Research

Cyber Security in Power Electronics Using Minimal Data - A Physics-Informed Spline Learning Approach

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Abstract

Cyberattacks can be strategically counterfeited to replicate grid faults, thereby manipulating the protection system and leading to accidental disconnection of grid-tied converters. To prevent such setbacks, we propose a physics-informed spline learning approach-based anomaly diagnosis mechanism to distinguish between both events using minimal data for the first time in the realm of power electronics. This methodology not only provides compelling accuracy with limited data, but also reduces the training and computational resources significantly. We validate its effectiveness and accuracy under experimental conditions to conclude how data availability problem can be handled.

Cyber attacks Machine Learning Physics informed machine learning Physics informed spline learning Cybersecurity Grid connected inverter PV systems Generative adversarial network Data quality Power electronics Anomaly diagnosis artificial intelligence photovoltaic inverters cyberattacks