Dans le cadre de la résolution du problème de déficit énergétique et de la pollution environnementale due aux énergies fossiles, l’utilisation des énergies renouvelables (ER) constitue une solution idoine. Cependant, l’instabilité de ces sources demeure une des limites considérables à leur exploitation. Notre travail porte sur l’évaluation du potentiel éolien en intégrant le paramètre de la densité massique locale de l’air. L’extrapolation de la demande d’énergie permet de prédire la solution envisageable. Le but de ce travail est de vérifier si l’énergie éolienne peut satisfaire à la demande d’énergie dans le village Wouro Kessoum. Les résultats montrent que ce n’est qu’entre 8 heures et 17 heures de la journée que la puissance récupérable satisfait à la demande d’énergie pour 195 ménages de ce village.
Titanium dioxide nanofluid is used in the thermosyphon solar flat plate collector at volume fractions of 0.1%, 0.3%, 0.5%, 0.7%, and 1% to test the collector’s effectiveness. The heat transfer coefficient, Nusselt number, friction factor, and pressure drop calculations were used to analyze the impact of adding nanoparticles to the working fluid. The effectiveness of the solar flat plate collector and the heat transfer coefficient are both greatly affected by the addition of titanium dioxide to the working fluid, with a tolerable rise in pressure drop. With concomitant variances of 15% and 13%, correlations were constructed for the Nusselt number and friction factor.
This paper presents a method for optimal sizing of a Micro grid connected to a hybrid source to ensure the continuity and quality of energy in a locality with a stochastically changing population. The hybrid system is composed of a solar photovoltaic system, a wind turbine, and an energy storage system. The reliability of the system is evaluated based on the voltage level regulation on IEEE 33-bus and IEEE 69-bus standards. Power factor correction is performed, despite some reliability and robustness constraints. This work focuses on energy management in a hybrid system considering climatic disturbances on the one hand, and on the other hand, this work evaluates the energy quality and the cost of energy. A combination of genetic algorithms of particle swarm optimization (CGAPSO) shows high convergence speed, which illustrates the robustness of the proposed system. The study of this system shows its feasibility and compliance with standards. The results obtained show a significant reduction in the total cost of production of this proposed system.
According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems.
This paper presents a mathematical model of 255 kW grid-connected solar photovoltaic (SPV) system. To study the performance characteristics of the grid-connected SPV system, a new hybrid adaptive grasshopper optimization algorithm with the recurrent neural network (AGO-RNN) control technique was implemented. Furthermore, the power quality at the point of common coupling (PCC) has been studied using the conventional (PSO) and proposed AGO-RNN controllers. The characteristics of the PV system were analyzed under varying environmental (variable irradiance and temperature) conditions considering 3 different cases such as (i) standard test conditions (STC), (ii) variable radiation with constant temperature, and (iii) variable radiation with variable temperature. For each case, the total harmonic distortion (THD) has been calculated using the proposed AGO-RNN control technique, and the results were compared with particle swarm optimization (PSO) technique. The 255 kW PV model is initially developed and connected to a three-level NPC inverter, an MPPT-based perturbation and observation algorithm. Later, the PV model is controlled by an AGO-RNN pulse width modulation (PWM) controller and is then integrated to the main grid at PCC. The main advantage of this technique is exploiting the separate DC-DC converter between the SPV module and the inverter. Finally, the proposed grid-connected SPV system was simulated on MATLAB for analyzing the performance of the system based on its I-V and P-V characteristics, inverter voltage, grid power, gird voltage, grid current, power factor, and THD under different environmental conditions. The simulation results demonstrate that the current magnitude and THD of the SPVGC system are improved with the cutting-edge AGO-RNN controller compared to PSO in all three different scenarios, and this value is less than 1.6%, which is within the permitted limits of IEC 61727 standards.
Microgrids with distributed generation (DG) are rapidly coming into distribution networks to supply load demand in order to preserve ecological balance and reduce greenhouse gas emissions. Power electronic advancements are making renewables dispatchable to loads while turning passive networks to active with bidirectional power flow. The IEEE-1547-2018 regulations enforced certain standards on microgrids, including the ability to detect unintended failures, island the microgrid in less than 2 seconds, and feed connected loads while maintaining voltage, frequency, and power quality. There are numerous islanding techniques accessible, including passive, active, hybrid, and communication techniques. The active techniques degrade power quality due to injection of deviations, passive type leaves larger nondetection zone (NDZ), and communication type are costly. The hybrid type combines both passive and active methods. To get away from all these issues, a passive technique is formulated in this paper, which measures the differential phase angle of voltage and current at DG output, to detect islanding. This approach reliably identifies islanding in 20 ms at nearly zero NDZ. This approach is also stable during transient conditions such as load switching and throwing off. There is also no power quality issue because there are no injections during testing. This method is tested in MATLAB/Simulink and evaluated using the differential frequency technique to get performance indices in accordance with UL-1741 testing standards.
In this research, a solar photovoltaic system with maximum power point tracking (MPPT) and battery storage is integrated into a grid-connected system using an improved three-level neutral-point-clamped (NPC) inverter. An NPC inverter with adjustable neutral-point clamping may achieve this result. To achieve this result, a modernized NPC inverter is used. Using the three-level vector modulation approach, the correct AC voltage may be generated when DC voltage conditions are present in an unbalanced situation involving an NPC inverter. A higher degree of precision is made possible by this. In-depth information on the many possible NPC inverter designs and modulation strategies was provided. By incorporating the necessary sophisticated algorithms into the NPC inverter, the necessary solar PV-MPPT functionality is made available, allowing for the regulation of power transfer among the solar PV system, the battery, and the grid. Modified INC method is proposed which has a tracking efficiency of 99.5% for varying irradiance. The use of sufficiently sophisticated algorithms makes this a reality. Using simulation with MATLAB/Simulink, we were able to examine an NPC inverter’s performance in several setups. Several amounts of solar irradiation were used to test the battery’s charging and discharging capabilities. The probe turned out to be fruitful. Laboratory testing ensures that the proposed method works by simulating real-world conditions.
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