State Estimation in Water Distribution Networks through a New Successive Linear Approximation

TL;DR

This paper introduces a scalable successive linear approximation method for water distribution network state estimation, effectively handling nonlinearities, uncertainties, and measurement inaccuracies.

Contribution

It presents a novel approach that solves the SE problem via a sequence of linear or quadratic programs, improving scalability and robustness over existing methods.

Findings

Effective in handling nonconvex valve/pump models

Can incorporate robust uncertainty modeling

Demonstrated success on simple test cases

Abstract

State estimation (SE) of water distribution networks (WDNs) is difficult to solve due to nonlinearity/nonconvexity of water flow models, uncertainties from parameters and demands, lack of redundancy of measurements, and inaccurate flow and pressure measurements. This paper proposes a new, scalable successive linear approximation to solve the SE problem in WDNs. The approach amounts to solving either a sequence of linear or quadratic programs---depending on the operators' objectives. The proposed successive linear approximation offers a seamless way of dealing with valve/pump model nonconvexities, is different than a first order Taylor series linearization, and can incorporate with robust uncertainty modeling. Two simple testcases are adopted to illustrate the effectiveness of proposed approach using head measurements at select nodes.