Detection of cleaning interventions on photovoltaic modules with machine learning

Heinrich, Matthias; Meunier, Simon; Samé, Allou; Quéval, Loïc; Darga, Arouna; Oukhellou, Latifa; Multon, Bernard

doi:10.1016/j.apenergy.2020.114642

Cited by 29 publications

(10 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…According to the study done for solar pumping system of 600 Wp in [81], it is found that the logistic regression model can give greater than 90% system accuracy for temporal resolution smaller than 300 seconds whereas for greater than 300s, complex tools like the random forest (RF) or artificial neural network (ANN) are needed. In the same system, the combination of the array voltage, the array current and the module temperature with Random Forest model gave the most accurate classifier (90%) which can be visualized by Figure 9.…”

Section: A Scheduling Of Solar Panel Cleaning Interventionmentioning

confidence: 99%

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

et al. 2020

View full text Add to dashboard Cite

Solar Photovoltaic System (SPV) is one of the growing green energy sources having immense penetration in the national grid as well as the off-grid around the globe. Regardless of different solar insolation level at various regions of the world, SPV performance is also affected by several factors: conversion efficiency of PV cell technology, ambient temperature and humidity, soiling and seasonal/weather patterns. The rise in PV cell temperature and soiling is found to be detrimental issues regarding power plant performance and life expectancy leading alterations in the levelised cost of energy (LCoE). In this paper, authors present a short glance about factors affecting the performance of photovoltaic modules and re-discuss their usability in cleaning intervention decision-making models. With some highlights on the essence of cleaning to mitigate the soiling issues in PV power plants, this paper presents the existing cleaning techniques and practices along with their evaluations. The need for an optimal cleaning intervention by using advanced scientific tools rather than by visual inspection is drawing the attention of PV experts. The authors finally suggest a schematic of a decision-making model which involves the use of probable parameters, data processing techniques and machine learning tools. The implementation of data science and machine learning in a solar PV panel cleaning system could be a remarkable advancement in the field of renewable energy.

show abstract

Section: A Scheduling Of Solar Panel Cleaning Interventionmentioning

confidence: 99%

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

et al. 2020

View full text Add to dashboard Cite

show abstract

“…MLTs applied for FDD consist of various methods with distinctive principles and structures. The most common ones include Artificial Neural Network (ANN) [10], Fuzzy Logic (FL) [11], Support Vector Machine (SVM) [12], k-Nearest Neighbor algorithm (kNN) [13] and Decision Tree (DT) [14]-based techniques (including random forest (RF) [15]). Through keyword research 1 and the subsequent content verification in common publishers or research platforms (e.g., Science Direct, IEEE Xplore, Google Scholar, Research gate), the number of reported publications on PV FDD from 2009 to July 2020, for different types of MLTs are summarized and presented in Fig.…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Delpha

Diallo

et al. 2021

Renewable and Sustainable Energy Reviews

168

View full text Add to dashboard Cite

“…In this scenario of the PV solar sector, it seems that the large production plants, as well as their energy production, will play a very important role. One of the major focuses of interest is the monitoring, inspection, and maintenance of PV solar plants, regardless of their power [2][3][4]. Operation and maintenance (O&M) are the main saving points for investors in solar PV, and for this reason, in recent years, there has been a greater emphasis on advanced techniques for PV systems design, operation, and maintenance [5].…”

Section: Introductionmentioning

confidence: 99%

Low-Cost Electronics for Online I-V Tracing at Photovoltaic Module Level: Development of Two Strategies and Comparison between Them

et al. 2021

View full text Add to dashboard Cite

The measurement of current–voltage (I-V) curves of single photovoltaic (PV) modules is at this moment the most powerful technique regarding the monitoring and diagnostics of PV plants, providing accurate information about the possible failures or degradation at the module level. Automating these measurements and allowing them to be made online is strongly desirable in order to conceive a systematic tracking of plant health. Currently, I-V tracers present some drawbacks, such as being only for the string level, working offline, or being expensive. Facing this situation, the authors have developed two different low-cost online I-V tracers at the individual module level, which could allow for a cost-affordable future development of a fully automated environment for the tracking of the plant status. The first system proposed implements a completely distributed strategy, since all the electronics required for the I-V measurement are located within each of the modules and can be executed without a power line interruption. The second one uses a mixed strategy, where some common electronics are moved from PV modules to the inverter or combiner box and need an automated very short disconnection of the modules string under measurement. Experiments show that both strategies allow the tracing of individual panel I-V curves and sending of the data afterwards in numerical form to a central host with a minimum influence on the power production and with a low-cost design due to the simplicity of the electronics. A comparison between both strategies is exposed, and their costs are compared with the previous systems proposed in the literature, obtaining cost reductions of over 80–90% compared with actual commercial traces.

show abstract

Detection of cleaning interventions on photovoltaic modules with machine learning

Cited by 29 publications

References 46 publications

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

Current Practices of Solar Photovoltaic Panel Cleaning System and Future Prospects of Machine Learning Implementation

Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review

Low-Cost Electronics for Online I-V Tracing at Photovoltaic Module Level: Development of Two Strategies and Comparison between Them

Contact Info

Product

Resources

About