In the field of PV research, several studies have focused on classifying defects within PV cells by utilizing EL images. However, these investigations solely address defect classification without predicting the power output of the entire PV module and parameters in the equivalent circuit of PV modules.
In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K -means, MobileNetV2 and linear discriminant algorithms to cluster solar cell images and develop a detection model for each constructed cluster.
Before the emergence of deep learning techniques, various traditional methods were employed for anomaly detection in photovoltaic (PV) cells. These methods can be broadly categorized into two groups: statistical analysis, and signal processing.
Visualizing feature map (The figure illustrates the change in the feature map after the SRE module.) We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively capturing diverse defect features, particularly for small flaws.
Accurately detecting faults in photovoltaic modules/cells and estimating their effective power output and parameters of the equivalent circuit representation of photovoltaic modules is becoming increasingly critical for both the reliability of associated systems and the efficiency of electricity production from renewable energy sources.
proposes a machine learning approach using Gaussian process regression (GPR) and a generalized likelihood ratio test (GLRT) chart to enhance fault detection performance in photovoltaic (PV) systems. While statistical analysis methods are relatively simple and computationally efficient, they often suffer from several limitations.
Stoicescu, " Automated Detection of Solar Cell Defects with Deep Learning," in 2018 26th European Signal Processing Conference (EUSIPCO), 2018, pp. 2035–2039.
The proposed shading detection methods are efficient, precise, and easy to implement for PV systems in any scale. ... (or more) solar cell(s) receive different irradiance …
In this study, we introduce a defect detection method for photovoltaic cells that integrates deep learning techniques. To develop and evaluate the proposed model, we trained it on a dataset …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
Solar photovoltaic (PV) arrays connected with the microgrid system consist of multiple strings interconnected in different ways. This paper deals the diagnosis of faults that …
Currently, photovoltaic (PV) modules, as the main part of solar energy, is growing rapidly all over the world due to the significant progress of the developed technology …
In this study, machine learning confusion matrices are employed to examine the significant number of errors present in the PV string. Faults in the PV array that are line-to …
Detecting and addressing these anomalies and defects in a timely manner is essential to ensuring that solar panels operate at optimal capacity. Anomaly and defect …
Anomaly detection in photovoltaic (PV) cells is crucial for ensuring the efficient operation of solar power systems and preventing potential energy losses. In this paper, we …
Download Citation | On Jan 1, 2024, Qing Liu and others published An efficient CNN-based detector for photovoltaic module cells defect detection in electroluminescence images | Find, …
To further improve detection performance of CNN-based PV cell defect detection method, in this paper, we propose a novel, efficient method for PV cell defect detection using …
This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: (1) providing …
The model detects faults within individual PV cells, predicts the overall power output of the PV module, and predicts the series resistance in the equivalent circuit …
Photovoltaic Cell is an electronic device that captures solar energy and transforms it into electrical energy. It is made up of a semiconductor layer that has been …
Photovoltaic cells represent a pivotal technology in the efficient conversion of solar energy into electrical power, rendering them integral to the renewable energy sector …
The main goal of the contribution is to develop a diagnosis method for PVM that is predictive, based on the online detection of a predictor symptom, centred and sampled on …
We propose a photovoltaic cell defect detection model capable of extracting topological knowledge, aggregating local multi-order dynamic contexts, and effectively...
Solar photovoltaic (PV) energy has quickly replaced conventional energy sources in recent decades due to its global availability, modularity, lack of pollution, ease of …
In this study, a novel system for discovering solar cell defects is proposed, which is compatible with portable and low computational power devices. It is based on K-means, …
The novelty of this proposal is the processing of voltage and current signals generated (ripple signals) by the electrical interaction between the photovoltaic string, the …
Taking into account the numerous factors that influence the fault detection processes in photovoltaic (PV) systems, several authors have proposed conventional reviews …