Performance Degradation Analysis of c-Si PV Modules Mounted on a Concrete Slab under Hot-Humid Conditions Using Electroluminescence Scanning Technique for Potential Utilization in Future Solar Roadways

The Hungarian society and the Hungarian state are constantly increasing their solar capacity. More and more solar power plants are being put into operation. The largest of these has a 100 MW peak capacity. Such power plants do not require constant maintenance. However, in the case of low productivity, a conditional assessment is required. The reason for production loss can also be manufacturing, installation, and operational errors. A flying drone was used for finding failures by thermographic scouting. Furthermore, electroluminescent (EL) and flash tests give a comprehensive view of the real state of the modules in a mobile laboratory. We had the opportunity to summarize these test results of more than a thousand modules operating in a solar power plant. The report on the power plant shows that a significant part of the modules became unusable in a short time. After four years, 10% of the 260 Wp modules suffered a performance reduction of more than 10%.

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“…Their maximal power was 87.2% and 89.2%. This assumption is supported by the electroluminescence test (Figures 6-11) [12,[20][21][22][23][24].…”

Section: Flash Testmentioning

confidence: 66%

Condition Assessment of Solar Modules by Flash Test and Electroluminescence Test

et al. 2021

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“…The PV modules were mounted on a concrete slab to protect the back surface from exposure in an environment [18]. The back surface of the PV modules was isolated from the environment by fixing on the metallic isolation box (filled with concrete), and the gap between the metallic wall and frame of the PV module was covered with insulating tape.…”

Section: Methodsmentioning

confidence: 99%

Reliability Study of c-Si PV Module Mounted on a Concrete Slab by Thermal Cycling Using Electroluminescence Scanning: Application in Future Solar Roadways

2020

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Several tests were conducted to ratify the reliability and durability of the solar photovoltaic (PV) devices before deployment in the real field (non-ideal conditions). In the real field, the temperature of the PV modules was varied during the day and night. Nowadays, people have been bearing in mind the deployment of PV modules on concrete roads to make use of the space accessible on roads. In this regard, a comparative study on the failure and degradation behaviors of crystalline Si PV modules with and without a concrete slab was executed via a thermal cycling stress test. The impact of the concrete slab on the performance degradation of PV modules was evaluated. Electroluminescence (EL) results showed that the defect due to thermal cycling (TC) stress was reduced in the PV module with a concrete slab. The power loss due to the thermal cycling was reduced by approximately 1% using a concrete slab for 200 cycles. The Rsh value was reduced to approximately 91% and 71% after thermal cycling of 200 cycles for reference PV modules, respectively. The value of I0 was increased to approximately 3.1 and 2.9 times the initial value for the PV modules without and with concrete, respectively.

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“…With the development of renewable energy, solar photovoltaic (PV) has become one of the key development projects [1]. However, photovoltaic systems are exposed for a long time to outdoor environments with a high temperature and high humidity, which will cause the system performance, reliability, optoelectronic and material properties of the PV module to degrade and become faulty over time [2][3][4]. In ref.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Module Fault Detection Based on a Convolutional Neural Network

Wang²,

Wei³

et al. 2021

Processes

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With the rapid development of solar energy, the photovoltaic (PV) module fault detection plays an important role in knowing how to enhance the reliability of the solar photovoltaic system and knowing the fault type when a system problem occurs. Therefore, this paper proposed the hybrid algorithm of chaos synchronization detection method (CSDM) with convolutional neural network (CNN) for studying PV module fault detection. Four common PV module states were discussed, including the normal PV module, module breakage, module contact defectiveness and module bypass diode failure. First of all, the defects in 16 pieces of 20W monocrystalline silicon PV modules were preprocessed, and there were four pieces of each fault state. When the signal generator delivered high frequency voltage to the PV module, the original signal was measured and captured by the NI PXI-5105 high-speed data acquisition system (DAS) and was calculated by CSDM, to establish the chaos dynamic error map as the image feature of fault diagnosis. Finally, the CNN was employed for diagnosing the fault state of the PV module. The findings show that after entering 400 random fault data (100 data for each fault) into the proposed method for recognition, the recognition accuracy rate of the proposed method was as high as 99.5%, which is better than the traditional ENN algorithm that had a recognition rate of 86.75%. In addition, the advantage of the proposed algorithm is that the mass original measured data can be reduced by CSDM, the subtle changes in the output signals are captured effectively and displayed in images, and the PV module fault state is accurately recognized by CNN.

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Performance Degradation Analysis of c-Si PV Modules Mounted on a Concrete Slab under Hot-Humid Conditions Using Electroluminescence Scanning Technique for Potential Utilization in Future Solar Roadways

Cited by 24 publications

References 17 publications

Condition Assessment of Solar Modules by Flash Test and Electroluminescence Test

Condition Assessment of Solar Modules by Flash Test and Electroluminescence Test

Reliability Study of c-Si PV Module Mounted on a Concrete Slab by Thermal Cycling Using Electroluminescence Scanning: Application in Future Solar Roadways

Photovoltaic Module Fault Detection Based on a Convolutional Neural Network

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