A recent article co-authored by Chinese scientists titled "Remaining Useful Life Prediction of PV Systems Under Dynamic Environmental Conditions" has been published in the flagship journal of the Institute of Electrical and Electronics Engineers (IEEE), the Journal of Photovoltaics, and is now available online in IEEE Xplore (Early Access). Dr. Jinfeng Zhou, Secretary-General of the China Biodiversity Conservation and Green Development Foundation (CBCGDF), was one of the co-authors of the paper.

The article addresses the critical issue of predicting the remaining useful life (RUL) of photovoltaic (PV) systems, which have great potential as a low-carbon technology during their long lifecycle. PV systems are one of the least carbon-intensive approaches for electricity generation, and RUL prediction is vital for the prognostics and health management of PV systems. Accurately predicting RUL can potentially prevent unexpected failure and maintenance due to PV degradation.

One of the main challenges in predicting RUL is the dynamic environmental conditions associated with PV outdoor operation, which is one of the major root causes of PV degradation. However, predicting RUL under such dynamic environmental conditions remains a challenging task. To address this issue, the authors propose a semiparametric prognostic framework for PV systems under dynamic environmental conditions.

The proposed framework establishes a quantitative relationship between PV degradation and environmental conditions to integrate environmental condition information into RUL prediction. The framework combines the cumulative damage model with multivariate Bernstein bases, and the block bootstrap method is used to estimate future environmental conditions as inputs for RUL prediction. The least-squares estimators of the model parameters can be obtained through the block coordinate descent method. The proposed method is applicable to most PV technologies.

To demonstrate the effectiveness of the proposed method, the authors present applications to field data of Australian PV systems. The results show that the proposed framework can accurately predict the RUL of PV systems under dynamic environmental conditions.

In conclusion, this article provides a valuable contribution to the field of PV systems by proposing a semiparametric prognostic framework for predicting the RUL of PV systems under dynamic environmental conditions. The proposed method has the potential to prevent unexpected failures and maintenance of PV systems, which is critical for the long-term sustainability and viability of the PV industry.

Full text see:
Q. Liu, Q. Hu, J. Zhou, D. Yu and H. Mo, "Remaining Useful Life Prediction of PV Systems Under Dynamic Environmental Conditions," in IEEE Journal of Photovoltaics, doi: 10.1109/JPHOTOV.2023.3272071.

Reporter: Littlejane
Editor:Daisy

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