Resource-Aware Adaptive Monitoring for Smart Grid Integrity: A Comparative Analysis of Sparse and Sentinel-Augmented Sensing modes

Continuous integrity monitoring of smart power grids is increasingly challenged by sensing, communication, and operational constraints that limit full observability. While many existing approaches assume dense measurement availability, practical deployments must balance monitoring costs with the timely detection of abnormal grid behavior. This study presents a resource-aware adaptive monitoring framework that explicitly models sensing as a constrained resource and evaluates how partial observability affects detection latency and reliability. Using a MATPOWER-based time-series simulation, two monitoring modes are compared: adaptive threshold-based monitoring and sentinel augmentation. The proposed adaptive strategy dynamically selects subsets of buses for observation and escalates sensing coverage in response to statistically significant voltage deviations. Experimental results demonstrate that pure adaptive monitoring can reduce sensing costs by more than 80% relative to always-on monitoring, albeit with non-negligible detection latency due to sparse observations. Introducing a small set of continuously monitored sentinel buses substantially reduces detection latency, often to zero, at the expense of increased sensing cost and false positives. Parameter sweep analyses further reveal non-monotonic cost-latency behavior, highlighting diminishing returns from uniform increases in sampling density. Rather than identifying a single optimal configuration, this study characterizes the fundamental trade-offs among cost, latency, and sensitivity in resource-constrained smart grid monitoring and provides practical insights for designing flexible integrity-monitoring strategies under limited observability.