Fault Detection in Grid Connected PV System using Artificial Neural Network

Soffiah, K.; Manoharan, P. S.; Deepamangai, P.

doi:10.1109/icees51510.2021.9383734

Cited by 11 publications

(2 citation statements)

References 13 publications

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“…They use Levemberg's algorithm Marquardt and the ANN in MATLAB/Simulink. The authors conclude that the algorithm effectively recognizes short-circuit faults through the trained data [11]. Similarly, Lu et al propose the random forests algorithm to detect partial shading, short circuit, open circuit, and aging faults on a simulated PV field.…”

Section: Introductionmentioning

confidence: 96%

“…In recent years, there has been a renewed interest in the industrial applications of digital methods, such as the use of machine learning for vehicle autonomy on public roads and fault diagnosis using data [7]. In the field of photovoltaics, various machine learning models, such as artificial neural networks (ANN), k nearest neighbors (kNN), the Adaptive Neuro-Fuzzy Inference System (ANFIS), Naïve Bayes (NB), decision trees (RF), and fuzzy logic, have been successfully employed for fault diagnosis [3,[8][9][10][11][12][13]. Several articles have demonstrated the effectiveness of supervised learning algorithms in improving the diagnosis of PV systems with the application of artificial intelligence [14,15].…”

Section: Introductionmentioning

confidence: 99%

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A Comprehensive Review of Supervised Learning Algorithms for the Diagnosis of Photovoltaic Systems, Proposing a New Approach Using an Ensemble Learning Algorithm

Toche Tchio,

Kenfack,

Kassegne

et al. 2024

Applied Sciences

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Photovoltaic systems are prone to breaking down due to harsh conditions. To improve the reliability of these systems, diagnostic methods using Machine Learning (ML) have been developed. However, many publications only focus on specific AI models without disclosing the type of learning used. In this article, we propose a supervised learning algorithm that can detect and classify PV system defects. We delve into the world of supervised learning-based machine learning and its application in detecting and classifying defects in photovoltaic (PV) systems. We explore the various types of faults that can occur in a PV system and provide a concise overview of the most commonly used machine learning and supervised learning techniques in diagnosing such systems. Additionally, we introduce a novel classifier known as Extra Trees or Extremely Randomized Trees as a speedy diagnostic approach for PV systems. Although this algorithm has not yet been explored in the realm of fault detection and classification for photovoltaic installations, it is highly recommended due to its remarkable precision, minimal variance, and efficient processing. The purpose of this article is to assist technicians, engineers, and researchers in identifying typical faults that are responsible for PV system failures, as well as creating effective control and supervision techniques that can minimize breakdowns and ensure the longevity of installed systems.

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Section: Introductionmentioning

confidence: 96%