Abstract:
Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as
the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high
impedance faults, low location mismatch, and the presence of Maximum Power Point
Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this
regard, there have been several attempts made by various researchers to identify PV array
faults. However, most of the previous work has focused on fault detection and classification
in only a few faulty scenarios. This paper presents a novel approach that utilizes deep twodimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D
scalograms generated from PV system data in order to effectively detect and classify PV
system faults. An in-depth quantitative evaluation of the proposed approach is presented and
compared with previous classification methods for PV array faults – both classical machine
learning based and deep learning based. Unlike contemporary work, five different faulty
cases (including faults in PS – on which no work has been done before in the machine
learning domain) have been considered in our study, along with the incorporation of MPPT.
We generate a consistent dataset over which to compare ours and previous approaches, to
make for the comprehensive and meaningful comparative evaluation of fault diagnosis. It is
observed that the proposed method involving fine-tuned pre-trained CNN outperforms
existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also
highlights the importance of representative and discriminative features to classify faults (as
opposed to the use of raw data), especially in the noisy scenario, where our method achieves
the best performance of 70.45%. We believe that our work will serve to guide future research
in PV system fault diagnosis
