Determining nightline leakage and usage

Every water company has a top hit list of problem DMAs ­ the ones with huge nightlines which elude all efforts to track down leakage, writes Charles Harris and Roger Ironmonger of Primayer. These invariably include unmeasured trade users ­ the number one headache for leakage practitioners.


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Socrates from Primayer is a new, patented tool for analysing DMA nightlines,

specifically designed for discriminating between areas of high use and high

leakage.

Socrates is much more than a conventional data logger. Its statistical

processing capability holds the key to teasing out the elusive boundary

between legitimate night use and leakage on a site by site basis. It has

applications in DMA demand analysis, consumption monitoring, control system

fault diagnosis and in leakage reporting.

Nightlines ­ water flows into DMAs during periods of low use by consumers ­

are monitored extensively by the water industry to estimate leakage levels.

This is carried out using data loggers or telemetry devices attached to flow

meters.

Conventionally, average values (usually via accumulated pulse counts) are

recorded over 15 minute intervals. This period has been chosen for

convenience; it appears to give a reasonably detailed view of network

operation without overloading supervisors with data. Unfortunately, new

demands on network efficiency mean much more precise diagnostic tools are

required.

The problem is that conventional averaging hides important details. Figure 1

shows an example on a real DMA. The minimum 15 minute average value is 5.34

litres/sec over the period 02:30 to 02:45. The actual flow pattern can be

seen varying above and below the average valves; it is markedly different

and, crucially, the minimum valve (an indicator of leakage) is considerable

lower. The example is taken from a mixed trade/domestic DMA and it

illustrates problems endemic to such sites. Varying trading and production

patterns will almost always defeat conventional sampling methods.

When accounting for leakage, practitioners also need to understand how

domestic customers use water at night. Traditionally, it is assumed all

domestic properties use the same average quantity of water every night

regardless of social or climatic circumstances. A common global estimate is

1.7 litres per property per hour. This is perhaps the biggest single flaw in

the system by which the industry now estimates leakage. Tests with

consumption monitors show the assumption is invalid. As an example, Figure 2

shows one week¹s data derived from such a site ­ the night to night

variation approaches 50 per cent.

Socrates tackles these problems by exploiting the maximum resolution

available from the flow measuring system. It collects thousands of data

values per night which it processes into a smaller set of micro-values. This

set (typically 500 values) exposes the crucial details of night lines which

are masked by conventional averaging. This has enormous value in diagnosing

discrepancies in trade use. It is also possible to analyse nightly

distributions of micro-values to find estimates of domestic consumption

which reflect prevailing site conditions.

On pulse generating flow meters, Socrates uses an advanced form of Pulse

Interval Timing (PIT). On analogue meters it carries out rapid sampling.

These methods potentially enable it to probe nightlines more thoroughly than

conventional pulse counting or averaging. However, simple PIT measurements

will give artificially low values. Before presenting results, Socrates

processes the raw data to remove extraneous artefacts associated with the

measurement system, pressure surges and hydraulic oscillations.

On pulse generating flow meters, there can be substantial variation in

output pulse values unrelated to variation in the flow being measured. On

some types of meter, the variation is systematic. On others it is random.

Socrates uses proprietary filtering algorithms to remove these errors.

Sudden hydraulic disturbances, such as pump starts, can generate surges in

networks causing flows to oscillate strongly abut mean levels. The troughs

of these oscillations are difficult to distinguish from genuine short term

reductions in user demand. PIT measurements which do not discount such

troughs will always give wrong estimates of baseline flows. The errors will

be serious. To avoid errors, Socrates checks for sudden changes in pressure.

Pressure change thresholds can be set by the user and if these are exceeded,

Socrates assumes a transient has occurred and ignores flow data around this

section of the nightline.

When searching for minimum valves, Socrates also sets restrictions on the

rate at which flows are allowed to change around minimum points. This

catches spikes and and other short transients which are not evident in the

pressure trace.

In addition to transient hydraulic artefact some systems exhibit continuous

low level hydraulic oscillation even after removing measurement artefact. If

this Œcyclic¹ hydraulic artefact is not filtered, errors will be caused by

confusing trough values in natural hydraulic cycles with periods of low user

activity. Socrates uses autocorrelation to determine the optimum averaging

time to smooth out such oscillations. This averaging time is calculated for

each and every nightline; it is the minimum time over which measurements

must be averaged to minimise errors. The averaging time is called the

micro-period and average values calculated over such periods are called

micro-values. Figure 3 shows an example of cyclic artefact.

For each nightline, Socrates searches for a minimum flow valve which

satisfies pressure and flow transient restrictions and which is free of

measurement and cyclic hydraulic artefact. Because of the measurement speed,

it is able to probe deep into nightlines to reveal the lowest baseline

flows.

