  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.
