Distributed Estimation via Iterative Projections with Application to Power Network Monitoring

TL;DR

This paper introduces two scalable distributed algorithms for power network state estimation, enabling control centers to collaboratively monitor grid conditions and detect data corruption efficiently using local measurements.

Contribution

It proposes novel distributed estimation methods with incremental and diffusive cooperation modes, tailored for scalable power network monitoring and anomaly detection.

Findings

Algorithms are computationally efficient and scalable.

Finite-memory approximation maintains high accuracy.

Effective detection of corrupted measurements achieved.

Abstract

This work presents a distributed method for control centers to monitor the operating condition of a power network, i.e., to estimate the network state, and to ultimately determine the occurrence of threatening situations. State estimation has been recognized to be a fundamental task for network control centers to ensure correct and safe functionalities of power grids. We consider (static) state estimation problems, in which the state vector consists of the voltage magnitude and angle at all network buses. We consider the state to be linearly related to network measurements, which include power flows, current injections, and voltages phasors at some buses. We admit the presence of several cooperating control centers, and we design two distributed methods for them to compute the minimum variance estimate of the state given the network measurements. The two distributed methods rely on different modes of cooperation among control centers: in the first method an incremental mode of cooperation is used, whereas, in the second method, a diffusive interaction is implemented. Our procedures, which require each control center to know only the measurements and structure of a subpart of the whole network, are computationally efficient and scalable with respect to the network dimension, provided that the number of control centers also increases with the network cardinality. Additionally, a finite-memory approximation of our diffusive algorithm is proposed, and its accuracy is characterized. Finally, our estimation methods are exploited to develop a distributed algorithm to detect corrupted data among the network measurements.