Controlling water supply measurement

15 May 2017

Effective management of water metering networks is crucial to ensure that the required accuracy is attained, says Alick MacGillivray.

The need for the water industry to understand and control its distribution networks has never been more pressing, with regulatory authorities such as OFWAT in the UK, and environmental pressures demanding better measurement accuracy. As a result, flow metering is increasingly being seen as an important factor in monitoring of leakage and other types of abstraction in the water industry. 

Network analysis
Flow meters are subject to a range of conditions within the water network and can be subject to electromagnetic or acoustical interference as well as mechanical damage. To manage this, the industry has developed analytical methods to detect when this happens. The water industry has, for example, made use of mass balances techniques to estimate the level of leakage in a system. Recently, this has been augmented by the development and application of powerful data analysis that take this process a stage further by helping to identify the individual instrumentation that is mainly responsible for the imbalance.

There are also more sophisticated techniques – originally designed for use in such diverse areas as missile tracking and computer vision – which can extract underlying trends from very noisy signals. These techniques can offer benefits in the water industry too.

Data validation 
The first and most basic of these tools is a technique known as data validation. This is effectively a collection of tools that are used to assess the quality of the acquired data. Part of this suite of tools applies numerical filters to acquired data to ensure that it ‘makes sense’. It is, for example, possible to set upper and lower limits on measured flows and to reject data that are outside this range. This is, in effect, a way of detecting cross-errors in the system. Another component of this technique is to set limits on the rate of change of measured quantities to detect spikes and other transient behaviour in the network. This allows the operator to base further analyses on data that lie within the normal operating parameters of the network.

Often analyses are not based on individually acquired points, but on data that have been smoothed or averaged over a period of time, which allows the operators to determine longer term trends in the data and assist in demand forecasting. This can be taken further by the application of neural networks or generic algorithms where a computer-based neural network ‘learns’ the behaviour of a metering network and then highlights anomalous behaviour.

Uncertainty analysis
Uncertainty is the degree of doubt about a measurement. Undertaking an analysis of uncertainty involves identifying the main influences that affect the result of the measurement. This will result in a number which represents the ‘margin of error’ in the measurement. Applying this to the network gives an uncertainty in the water balance; that is a margin of error within which the mass balance should lie. Identifying the main contributors to this figure can ensure that capital expenditure is targeted to areas in the network where it will produce the most benefit.

Data reconciliation
Data reconciliation is basically a self-consistency check designed to complement the existing metering infrastructure. It applies corrections to individual flows, to remove imbalances from pipe intersections in the distribution network. The size of each correction is compared with the expected uncertainty of the measurement to assign a measurement index to each value. 

If this index exceeds a given threshold, it is likely that the meter measuring this flow is a major contributor to the imbalance in the system. Alternatively, it may indicate that there is a leak in the pipe section containing the meter. By trending the index over time, it is possible to detect meter drift or leak development before significant operational problems arise. 

Going forward, optimising data use is an operational imperative, especially to water companies under environmental, regulatory and resource pressure. Failure to protect significant metering investments, by not complementing it with modern, cost-effective, data analysis techniques, risks increased capital and operational expenditure through poor targeting of effort. 

Alick MacGillivray is a senior consultant at NEL, part of the TÜV SÜD Group.

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