Figure 4 shows results from a rural DMA. Socrates reported a 25 per cent

lower nightline than 15 minute averaging on the night shown. The selected

minimum point at approximately 04:35 shows a stable dip in the nightline

some 30 seconds long. Socrates rejects the minima around 04:26 as

transients.

In another example, a data logger on a city centre site had detected high

Œleakage¹ ­ the logger was reporting a minimum flow of 11 litres/sec.

Socrates, however, revealed a true minimum of 6.3 litres/sec with an

additional varying flow component superimposed. This extra use explained the

high nightline and saved time consuming investigation.

On many conventional logging systems, Œfast’ may not be fast enough. High

speed measurements are required to detect surges. No conventional logger

automatically detects transients. Additionally, no conventional logger

detects and compensates for measurements system error nor for

micro-periodicity.

Fast logging on pulse counting systems is subject to large quantisation

errors. For example, a 3.5 litre/sec flow logged every second from a

pulse-head generating one pulse/litre will store the sequence of flow values

as 3, 4, 3, 4, 3. There will be no intermediate values. This is not precise

enough.

Even if fast logging was suitable, the shear volume of raw data would

overload network supervisors. Socrates avoids this by processing raw

measurements before presenting a limited set of key facts describing the

state of the distribution system.

Socrates can be used in cul-de-sac type consumption monitors where flows

into statistically chosen small groups of houses are measured to calculate

per capita consumption.

Figure 5 shows a Socrates nightline plot from a 111 property ACORN F50 site.

Clearly evident is a Œcore’ flow on which intermittent Œevents’ such as

toilet flushes and washing machine fills are superimposed. Because these

sites are small, events rarely coalesce. The Œcore¹ flow will thus be very

close to, if not actually at, the leakage level of the site. Figure 6

compares the reporting of the lowest nightline flows using conventional

averaging and Socrates over one week.

Conventional averaging over-estimates

the minimum flow, and hence the leakage, on this site by, on average, 37 per

cent. This improved accuracy clearly has a positive implications for Per

Capita Consumption calculations used in integrated flow analyses of leakage.

Compared with conventional monitoring, on sites of up to approximately 500

properties, Socrates routinely records minimum flows undistorted by

intermittent night use. This opens the possibility of using larger

consumption monitor areas. Core flow levels would still be exposed, but the

larger property count would improve the statistical analysis of night use.

An average night flow value, say over one hour, from a consumption monitor

comprises two components: intermittent use and core flow. Socrates always

penetrates to the core flow. Core flow itself comprises three parts:

continuous use, plumbing losses and leakage. On a consumption monitor, core

flow is arguably all waste. An example of continuous use is a tap dripping

because it hasn¹t been turned fully off. A plumbing loss might be a tap

dripping because of a faulty washer. These two components vary randomly and

are the principal reason why consumption monitor minimum micro-values vary

about mean levels. A statistical estimate of the actual leakage boundary may

be derived from the distribution of the minimum micro-values over a series

of nights. An example is shown in Figure 7.

The concept of core flow can be extended to DMAs. Larger DMAs, up to 2,000

properties, have been simulated by adding together random sequences of real

Socrates nightlines. These studies indicate that Socrates will typically

penetrate the intermittent use on a 500 property DMA to within four per cent

of the actual core flow. On a 2,000 property DMA it penetrates to within 16

per cent.

Core flow on a DMA will comprise the same components as on a consumption

monitor but with two extra random elements: continuous trade use and the

penetration error described above.

These four components vary randomly and cause minimum nightly micro-values

to vary about mean levels. A statistical estimate of the actual leakage

boundary may be derived from the distribution of these minimum values over a

series of nights. Figure 8 shows a typical analysis applied to a DMA

comprising 1,700 properties and a harbour. It is worth noting in this

example, and it is by no means unique, that conventional analysis yields

ridiculous leakage values, even before trade allowances are subtracted.

In contrast to these dubious speculations, the Socrates method makes no

assumptions. It needs neither domestic night use, or trade night use values.

The estimate is based solely on the evidence of the nightline.

New demands being made on network efficiency require much more precise tools

to monitor performance. Conventional monitoring, based on limited average

data-sets, masks important details of night flows, so making troubleshooting

difficult. In this article, we have seen practical cases where Socrates’

high resolution data has helped the leakage manager determine what is or

isn’t leakage. We have also seen how simplistic measurements based solely on

fast or PIT sampled absolute minimum flow rates will be dangerously

misleading. We have also seen how tangible benefits are gained by using

Socrates on consumption meters. The errors caused by counting in

intermittent night use are eliminated. The concept of core flow has been

introduced and with it the potential for a completely default-free analysis

of leakage.


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