Machine Learning Assisted Inertia Estimation using Ambient Measurements

TL;DR

This paper introduces machine learning methods using LRCN and GCN networks to estimate power system inertia from ambient PMU data, enhancing accuracy and system observability.

Contribution

It presents two novel ML-based inertia estimation techniques incorporating spatial and graphical features, along with an optimal PMU placement strategy.

Findings

LRCN model achieves 97.34% accuracy

GCN model achieves 98.15% accuracy

Optimal PMU placement improves estimation performance

Abstract

With the increasing penetration of converter-based renewable resources, different types of dynamics have been introduced to the power system. Due to the complexity and high order of the modern power system, mathematical model-based inertia estimation method becomes more difficult. This paper proposes two novel machine learning assisted inertia estimation methods based on long-recurrent convolutional neural (LRCN) network and graph convolutional neural (GCN) network respectively. Informative features are extracted from ambient measurements collected through phasor measurement units (PMU). Spatial structure with high dimensional features and graphical information are then incorporated to improve the accuracy of the inertia estimation. Case studies are conducted on the IEEE 24-bus system. The proposed LRCN and GCN based inertia estimation models achieve an accuracy of 97.34% and 98.15% respectively. Furthermore, the proposed zero generation injection bus based optimal PMU placement (ZGIB-OPP) has been proved to be able to maximize the system observability, which subsequently improves the performance of all proposed inertia estimation models.