Investigating defects and annual degradation in UK solar PV installations through thermographic and electroluminescent surveys

Dhimish, Mahmoud; Badran, Ghadeer

doi:10.1038/s41529-023-00331-y

Cited by 10 publications

(8 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Electroluminescence (EL) imaging is a technique that is used to evaluate the performance of photovoltaic (PV) modules by measuring the light emission that occurs when an electrical current is applied to the module [15]. EL imaging is typically used to identify defects or issues in PV modules that may be affecting their performance, such as cracks, damaged electrodes, or other issues [16]. This information can be used to identify and address any issues that may be contributing to the durability and reliability issues of the PV modules.…”

Section: Pid Mechanismmentioning

confidence: 99%

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

Badran

Dhimish

2023

Solar

Self Cite

View full text Add to dashboard Cite

Photovoltaic (PV) technology plays a crucial role in the transition towards a low-carbon energy system, but the potential-induced degradation (PID) phenomenon can significantly impact the performance and lifespan of PV modules. PID occurs when a high voltage potential difference exists between the module and ground, leading to ion migration and the formation of conductive paths. This results in reduced power output and poses a challenge for PV systems. Research and development efforts have focused on the use of new materials, designs, and mitigation strategies to prevent or mitigate PID. Materials such as conductive polymers, anti-reflective coatings, and specialized coatings have been developed, along with mitigation strategies such as bypass diodes and DC-DC converters. Understanding the various factors that contribute to PID, such as temperature and humidity, is critical for the development of effective approaches to prevent and mitigate this issue. This review aims to provide an overview of the latest research and developments in the field of PID in PV modules, highlighting the materials, designs, and strategies that have been developed to address this issue. We emphasize the importance of PID research and development in the context of the global effort to combat climate change. By improving the performance and reliability of PV systems, we can increase their contribution to the transition towards a low-carbon energy system.

show abstract

Section: Pid Mechanismmentioning

confidence: 99%

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

Badran

Dhimish

2023

Solar

Self Cite

View full text Add to dashboard Cite

show abstract

“…The methods to detect anomalies and faults in PV modules are visible inspection, electrical characterization and imaging [6]. However, visible inspection method involves human labor and not suitable for large scale PV plants.…”

Section: Introductionmentioning

confidence: 99%

NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

Gopalakrishnan,

Wahab,

Veerasamy

et al. 2024

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Many nations are increasing the installations of solar photovoltaic (PV) modules for clean energy production. Such PV modules are considered to be cost effective if the module’s operation lifetime is more than twenty years. In real-time, the PV modules undergo degradation due to hotspots, defects and other anomalies resulting in reduced operation lifetime. Infrared (IR) Thermography is a Non-Destructive Testing (NDT) method that can be used in identifying such anomalies present in PV modules. However, the IR thermography requires Artificial Intelligence (AI) based classification techniques to detect the anomalies. This research article proposes a deep learning classifier, based on NASNet-LSTM for the identification of electrical and non-electrical anomalies occurring in PV modules. NASNet is a convolutional neural network (CNN) based classifier when combined with Long Short-Term Memory (LSTM) Networks performs classification with an accuracy of 84.75% considering the raw dataset used in this research. The results are validated by comparing the accuracies with other models. The study concludes that NASNet-LSTM performs well in the anomaly detection of PV modules.

show abstract

“…In recent years, (i.e., last 3 years), the market for solar PV inspection has shifted towards electroluminescence (EL) imaging [13][14][15], as this technique has been shown to be highly effective in detecting defects and other issues in solar PV modules that are not visible through traditional inspection methods. While thermal imaging has been a popular method for solar PV inspection in the past, it has limitations in identifying certain types of defects, such as cracks and cell damage.…”

Section: Introductionmentioning

confidence: 99%

“…EL imaging has several advantages over thermal imaging, including its ability to detect a wider range of defects, its high resolution and sensitivity, and its ability to capture images at high speeds. Additionally, EL imaging is a non-destructive testing method, which means that it does not damage the solar PV module or require the removal of any components [15]. Overall, the increasing importance of EL imaging in solar PV inspection reflects the growing recognition of its effectiveness and accuracy in detecting defects and other issues that may affect the performance of solar PV modules.…”

Section: Introductionmentioning

confidence: 99%

Optical Filter Design for Daylight Outdoor Electroluminescence Imaging of PV Modules

Dhimish,

Tyrrell

2024

Photonics

Self Cite

View full text Add to dashboard Cite

This paper presents an advanced outdoor electroluminescence (EL) imaging system for inspecting solar photovoltaic (PV) modules under varying daylight conditions. EL imaging, known for its effectiveness in non-destructively detecting PV module defects, is enhanced through specialized optical filters. These filters, including a bandpass filter targeting EL emissions and a neutral density filter to reduce background light, significantly improve the system’s signal-to-noise ratio (SNR). The experimental results demonstrate the system’s enhanced performance, with superior clarity and detail in EL emissions, enabling precise defect localization and characterization at the cellular level. Notably, the system achieves an SNR improvement, with values consistently above two, outperforming previous systems and confirming its suitability for efficient solar PV maintenance and diagnostics. This research offers a flexible approach to optimizing EL imaging quality across various solar irradiance levels and angles, essential for improved PV module performance and reliability. The system effectively handles different PV module configurations, orientations, and types, including monofacial and bifacial arrays. It showcases robust imaging capabilities under high solar irradiance and different sun illumination levels, maintaining high-quality imaging due to its optimized filter design. Additionally, the system’s adaptability in detecting EL emissions from series-connected PV modules is highlighted, demonstrating its comprehensive evaluation capabilities for PV array performance.

show abstract

Investigating defects and annual degradation in UK solar PV installations through thermographic and electroluminescent surveys

Cited by 10 publications

References 31 publications

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

Potential Induced Degradation in Photovoltaic Modules: A Review of the Latest Research and Developments

NASNet-LSTM based Deep learning Classifier for Anomaly Detection in Solar Photovoltaic Modules

Optical Filter Design for Daylight Outdoor Electroluminescence Imaging of PV Modules

Contact Info

Product

Resources

About