Can yolov7 be used to detect anomaly in PV cells?

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 propose an enhanced YOLOv7-based deep learning framework for fast and accurate anomaly detection in PV cells.

Are IBTS and ETTs suitable for solar cell defect detection?

Although several review papers have investigated recent solar cell defect detection techniques, they do not provide a comprehensive investigation including IBTs and ETTs with a greater granularity of the different types of each for PV defect detection systems.

What data analysis methods are used for PV system defect detection?

Nevertheless, review papers proposed in the literature need to provide a comprehensive review or investigation of all the existing data analysis methods for PV system defect detection, including imaging-based and electrical testing techniques with greater granularity of each category's different types of techniques.

Can deep learning be used for fault detection in PV systems?

Mansouri et al., have only reviewed fault diagnosis and detection techniques based on Deep Learning (DL) for PV systems from the perspective of methodology and five basic architectures: stacked autoencoder network, deep belief network, Convolutional Neural Network (CNN), recurrent neural network, and deep transfer learning.

What methods are used for anomaly detection in photovoltaic (PV) cells?

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.

Can machine learning improve fault detection performance in photovoltaic systems?

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.