A Semi-self-supervised Fault Detection Method for Photovoltaic Systems
Fault detection for photovoltaic(PV)system components has gained increasing attention from operations and maintenance due to its ability to improve system electricity generation and reduce safety hazards.Conventional machine learning and deep learning methods typically rely on a considerable a-mount of labeled data to establish effective models.However,the labeling is limited to support effective models in PV systems.Moreover,manul labelling is both costly and prone to errors.To address these challenges,this work proposes a semi-self-supervised learning fault detection method.The proposed method first pre-trains a model based on self-learning using a large amount of unlabeled data.It then fine-tunes the pre-trained model using limited supervised information to achieve effective fault detec-tion for the main components of PV systems,such as PV strings and PV modules.This approach has been deployed at a 2.5 MW PV system located in North China.Months of operation and maintenance demon-strate that the proposed fault detection method improves fault detection performance by 4.49%or more in terms of F1-score compared with conventional machine learning based method,thereby effectively en-hancing on-site operation and maintenance.
NETL
NSTL
万方数据
