Improving the Efficiency of Photovoltaic Panels Using Machine Learning Approach

Abstract

Photovoltaic (PV) solar panels account for a major portion of the smart grid capacity. On the other hand, the accumulation of solar panels dust is a significant challenge for PV-based systems. The accumulation of solar panels dust results in a significant reduction in the amount of energy produced. Because of the country’s low wind velocity and rainfall, frequent cleaning of solar panels is necessary either by manual or automated means. Cleaning activities should only be initiated when absolutely essential to reduce maintenance costs and increase the power output of solar panels that have been projected to be affected by dust accumulation. In this paper, we develop a deep belief network model to detect the dust particles in the solar panels installed as a large unit. The study takes into account various input metrics that includes solar irradiance, temperature level, and dust level on the panels. These metrics are used for the estimation of the level of dust present in the atmosphere and how often the panels can be cleaned at regular intervals. The simulation is conducted to test the efficacy of the model in cleaning the panels. The results are estimated in terms of accuracy, precision, recall, and F-measure. The results of the simulation show that the proposed model achieves higher accuracy rate of more than 99% than other methods.

A Survey of Photovoltaic Panel Overlay and Fault Detection Methods

“…In the future, we can try to use other deep learning models or improve the structure and parameters of the DRNN model to improve the generalization ability and efficiency of the model. Khilar et al [85] proposed a deep belief network model for dust detection in largescale solar panel systems. The model used multiple features, such as solar irradiance, temperature, and dust accumulation on the panels, to estimate the atmospheric dust concentration and to determine the optimal cleaning schedule.…”

“…The method in [85] is able to consider multiple input metrics, including solar radiation, ambient temperature, and dust levels on the panels, resulting in a more accurate estimate of the dust content in the atmosphere and panel cleaning frequency. The method in [87] does not rely on fixed backgrounds, fixed cameras, or manually edited images, but uses ground and aerial images and applies automatic background removal and complex feature extraction.…”

“…The method in [87] does not rely on fixed backgrounds, fixed cameras, or manually edited images, but uses ground and aerial images and applies automatic background removal and complex feature extraction. Compared with the methods in [85,87], the model used in the method in [86] uses some novel algorithms for preprocessing and feature extraction, such as the regional histogram approximation algorithm and gray quantization algorithm. These algorithms can effectively remove noise from images, enhance image quality, and extract features that aid in defect detection and localization.…”

“…The methods in [85][86][87] can all increase the diversity of data sets in the future and use photovoltaic image data sets from more sources and scenes to improve the generalization ability and robustness of the model. In the future, the method in [85] can also use more hidden layers or different types of neurons to build deep belief network models, increase the nonlinearity and expressiveness of the model, and improve the fitting effect and classification performance of the model.…”

“…The methods in [85][86][87] can all increase the diversity of data sets in the future and use photovoltaic image data sets from more sources and scenes to improve the generalization ability and robustness of the model. In the future, the method in [85] can also use more hidden layers or different types of neurons to build deep belief network models, increase the nonlinearity and expressiveness of the model, and improve the fitting effect and classification performance of the model. The method in [87] can explore more deep learning network structures and parameters in the future, such as convolutional neural networks, residual networks, attention mechanisms, etc., to improve the expression ability and efficiency of the model.…”