instrument location sensitivity to leak/burst
events reasonably well.
Farley et al. [11, 34] built on the work carried out in [30, 33] and proposed to
search the sensitivity matrix to achieve selective sensitivity to events in different
network areas. By doing this, their approach enabled providing useful leak/burst
localisation information by subdividing a DMA in smaller detection zones. The main
differences from the work presented in Farley et al. [30, 33] are that a genetic
algorithm (GA – see, e.g. [35]) is used to improve the search efficiency when
identifying the best sensor locations and an uncertainty band is applied to either
side of the threshold to (somehow) account for a certain degree of model/measurements uncertainty. The single objective function of the GA search aims to identify
combinations of instruments that provide an even division of the DMA and to
minimise the number of nodes within the penalty zone (i.e. a zone whereby a
response within the uncertainty band is produced at one or more instruments). The
authors presented results from field tests using hydrant flushing to simulate leak/
burst events in real DMAs. The field tests’ results demonstrated the practical
applicability of the method, showing that by combining quantification of differential
sensitivities with event detection techniques for data analysis (i.e. [12, 13]), events
can effectively be localised using a small number of instruments (i.e. taking into
account existing instrumentation and one or two additional pressure sensors). However, it was noted that the effectiveness of the localisation method was dependent on
where in a DMA a leak/burst event occurs (e.g. event near a DMA inlet are likely to
be missed) and, most importantly, that the method only works if all the considered
instruments are working and the event detections from all the sensors’ data agree
with what the model expects to happen (i.e. an incorrect or uncertain detection even
at a single sensor location would cause the method to indicate an incorrect leak/burst
search area).
Pérez et al. [31] proposed a sensor placement method conceptually similar to the
one presented in Farley et al. [30]. This method is based on computing and analysing
the differences (i.e. residuals) between the pressure measurements at the sensor
locations following a leak/burst and their estimations obtained using a hydraulic
model. The basic idea is that the values of these residuals for a particular leak/burst
32
M. Romano