Year: 2025
Type: article
Abstract: The development of new tools to assist the system operator has been crucial in modern power systems due to the system complexity and operational challenges. Among these tools, the system’s load margin, which indicates the maximum load level allowed without instability occurring, stands out. The physical characteristics of the modern power system in the stability threshold condition and the abundant data from Phasor Measurement Units (PMUs) can be used by machine learning techniques to predict the load margins of power systems. This paper proposes a new Physics-Informed Neural Network for computing the precise value of the load margin of power systems equipped with PMUs adopting experimental and physical knowledge in the training process through three loss functions. A PMU allocation procedure is applied to reduce the number of PINN entries. Case studies applying the proposed PINN are performed on the IEEE 68-bus system, and comparative analyses are conducted with traditional Artificial Neural Networks (ANNs), Graph Neural Networks (GNNs) and Physics-Guided Neural Networks (PGNNs). Results show better Root Mean Square Error values for the proposed PINN compared to the ANN, GNN and PGNN for different numbers of PMUs allocated in the test system.
Source: Applied Sciences
DOI: 10.3390/app152312396
Author: Murilo Eduardo Casteroba Bento
Institution: Universidade Federal do Rio de Janeiro
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G29316538, G4402799499, G5415330376
OpenAlex ID: W4416504370