Dynamic state estimation under stressed conditions in modern power networks

Abstract

This thesis presents the way dynamic state estimation can be conducted in the context of power networks under stressed operating conditions, in order to enhance grid monitoring. Resilience is fundamental for modern power systems, which are experiencing significant changes; renewable energy based technologies diversify power generation, dramatically affecting system response to contingencies, whereas increased energy consumption, in combination with lack of investments and aged equipment, lead to system operation close to its limits. This stressed system operation is associated with the activation of limiting devices, such as overexcitation limiters, whose impact on the power system stability margin has been proven and quantified in this thesis. This is based on a dynamic security assessment (DSA) methodology developed in this context, and two stability limit metrics have been used, the critical clearing time (CCT) and the power limits (PLs).

Having proven that the system stability margin is likely to decrease under stressed operating conditions, a stressed system conditions indicator has been proposed, considering the overexcitation limiter activation as an event denoting system stress. The proposed stress identification technique is based on Unscented Kalman filtering (UKF) dynamic state estimation, where phasor measurement units (PMUs) play a significant role in data transmission. In order to enhance the accuracy of power system monitoring, even in cases when limiting devices with unknown dynamics have been activated during stressed system operation, a novel UKF based dynamic state estimation algorithm has been developed in this thesis, considering unknown inputs in the power system models used. These studies have been carried out using two model power systems: A small 9-bus 3-machine system, and a large 68-bus 16-machine system.