Machine Learning-based Adaptive Anomaly Detection for Control Feedback Interferences in Solid State Transformers

• Published in: IEEE journal of emerging and selected topics in industrial electronics (Print) pp. 1 - 12
• Main Authors: Bhattacharya, Souradeep, Greidanus, Mateo D. Roig, Gupta, Shantanu, Sur, Debotrinya, Mazumder, Sudip K., Govindarasu, Manimaran
• Format: Journal Article
• Language: English
• Published: IEEE 12.07.2025
• Subjects: Accuracy; Adaptation models; Anomaly detection; Computer architecture; Concept drift; Cyber-physical attacks; Cybersecurity; Data models; Machine learning; Noise interference; Real-time systems; Solid-state transformers; Training; Transformers; Voltage control
• ISSN: 2687-9735; 2687-9743
• DOI: 10.1109/JESTIE.2025.3589164

Solid-State Transformer (SST)-based Power Substations (SSPS) have emerged as a pivotal innovation for integrating distributed generation and energy storage systems within modern grid. However, SSTs' mixed-signal nature and network-dependent control make them vulnerable to evolving cyber-physical threats, which can disrupt real-time operations, especially as attack patterns continuously evolve, making static, batch-trained anomaly detection systems (ADS) ineffective. To address this, this paper proposes a Machine Learning (ML)-based Adaptive ADS (ML-A2D) designed to detect control feedback noise interference attacks that compromise the low-frequency closed-loop performance of SSTs. The proposed framework employs a semi-supervised online learning approach, enabling continuous adaptability to new anomalies while maintaining fine-grained, real-time detection. The system was evaluated in a realistic SST hardware testbed under practical and varying attack scenarios, demonstrating robust performance with detection accuracy exceeding 96%. With an effective detection time of 0.07 ms and an overall latency of less than 200 ms within a hierarchically controlled network of AC/AC converter modules, the proposed ML-A2D offers a scalable and reliable solution to enhance the resilience of SSTs in next-generation power systems.