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Islam, M.; Rashel, M.R.; Ahmed, M.T.; Islam, A.K.M.K.; Tlemçani, M. Artificial Intelligence and Photovoltaic Fault. Encyclopedia. Available online: https://encyclopedia.pub/entry/51754 (accessed on 05 July 2024).
Islam M, Rashel MR, Ahmed MT, Islam AKMK, Tlemçani M. Artificial Intelligence and Photovoltaic Fault. Encyclopedia. Available at: https://encyclopedia.pub/entry/51754. Accessed July 05, 2024.
Islam, Mahmudul, Masud Rana Rashel, Md Tofael Ahmed, A. K. M. Kamrul Islam, Mouhaydine Tlemçani. "Artificial Intelligence and Photovoltaic Fault" Encyclopedia, https://encyclopedia.pub/entry/51754 (accessed July 05, 2024).
Islam, M., Rashel, M.R., Ahmed, M.T., Islam, A.K.M.K., & Tlemçani, M. (2023, November 17). Artificial Intelligence and Photovoltaic Fault. In Encyclopedia. https://encyclopedia.pub/entry/51754
Islam, Mahmudul, et al. "Artificial Intelligence and Photovoltaic Fault." Encyclopedia. Web. 17 November, 2023.
Artificial Intelligence and Photovoltaic Fault
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This entry is adapted from the peer-reviewed paper 10.3390/en16217417

The global transition to sustainable energy has positioned photovoltaic (PV) systems at the top of renewable energy solutions. Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. 

photovoltaic fault detection Artificial Intelligence (AI) machine learning deep learning computer vision Machine Vision Natural Language Processing (NLP)

1. Introduction

The global transition to sustainable energy has positioned photovoltaic (PV) systems at the top of renewable energy solutions. Robust fault detection and diagnosis systems are crucial to ensure the efficiency and longevity of PV systems. Historically, fault detection in PV systems was dependent on manual inspections and traditional electrical measurements [1]. However, with the vast arrays of panels installed, especially in large solar farms, this method proved to be inefficient, labor-intensive, and occasionally inaccurate. With the rapid progression in ML and DL techniques, researchers are exploring various computational methodologies to effectively identify and classify PV faults [2]. In recent times, many AI techniques have been developed for fault detection and diagnosis in PV Systems, but it is still unclear which one is the best. A significant challenge is the lack of public datasets for PV fault detection [3]. This limits the potential of AI techniques in this field. There are some methods that are precise, but they are either too slow or too complex for real-world use [4]. Furthermore, there seems to be an imbalance in research attention, such as specific PV faults being extensively studied while others are overlooked [5].

2. Artificial Intelligence

Using AI in the field of green energy, especially PV systems, has opened new opportunities for identifying and resolving issues. Data-driven decision-making systems can assist in efficient energy management [6]. This section highlights the different types of AI and their roles in PV fault detection.

2.1. Types of Artificial Intelligence

Machine Learning (ML): ML allows computers to learn from past data [7]. In PV systems, ML can analyze past performance data to predict and detect PV faults and abnormalities.
Deep Learning: A subset of ML, deep learning uses complex structures called Neural Networks [8]. It is especially useful in analyzing images of PV panels using Convolutional Neural Networks (CNNs) to find defects or performance issues.
Computer Vision: Computer Vision (CV) is about making computers interpret and act on visual data [9]. With PV systems, it can process images from drones or satellites to identify PV faults.
Machine Vision: Machine Vision is like computer vision, but it is mainly used in manufacturing [10]. For PV panels, it can ensure quality control during production.
Natural Language Processing (NLP): NLP is not related to PV fault detection, but it has the capacity to analyze textual data [11]. Therefore, NLP can be used to maintain reports, logs, and other textual information related to PV panels.

2.2. The Role of Artificial Intelligence in PV Systems

Image Analysis: Drones take high-quality pictures of PV farms. With deep learning, these pictures show small cracks or mismatched panels [12].
Predictive Maintenance: ML predicts possible faults before they happen by understanding past performance and current conditions. This allows for timely intervention.
Anomaly Detection: Modern PV systems are always monitored. AI-based technology helps these systems quickly find and point out any unusual changes, which ensures that no fault goes unnoticed [13].
Optimization: By analyzing environmental data, AI-based technology can suggest optimal operating conditions as optimal panel angles for PV panels. AI-based technology can also suggest cleaning schedules and energy storage strategies based on real-time data.
As AI technologies become more sophisticated, their capability to detect and even prevent faults in PV systems will grow. It can expect more automated solutions and a higher degree of reliability in PV installations.

