Scalable Sensor Placement for Cyclic Networks with Observability Guarantees: Application to Water Distribution Networks

TL;DR

This paper introduces a scalable, graph-based sensor placement algorithm that guarantees structural observability in large cyclic networks with uncertainties, demonstrated on water distribution networks with thousands of nodes.

Contribution

It presents a novel, efficient method for sensor placement that ensures observability in complex, uncertain, large-scale cyclic networks, addressing computational challenges.

Findings

Successfully applied to large water networks with 1694 nodes and 124 cycles

Achieved sensor placement in under 0.1 seconds for complex networks

Guarantees structural observability despite parametric uncertainties

Abstract

Optimal sensor placement is essential for state estimation and effective network monitoring. As known in the literature, this problem becomes particularly challenging in large-scale undirected or bidirected cyclic networks with parametric uncertainties, such as water distribution networks (WDNs), where pipe resistance and demand patterns are often unknown. Motivated by the challenges of cycles, parametric uncertainties, and scalability, this paper proposes a sensor placement algorithm that guarantees structural observability for cyclic and acyclic networks with parametric uncertainties. By leveraging a graph-based strategy, the proposed method efficiently addresses the computational complexities of large-scale networks. To demonstrate the algorithm's effectiveness, we apply it to several EPANET benchmark WDNs. Most notably, the developed algorithm solves the sensor placement problem with guaranteed structured observability for the L-town WDN with 1694 nodes and 124 cycles in under 0.1 seconds.