Iterative Sensor Placement for Water Distribution Network Uncertainty Reduction
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Solution Overview
Problem
Water distribution network models face significant uncertainty due to scarce measurement points, making it difficult to accurately calibrate and identify sources of uncertainty, such as leaks or incorrect parameters, within the network.
Innovation Solution
An iterative method and system that optimally places sensors, partitions the network into observable and unobservable sections, corrects uncertain parameters, and calculates global uncertainty values, allowing for the localization and correction of uncertainties across the network through repeated iterations until all uncertain sections are resolved.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional calibration methods are used to reduce uncertainty in WDN models, then model accuracy improves, but the number of measurement points required becomes prohibitively large
Solution Approach 1:
The patent divides the water distribution network into multiple zones based on topological connectivity and uncertainty characteristics. Each zone is calibrated independently using local measurement points, rather than requiring global coverage. This segmentation allows accurate calibration with fewer total measurement points by focusing resources on representative locations within each zone.
Solution Approach 2:
The patent introduces an uncertainty propagation model that acts as an intermediary to predict how uncertainties in unmeasured parameters affect measured parameters. This model allows the calibration process to infer unmeasured parameters from measured ones, reducing the need for direct measurement points while maintaining model accuracy.
2Measurement precision
If more sensors are deployed to reduce uncertainty, then fault localization accuracy improves, but system cost and complexity increase
Solution Approach 1:
The patent segments the network into zones where each zone can be monitored with a minimal set of sensors. By calculating uncertainty propagation within each zone, the system identifies the minimum number of measurement points needed to achieve adequate fault localization accuracy without deploying sensors throughout the entire network.
Solution Approach 2:
The patent replaces physical sensors with a virtual sensing approach using uncertainty propagation models. The model predicts parameter values and identifies faults by analyzing relationships between measured and unmeasured parameters, substituting computational inference for physical measurement devices in many cases.
3Measurement precision
If calibration is performed on all parameters simultaneously, then comprehensive uncertainty reduction is achieved, but computational time and resources increase significantly
Solution Approach 1:
The patent segments the calibration process into zone-level iterations rather than attempting to calibrate all parameters globally at once. Each iteration focuses on a specific zone, calibrating parameters locally using measurements from that zone. This segmented approach reduces computational complexity and time while achieving comprehensive uncertainty reduction across the entire network through multiple passes.
Solution Approach 2:
The patent performs preliminary uncertainty analysis to identify which parameters have the greatest impact on model accuracy before calibration. This allows the calibration process to prioritize adjusting only the most influential parameters first, achieving significant uncertainty reduction with fewer computational iterations rather than adjusting all parameters simultaneously.
Data Source
AI summary
In one aspect, a method for reducing uncertainty in a hydraulic model of a water distribution network due to uncertain parameters and faults in the water distribution network is provided which includes the steps of: (i) calculating an optimized placement of sensors throughout a given uncertain section of the water distribution network; (ii) collecting data from the sensors; (iii) partitioning the given uncertain section of the water distribution network into observable and unobservable sub-sections based on the hydraulic model and a) a position, b) a number, and/or c) a type of the sensors that are available; (iv) correcting uncertain parameters and identifying faults for each of the observable sub-sections; (v) calculating a global uncertainty value for each of the unobservable sub-sections; and (vi) repeating the steps (i)-(vi) iteratively, at each iteration selecting an uncertain sub-section of the water distribution network, until no uncertain sub-sections of the water distribution network remain.