A Mathematical Model for the Optimization of Renewable Energy Systems

Sánchez, Mariam Gómez; Macía, Yunesky Masip; Gil, Alejandro Fernández; Castro, Carlos; González, Suleivys M. Nuñez; Yanes, Jacqueline Pedrera

doi:10.3390/math9010039

Cited by 12 publications

(4 citation statements)

References 47 publications

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“…However, the application arrangement varies since couples are employed in the construction of some homes, which is not a serious issue that needs to be examined. The majority of energy resources will operate directly in subsurface areas thanks to wireless sensor networks, and if any faults are found, the entire system can be switched to a distributed mode of operation (Sánchez et al, 2020). The aforementioned procedure is completed automatically using the least squares algorithm, which increases data on the generation of energy sources.…”

Section: Existing Approachesmentioning

confidence: 99%

Mathematical approach of fiber optics for renewable energy sources using general adversarial networks

Hasanin

Manoharan²,

Alterazi³

et al. 2023

Front. Ecol. Evol.

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It is significantly more challenging to extend the visibility factor to a higher depth during the development phase of a communication system for subterranean places. Even if there are numerous optical fiber systems that provide the right energy sources for intended panels, the visibility parameter is not optimized past a certain point. Therefore, the suggested method looks at the properties of a fiber optic communication system that is integrated with a certain energy source while having external panels. A regulating state is established in addition to characteristic analysis by minimizing the reflection index, and the integration of the general adversarial network (GAN) optimizes both central and layer formations in exterior panels. Thus, the suggested technique uses the external noise factor to provide relevant data to the control center via fiber optic shackles. As a result, the normalized error is smaller, boosting the suggested method's effectiveness in all subsurface areas. The created mathematical model is divided into five different situations, and the results are simulated using MATLAB to test the effectiveness of the anticipated strategy. Additionally, comparisons are done for each of the five scenarios, and it is found that the proposed fiber-optic method for energy sources is far more effective than current methodologies.

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Section: Existing Approachesmentioning

confidence: 99%

Mathematical approach of fiber optics for renewable energy sources using general adversarial networks

Hasanin

Manoharan²,

Alterazi³

et al. 2023

Front. Ecol. Evol.

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“…T ps = 2H B f 0 , H be the inertia constant of a generator (MW-s/MVA) and P r the rating of the turbo-generator (MVA). f o is frequency (6,(8)(9)(10)(11)15) .…”

Section: Mathematical Approachmentioning

confidence: 99%

Dynamic Response of Multi Area Load Frequency Control through Different Computational Techniques

Dhamanda¹,

Dutt²

2022

IJST

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Objective: To employ optimization technique (GA (Genetic Algorithm) Technique) to solve the problem of power generation, by means of mathematical approach to formulate the problem mathematically. Methods: Thermal units have been taken as a source of power generation. Transfer function model of thermal generating units has been developed and simulated through MATLAB Simulink software. The main issue of power generation is the continuous mismatching in the generation and consumption of electricity due to which continuous the change in frequency and corresponding power becomes a major issue, which need to be maintained in constancy for regular operation. For this purpose, optimization technique (GA Technique), PI (Proportional Plus Integral), PID (Proportional Plus Integral Plus Derivative) technique have been used to solve the issue of frequency and corresponding power. Findings: Solve the issues of change in frequency and corresponding tie line power, a table has been drawn for batter comparison. Novelty: The comparative results obtained from all technique have been put in table which shows that the optimization technique (GA Technique) gives very effective, efficient and dynamic results (8 Sec and 11 Sec in Frequency & Power Variation) compared to traditional technique (PI, PID) (28 Sec, 44 Sec & 27 Sec & 43 Sec in Frequency & Power Variation).

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“…k 2 = c 2 e a 2 ω r opt = 0.046404e 0.051246ω r opt (15) On the other hand, Equation (16) represents the power expansion for calculating k 1 , while Equation (17) shows the function structure for determining k 2 . Table 5 contains the different coefficients associated to the polynomial equation for k 1 while Table 6 presents the diverse coefficients related to the polynomial function for k 2 .…”

Section: Mathematical Modelmentioning

confidence: 99%

“…One of the significant problems related to wind power generation is that the wind is continuously varying its velocity and direction [13]. Because of this, diverse and advanced technologies have been developed in order to obtain the highest wind power efficiency and to ensure the most profitable wind energy technologies [14][15][16][17]. Some studies, for example, are specialized in methods, techniques, and technologies to guarantee a proper connection between wind power generation and the electrical grid [18,19].…”

Section: Introductionmentioning

confidence: 99%

Wind Power Extraction Optimization by Dynamic Gain Scheduling Approximation Based on Non-Linear Functions for a WECS Based on a PMSG

González-Hernández

Salas-Cabrera

2021

Mathematics

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Mathematical models and algorithms for maximizing power extraction have become an essential topic in renewable energies in the last years, especially in wind energy conversion systems. This study proposes maximum power point tracking using gain scheduling approximations for an emulated wind system in a direct-drive connection. Power extraction is obtained by controlling the duty cycle of a Multilevel Boost Converter, which directly varies the rotational speed of a permanent magnet synchronous generator directly coupled to a three-phase induction motor that emulates the wind turbine. The system’s complexity is linked to the inherent non-linearities associated with the diverse electrical, mechanical, and power electronic elements. In order to present a synthesized model without losing the system dynamic richness, several physical tests were made to obtain parameters for building several mathematical approaches, resulting in non-linear dynamic equations for the controller gains, which are dependant on wind speed. Thirty real operational wind speeds considering typical variations were used in several tests to demonstrate the mathematical models’ performance. Results among these gain scheduling approaches and a typical controller constant gains mathematical model were compared based on standard deviations, absolute error, and the time for reaching the optimum generator angular speed related to every wind speed.

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A Mathematical Model for the Optimization of Renewable Energy Systems

Cited by 12 publications

References 47 publications

Mathematical approach of fiber optics for renewable energy sources using general adversarial networks

Mathematical approach of fiber optics for renewable energy sources using general adversarial networks

Dynamic Response of Multi Area Load Frequency Control through Different Computational Techniques

Wind Power Extraction Optimization by Dynamic Gain Scheduling Approximation Based on Non-Linear Functions for a WECS Based on a PMSG

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