The Experimental Study of Dust Effect on Solar Panel Efficiency

Coşgun, Atıl Emre; Demir, Hasan

doi:10.2339/politeknik.903989

Cited by 12 publications

(7 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This point is where the roulette wheel selection acts. This selection method uses (24) in order to work:…”

Section: Optimized Feature Selectionmentioning

confidence: 99%

“…, 40. After applying (24), the individuals are sorted from the highest to the lowest, and only the first 20 are selected to be part of the new population. Additionally, and to prevent stagnation at a local maximum, the mutation operation is applied, considering a probability of mutation of 30%.…”

Section: Optimized Feature Selectionmentioning

confidence: 99%

“…These works conduct a series of experiments wherein different concentrations of dust are placed on the surface of a PV panel with the only purpose of evaluating the reduction of the output power delivered by the PV system. On their part, the authors in [24] demonstrated that the dust accumulation directly affects the efficiency achieved by a PV panel. Moreover, they trained an artificial neural network (ANN) to predict the level of affection in the PV panel efficiency in terms of the size of the dust particles.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

Perez-Anaya,

Elvira-Ortiz,

Osornio-Rios

et al. 2022

Electronics

View full text Add to dashboard Cite

The use of renewable energies is increasing around the world in order to deal with the environmental and economic problems related with conventional generation. In this sense, photovoltaic generation is one of the most promising technologies because of the high availability of sunlight, the easiness of maintenance, and the reduction in the costs of installation and production. However, photovoltaic panels are elements that must be located outside in order to receive the sun radiation and transform it into electricity. Therefore, they are exposed to the weather conditions and many environmental factors that can negatively affect the output delivered by the system. One of the most common issues related to the outside location is the dust accumulation in the surface of the panels. The dust particles obstruct the passage of the sunlight, reducing the efficiency of the generation process and making the system prone to experimental long-term faults. Thus, it is necessary to develop techniques that allow us to assess the level of dust accumulation in the panel surface in order to schedule a proper maintenance and avoid losses associated with the reduction of the delivered power and unexpected faults. In this work, we propose a methodology that uses a machine learning approach to estimate different levels of dust accumulation in photovoltaic panels. The developed method takes the voltage, current, temperature, and sun radiance as inputs to perform a statistical feature extraction that describes the behavior of the photovoltaic system under different dust conditions. In order to retain only the relevant information, a genetic algorithm works along with the principal component analysis technique to perform an optimal feature selection. Next, the linear discrimination analysis is carried out using the optimized dataset to reduce the problem dimensionality, and a multi-layer perceptron neural network is implemented as a classifier for discriminating among three different conditions: clean surface, slight dust accumulation, and severe dust accumulation. The proposed methodology is implemented using real signals from a photovoltaic installation, proving to be effective not only to determine if a dust accumulation condition is present but also when maintenance actions must be performed. Moreover, the results demonstrate that the accuracy of the proposed method is always above 94%.

show abstract

“…This point is where the roulette wheel selection acts. This selection method uses (24) in order to work:…”

Section: Optimized Feature Selectionmentioning

confidence: 99%

Section: Optimized Feature Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

Perez-Anaya,

Elvira-Ortiz,

Osornio-Rios

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…The relation between the PV output voltage and the particle size of soiling was investigated in [21]. The study showed that as the size of the soiling particles covering the panel gets smaller, the output voltage of the panel decreases linearly, thus negatively affecting the PV panel's efficiency.…”

Section: Introductionmentioning

confidence: 99%

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

Alfaris

2023

Energies

View full text Add to dashboard Cite

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.

show abstract

“…The main constraint of solar power as a renewable energy source is our inability to convert it into electricity in an efficient and costeffective manner. Photovoltaic (PV) energy, which is one of the leading renewable energy sources, is based on the principle of converting photons directly from sunlight into electrical energy by the use of the photovoltaic effect [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of P&O and IC MPPT Techniques under Different Atmospheric Conditions

Endiz

2022

ECJSE

View full text Add to dashboard Cite

Solar photovoltaic (PV) systems have rapidly become a significant energy supply in the world over the last two decades. PV energy has demonstrated its importance among the other renewable energy sources concerning the greenhouse gas emissions and several negative impacts of other energy sources. According to the solar irradiation and ambient temperature, the output power of the PV panels can be of various values. Therefore, different types of power electronics strategies have been used to extract the maximum energy from the PV panels. Maximum Power Point Tracking (MPPT) strategy is the most used technique for PV systems. In this study, the comparative analysis of Perturb and Observe (P&O) and Incremental Conduction (IC) MPPT techniques are performed under different atmospheric conditions. The performance of the P&O and IC techniques is observed in the simulation environment using Matlab/Simulink model. Based on the tracking time and the fluctuation at the output of the PV system, two MPPT techniques are compared. The advantages, disadvantages, and limitations are discussed. The proposed PV system can properly track the maximum power point for both MPPT techniques. The IC technique offers a reduction of the steady-state oscillation and has a better response time to find the maximum power point at the start of the simulation.

show abstract

The Experimental Study of Dust Effect on Solar Panel Efficiency

Cited by 12 publications

References 16 publications

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

Methodology for the Identification of Dust Accumulation Levels in Photovoltaic Panels Based in Heuristic-Statistical Techniques

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

Comparative Analysis of P&O and IC MPPT Techniques under Different Atmospheric Conditions

Contact Info

Product

Resources

About