Proactive and Automatic Underfrequency Load Shedding via PMUs and Particle Filters

TL;DR

This paper introduces a proactive, automatic underfrequency load shedding method using PMUs and particle filters to predict system frequency and optimize load shedding, demonstrated through simulated case studies.

Contribution

It presents a novel proactive load shedding approach that leverages PMU data and particle filters for real-time frequency prediction, improving over traditional reactive schemes.

Findings

Effective frequency prediction with particle filters

Potential for real-time, optimized load shedding

Improved system stability in simulations

Abstract

Underfrequency (UF) load shedding schemes are traditionally implemented in two ways: One approach is based on manual load shedding, with system operators requesting loads to be shed ahead of anticipated stressful operating conditions. Manual load shedding is usually done through phone calls. The second method is automatic load shedding via underfrequency relays. Using static settings, these schemes can be designed to operate in stages and drop previously identified loads. The main limitation of traditional load shedding schemes is that they are reactive and leave little room for optimized corrective actions. This work presents a proactive and automatic underfrequency load shedding solution for power systems. Measurements are captured via phasor measurement units (PMUs) at relatively low sampling rates of 30 Hz. These measurements are then processed by particle filters who predict the future state of the system's frequency. Based on these predictions excess load is determined and shed. Comparative case studies are performed in simulated environments. Easy-to-implement models, without hard-to-derive parameters, highlight potential aspects for real-life implementation.