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 two-dimensional (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 first (to the best of our knowledge) 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.
Highlights
The photovoltaic (PV) industry has garnered prominence in recent years due to the economic and environmental benefits of freely available solar energy
As PV systems are exposed to harsh outdoor environment, they are susceptible to several faults and anomalies such as line-to-line (LL), line-to-ground (LG), open-circuit (OC), Hot spot (HS), environmental effects, wiring losses and malfunctioning of power conditioning units
To address the aforementioned challenges and shortcomings of existing work in fault diagnosis, this paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Network (CNN) to extract features from 2-D scalograms generated from PV system data, in order to effectively detect and classify PV system faults in severe conditions
Summary
The photovoltaic (PV) industry has garnered prominence in recent years due to the economic and environmental benefits of freely available solar energy. As PV systems are exposed to harsh outdoor environment, they are susceptible to several faults and anomalies such as line-to-line (LL), line-to-ground (LG), open-circuit (OC), Hot spot (HS), environmental effects (uniform and non-uniform shading, humidity, snow and dust accumulation), wiring losses and malfunctioning of power conditioning units These faults may reduce the energy conversion efficiency and lifetime of PV arrays and are reported to be the major reason behind their catastrophic failure [3], [4]. To address the aforementioned challenges and shortcomings of existing work in fault diagnosis, this paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Network (CNN) to extract features from 2-D scalograms generated from PV system data, in order to effectively detect and classify PV system faults in severe conditions.
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