| Title | Collaboratively Diagnosing IGBT Open-circuit Faults in Photovoltaic Inverters: A Decentralized Federated Learning-based Method |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Wang, Xinyi, Yang, Bo, Liu, Qi, Jin, Tiankai, Chen, Cailian |
| Conference Name | IECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society |
| Keywords | Cyber-physical systems, data privacy, fault detection, fault detection and diagnosis, fault diagnosis, federated learning, human factors, IGBT open-circuit fault, insulated gate bipolar transistors, Learning systems, machine learning, Metrics, multiple fault diagnosis, Packet loss, photovoltaic system, Photovoltaic systems, pubcrawl, Resiliency |
| Abstract | In photovoltaic (PV) systems, machine learning-based methods have been used for fault detection and diagnosis in the past years, which require large amounts of data. However, fault types in a single PV station are usually insufficient in practice. Due to insufficient and non-identically distributed data, packet loss and privacy concerns, it is difficult to train a model for diagnosing all fault types. To address these issues, in this paper, we propose a decentralized federated learning (FL)-based fault diagnosis method for insulated gate bipolar transistor (IGBT) open-circuits in PV inverters. All PV stations use the convolutional neural network (CNN) to train local diagnosis models. By aggregating neighboring model parameters, each PV station benefits from the fault diagnosis knowledge learned from neighbors and achieves diagnosing all fault types without sharing original data. Extensive experiments are conducted in terms of non-identical data distributions, various transmission channel conditions and whether to use the FL framework. The results are as follows: 1) Using data with non-identical distributions, the collaboratively trained model diagnoses faults accurately and robustly; 2) The continuous transmission and aggregation of model parameters in multiple rounds make it possible to obtain ideal training results even in the presence of packet loss; 3) The proposed method allows each PV station to diagnose all fault types without original data sharing, which protects data privacy. |
| DOI | 10.1109/IECON48115.2021.9589794 |
| Citation Key | wang_collaboratively_2021 |
Collaboratively Diagnosing IGBT Open-circuit Faults in Photovoltaic Inverters: A Decentralized Federated Learning-based Method