approximately 300–5,000 properties. The current UK industry practice is to install a
flow sensor (and sometimes a pressure sensor as well) at the inlet of a DMA and any
outlet (e.g. to another DMA or to a large industrial user) and a (supplementary)
pressure sensor at the so-called critical monitoring point within the DMA (i.e. the
point located either at the point of highest elevation or alternatively at a location
farthest away from the inlet). With recent improvements in sensor technology and
communication technologies (such as GSM, GPRS and, more recently,
LORAWAN, Sigfox, NB-IoT and 5G), data can now be transferred via wireless
systems, and batteries last much longer, meaning that sensors can be placed in less
accessible areas and data from these devices can be received in near real time
(e.g. every 15 min). Furthermore, it is becoming more feasible to deploy larger
numbers of instruments per DMA, as the cost of both pressure and flow instrumentation (and their maintenance) has been reduced considerably. As a result, a vast
amount of pressure and flow data originating from the many DMAs that typically
form a UK WDS is now frequently available and expected to quickly grow over
time. This data can give insights into the operation and current/future status of water
networks and support many water loss-related activities, such as estimating background leakage levels, establishing and maintaining hydraulic models of water
systems and detecting and localising new leaks and bursts as they occur. With regard
to the latter, data-driven techniques utilising machine learning and advanced statistical tools have been developed that automatically manage and analyse in an on-line
fashion increasing numbers of near real-time data streams aiming at enabling the
detection and (in certain instances) the approximate location of leaks, bursts and
other similar network events (e.g. [8–18]). These techniques can complement traditional leak/burst localisation methods such as acoustic surveys, which can then be
used for accurately determining the exact leak/burst position (i.e. pinpointing). The
value of the information that can be derived through analysis of sensor data and
hence the success of the aforementioned methods (especially for localisation),
however, is critically linked to the number and types of sensors deployed and their
locations. As previously mentioned, it is envisaged that in the near future higher
numbers of sensors (especially pressure, for their lower cost and easier installation
and maintenance when compared to flow sensors) will be used to monitor WDSs.
However, due to the financial constraints placed on water companies, the costs of
increased instrumentation in WDSs (both capital and ongoing maintenance) must be
weighed against the operational and other cost savings which can be made by
improving network operations and management. It is therefore desirable to limit
the number of additional instruments to be deployed by selecting the optimal number
and location of sensors in a DMA.
This chapter provides a critical state-of-the-art literature review on the subject of
optimal sensor placement in WDSs for leak/burst detection and localisation. It
provides details of a number of existing sensor macro-location design methodologies
intended to facilitate the efficient collection of relevant measurements in WDSs for
that specific purpose. Generally speaking, the optimal placement of a limited number
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
29
flow sensor (and sometimes a pressure sensor as well) at the inlet of a DMA and any
outlet (e.g. to another DMA or to a large industrial user) and a (supplementary)
pressure sensor at the so-called critical monitoring point within the DMA (i.e. the
point located either at the point of highest elevation or alternatively at a location
farthest away from the inlet). With recent improvements in sensor technology and
communication technologies (such as GSM, GPRS and, more recently,
LORAWAN, Sigfox, NB-IoT and 5G), data can now be transferred via wireless
systems, and batteries last much longer, meaning that sensors can be placed in less
accessible areas and data from these devices can be received in near real time
(e.g. every 15 min). Furthermore, it is becoming more feasible to deploy larger
numbers of instruments per DMA, as the cost of both pressure and flow instrumentation (and their maintenance) has been reduced considerably. As a result, a vast
amount of pressure and flow data originating from the many DMAs that typically
form a UK WDS is now frequently available and expected to quickly grow over
time. This data can give insights into the operation and current/future status of water
networks and support many water loss-related activities, such as estimating background leakage levels, establishing and maintaining hydraulic models of water
systems and detecting and localising new leaks and bursts as they occur. With regard
to the latter, data-driven techniques utilising machine learning and advanced statistical tools have been developed that automatically manage and analyse in an on-line
fashion increasing numbers of near real-time data streams aiming at enabling the
detection and (in certain instances) the approximate location of leaks, bursts and
other similar network events (e.g. [8–18]). These techniques can complement traditional leak/burst localisation methods such as acoustic surveys, which can then be
used for accurately determining the exact leak/burst position (i.e. pinpointing). The
value of the information that can be derived through analysis of sensor data and
hence the success of the aforementioned methods (especially for localisation),
however, is critically linked to the number and types of sensors deployed and their
locations. As previously mentioned, it is envisaged that in the near future higher
numbers of sensors (especially pressure, for their lower cost and easier installation
and maintenance when compared to flow sensors) will be used to monitor WDSs.
However, due to the financial constraints placed on water companies, the costs of
increased instrumentation in WDSs (both capital and ongoing maintenance) must be
weighed against the operational and other cost savings which can be made by
improving network operations and management. It is therefore desirable to limit
the number of additional instruments to be deployed by selecting the optimal number
and location of sensors in a DMA.
This chapter provides a critical state-of-the-art literature review on the subject of
optimal sensor placement in WDSs for leak/burst detection and localisation. It
provides details of a number of existing sensor macro-location design methodologies
intended to facilitate the efficient collection of relevant measurements in WDSs for
that specific purpose. Generally speaking, the optimal placement of a limited number
Review of Techniques for Optimal Placement of Pressure and Flow Sensors. . .
29