A Sensorless Intelligent System to Detect Dust on PV Panels for Optimized Cleaning Units

Abstract

Deployment of photovoltaic (PV) systems has recently been encouraged for large-scale and small-scale businesses in order to meet the global green energy targets. However, one of the most significant hurdles that limits the spread of PV applications is the dust accumulated on the PV panels’ surfaces, especially in desert regions. Numerous studies sought the use of cameras, sensors, power datasets, and other detection elements to detect the dust on PV panels; however, these methods pose more maintenance, accuracy, and economic challenges. Therefore, this paper proposes an intelligent system to detect the dust level on the PV panels to optimally operate the attached dust cleaning units (DCUs). Unlike previous strategies, this study utilizes the expanded knowledge and collected data for solar irradiation and PV-generated power, along with the forecasted ambient temperature. An expert artificial intelligence (AI) computational system, adopted with the MATLAB platform, is utilized for a high level of data prediction and processing. The AI was used in this study in order to estimate the unprovided information, emulate the provided measurements, and accommodate more input/output data. The feasibility of the proposed system is investigated using actual field data during all possible weather conditions.

Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

“…), solar radiation and insolation have been collected in hourly intervals, and analyses have been made using deep neural networks (LSTM, GRU and transformers), with the latter proven to be the most effective (RMSE in the range of 0.21-0.24) [29]. In the case of a risk of PV panels being covered with dust, an AI-based system for detecting the level of dust on PV panels was developed, combined with dust cleaning units, and tested in real field conditions in various weather conditions [30]. AIbased models can recognize location/region specifics, long-term spatial and temporal variables, and anomalies in insolation patterns.…”

Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting

“…In [5], Faris E. Alfaris investigated a significant challenge in deploying PV systems, particularly in desert regions, where dust accumulation on PV panels can hinder their performance. Unlike traditional methods involving cameras, sensors, and power datasets, this study proposes an intelligent, sensorless approach to detect dust levels on PV panels, optimizing attached Dust Cleaning Units (DCUs).…”