Monitoring water supply systems for anomaly detection and
response
M. Bakker*/**, T. Lapikas***, B.H. Tangena****, J.H.G. Vreeburg*/*****
* Delft University of Technology, PO Box 5048, 2600 GA Delft, the Netherlands
** Royal HaskoningDHV B.V., PO Box 1132, 3800 BC Amersfoort, the Netherlands (E-mail: martijn.bakker@rhdhv.com, telephone :+31 33 468 2327, fax +31 33 468 2801)
*** UReason, Pompoenweg 9, 2321 DK Leiden, the Netherlands **** RIVM, PO Box 1, 3720 BA Bilthoven, the Netherlands
***** KWR Watercycle Research Institute, P.O. Box 1072, 4330 BB Nieuwegein, The Netherlands
Abstract
Water supply systems are vulnerable to damage caused by unintended or intended human actions, or due to aging of the system. In order to minimize the damages and the inconvenience for the customers, a software tool was developed to detect anomalies at an early stage, and to support the responsible staff in taking the right decisions to restore the normal situation. The software is designed for water quantity events as well as for water quality events. The model aims to detect events which occur relatively frequently in water distribution systems, like pipe bursts events and water discolouration events. The model does not aim to detect more severe and rare water contaminations.
Keywords
demand prediction; pipe burst; discolouration, anomaly detection
INTRODUCTION
Vulnerability of water distribution systems
Water distribution systems are extensive systems, comprised of numerous exposed elements in public areas. This makes the systems inherently vulnerable for both unintended damage, for instance like pipe damage due to excavation by a contractor, or intended damage, like water contamination by malicious actions (Perelman et al. (2012)). Beside damage caused by human actions, the systems are vulnerable for spontaneous damage or water quality deterioration. Failures of the system can occur as a result of the life cycle of pipes and infrastructures, and can be induced by factors like soil corrosivity and movement, high water pressures, traffic loading and changing operational conditions (Farley et al. (2010)). Based on this high vulnerability of the water supply systems, one would expect high rates of outbreaks caused by failures or intended contamination of the systems.
Water quality outbreaks in water distribution systems
Outbreaks in the Netherlands
Smeets et al. (2009) and Van Lieverloo et al. (2007) report that three outbreaks occurred in the Netherlands since the end of World War II (65 year period). This results in an average outbreak rate in the Netherlands of 0.046 outbreaks per year. All three cases were caused by a distribution deficiency (cross connection which caused inflow of non drinking water into the water distribution system), so the outbreak rate caused by distribution deficiency is also 0.046 outbreaks per year. The outbreak rate seems rather low and is based on only three cases. The low number of outbreaks (cases) results in a low statistical certainty of the number. In order to get a higher confidence in the outbreak number, a comparison is made to reported outbreaks in Western Europe and in the United States.
Outbreaks in Western Europe and in the United States
Risebro et al. (2005) report all outbreaks associated with drinking water in 10 countries in Western Europe (Finland, France, Germany, Greece, Italy, the Netherlands, Ireland, Spain, Sweden and the United Kingdom, 350 million people) in the period 1990 to 2004. A total number of 86 outbreaks in water supply systems in the period of 15 years were reported. This results in an outbreak rate of 5.7 outbreaks per year (0.016 outbreaks per million people per year). This number would result in the Netherlands (16.7 million inhabitants) in 0.27 outbreaks per year. Of the 86 reported outbreaks, at 19 (22.1%) a deficiency in the distribution system was identified to be cause of the outbreak. The normalized outbreak rate caused by distribution deficiency is therefore 0.0036 outbreaks per million people per year, which would result in 0.060 outbreaks per year in the Netherlands.
