A Controlled Benchmark for Sampling-Based Monitoring of Compound Power Quality Disturbances in Nonlinear Three-Phase Systems: Trade-Offs Between Adaptive, Uniform, and Event-Triggered Strategies

This paper presents a reproducible MATLAB R2025b benchmark for evaluating uniform, adaptive, and event-triggered sampling under a compound power-quality disturbance in a nonlinear three-phase system. The benchmark combines voltage swell, harmonic distortion, a damped transient, low-frequency oscillation, and phase unbalance within a finite interval. Performance is assessed using phase-specific and aggregated three-phase indicators, including RMSE, maximum error, detection delay, reconstruction percentage, spectral deviation, critical-time error, symmetrical components, and voltage unbalance factor. To ensure methodological fairness, the strategies are compared using a common linear reconstruction method and an additional experiment with an equal sample budget. Robustness is evaluated for different disturbance severities and durations, as well as under 40 dB and 30 dB noise, using 30 Monte Carlo realizations. The Fault Detection Sensitivity Index (FDSI) is introduced as a benchmark-specific composite metric and examined through 1000 weight perturbations of ±20%. Under the natural acquisition configuration, event-triggered sampling achieved the lowest three-phase RMSE (0.0302 p.u.), the highest reconstruction percentage (96.149%), and the shortest detection delay (7.75 ms). Under equal-budget conditions, uniform sampling provided the lowest RMSE, whereas event-triggered sampling retained the fastest temporal response. The complete FDSI ranking remained stable in 100% of the sensitivity trials. The proposed framework provides a traceable pre-validation tool for sampling-based monitoring in industrial networks, microgrids, and converter-dominated electrical systems.