| Summary: | Modern photovoltaic (PV) systems face increasing cybersecurity threats due to their integration with smart grid infrastructure. While previous research has identified vulnerabilities, the lack of standardized datasets has hindered the development and evaluation of detection algorithms. Building upon our previously introduced Photo-Set dataset, this paper presents a benchmark evaluation of anomaly detection algorithms for PV cybersecurity applications. We evaluate three state-of-the-art algorithms (One-Class SVM, Isolation Forest, and Local Outlier Factor) across 12 attack scenarios, establishing performance baselines and identifying algorithm-specific strengths and limitations. Our experimental results reveal a clear detectability hierarchy. This work proposes a standardized benchmark for PV cybersecurity research and provides the industry with evidence-based guidance for security system deployment.
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