Craun et al. (2010) report all outbreaks associated with drinking water in the United States in the period 1971 to 2006. A total number of 780 outbreaks in the period of 36 years were reported. 338 outbreaks occurred in community water systems, which are comparable to water supply systems in Western Europe. The other outbreaks occurred in non community systems or individual systems, or were related to the use of commercially bottled water or to the purchase of bulk water. The average outbreak rate for community water systems is 9.4 outbreaks per year. 96% of the consumers in the United States (298 million people) are served by a community water system (EPA 2011). Therefore the normalized outbreak rate equals 0.032 outbreaks per million consumers per year. This number would result in the Netherlands (16.7 million inhabitants) in 0.52 outbreaks per year. Of the 338 reported outbreaks in community systems, at 49 (14.4%) a deficiency in the distribution system was identified to be cause of the outbreak. The normalized outbreak rate caused by distribution deficiency is therefore 0.0046 outbreaks per million people per year, which would result in 0.076 outbreaks per year in the Netherlands.
The outbreak rates (translated to the Dutch population) in Western Europe (0.060 outbreaks per year) and in the United States (0.076 outbreaks per year) are somewhat higher than the actual observed outbreak rate in the Netherlands (0.046 outbreaks per year). The numbers, however, have the same magnitude, which confirms the observed outbreak rate in the Netherlands, though based on 3 cases only. Assuming comparable water distribution systems in The Netherlands, Western Europe and the United States, an outbreak rate between 0.05 and 0.07 outbreaks per year in the Netherlands may be expected (one event per 15 to 20 years).
Detection possible?
The etiology of all reported outbreaks in the Netherlands was determined to be (coliform) bacteria (Smeets et al. (2009)). Risebro et al. (2005) reported that the contamination causing outbreaks in Western Europe related to distribution system failures consisted of Gastroenteritis (n=7), followed by Protozoal (n=5), Bacterial (n=3) and Mixed (n=3) and Viral (n=1). Craun et al. (2010) reported that the etiology could not be determined for 44.6% of all outbreaks in the United States. Also, the paper does not mention the causes of the outbreaks related to distribution system failures specifically. Most common among all identified contaminations were parasites, bacteria, chemicals and viruses. The above mentioned examples show that the contamination can be (a wide range) of microbiological agents as well as chemical, and that in some cases the contaminant could not be determined. This diversity in possible contaminants puts researchers for a complex challenge, in finding the right (combination of) sensor(s) that is able to detect most of the contaminants. Although researchers progress in developing a multi sensible sensor on a chip (Elad et al. (2011)), a sensitive, reliable and affordable general sensor which can be installed in a water distribution system still seems to be far away.
Although quite a number of researchers have addressed the question of finding the optimal location to install sensors, the number of sensors to install is considered as a given fact imposed by the available budget. In some work a case study is presented, which gives an idea of the number of sensors in relation the number of people served in the area in a practical application. For example, Cozzolino et al. (2011) present a case study where the optimal locations for 5 sensors in a network serving 18,000 people is researched. The “sensor rate” in this case equals 5 per 18,000 people, or 28 per 100,000 people. If this number would be considered typical for protecting the population against drinking water contamination events, the utilities in the Netherlands would have to install over 4,500 sensors in their distribution systems. This number of sensors would have to be installed and would have to monitor continuously in order to detect events with a likelihood of once per 15 to 20 years. It’s likely that in this case the number of false alarms will largely exceed the number of detected real events. By installing the sensors in order to protect the customers from water contamination events, the utilities will have to accept false alarms which will evoke customer complaints and will reduce the confidence customers have in the utilities.
This paragraph shows that there are still serious hurdles to overcome towards the real implementation of monitoring systems to protect people from water contamination events: 1. The sensors have limited sensitivity to all possible water contamination events, and 2. The necessary number of sensors is large, and therefore also the expected number of false alarms evoked by the sensors.
