Robust Decentralized State Estimation and Tracking for Power Systems via Network Gossiping

TL;DR

This paper introduces a decentralized adaptive state estimation method for power systems using network gossiping, enabling robust, accurate, and resilient global state tracking with local computations and near-neighbor communications.

Contribution

It presents the Gossip-based Gauss-Newton algorithm and a fully decentralized adaptive re-weighted scheme that improve robustness and flexibility in power system state estimation.

Findings

Accurately estimates and tracks power system state online.

Robust against bad data and random communication failures.

Demonstrates effectiveness on IEEE-118 system simulations.

Abstract

This paper proposes a fully decentralized adaptive re-weighted state estimation (DARSE) scheme for power systems via network gossiping. The enabling technique is the proposed Gossip-based Gauss-Newton (GGN) algorithm, which allows to harness the computation capability of each area (i.e. a database server that accrues data from local sensors) to collaboratively solve for an accurate global state. The DARSE scheme mitigates the influence of bad data by updating their error variances online and re-weighting their contributions adaptively for state estimation. Thus, the global state can be estimated and tracked robustly using near-neighbor communications in each area. Compared to other distributed state estimation techniques, our communication model is flexible with respect to reconfigurations and resilient to random failures as long as the communication network is connected. Furthermore, we prove that the Jacobian of the power flow equations satisfies the Lipschitz condition that is essential for the GGN algorithm to converge to the desired solution. Simulations of the IEEE-118 system show that the DARSE scheme can estimate and track online the global power system state accurately, and degrades gracefully when there are random failures and bad data.