Modeling, Development and Analysis Performance of an Intelligent Control of Photovoltaic System by Fuzzy Logic approach for Maximum Power Point Tracking

Barakate, Ahmed Amine; Abdelmoumene, Rida; Wahbi, Azeddine; Maddi, Mohamed; Hlou, Laamari; Hadjoudja, Abdelkader

doi:10.17762/ijcnis.v13i1.4881

Cited by 3 publications

(1 citation statement)

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“…The comparison between the simulated model values and the actual values showed an accuracy rate of approximately 98.6%. Furthermore, ANNs made it possible to detect the open switch fault of the inverter based on the characteristics of the phase current [85]. This technique was found to provide better control than using basic DTC in terms of sound.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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: Artificial Neural Network (Ann)mentioning

confidence: 99%