This algorithm was successful in identifying the most important features that affected solar power generation, including weather conditions, time of day, and solar panel tilt angle. In conclusion, the proposed X-LSTM-EO
An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from the PVPP, is introduced in this paper. The key novelty
Experimental comparison and analysis show that the algorithm effectively combines the azimuth tracking and the electrical maximum power tracking algorithm without a
Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated
Consequently, constant power generation (CPG) is imposed by grid codes. An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from...
A new proposed objective function of PV module power and constraints is also presented. Genetic algorithm is performed under varying the initial population of independent
The presented research aimed to conduct a comprehensive analysis of both individual and hybrid MPPT techniques for efficient solar power generation. The primary focus is on evaluating the...
In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works...
This study introduces a novel approach to maximum power point tracking in solar photovoltaic systems by combining the super-twisting algorithm with the grey wolf optimizer. Abstract This study presents a new Maximum Power Point Tracking (MPPT) approach for solar photovoltaic (PV) systems, combining the Super-Twisting Algorithm (STA) and Grey Wolf
Solar photovoltaic PV power generated is the most prominent methods among the renewable energy power generation methods. The solar PV system are acquiring much acclaim due to the advantage and
An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from the PVPP, is introduced in this paper. The key novelty of the proposed algorithm is its applicability for both single- and two-stage PVPPs and flexibility to move the operation point to the right or left side of the MPP
Experimental comparison and analysis show that the algorithm effectively combines the azimuth tracking and the electrical maximum power tracking algorithm without a position sensor, and realizes the maximum power output of a single PV module.
Consequently, constant power generation (CPG) is imposed by grid codes. An algorithm for the calculation of the photovoltaic panel voltage reference, which generates a constant power from...
In this study, in order to predict a photovoltaic module power output, weather data are simultaneously collected while recording the module''s power generation. A six-days dataset of record was used to train, validate, and test a FFNN, compare the performance of different training algorithms and their effect on ANN prediction performance. In
Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated with PV power output. Moreover, the Levenberg-Marquardt algorithm was found to
Due to the implementation of the "double carbon" strategy, renewable energy has received widespread attention and rapid development. As an important part of renewable energy, solar energy has been widely used worldwide due to its large quantity, non-pollution and wide distribution [1, 2].The utilization of solar energy mainly focuses on photovoltaic (PV)
A new proposed objective function of PV module power and constraints is also presented. Genetic algorithm is performed under varying the initial population of independent variables, then Lagrange multiplier algorithm is simulated. The optimal PV module power obtained from both algorithms is compared. The analysis is based on real
The presented research aimed to conduct a comprehensive analysis of both individual and hybrid MPPT techniques for efficient solar power generation. The primary focus
As a clean, non-polluting renewable energy source, solar energy can be converted to electricity directly without consuming other fossil energy sources, thus it is widely used in power generation 2
Due to the updated formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable performance and accuracy of the new algorithm, providing power regulation capability in the range 20%–100% of the maximum available power. Moreover, the impact of the
A proper MPPT algorithm is required to capture the maximum power point (MPP) from the characteristic curves of a solar PV under partial shaded conditions (PSC). An optimized maximum power point tracking (MPPT) and fault classification in solar PV systems are presented in this research work. To select the best optimization model for
This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining...
This paper presents an algorithm for implementing an ANN-GA for predicting solar power generation. The algorithm involves preprocessing the data, defining the ANN architecture, defining...
The solar power generation capacity has increased by nearly 100 GWp in 2017, which is about 31 per cent more from 2017 [5, 6]. However, the extensive use of a PV system is not so common because of its high starting cost. Again, there is no assurance that the energy delivered from PV exhibits steady output since it relies completely on the sun-oriented
This article presents an innovative model-based (MB) tracking algorithm devoted to supporting power network regulation. Due to the updated formulation, the algorithm can
In shaded/unshaded photovoltaic (PV) systems, tracking of maximum power under different environmental conditions is provided by maximum power point tracking (MPPT). In recent years many works...
Renewable energy sources, such as solar power, play a pivotal role in addressing the challenges of energy sustainability and climate change mitigation [1, 2].Accurately forecasting photovoltaic (PV) AC power generation is crucial for effectively managing power grids, seamlessly incorporating renewable energy sources, and making informed decisions.
The rated power of a solar power generation system is increased by several string connections of power modules where the series-connected PV modules comprise the strings. Furthermore, several strings can be connected in parallel to achieve higher power ranges. The grid connection of PV plants that are constructed by using such strings is performed with
This article presents an innovative model-based (MB) tracking algorithm devoted to supporting power network regulation. Due to the updated formulation, the algorithm can vary the power curtailment according to a reduction factor given by the power system operators. Results show the remarkable performance and accuracy of the new
The algorithm involves preprocessing the data, defining the ANN architecture, defining the fitness function, and implementing the GA to optimize the ANN’s parameters. The results of this approach can be useful for predicting future solar power generation and optimizing the performance of solar power systems.
In photovoltaic systems, one of the most used MPPT algorithms is the P&O algorithm. Its basic idea is to gradually alter the PV system's operating point while closely observing how the power output changes in response. The operating point is changed to improve power output after reaching the maximum power point 32.
Experimental comparison and analysis show that the algorithm effectively combines the azimuth tracking and the electrical maximum power tracking algorithm without a position sensor, and realizes the maximum power output of a single PV module.
Therefore, the output power of the PV module can be controlled by controlling the irradiance and output voltage. For PV modules, the irradiance cannot be directly controlled. The incident angle of sunlight on the photovoltaic module affects the irradiance received by the module .
Results show that solar irradiation, ambient and module temperatures are key factors in predicting PV module power generation, as these variables are strongly correlated with PV power output. Moreover, the Levenberg-Marquardt algorithm was found to be the best training procedure.
As can be seen from the front view of the surface plot in Fig. 4, the maximum output power voltage points at all of the azimuth of the PV module are around 18 V, which is in line with the P–V characteristic curve of the PV module. In the left-view plot, the PV output power and irradiance are nonlinear.
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