Edge-to-Cloud PMU Digital Twin with Adaptive Recommendation and Forecasting

Wide-area grid monitoring increasingly depends on high-rate phasor measurement unit (PMU) streams, cloud-native telemetry fabrics, predictive calibration models, and operator-facing decision support. These capabilities are usually deployed as separate systems: PMU analytics detect events, maintenance models estimate instrument risk, forecasting models project future load or stability margins, and schedulers route workloads across edge and cloud resources. This paper proposes an Edge-to-Cloud PMU Digital Twin (ECPDT), a synthetic architecture that binds those functions into one governed control loop. ECPDT maintains a continuously updated grid-state twin from PMU streams, predicts calibration time-to-degradation, forecasts long-horizon operating envelopes, and recommends bandwidth, placement, inspection, and control-support actions under policy and latency constraints. The design extends Low-Latency Grid Intelligence with Self-Governing Stream and Calibration Agents by adding a persistent digital-twin state and extends Cross-Cloud LLMOps Scheduler for Privacy-Budgeted RAG and Inference by using provider-aware scheduling for grid analytics and model inference. A simulated evaluation over synthetic PMU disturbances, calibration drift, and cross-cloud workload variation suggests that ECPDT improves event localization F1 from 0.72 to 0.89, reduces calibration-warning lead-time error by 37.5%, and lowers P95 decision-support latency by 34.1% relative to separated edge and cloud baselines.