Implementation of an IEC 61215-oriented photovoltaic module test emulator with integrated predictive maintenance capabilities

This paper presents a hybrid software-based framework for photovoltaic (PV) testing, monitoring, and predictive maintenance that combines a physically validated PV emulator with data-driven diagnostic models. The emulator reproduces selected IEC 61215-inspired test scenarios, including electrical characterisation, irradiance/temperature variations, simulated accelerated ageing, and electrical fault scenarios—without relying on real solar irradiation, enabling realistic I–V and P–V curve generation under controlled settings for performance assessment and algorithm validation. Note that the emulator is a complementary validation tool and does not substitute for formal IEC 61215 certification. Building on this emulator, a two-stage intelligent monitoring architecture is proposed. First, an unsupervised autoencoder learns normal PV behaviour and detects anomalies by analysing reconstruction errors. It shows strong discriminative capability, achieving a high ROC–AUC while remaining robust across varying environmental conditions, thereby enabling early detection of abrupt faults and progressive degradation. Second, a supervised bidirectional gated recurrent unit (BiGRU) model performs fault classification by explicitly exploiting temporal dependencies in electrical signals. The BiGRU classifier achieves an overall accuracy exceeding 99%, with high class-wise precision and recall across multiple fault categories, including resistive faults, degradation, soiling, and severe electrical failures. The framework is validated on emulator-based and analytically derived datasets (Sandia, Madison, Cenerg models), demonstrating strong generalisation and stability across operating conditions; field validation on real PV installations is identified as a key next step. Overall, integrating physical emulation, unsupervised anomaly detection, and supervised temporal classification provides a robust and scalable predictive maintenance solution for PV installations, particularly in environments with high climatic variability.