Work

An online intrusion detection system for photovoltaic generators through physics-based neural networks

Year: 2025

Type: article

Abstract: The rapid proliferation of photovoltaic (PV) systems in modern power grids has introduced new cybersecurity vulnerabilities. This paper presents a novel approach to cybersecurity monitoring of PV systems based on real-time analysis of their physical and electrical behavior. We propose an anomaly detection algorithm that combines Long Short-Term Memory (LSTM) networks with Physics-Informed Neural Networks (PINNs) to identify potential cyberattacks. Experimental results demonstrate that the proposed method outperforms conventional techniques in identifying anomalies, offering faster and more reliable detection capabilities.

Source: Electric Power Systems Research

DOI: 10.1016/j.epsr.2025.112528

Authors: Daniel Fernández Valderrama, Giovanni Battista Gaggero, Giulio Ferro, Afroz Mokarim, Michela Robba, Paola Girdinio, Mario Marchese

Institution: University of Genoa

Language: English

Topic: Smart Grid Security and Resilience

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: hybrid

Estimated APC paid: $2,810