Abstract
Cyber attacks 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 (PiSL) 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.
Original language | English |
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Pages (from-to) | 12938-12943 |
Journal | IEEE Transactions on Power Electronics |
DOIs | |
Publication status | Published - Nov 2022 |
Externally published | Yes |
Keywords
- Splines (mathematics)
- Cyberattack
- Voltage measurement
- Mathematical models
- artificial intelligence
- Circuit faults
- Current measurement
- Anomaly diagnosis
- photovoltaic inverters