29 research outputs found
Overview on the STATCOM Performance in the Power Quality Improvement in the Electrical Grid
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Towards the prospection of an optimal thermal response of ZnO surge arrester in HV power system
Get PDFIn order to understand the thermal and electrical properties of surge arrester under standard climatic conditions, it would be useful to evaluate the heating progression in its different elements. These are constituted of heterogeneous materials, which have physical and electrical nonlinear properties along the surge arrester. Temperature predicting solutions for zinc oxide ceramic blocks provide fundamental elements of the electrothermal material characteristics in view of the lifetime estimation. Electrothermal phenomenon analysis of surge arresters is based on empirically-determined parameter models, using finite element method (FEM) simulated on COMSOL Multiphysics software which is a more precise approach compared to the existing models. In this paper, the behavioural model has been developed to study the surge arresters in both cases: the steady state and overvoltage state (lightning). Previous published works validate the first simulation results. This work is therefore extended to consider other operating states, which illustrate the dependence of the type of surge on the amplitudes and durations of electrothermal phenomena
Contribution to the study of the aggression of lightning phenomenon on the wind turbine structures
No full textOne-Hour Ahead Electric Load Forecasting Using Neuro-fuzzy System in a Parallel Approach
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One-hour ahead electric load and wind-solar power generation forecasting using artificial neural network
No full textOptimization and dimensioning of stand-alone systems: enhancing MPPT efficiency through DLGA integration
Get PDFThis paper explores optimizing and sizing stand-alone solar power systems using an intelligent maximum power point tracking (MPPT) method, enhanced by artificial neural networks (ANN). The study focuses on both system sizing and energy optimization, integrating genetic algorithms (GA) with deep learning (DL) to optimize the architecture of the ANN for improved performance in predicting solar energy output. The hybrid method, deep learning genetic algorithms (DLGA), efficiently reduces computational complexity and enhances flexibility through parameter tuning, significantly improving the performance of multi-layer perceptron networks. Additionally, a precise sizing methodology based on solar irradiance data was implemented to ensure the system is neither oversized nor undersized. The system's performance was tested and validated using MATLAB/Simulink simulations, which demonstrated superior predictive accuracy, faster convergence, and optimized energy capture. This combined approach of intelligent MPPT and accurate sizing presents a highly effective solution for improving the efficiency and reliability of stand-alone solar energy systems under varying environmental conditions
Very short-term electricity demand forecasting using adaptive exponential smoothing methods
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