Performance Limitations of Distributed Integral Control in Power Networks Under Noisy Measurements

TL;DR

This paper investigates the performance of the distributed averaging PI controller in power networks, revealing that measurement noise can significantly impact its effectiveness and scalability, especially in large, sparse networks.

Contribution

The study provides a detailed analysis of how noisy measurements affect the transient performance of DAPI controllers, highlighting conditions for optimal performance and when centralized control may be preferable.

Findings

Measurement noise can significantly degrade DAPI performance.

Increased inter-nodal alignment improves robustness to noise.

Large sparse networks may favor centralized control over distributed methods.

Abstract

Distributed approaches to secondary frequency control have become a way to address the need for more flexible control schemes in power networks with increasingly distributed generation. The distributed averaging proportional-integral (DAPI) controller presents one such approach. In this paper, we analyze the transient performance of this controller, and specifically address the question of its performance under noisy frequency measurements. Performance is analyzed in terms of an H2 norm metric that quantifies power losses incurred in the synchronization transient. While previous studies have shown that the DAPI controller performs well, in particular in sparse networks and compared to a centralized averaging PI (CAPI) controller, our results prove that additive measurement noise may have a significant negative impact on its performance and scalability. This impact is shown to decrease with an increased inter-nodal alignment of the controllers' integral states, either through increased gains or increased connectivity. For very large and sparse networks, however, the requirement for inter-nodal alignment is so large that a CAPI approach may be preferable. Overall, our results show that distributed secondary frequency control through DAPI is possible and may perform well also under noisy measurements, but requires careful tuning.