Photovoltaic System Performance and Fault Diagnosis

Photovoltaic (PV) systems convert solar irradiance into electrical energy through arrays of semiconductor modules. System performance is commonly assessed by metrics such as efficiency, energy yield and performance ratio, which reflect the combined effects of environmental conditions, component quality and operational ageing. Over time, modules experience degradation driven by thermal cycling, ultraviolet exposure, moisture ingress and mechanical stress, which manifest as reduced output, hot spots, delamination or encapsulant discolouration. Fault diagnosis encompasses the detection, classification and localisation of anomalies—from partial shading and soiling to electrical interconnection failures and arc faults—using data-driven or physics-based methods. Modern approaches integrate electrical measurements, electroluminescence and infrared imaging, unmanned aerial vehicles and advanced signal processing to monitor large-scale installations. Machine learning and deep learning algorithms now enable real-time anomaly recognition and predictive maintenance, thereby extending operational lifetime, improving economic returns and ensuring grid reliability.

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Photovoltaic System Performance and Fault Diagnosis publication trend

The graph below shows the total number of articles in photovoltaic system performance and fault diagnosis across all publications each year (not limited to Nature Index journals).

Technical terms

Photovoltaic system: An assembly of interconnected solar cells, modules and balance-of-system components that convert sunlight into electricity.

Performance ratio: The ratio of actual to theoretical energy output, accounting for site-specific irradiance and temperature effects.

Degradation: The irreversible loss of module performance over time due to environmental, mechanical or chemical stressors.

Fault diagnosis: The process of detecting, identifying and locating anomalies or failures within a PV system.

Partial shading: Uneven illumination of a module or array segment that causes mismatched current and power loss.

Convolutional neural network: A deep learning architecture that automatically extracts spatial features from input data for classification tasks.

Infrared thermography: A non-contact imaging technique that maps surface temperature distributions to reveal hot spots and thermal anomalies.

References

1. Review of degradation and failure phenomena in photovoltaic modules. Renewable and Sustainable Energy Reviews (2022).
2. A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays. IEEE Access (2020).
3. Automatic Detection System of Deteriorated PV Modules Using Drone with Thermal Camera. Applied Sciences (2020).