AI-Powered Anomaly Detection in Solar SCADA Systems: Challenges and Solutions

Abstract

The digital transformation of solar energy environments has left them exposed to cyber and operational threats, especially in SCADA systems that control photovoltaic (PV) plants. Conventional rules-based monitoring solutions are becoming inadequate to manage how large, complex and real-time the smart grid has become. In this paper, an AI-based anomaly detection framework using deep learning models (CNN and LSTM) is proposed to detect abnormal patterns in solar SCADA data streams. Both process variables (e.g., voltage, current, irradiance, inverter temperature) and network traffic metrics are considered by the proposed system while querying false data injection, DoS attack and communication-layer anomalies. Experimental subject results obtained on hybrid real-time datasets prove that the proposed model attains more than 96% detection efficiency, substantial decrease in false alarm rate (FAR) and latency when compared to conventional machine learning classifiers. The framework further includes online adaptation that provides adaptive retraining capabilities for addressing concept drift due to environmental and operational changes. These results demonstrate that the AI-based anomaly detection capability not only improves cybersecurity of cyber-physical systems and the data integrity, but it also helps predictive based maintenance and operation robustness of PVs. Lastly, the paper reviews major hurdles (e.g., paucity of datasets, model explanation and edge deployment limitations) and proposes practical solutions to embed intelligent anomaly detection in nextgeneration secure-, autonomous- and sustainable-based solar SCADA ecosystems.