Online Fault Diagnosis for Photovoltaic Arrays Based on Fisher Discrimination Dictionary Learning for Sparse Representation

The nonlinear output characteristics of PV arrays and maximum power point tracking (MPPT) techniques bring more difficulties to fault diagnosis. The fault diagnosis model based on electrical transient time-domain analysis is an effective method for solving the above problems. However, existing studies using transient processes usually train their models by extensive labeled datasets, and some approaches apply normalization methods with environmental condition sensors or reference PV panels. Therefore, Fisher discrimination dictionary learning (FDDL) for sparse representation is explored for diagnosing PV array faults, including line-to-line faults (LLF), open-circuit faults (OCF), and partial shading faults (PSF), with a small labeled dataset, and a dynamic normalization method without additional sensors is proposed to process transient data. Moreover, LLF and PSF that have similar characteristics under low mismatch should be further distinguished. The proposed model is designed with two stages. In the first stage, a multiple classifier trained using small labeled datasets with all fault types is applied to diagnose all kinds of studied PV array faults. Then, a dictionary only for PSF and LLF is learned in the second stage to further identify LLF and PSF. Finally, a 1.8 kW rooftop grid-connected PV system with 6 x 3 PV arrays is applied to validate the performance of the proposed model. The comparison result shows the superiority of the proposed model.

Automated summary

The study explores the Fisher discrimination dictionary learning for sparse representation in diagnosing PV array faults, proposes a dynamic normalization method for processing transient data, and designs a two-stage model to diagnose different types of faults, demonstrating the superior performance of the proposed model.