Khaled Dhibi
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View article: Digital Twin-Based Kernel RF for Fault Detection and Diagnosis in Photovoltaic Systems
Digital Twin-Based Kernel RF for Fault Detection and Diagnosis in Photovoltaic Systems Open
Ensuring the reliability, efficiency, and economic viability of photovoltaic (PV) systems requires effective fault detection and diagnosis, which remains a significant challenge. In this work, we propose a novel and integrated framework th…
View article: Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm
Effective uncertain fault diagnosis technique for wind conversion systems using improved ensemble learning algorithm Open
This paper introduces a pioneering fault diagnosis technique termed Interval Ensemble Learning based on Sine Cosine Optimization Algorithm (IEL- SCOA), tailored to tackle uncertainties prevalent in wind energy conversion (WEC) systems. The…
View article: Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems
Enhanced PSO-Based NN for Failures Detection in Uncertain Wind Energy Systems Open
Ensuring the validity of measurements in wind energy systems (WES) is a challenging task in system diagnosis and data validation. This work, therefore, elaborates on the development of new approaches aimed at improving the operation of WES…
View article: An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization
An Effective Fault Diagnosis Technique for Wind Energy Conversion Systems Based on an Improved Particle Swarm Optimization Open
The current paper proposes intelligent Fault Detection and Diagnosis (FDD) approaches, aimed to ensure the high-performance operation of Wind energy conversion (WEC) systems. First, an efficient feature selection algorithm based on particl…
View article: Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems
Interval-Valued SVM Based ABO for Fault Detection and Diagnosis of Wind Energy Conversion Systems Open
In this paper, special attention is paid to the detection and diagnosis of various incipient faults of uncertain wind energy conversion (WEC) systems. The proposals will enhance the monitoring and diagnosis of the WEC system while taking i…
View article: Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems
Interval-Valued Reduced Ensemble Learning Based Fault Detection and Diagnosis Techniques for Uncertain Grid-Connected PV Systems Open
One of the most promising renewable energy technologies is photovoltaics (PV). Fault detection and diagnosis (FDD) becomes more and more important in order to guarantee high reliability in PV systems. FDD of PV systems using machine learni…
View article: A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation
A Hybrid Fault Detection and Diagnosis of Grid-Tied PV Systems: Enhanced Random Forest Classifier Using Data Reduction and Interval-Valued Representation Open
This paper proposes a novel fault detection and diagnosis (FDD) technique for grid-tied PV systems. The proposed approach deals with system uncertainties (current/voltage variability, noise, measurement errors, ⋯) by using an interval-valu…
View article: An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems
An Enhanced Ensemble Learning-Based Fault Detection and Diagnosis for Grid-Connected PV Systems Open
The main objective of this article is to develop an enhanced ensemble learning (EL) based intelligent fault detection and diagnosis (FDD) paradigms that aim to ensure the high-performance operation of Grid-Connected Photovoltaic (PV) syste…