Moving-Horizon State Estimation for Power Networks and Synchronous Generators

TL;DR

This paper introduces a dynamic moving-horizon estimation method for power networks and generators, improving accuracy and robustness over static methods, even with incomplete models and limited sensor data.

Contribution

It presents a unified estimation framework that handles incomplete models, detects bad data, and estimates generator states without terminal PMUs.

Findings

Improved estimation accuracy over static methods

Effective internal generator state estimation without terminal PMUs

Capability to detect and identify bad data

Abstract

Power network and generators state estimation are usually tackled as separate problems. We propose a dynamic scheme for the simultaneous estimation of the network and the generator states. The estimation is formulated as an optimization problem on a moving-horizon of past observations. The framework is a generalization of static state estimation; it can handle incomplete model knowledge and does not require static network observability by PMUs. The numerical results show an improved estimation accuracy compared to static state estimation. Moreover, accurate estimation of the internal states of generators without PMUs on their terminals can be achieved. Finally, we highlight the capability of the proposed estimator to detect and identify bad data.