3. Photovoltaic Faults

PV panels are an essential source of green energy, but they can face various types of issues that can degrade their performance. To obtain good performance from PV systems, it is essential to understand potential faults and strategies to mitigate them. This section discusses the types of PV faults, their causes, and potential mitigation techniques.

3.1. Types of Photovoltaic Faults

PV systems can encounter different types of faults that can negatively impact their performance. Some of the most common PV faults are described below.
Module Mismatching: This is usually caused by uneven aging or differences in manufacturing between modules. This can lead to reduced efficiency and hot spots [14].
Micro-Cracks: These often result from mechanical stress and can reduce the module’s performance over time [15].
Hot Spots: These are areas of localized heating in the PV module due to high resistance. They may arise from shading, dirt, or uneven aging [16].
Shadowing: Objects like trees or nearby buildings can block sunlight and shade panels. This causes a decrease in their efficiency [17].
Degradation: Over time, PV modules can degrade, which leads to a gradual drop in power output.

3.2. Causes of PV Faults

Module aging: Over time, modules experience natural degradation, which can affect their efficiency and output [18].
Manufacturing inconsistencies: Differences in the production process can lead to slight differences in module quality, which causes performance variations [19].
Temperature fluctuations: Rapid changes in temperature can make materials expand or shrink, which might damage the modules [20].
UV exposure: Continuous exposure to UV rays can deteriorate the protective layers of PV modules, which reduces their efficiency and lifespan [21].
Weather-related factors: Factors like hailstorms, snow, or persistent rain can directly or indirectly lead to faults such as micro-cracks or other structural faults in the PV system.
During Installation: Rough handling or physical pressure during the installation can cause defects in the PV panels.

3.3. Mitigating PV Faults

Efficient fault detection is the first step toward mitigation. Some strategies are discussed in this section to mitigate PV faults.
Image-Based Approaches: Aerial images, especially from Unmanned Aerial Vehicles (UAVs), combined with deep learning techniques like CNNs, have proven effective in detecting faults. Infrared and thermographic images can precisely locate hot spots and micro-cracks [22].
Machine Learning and Deep Learning: Methods like CNNs, RNNs, and quantum circuits have been used to analyze data and spot inconsistencies in PV outputs. These methods can be used effectively to indicate potential faults.
Routine Maintenance: Regularly cleaning PV panels to remove obstructions like dust and debris ensures consistent sunlight absorption. Periodic inspections help to identify defects or connection issues early and preserve the system’s efficiency and lifespan.
Optimal Panel Positioning: To maximize energy production, PV panels should be placed in locations with minimal shadowing from obstructions like trees or buildings. They should be angled and oriented to capture consistent sunlight throughout the day. Adjustments based on geographical and weather factors can enhance their efficiency [23].
To maintain the efficiency and longevity of PV systems, understanding potential faults and proactively addressing them is essential. With technologies like machine learning and regular maintenance, PV system issues can be managed effectively.

