Trust-Based Adjustment of Measurement Discrepancies in Distribution Networks

This paper addresses security challenges in modern electricity distribution systems, where supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) networks are increasingly exposed to cyber threats and fraudulent activities, leading to metering discrepancies. As smart grids become more interconnected, identifying compromised meters and devices at scale requires a robust inference framework. While alarm systems provide real-time detection of anomalies such as abrupt energy consumption changes, data losses, and security breaches, the lack of seamless integration between SCADA and AMI alarm data limits their effectiveness. To enhance grid security and resilience, this paper presents a data-driven approach for evaluating metering network trustworthiness by analyzing measurement variations from feeder remote terminal units (FRTUs) and IP-based energy meters (EMs) across primary and secondary distribution networks. The proposed probabilistic trust model leverages historical alarm data and event logs, demonstrating its ability to detect discrepancies in a simulated environment. The inferred trust scores are then used to re-weight and reconcile conflicting measurements, allowing the system to attenuate the influence of untrusted data during anomaly detection and state estimation. By correlating alarm patterns with metering anomalies, this approach strengthens cyber-physical security, enhances operational transparency, and supports the transition to more secure and intelligent distribution networks.