Performance Evaluation and Voltage-Domain Error Decomposition of the Single-Diode Photovoltaic Model Under Dynamic Operating Conditions

Accurate modeling of photovoltaic array performance under dynamic field conditions is essential for power forecasting and electrical grid integration. Despite the widespread use of five-parameter single-diode models for annual yield estimation, their instantaneous predictive fidelity under operational extremes remains unconstrained. This study evaluates the dynamic accuracy of the standard single-diode framework using a 21-day telemetry dataset (2,909 active daylight observations, plane-of-array irradiance > 50 W/m²) from a 648-module SunPower SPR-327 photovoltaic array (System 1283, Golden, Colorado). All reported voltages and currents are array-level maximum-power-point quantities measured at the inverter DC input. Across all records, the model predicted DC power with a Mean Absolute Error (MAE) of 5.29 kW (5.57%), a Root Mean Square Error (RMSE) of 5.95 kW (6.26%), and a net bias of −3.98 kW (−4.19%). Under high-irradiance conditions (>800 W/m²), with module temperatures predominantly exceeding 55°C, systematic power under-prediction reached −6.29 kW. Error decomposition revealed that current predictions remained accurate to within 0.4% (bias of −1.91 A), while a 3.1% voltage deficit (−12.02 V) drove the power error. Conversely, low-irradiance (50–200 W/m²) discrepancies were highly sensitive to sensor selection, with voltage bias shifting from −0.84 V (reference cell) to +5.11 V (pyranometer). The deficit in high-irradiance voltage prediction points toward physical mechanisms in the voltage domain, such as the model's assumption of a temperature-independent series resistance or standard silicon thermal coefficient adjustments; however, these proposed mechanisms represent hypotheses generated by this analysis rather than confirmed causes. These results isolate the primary source of high-irradiance modeling error and establish target parameters for future calibration.