Keywords

water supply systems, neural network, fuzzy logic, modelling

Start Date

1-7-2006 12:00 AM

Description

The objective of a Water Supply System (WSS) is to convey treated water to consumers through a pressurized network of pipes. A number of meters and gauges are used to take continuous or periodic measurements that are sent via a telemetry system to the control and operation centre and used to monitor the network. Using this typically limited number of measures together with demand predictions the state of the system must be assessed. Suitable state estimation is of paramount importance in diagnosing leaks and other anomalies in WSS. But this task can be really cumbersome, if not unattainable, for human operators. The aim of this paper is to explore the possibility for a neural network to perform such a task. For one thing, state estimation of a network is performed by using optimization techniques that minimize the discrepancies between the measures taken by telemetry and the values produced by the mathematical model of the network, which tries to reconcile all the available information. But, for the other, although the model can be completely accurate, the estimation is based on data containing non negligible levels of uncertainty, what definitely influences the precision of the estimated states. The quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate estimation. By making use of the mathematical model of the network, estimated states together with uncertainty levels, that is to say, fuzzy estimated states, for different anomalous states of the network can be obtained. Also a description of the anomaly associated with such fuzzy state must be stored. The final aim is to train a neural network capable of assessing WSS anomalies associated with particular sets of measurements received by telemetry and demand predictions.

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Jul 1st, 12:00 AM

Neural Identification of Fuzzy Anomalies in Pressurized Water Systems

The objective of a Water Supply System (WSS) is to convey treated water to consumers through a pressurized network of pipes. A number of meters and gauges are used to take continuous or periodic measurements that are sent via a telemetry system to the control and operation centre and used to monitor the network. Using this typically limited number of measures together with demand predictions the state of the system must be assessed. Suitable state estimation is of paramount importance in diagnosing leaks and other anomalies in WSS. But this task can be really cumbersome, if not unattainable, for human operators. The aim of this paper is to explore the possibility for a neural network to perform such a task. For one thing, state estimation of a network is performed by using optimization techniques that minimize the discrepancies between the measures taken by telemetry and the values produced by the mathematical model of the network, which tries to reconcile all the available information. But, for the other, although the model can be completely accurate, the estimation is based on data containing non negligible levels of uncertainty, what definitely influences the precision of the estimated states. The quantification of the uncertainty of the input data (telemetry measures and demand predictions) can be achieved by means of robust estate estimation. By making use of the mathematical model of the network, estimated states together with uncertainty levels, that is to say, fuzzy estimated states, for different anomalous states of the network can be obtained. Also a description of the anomaly associated with such fuzzy state must be stored. The final aim is to train a neural network capable of assessing WSS anomalies associated with particular sets of measurements received by telemetry and demand predictions.