English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a novel algorithm for uncertainty quantification of waterdistribution system measurement data including nodal demands/consumptions aswell as real pressure and flow measurements. This procedure, referred to asConfidence Limit Analysis (CLA), is concerned with a deployment of a LeastSquares (LS) state estimator based on the loop corrective flows and thevariation of nodal demands as independent variables. The confidence limitsobtained for the nodal pressures and the inflows/outflows of a water networkare determined with the novel algorithm called Error Maximization (EM) methodand are evaluated with respect to two other more established CLA algorithmsbased on an Experimental Sensitivity Matrix (ESM) and on the sensitivity matrixmethod obtained with the LS nodal heads equations state estimator. Theestimated confidence limits obtained for two real water networks show that theproposed EM algorithm is comparable to the other two CLA benchmark algorithmsbut due to its computational efficiency it is more suitable for online decisionsupport applications in water distribution systems. Both ESM and EM methodswork for any operating point, whether arbitrarily or randomly chosen, for anywater network although EM method has the advantage of being computationallysuperior and working with any sets of measurements.

Uncertainty Quantification of Water Distribution System Measurement Data based on a Least Squares Loop Flows State Estimator