A Framework of Statistical Process Control for Wind Energy Systems: A Digital Twin Approach
Arabian Journal for Science and Engineering, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s13369-026-11616-0
- Dergi Adı: Arabian Journal for Science and Engineering
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, zbMATH, Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Digital twin, I-MR control charts, NREL SAM, Statistical process control, Wind energy, Wind power plants
- Çanakkale Onsekiz Mart Üniversitesi Adresli: Evet
Özet
The inherent stochasticity of wind resources poses fundamental challenges to the reliable integration of wind power plants (WPPs) into modern grid infrastructures. This study introduces a novel, integrated framework that synthesizes high-fidelity, empirically calibrated digital twin modeling—built upon the National Renewable Energy Laboratory’s System Advisor Model (NREL SAM) using validated operational data from existing WPPs—with statistical process control (SPC) methodology to establish a robust, multi-scale early warning system for wind energy systems. A comprehensive, empirically calibrated digital twin model of a 48 MW WPP, comprising 32 GE 1.5 SLE turbines situated in Colorado, USA, was constructed using the NREL SAM platform, which is extensively validated against real-world operational data from existing wind power plants. The model incorporated site-specific meteorological parameters, detailed turbine specifications, rectangular farm configuration with eight rotor diameter spacing, and an extensive loss taxonomy yielding an 11.02% total fixed loss ratio. The simulation generated 8760 h of high-resolution energy production data, which were subsequently analyzed using Individual and Moving Range (I-MR) control charts at six temporal scales—daily, weekly, monthly, seasonal, semiannual, and annual. Analysis of variance confirmed wind speed as the dominant exogenous driver with exceptional statistical significance (F-Value = 67,017.96, p < 0.001), while the coefficient of determination (R2 = 89.93%) validated the digital twin’s predictive fidelity. The I-MR control charts revealed that the energy generation process exhibits statistically uncontrolled behavior on daily timescales, with numerous out-of-control signals indicating substantial special-cause variation. However, as the temporal aggregation extended to weekly and monthly horizons, the process mean remained statistically stable, while persistent anomalies in the standard deviation charts indicated intermittent volatility in production variability. Control limits progressively narrowed from daily (UCL/LCL: 38,263 kWh/–2572 kWh) to semiannual (20,157 kWh/15,535 kWh) periods, quantitatively illustrating the timescale-dependent nature of process controllability. By establishing statistical control benchmarks in a virtual environment, the proposed methodology enables pre-emptive performance management and risk mitigation before physical asset deployment, offering a replicable archetype to enhance the predictability and reliability of renewable energy systems.