Terrorist attacks on water distribution systems
The events on 11 September 2011 showed the vulnerability of modern society to terrorist attacks. The events led to many actions to improve the security in virtually all large scale public and private organizations and infrastructure. The first reaction of many water utilities was to improve the access security of their visible assets, like treatment facilities, pumping stations, reservoirs and offices. Different universities started doing research about the possibilities to secure the water distribution systems. Because a direct surveillance of the water distribution system is not possible as a result of its extensiveness, researchers focus on topics to detect and respond to eventual attacks. The main fields of research include: 1. Sensor development (like Elad et al. (2011), Gerhardt et al. (2006) and Lee and Gu (2005)); 2. Optimal sensor placement in a distribution system (like Farley et al. (2010), Hart and Murray (2010), Krause et al. (2008); Cozzolino et al. (2011), Berry et al. (2005)); 3. Source identification after detection of a contamination (like De Sanctis et al. (2010), Mann et al. (2012), Zeng et al. (2012), Tryby et al. (2010), Preis and Ostfeld (2007), Laird et al. (2006), Guan et al. (2006)); 4. Optimal response to minimize the effects after detection of a contamination (like Alvisi et al. (2012), Alfonso et al. (2010), Guidorzi et al. (2009), Poulin et al. (2008), Preis and Ostfeld (2008), Baranowski and LeBoeuf (2006).
Despite the tremendous efforts by the above mentioned researchers, there is still no ready to market system available to be implemented by the water utilities. The hurdles identified earlier for a monitoring system to detect “normal” contamination events, are even higher in relation to a monitoring system to detect terrorist attacks: the range of possible contaminants is wider, and the expected number of events is lower.
Pipe bursts and discolouration events
Pipe bursts
Pipe bursts are considered part of the normal operation of a water supply system, because of the daily occurrence of bursts. Trietsch and Vreeburg (2005) report an average value of 0.09 failures per km of water main per year in the Netherlands. With a total length of all water mains of some 115,000 km, the number of pipe failures in the Netherlands amounts 10,000 per year (28 per day).
Most bursts have only limited effect on the water supply and cause an interruption of supply to only a few customers. However, every (large size) utility is confronted almost every year with a number of pipe burst events which have a larger impact on the water supply. Those events cause considerable damage to the surroundings en impose an interruption of supply to a large number of consumers. Vreeburg and Boxall (2007) show that 59% of all customer complaints at UK water companies are about receiving no water at all or receiving water at low pressure.
Figure 1. Example of a pipe burst event
Discolouration events
Other “normal” disturbances in water distribution system are events which result in discolouration of the water. A relatively large part of all customer complaints are related to discolouration, varying from 34% of all complaints in the UK (Vreeburg and Boxall (2007)) to 75% of all complaints at an Australian water company (Kjellberg et al. (2009)). Discolouration events are often evoked by abnormal flow conditions in a part of the network where particles have settled.
DisConTO project
A collaboration of Dutch companies started the project “Distribution Control Training and Operation”, DisConTO. The collaboration consists of four Dutch water utilities, the Delft University of Technology, the National Institute for Public Health and the Environment, Consulting and Engineering firm Royal HaskoningDHV and intelligence software provider UReason. The project is financially supported by a grant from the Dutch government. The project aims (among others) to generate knowledge about disturbing water quality and water quantity events in water distribution networks, and to translate this knowledge in software for real-time anomaly detection and off-line operator training. The collaboration has chosen to aim at “normal” disturbances like pipe bursts and discolouration events, rather than at water contamination events or at terrorist attacks. The project recognizes intended and untended water contamination as serious threats, which should be addressed to by researchers. However, the goal of the DisConTO project is to develop ready to use software tools. The knowledge and sensors for detection of water contamination events is still lagging too much behind for the software development. Therefore the DisConTO project has chosen to develop software that aims at “normal” disturbances.
The next section describes of which modules the DisConTO software will be built up, and what the current status of the modules is.
MATERIALS
For an early detection and a quick response to anomalies, a monitoring system for water supply systems is developed, called the Distribution Monitor. This system monitors water quantity and water quality aspects, and alarms after detecting anomalies. The Distribution Monitor consists of the following components:
1. A network of flow, pressure and quality sensors
2. A real-time running hydraulic network model with quality module 3. An algorithm for anomaly detection (flow, pressure quality) 4. A demand prediction and operations prediction / controlling model
The Distribution Monitor will be made available to operators at the central control rooms of water utilities or to the departments of the utilities that are responsible for the response to disturbances (water quality and bursts) in the distribution system. The functional design of the Distribution Monitor is shown in Figure 2.
