Doctoral Dissertations

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Browsing Doctoral Dissertations by Subject "Bidirectional Gated Recurrent Unit"

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    Big Data and Artificial Intelligence for Improving the Performance and Efficiency of Large-Scale Grid-Connected PV Power Plant
    (University of M'Sila, 2024-11-17) Amiri Ahmed Faris
    This thesis presents reliable methods for fault detection and diagnosis in Photovoltaic (PV) systems. The first method proposes a two step approach for developing a reliable PV model and constructed a fault detection procedure using Random Forest Classifiers (RFCs). The first step involves identifying the unknown parameters of the One Diode Model (ODM) using the Modified Grey Wolf Optimization (MGWO) algorithm and simulating the PV array t o extract maximum power point (MPP) coordinates. The second step involves developing two RFCs: one for fault detection and another for fault diagnosis. The second method uses the Sandia Array Performance Model (SAPM) for accurate photovoltaic system si mulation. Parameters are extracted with the Artificial Bee Colony (ABC) algorithm to optimize and reduce errors between measured and simulated data. Additionally, deep learning is employed by combining Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Units (Bi GRU) to analyze dynamic system power outputs at the MPP for fault detection and diagnosis with high precision. The third work develops a predictive modeling method for PV generation using rigorous feature selection, outlier re moval, and hyperparameter tuning. The method is implemented in a MATLAB interface to predict key parameters and evaluate system performance. The efficiency of these methods is evaluated using real data from actual PV systems.