“…Figure 5 depicts the schematic representation of a static boost converter coupled to a solar generator 36 , 37 . Figure 5 A solar generator coupled to a DC-DC boost converter.…”
Section: Description Of the Grid-connected Pv Systemmentioning
confidence: 99%
“…The collected findings validated the suggested MPPT’s efficiency under various partial shadow patterns. In recent years, several important recent studies regarding intelligent control techniques, such as neural networks 34 , 35 , fuzzy logic 36 , 37 , particularly those relying on GMPPT approaches, such as particle swarm optimization (PSO), have demonstrated remarkable stability in the face of unpredictable climatic fluctuations. The literature review highlights the persistent challenge faced by traditional MPPT systems in efficiently extracting energy from PV fields under varying environmental conditions.…”
This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.
“…Figure 5 depicts the schematic representation of a static boost converter coupled to a solar generator 36 , 37 . Figure 5 A solar generator coupled to a DC-DC boost converter.…”
Section: Description Of the Grid-connected Pv Systemmentioning
confidence: 99%
“…The collected findings validated the suggested MPPT’s efficiency under various partial shadow patterns. In recent years, several important recent studies regarding intelligent control techniques, such as neural networks 34 , 35 , fuzzy logic 36 , 37 , particularly those relying on GMPPT approaches, such as particle swarm optimization (PSO), have demonstrated remarkable stability in the face of unpredictable climatic fluctuations. The literature review highlights the persistent challenge faced by traditional MPPT systems in efficiently extracting energy from PV fields under varying environmental conditions.…”
This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.
“…The final yield ( F y ) of the system is the net AC output power of the system for the year, month, or day divided by the rated power, which is given as follows: [ 36,37 ] where E ac is the final AC electric energy produced by the system (kWh) and P r is the rated power (kW).…”
Section: Methodsmentioning
confidence: 99%
“…The reference yield ( R y ) is defined as the total insolation in a plane divided by the reference irradiance under standard test conditions (STC), which is calculated as follows: [ 36,37 ] where I r is the total insolation in a plane (kWh m −2 ) and H r is the reference solar irradiance of 1 kW m −2 at STC.…”
Herein, the feasibility of a grid‐connected photovoltaic (PV) power generation system with five tracking models in Hefei is evaluated comprehensively. The comprehensive evaluation of grid‐connected PV power generation system mainly includes three aspects: technology, economy, and environment. The results show that the performance ratio of grid‐connected PV system with dual‐axis tracking device is the highest among the five kinds of PV systems with a value of 81.2%, while the performance ratio of grid‐connected PV system without tracking device is the lowest with a value of 77.3%. The grid‐connected PV system with dual‐axis tracking device is economically attractive, so users can be encouraged to purchase and install such systems to meet their electrical energy needs in Hefei. Furthermore, the grid‐connected PV system with dual‐axis tracking device is the most environmentally friendly and contributes to carbon emission reduction. Finally, it is concluded that the adoption of dual‐axis tracking grid‐connected PV power generation system in Hefei area is very attractive, and can provide a certain reference for the local promotion and use of such grid‐connected PV power generation system.
“…Where 𝛼𝛼𝑅𝑅 𝑅𝑅 coefficient of degradation and 𝑅𝑅 𝑅𝑅0 is the series resistor (before degradation). The state-space averaged model of the boost converter can be written as [35], [36]:…”
Photovoltaic conversion is an optimal solution for the electrification of rural areas, especially deserts for the abundance of solar energy in these regions. In recent years, many studies have been carried out to maximize the energy productivity of a PV array system and increase its efficiency. However, the arid and semi-arid region is characterized by a climate whose parameters significantly influence the operation of PV installations. This paper presents an overview investigation of the major internal and external factors significantly affecting both the efficiency and the performance of solar cells and the power of PV systems. These factors include the type of PV material, solar radiation intensity received, cell temperature, parasitic resistances, cloud, and other shading effects, inverter efficiency, dust, module orientation, weather conditions, geographical location, and cable thickness. Simulation of a PV system has been carried out in MATLAB-SIMULINK to prove the effectiveness of the proposed modeling method. These simulation results are useful to predict the production of the PV module under real operating conditions.
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