Figure 2. Modules in the Distribution Monitor
1. Network of flow, pressure and quality sensors
The availability of flow, pressure and quality sensors installed in the distribution system is a condition for a meaningful implementation of the Distribution Monitor. The measurements must be available in real-time in order to detect anomalies in (near) real-time. In most water supply systems a number of flow and pressure measurements are standard available in the SCADA system. Those measurements are needed for the operational control of the system. In the current practise, the water utilities in the Netherlands typically have no water quality sensors installed. Water quality sensors must be installed in order to measure disturbances in water quality. In the first implementation standard water quality sensors measuring conductivity, temperature and turbidity will be installed and connected to the Distribution Monitor.
2. Real-time running hydraulic network model with quality module
The real time measurements of flow and pressure at the pumping facilities or other pressure / flow controlling facilities will be used as inputs for the real-time running hydraulic network model. Machell et al. (2010) show the advantages and practical issues of running a real-time hydraulic network model. A hydraulic network model with a connected water quality model (EPANET-MSX) will be used to generate predicted values for the actual measured pressures, flows and water qualities. This so-called “now cast” for flow and pressure is generated by doing a static (1 time step) simulation every 5 minutes. The “now cast” of the water quality parameters is derived by doing a water quality simulation based on the result of the hydraulic simulation, and applying the initial concentrations derived from the previous water quality simulation.
3. Algorithm for anomaly detection (flow, pressure quality)
The measured and predicted (“now cast”) values of flow, pressure and quality are used as input in the anomaly detection modules. In order to detect burst events based on the flow data, the burst detection method described by Bakker (2012) will be applied. For the location (and detection) of burst events, the difference between measured and predicted pressure will be evaluated in the hydraulic model. Based on the results of iterative calculations an approximation of the possible burst location can be derived. For water quality event detection the Canary software will be used (Fisher (2010)).
4. Demand prediction and operations prediction / controlling model
Based on the (real-time) measured flows, the water demand in each individual supply area or DMA will be predicted. This prediction will be done by the adaptive water demand prediction model described by Bakker et al. (2003). The predicted water demand as well as the actual reservoir levels will be used as input in the operations prediction / controlling model. This model predicts the operation of the water supply system (treatment flow, pump switching / pump flows, valve manipulation) based on the predicted water demand and the modelled operation strategy. This model can also be used to actually control the water supply system, by writing back the calculated control as real-time set-points to the SCADA system.
The results of both water demand prediction as well as operations prediction will be fed into the hydraulic network model. With this input the hydraulic network will calculate all flows, pressures and reservoir levels in the network for the next 48 hours.
User interface
The user interface of the Distribution Monitor will be web-based and will have an intuitive design. The interface will not contain all advanced functionality available in hydraulic network models, but will only present both measured and predicted (simulated values). The measurements and the predictions will be shown on a geographic overview for the actual situation, as well as in trend graphs. The operator will have the opportunity to look ahead at predictions for the near future, and to look back to the differences / similarities between predicted and measured values.
RESULTS
All modules are now being developed and tested. Therefore is not possible to show results obtained by the Distribution Monitor at this stage. After developing the Monitor a prototype will be installed at the utilities who participate in the DisConTO project. After a testing and implementation phase the first results are expected.
CONCLUSIONS
The Distribution Monitor helps utilities to respond quicker and better to anomalies in the system, minimizing the consequences of both pipe burst events and water quality events. This not only results a higher service level to the customers, but also to a better image of the utilities by showing proactive action during disturbed circumstances. The Distribution Monitor therefore provides a powerful new functionality in the control centres of water utilities.
DISCUSSION
When initially installing the Distribution Monitor, the utility could decide to only connect the readily available (flow and pressure) measurements to the Distribution Monitor. In that case the monitoring will be imprecise, for small scale anomalies will not be noticed by the Monitor. However the Monitor can be tested and predicted and measured values can be visualized to the operator. This will help the operator to look at the system in another way than to what he is used. If the utility decides install extra measurements, the Distribution Monitor will be able to monitor more precisely. The method proposed by Farley et al. (2010) can be used to determine the optimal locations for pressure and flow sensors.
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