Is degradation of photovoltaic modules predictable and preventable?

该项目旨在确定导致商业太阳能电池组件退化的氢相关缺陷的基本特性，并开发模型来预测其影响。该缺陷导致高达16%的功率损失，并且由于氢在半导体中的普遍行为，可能会影响所有的光致发光器件。通过将氘（重氢）和机器学习相结合的新技术，项目的主要成果是对氢行为的新认识，减缓退化和预测模型，以测试和预测受影响模块的未来输出。这对于系统设计和可靠性、制造商保修条款、投资者回报、消费者信心以及最终缓解气候危机至关重要。

This project aims to determine the fundamental properties of the hydrogen related defect causing degradation of commercial solar modules and develop models to predict its impact. The defect causes up to 16% power loss and is likely to affect all photovoltaics due to the universal behaviour of hydrogen in semiconductors. Through new techniques combining deuterium (heavy hydrogen) and machine learning, the key project outcomes are new knowledge of hydrogen behaviour, mitigation of degradation and predictive models to test and forecast the future output of affected modules. This is critical for system design and reliability, manufacturer warranty terms, investor returns, consumer confidence, and ultimately mitigating the climate crisis.