Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms

Hwang, Humble Po-Ching; Ku, Cooper Cheng-Yuan; Chan, James Chi-Chang

doi:10.1109/access.2021.3063461

Cited by 38 publications

(15 citation statements)

References 25 publications

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“…However, there are several ways to split the dataset, e.g., 90% for training and 10% for testing [ 38 , 39 ], or 80% for training and 20% for testing [ 40 , 41 ]. However, this study refers to the scenario of 70% for the training dataset, 15% for the validation dataset and 15% for the test dataset, based on references [ 15 , 42 , 43 , 44 , 45 ]. The training and validation dataset was split using the train test split library of the scikit learn framework.…”

Section: Methodsmentioning

confidence: 99%

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Aksan

Suresh

et al. 2023

Sensors

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The massive installation of renewable energy sources together with energy storage in the power grid can lead to fluctuating energy consumption when there is a bi-directional power flow due to the surplus of electricity generation. To ensure the security and reliability of the power grid, high-quality bi-directional power flow prediction is required. However, predicting bi-directional power flow remains a challenge due to the ever-changing characteristics of power flow and the influence of weather on renewable power generation. To overcome these challenges, we present two of the most popular hybrid deep learning (HDL) models based on a combination of a convolutional neural network (CNN) and long-term memory (LSTM) to predict the power flow in the investigated network cluster. In our approach, the models CNN-LSTM and LSTM-CNN were trained with two different datasets in terms of size and included parameters. The aim was to see whether the size of the dataset and the additional weather data can affect the performance of the proposed model to predict power flow. The result shows that both proposed models can achieve a small error under certain conditions. While the size and parameters of the dataset can affect the training time and accuracy of the HDL model.

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Section: Methodsmentioning

confidence: 99%

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Aksan

Suresh

et al. 2023

Sensors

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“…In a small number of photovoltaic panel detection tasks, many scholars are still using infrared photovoltaic panel images taken on the ground for hot-spot fault detection. Hwang et al [24] converted the image format from RGB to HSV, and then used the gamma correction function to enhance the S and V values of red pixels, before finally sending them into the convolutional neural network to learn hot-spot fault features. Ali et al [25] used RGB, texture, directional gradient histogram (HOG), and local binary mode (LBP) features to form a new hybrid feature vector by data fusion.…”

Section: Hot-spot Fault Detection Based On the Infrared Image Feature...mentioning

confidence: 99%

Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image

Zheng

Liu

et al. 2022

Sensors

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Photovoltaic panels exposed to harsh environments such as mountains and deserts (e.g., the Gobi desert) for a long time are prone to hot-spot failures, which can affect power generation efficiency and even cause fires. The existing hot-spot fault detection methods of photovoltaic panels cannot adequately complete the real-time detection task; hence, a detection model considering both detection accuracy and speed is proposed. In this paper, the feature extraction part of YOLOv5 is replaced by the more lightweight Focus structure and the basic unit of ShuffleNetv2, and then the original feature fusion method is simplified. Considering that there is no publicly available infrared photovoltaic panel image dataset, this paper generates an infrared photovoltaic image dataset through frame extraction processing and manual annotation of a publicly available video. Consequently, the number of parameters of the model was 3.71 M, mAP was 98.1%, and detection speed was 49 f/s. A comprehensive comparison of the accuracy, detection speed, and model parameters of each model showed that the indicators of the new model are superior to other detection models; thus, the new model is more suitable to be deployed on the UAV platform for real-time photovoltaic panel hot-spot fault detection.

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“…Accordingly, three fault modes (i.e., one anomaly, non-contiguous cells with anomalies, and contiguous cells with anomalies) of degradation were studied. Hwang et al [81] designed a hybrid model that included three embedded learning systems, namely, Improved Gamma Correction Function (IGCF), CNN, and eXtreme Gradient Boosting (XGBoost) algorithm. These algorithms were combined in series to perform better preprocessing, extraction, and classification tasks, respectively.…”

Section: Dl-based Image Acquisitionmentioning

confidence: 99%

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

et al. 2021

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To ensure the continuity of electric power generation for photovoltaic systems, condition monitoring frameworks are subject to major enhancements. The continuous uniform delivery of electric power depends entirely on a well-designed condition maintenance program. A just-in-time task to deal with several naturally occurring faults can be correctly undertaken via the cooperation of effective detection, diagnosis, and prognostic analyses. Therefore, the present review first outlines different failure modes to which all photovoltaic systems are subjected, in addition to the essential integrated detection methods and technologies. Then, data-driven paradigms, and their contribution to solving this prediction problem, are also explored. Accordingly, this review primarily investigates the different learning architectures used (i.e., ordinary, hybrid, and ensemble) in relation to their learning frameworks (i.e., traditional and deep learning). It also discusses the extension of machine learning to knowledge-driven approaches, including generative models such as adversarial networks and transfer learning. Finally, this review provides insights into different works to highlight various operating conditions and different numbers and types of failures, and provides links to some publicly available datasets in the field. The clear organization of the abundant information on this subject may result in rigorous guidelines for the trends adopted in the future.

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Detection of Malfunctioning Photovoltaic Modules Based on Machine Learning Algorithms

Cited by 38 publications

References 25 publications

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

CNN-LSTM vs. LSTM-CNN to Predict Power Flow Direction: A Case Study of the High-Voltage Subnet of Northeast Germany

Lightweight Hot-Spot Fault Detection Model of Photovoltaic Panels in UAV Remote-Sensing Image

Machine Learning-Based Condition Monitoring for PV Systems: State of the Art and Future Prospects

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