Probabilistic State Estimation in Water Networks

State estimation (SE) in water distribution networks (WDNs), the problem of estimating all unknown network heads and flows given select measurements, is challenging due to nonconvexity hydraulic models significant uncertainty from demands, parameters, measurements. To this end, a probabilistic modeling for SE WDNs proposed. After linearizing nonlinear WDN model, proposed (PSE) shows that covariance matrix system states (unmeasured flows) can be linearly expressed by three sources (i.e., measurement noise, demands). Instead providing deterministic results states, PSE approach: 1) regards as random variables yields variances individual states; 2) considers thorough various types valves scenarios WDNs; 3) also useful quantification, extended period simulations, confidence limit analysis. The effectiveness scalability approach are tested using several case studies.