Browsing by Author "MANSOURI Smail"

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    Advancing Photovoltaic System Performance Analysis through Data Visualization and Intelligent Algorithms
    (National Higher School Of Technology And Engineering-ANNABA-, 2024) MANSOURI Smail; MADI Sarra; DEKHANE Azzeddine (Encadrant); BOUZITOUNA Abdallah (Encadrant)
    Accurate prediction of the maximum power point (Pmpp) of photovoltaic (PV) systems is crucial to optimize the energy yield and maximizing the efficiency of solar energy harvesting. This master thesis explores the potential of data-driven approaches for improving Pmpp prediction, utilizing regression techniques and feature importance analysis. Thestudyanalyzedadatasetofirradiance, temperature, andPmpp measurements, investigating the relationships between these variables and employing various regression models, including Ridge Regression, Lasso Regression, Decision Tree Regression, and Random Forest Regression. Performance comparisons revealed that tree-based models, notably Random Forest, outperformed linear models in capturing the complex interactions between input features and Pmpp. Furthermore, feature importance analysis highlighted the significant influence of irradiance (GPOA)onPmpp, particularly for tree-based models, underscoring the need for accurate irradiance data and modeling techniques that effectively capture non-linear relationships. This master thesis concludes that data-driven approaches, specifically those employing tree-based models, hold significant potential for advancing Pmpp prediction and optimizing PV system performance. Future research should explore the integration of additional features, such as solar panel characteristics, atmospheric conditions, and system degradation factors, along with dvanced machine learning techniques, to further enhance Pmpp prediction accuracy
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    Enhanced Real-Time Monitoring System for Photovoltaic (PV) Systems: Integration of I-V Curve Analysis
    (National Higher School Of Technology And Engineering -ANNABA-, 2024) MANSOURI Smail; MADI Sarra; DEKHANE Azzeddine (Encadrant)
    The increasing adoption of photovoltaic (PV) systems Requires advanced monitoring solutions to ensure optimal performance and reliability. This thesis presents the development and implementation of a real-time PV system monitoring system, incorporating an I-V tracer to enhance the accuracy and efficiency of performance assessments. Two distinct approaches were explored to achieve this goal. The first approach follows the IEC 60891 standard, which facilitates the conversion of measured I-V characteristics in operating conditions (OPC) to Standard Test Conditions (STC). The correction under STC conditions allows the estimation of the deviation between the tested module’s power and the maximum power specified by the manufacturer. The second approach involves the estimation of the single diode model (SDM) parameters using a metaheuristic optimization algorithm known as Teaching-Learning-Based Optimization (TLBO). This algorithm effectively estimates the parameters by mimicking the pedagogical process of teaching and learning, thereby providing a robust solution for parameter extraction under varying environmental conditions.