Artificial neural network-based all-sky power estimation and fault detection in photovoltaic modules

Kian Jazayeri; Moein Jazayeri; Sener Uysal

doi:10.1117/1.JPE.7.025501

19 April 2017 Artificial neural network-based all-sky power estimation and fault detection in photovoltaic modules

Kian Jazayeri, Moein Jazayeri, Sener Uysal

Author Affiliations +

Journal of Photonics for Energy, Vol. 7, Issue 2, 025501 (April 2017). https://doi.org/10.1117/1.JPE.7.025501

Abstract

The development of a system for output power estimation and fault detection in photovoltaic (PV) modules using an artificial neural network (ANN) is presented. Over 30,000 healthy and faulty data sets containing per-minute measurements of PV module output power (W) and irradiance (W/m2) along with real-time calculations of the Sun’s position in the sky and the PV module surface temperature, collected during a three-month period, are fed to different ANNs as training paths. The first ANN being trained on healthy data is used for PV module output power estimation and the second ANN, which is trained on both healthy and faulty data, is utilized for PV module fault detection. The proposed PV module-level fault detection algorithm can expectedly be deployed in broader PV fleets by taking developmental considerations. The machine-learning-based automated system provides the possibility of all-sky real-time monitoring and fault detection of PV modules under any meteorological condition. Utilizing the proposed system, any power loss caused by damaged cells, shading conditions, accumulated dirt and dust on module surface, etc., is detected and reported immediately, potentially yielding increased reliability and efficiency of the PV systems and decreased support and maintenance costs.

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Kian Jazayeri, Moein Jazayeri, and Sener Uysal "Artificial neural network-based all-sky power estimation and fault detection in photovoltaic modules," Journal of Photonics for Energy 7(2), 025501 (19 April 2017). https://doi.org/10.1117/1.JPE.7.025501

Received: 24 January 2017; Accepted: 4 April 2017; Published: 19 April 2017

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