Wireless sensor network for photovoltaic modules monitoring

Ortega, E.; Aranguren, Gerardo; Saenz, M.J.; Gutierrez, R.; Jimeno, J.C.

doi:10.1109/pvsc.2016.7750141

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Data collected during the tests and the rules that can be defined based on them will be used to develop an expert application based on machine learning process. As shown in publications [21,22], neural networks and machine learning can be successfully used in monitoring and diagnostics of the systems in photovoltaic installations. Machine learning algorithms create an analytical model based on training data to predict operational parameters or to make direct decisions without a human intervention.…”

Section: Description Of Smartpv Functionalitymentioning

confidence: 99%

In Situ Tests of the Monitoring and Diagnostic System for Individual Photovoltaic Panels

et al. 2021

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The dynamic development of photovoltaic systems in the world and in Poland is mainly related to the drop in prices of installation components. Currently, electricity from photovoltaics is one of the cheapest renewable energy sources. The basis for effective energy generation is, first of all, failure-free operation of the photovoltaic system over a long period of operation, up to 30 years. The paper presents the results of a study of a low-cost distributed system for monitoring and diagnosis of photovoltaic installations (SmartPV), capable of assessing the operating parameters of individual photovoltaic panels. The devices were tested by connecting them to an existing photovoltaic installation, allowing the measurement of operational parameters of individual photovoltaic panels as well as operating conditions such as illuminance and panel surface temperature. The data were recorded on a server using wireless Wi-Fi transmission. Interesting data were collected during the tests, confirming the usefulness of the suggested device for monitoring the photovoltaic installations. Differences in performance of the photovoltaic panel depending on solar radiation and surface temperature were recorded. The temperature coefficient of power was determined, allowing for increased accuracy in the prediction of generated power. The correct recording in different situation, i.e., shading, sensor damage or weather anomalies, was verified. Based on the collected data, rules will be defined for an expert application which, in combination with SmartPV devices, will ensure a quick response to any malfunctions of the photovoltaic system, both related to failures and those resulting from natural degradation during operation.

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Section: Description Of Smartpv Functionalitymentioning

confidence: 99%

In Situ Tests of the Monitoring and Diagnostic System for Individual Photovoltaic Panels

et al. 2021

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“…in the circuit model of a PV panel, and uses these parameter estimates to predict when the PV panel is partially shaded or in a hot-spot condition. Ortega et al [18], developed a system to individually measure each PV module and to detect individual faults in the PV plant. However, their proposed system cause energy loss due to failure.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks

Samara

Natsheh

2019

IEEE Access

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Photovoltaic panels system is becoming a popular choice as an alternative source of energy. This system comes with many challenges. To harness reliable energy efficiently, the photovoltaic panels system must remain in its best condition. This requires continuous maintenance and monitoring. However, in case of weather dependable energy yield change, and in order to identify if this change is normal due to environmental conditions, or is not normal because of faulty, or shaded, or dust-covered panel, an intelligent monitoring system is required. In this paper, we present a novel real-time monitoring system utilizing a small but efficient artificial neural network that is adequate to run on a low-cost system. The presented PV monitoring system can identify if the photovoltaic panel exhibit degradation due to fault conditions. In order to do that, the monitoring system implements an efficient small artificial neural network reference model. This artificial intelligent reference model is used to predict the output power of a normal operational photovoltaic panel under a set of changing environmental conditions. Moreover, the introduced monitoring system can monitor heterogamous PV panels with different manufacturing characteristics. In addition, the proposed monitoring system has the ability to log data online, over the internet, to facilitate other important features such as notification, system configuration update and further training, and data analysis. This paper discusses the different components of this novel artificial intelligent heterogeneous PV panels monitoring system. INDEX TERMSArtificial neural network, heterogeneous photovoltaic panel, low cost microcontroller, modeling, monitoring. NOMENCLATURE

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Wireless sensor network for photovoltaic modules monitoring

Cited by 2 publications

References 6 publications

In Situ Tests of the Monitoring and Diagnostic System for Individual Photovoltaic Panels

In Situ Tests of the Monitoring and Diagnostic System for Individual Photovoltaic Panels

Intelligent Real-Time Photovoltaic Panel Monitoring System Using Artificial Neural Networks

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