Data-Driven Diagnosis of PV-Connected Batteries: Analysis of Two Years of Observed Irradiance
Abstract
The diagnosis and prognosis of PV-connected batteries are complicated because cells might never experience controlled conditions during operation as both the charge and discharge duty cycles are sporadic. This work presents the application of a new methodology that enables diagnosis without the need for any maintenance cycle. It uses a 1-dimensional convolutional neural network trained on the output from a clear sky irradiance model and validated on the observed irradiances for 720 days of synthetic battery data generated from pyranometer irradiance observations. The analysis was performed from three angles, the impact of sky conditions, degradation composition, and degradation extent. Our results indicate that for days with over 50% clear sky or with an average irradiance over 650 W/m2, diagnosis with an average RMSE of 1.75% is obtainable independent of the composition of the degradation and of its extent.
