Convolutional Neural Network based Automatic Detection of Visible Faults in a Photovoltaic Module
29 Mar 2021-Energy Sources Part A-recovery Utilization and Environmental Effects (Informa UK Limited)-pp 1-16
TL;DR: In this paper, the primary objective is to distinguish several visual faults which hinder the performance, reliability and lifetime of photovoltaic (PV) modules, and to distinguish the visual faults that hinders the performance and reliability of PV modules.
Abstract: Background/Objective: The primary objective of the present study is to distinguish several visual faults which hinder the performance, reliability and lifetime of photovoltaic (PV) modules. Researc...
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TL;DR: In this article, the authors present the development of DL-based FDD for photovoltaic (PV) systems and provide guidelines for future research in the field of FDD.
Abstract: Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.
60 citations
TL;DR: A review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection can be found in this article , where the authors focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%.
Abstract: In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.
14 citations
TL;DR: To evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation.
Abstract: Faults on individual modules within a photovoltaic (PV) array can have a significant detrimental effect on the power efficiency and reliability of the entire PV system. In addition, PV module faults can create risks to personnel safety and fire hazards if they are not detected quickly. As IoT hardware capabilities increase and machine learning frameworks mature, better fault detection performance may be possible using low-cost sensors running machine learning (ML) models that monitor electrical and thermal parameters at an individual module level. In this paper, to evaluate the performance of ML models that are suitable for embedding in low-cost hardware at the module level, eight different PV module faults and their impacts on PV module output are discussed based on a literature review and simulation. The faults are emulated and applied to a real PV system, allowing the collection and labelling of panel-level measurement data. Then, different ML methods are used to classify these faults in comparison to the normal condition. Results confirm that NN obtain 93% classification accuracy for seven selected classes.
9 citations
TL;DR: In this paper , a model-based procedure for fault detection and diagnosis of photovoltaic modules is presented, where a four-layered feed-forward artificial neural network learns the correlation between the features of the current vs. voltage curve and the environmental variables, which are the irradiance and the temperature.
6 citations
TL;DR: In this paper , the authors systematically reviewed the research progress of Remote Sensing (RS) technology applied throughout various stages of the PV system development, including PV potential estimation, PV array detection, PV fault monitoring and diagnosis, and other cross-cutting areas where RS can facilitate PV development.
5 citations
References
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TL;DR: A new TCNN with the depth of 51 convolutional layers is proposed for fault diagnosis of ResNet-50 and achieves state-of-the-art results, which demonstrates that TCNN(ResNet- 50) outperforms other DL models and traditional methods.
Abstract: With the rapid development of smart manufacturing, data-driven fault diagnosis has attracted increasing attentions. As one of the most popular methods applied in fault diagnosis, deep learning (DL) has achieved remarkable results. However, due to the fact that the volume of labeled samples is small in fault diagnosis, the depths of DL models for fault diagnosis are shallow compared with convolutional neural network in other areas (including ImageNet), which limits their final prediction accuracies. In this research, a new TCNN(ResNet-50) with the depth of 51 convolutional layers is proposed for fault diagnosis. By combining with transfer learning, TCNN(ResNet-50) applies ResNet-50 trained on ImageNet as feature extractor for fault diagnosis. Firstly, a signal-to-image method is developed to convert time-domain fault signals to RGB images format as the input datatype of ResNet-50. Then, a new structure of TCNN(ResNet-50) is proposed. Finally, the proposed TCNN(ResNet-50) has been tested on three datasets, including bearing damage dataset provided by KAT datacenter, motor bearing dataset provided by Case Western Reserve University (CWRU) and self-priming centrifugal pump dataset. It achieved state-of-the-art results. The prediction accuracies of TCNN(ResNet-50) are as high as 98.95% ± 0.0074, 99.99% ± 0 and 99.20% ± 0, which demonstrates that TCNN(ResNet-50) outperforms other DL models and traditional methods.
319 citations
TL;DR: This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments using a novel cascaded autoencoder (CASAE) architecture.
Abstract: Automatic metallic surface defect inspection has received increased attention in relation to the quality control of industrial products. Metallic defect detection is usually performed against complex industrial scenarios, presenting an interesting but challenging problem. Traditional methods are based on image processing or shallow machine learning techniques, but these can only detect defects under specific detection conditions, such as obvious defect contours with strong contrast and low noise, at certain scales, or under specific illumination conditions. This paper discusses the automatic detection of metallic defects with a twofold procedure that accurately localizes and classifies defects appearing in input images captured from real industrial environments. A novel cascaded autoencoder (CASAE) architecture is designed for segmenting and localizing defects. The cascading network transforms the input defect image into a pixel-wise prediction mask based on semantic segmentation. The defect regions of segmented results are classified into their specific classes via a compact convolutional neural network (CNN). Metallic defects under various conditions can be successfully detected using an industrial dataset. The experimental results demonstrate that this method meets the robustness and accuracy requirements for metallic defect detection. Meanwhile, it can also be extended to other detection applications.
288 citations
TL;DR: An in depth analysis of various fault occurrences, protection challenges and ramifications due to undetected faults in PV systems is carried out.
Abstract: With the exponential growth in global photovoltaic (PV) power capacity, protection of PV systems has gained prodigious importance in last few decades. Even with the use of standard protection devices in a PV system, faults occurring in a PV array may remain undetected. Inspired by the ever increasing demand for a reliable fault detection technique, several advanced techniques have been proposed in literature; especially in the last few years. Hence, this paper carries out an in depth analysis of various fault occurrences, protection challenges and ramifications due to undetected faults in PV systems. Furthermore, with a widespread literature, the paper critically reviews numerous fault detection algorithms/techniques available for PV systems which are proven to be effective and feasible to implement. The proposed study is not only limited to surveying the existing techniques but also analyzes the performance of each technique with an emphasis on its: 1) Approach, 2) Sensor requirements, 3) Ability to diagnose and localize faults, 4) Integration complexity, 5) Accuracy, 6) Applicability and 7) Implementation cost. Overall, this paper is envisioned to avail the researchers working in the field of PV systems with a valuable resource, which will assist them to enrich their research works.
230 citations
TL;DR: Two automated approaches for automatic detection of defects in a single image of a PV cell are investigated, each based on an end-to-end deep Convolutional Neural Network that runs on a Graphics Processing Unit (GPU).
205 citations
TL;DR: A novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults is presented.
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.
123 citations