Big Datasets for Intelligent Anomaly Detection, Mitigation, and Forecasting in Feeders to Secure Smart Grid

Big Data (BD) offers new opportunities for scientists to detect both normal and anomalous events in the Smart Grid (SG). However, the diverse and complex nature of Internet of Things (IoT)-enabled Feeder (FED) datasets poses significant challenges in analyzing and visualizing the real impacts. As a result, existing Centralized Protection, Control, and Monitoring (PCM) solutions often fail to uncover hidden patterns, making it difficult to distinguish between original and anomalous events in FEDs. Consequently, stealthy anomalies can bypass PCM anomaly detection layers, leading to a decline in the overall resilience and security of the smart grid. Artificial Intelligence (AI) shows great potential for uncovering hidden correlations and patterns in large datasets generated from FEDs in the SG. To address this, a Self-learning Hybrid Machine Learning (SHEL) model has been designed to process BD for detecting, mitigating, and forecasting anomalies caused by false data injection in the energy network. The BD first undergoes rigorous preprocessing, including noise reduction, temporal alignment, and statistical feature extraction in FEDs. Afterward, the SHEL model learns the hidden patterns with high precision and exposes stealthy anomalies based on the real-time input BD stream and historical BD in coordination with the Supervisory Control and Data Acquisition (SCADA) system. This enables the Distribution System Operator (DSO) to gain deeper insights into cybersecurity threats that escape PCM mechanisms and could compromise the smart grid security. Moreover, the regenerated BD can support further research in fault diagnosis and forecasting, enhancing the resilience and security of the SG.