References

  1. Livera, A.; Theristis, M.; Makrides, G.; Georghiou, G.E. Recent advances in failure diagnosis techniques based on performance data analysis for grid-connected photovoltaic systems. Renew. Energy 2019, 133, 126–143.
  2. Zhang, D.; Han, X.; Deng, C. Review on the research and practice of deep learning and reinforcement learning in smart grids. CSEE J. Power Energy Syst. 2018, 4, 362–370.
  3. Li, B.; Delpha, C.; Diallo, D.; Migan-Dubois, A. Application of Artificial Neural Networks to photovoltaic fault detection and diagnosis: A review. Renew. Sustain. Energy Rev. 2021, 138, 110512.
  4. Machlev, R.; Heistrene, L.; Perl, M.; Levy, K.Y.; Belikov, J.; Mannor, S.; Levron, Y. Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities. Energy AI 2022, 9, 100169.
  5. Hong, Y.-Y.; Pula, R.A. Methods of photovoltaic fault detection and classification: A review. Energy Rep. 2022, 8, 5898–5929.
  6. Islam, M.; Hasan, M. Big Data Analysis Driven Decision Making System Ensuring Energy Security of a Country. In Proceedings of the 7th International Conference on Computer Technology Applications, Vienna, Austria, 13–15 July 2021; pp. 60–65.
  7. Zhou, Z.-H. Machine Learning; Springer Nature: Berlin/Heidelberg, Germany, 2021.
  8. LeCun, Y.; Bengio, Y.; Hinton, G. Deep learning. Nature 2015, 521, 436–444.
  9. Forsyth, D.A.; Ponce, J. Computer Vision: A Modern Approach; Prentice Hall Professional Technical Reference; ACM Digital Library: New York, NY, USA, 2022.
  10. Steger, C.; Ulrich, M.; Wiedemann, C. Machine Vision Algorithms and Applications; John Wiley & Sons: Hoboken, NJ, USA, 2018.
  11. Chowdhary, K.; Chowdhary, K.R. Natural language processing. In Fundamentals of Artificial Intelligence; Springer: Berlin/Heidelberg, Germany, 2020; pp. 603–649.
  12. Tang, W.; Yang, Q.; Xiong, K.; Yan, W. Deep learning based automatic defect identification of photovoltaic module using electroluminescence images. Sol. Energy 2020, 201, 453–460.
  13. De Benedetti, M.; Leonardi, F.; Messina, F.; Santoro, C.; Vasilakos, A. Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing 2018, 310, 59–68.
  14. Niazi, K.A.K.; Yang, Y.; Sera, D. Review of mismatch mitigation techniques for PV modules. IET Renew. Power Gener. 2019, 13, 2035–2050.
  15. Haque, A.; Bharath, K.V.S.; Khan, M.A.; Khan, I.; Jaffery, Z.A. Fault diagnosis of photovoltaic modules. Energy Sci. Eng. 2019, 7, 622–644.
  16. Appiah, A.Y.; Zhang, X.; Ayawli, B.B.K.; Kyeremeh, F. Review and performance evaluation of photovoltaic array fault detection and diagnosis techniques. Int. J. Photoenergy 2019, 2019, 6953530.
  17. Khalil, I.U.; Ul-Haq, A.; Mahmoud, Y.; Jalal, M.; Aamir, M.; Ahsan, M.U.; Mehmood, K. Comparative analysis of photovoltaic faults and performance evaluation of its detection techniques. IEEE Access 2020, 8, 26676–26700.
  18. Hwang, M.-H.; Kim, Y.-G.; Lee, H.-S.; Kim, Y.-D.; Cha, H.-R. A study on the improvement of efficiency by detection solar module faults in deteriorated photovoltaic power plants. Appl. Sci. 2021, 11, 727.
  19. Mahesh, N.; Divyadharshini, E.; GS, D.P.; Gokul, V. A Novel Method for Fault Detection and Protection in Solar Photo Voltaic Arrays. In Proceedings of the 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), Mysuru, India, 16–17 October 2022; pp. 1–5.
  20. Wang, M.; Xu, X.; Yan, Z. Online fault diagnosis of PV array considering label errors based on distributionally robust logistic regression. Renew. Energy 2023, 203, 68–80.
  21. Segbefia, O.K.; Akhtar, N.; Saetre, T.O. Defects and fault modes of field-aged photovoltaic modules in the Nordics. Energy Rep. 2023, 9, 3104–3119.
  22. Dissawa, L.H.; Godaliyadda, R.I.; Ekanayake, P.B.; Agalgaonkar, A.P.; Robinson, D.; Ekanayake, J.B.; Perera, S. Sky image-based localized, short-term solar irradiance forecasting for multiple pv sites via cloud motion tracking. Int. J. Photoenergy 2021, 2021, 1–27.
  23. Santhakumari, M.; Sagar, N. A review of the environmental factors degrading the performance of silicon wafer-based photovoltaic modules: Failure detection methods and essential mitigation techniques. Renew. Sustain. Energy Rev. 2019, 110, 83–100.
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Subjects: Computer Science, Artificial Intelligence
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Mahmudul Islam
,
Masud Rana Rashel
,
Md Tofael Ahmed
,
A. K. M. Kamrul Islam
,
Mouhaydine Tlemçani
View Times: 220
Revisions: 2 times (View History)
Update Date: 20 Nov 2023
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