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Title: :  Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved
PaperId: :  23234
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 10 Issue 2 2024
DUI:    16.0415/IJARIIE-23234
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
MOHAMMED YASEENSanketika vidhya parishad engineering college
TELLA DEEPASanketika vidhya parishad engineering college
ALAGALA KEVINSanketika vidhya parishad engineering college
DONKA ANANDSanketika vidhya parishad engineering college
YAMALI SRINIVASUSanketika vidhya parishad engineering college
CH.VISHNU CHAKRAVARTHISanketika vidhya parishad engineering college

Abstract

Electrical engineering
sliding mode control, hybrid renewable energy system Artificial Neural Network
In hybrid renewable energy source (HRES) systems, the primary goal of this research is to evaluate and analyse three different types of controllers for three-phase DC-AC inverters. To do this, two contemporary controllers based on artificial neural network and sliding mode control (SMC) methodologies are designed and compared. Among the HRESs are solar (PV), step-up transformers connecting transmission lines, battery storage systems and wind turbines to infinite bus bars. Both voltage control and current regulation are used by the developed controllers at the inverter side. To provide a voltage demand at the point of common coupling, a DC–DC boost converter is used (PCC). Next, a presentation of the HRES formulation using the created controllers follows. It is thought that the created controllers will function under a range of solar radiation, temperature, and wind speed loading scenarios. To confirm the effectiveness of the constructed controllers, MATLAB/Simulink is used to simulate the HRESs with the controllers. The acquired outcomes show that adaptive SMC additionally. When compared to traditional PI control, artificial neural network (ANN) control techniques yield superior outcomes in terms of input power, output power, current, and voltage

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IJARIIE MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. "Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved" International Journal Of Advance Research And Innovative Ideas In Education Volume 10 Issue 2 2024 Page 3305-3324
MLA MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. "Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved." International Journal Of Advance Research And Innovative Ideas In Education 10.2(2024) : 3305-3324.
APA MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, & CH.VISHNU CHAKRAVARTHI. (2024). Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved. International Journal Of Advance Research And Innovative Ideas In Education, 10(2), 3305-3324.
Chicago MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. "Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved." International Journal Of Advance Research And Innovative Ideas In Education 10, no. 2 (2024) : 3305-3324.
Oxford MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. 'Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved', International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, 2024, p. 3305-3324. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Using_artificial_neural_networks_and_sliding_mode_control__the_performance_of_hybrid_renewable_energy_sources_connected_to_the_grid_is_improved_ijariie23234.pdf (Accessed : ).
Harvard MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. (2024) 'Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved', International Journal Of Advance Research And Innovative Ideas In Education, 10(2), pp. 3305-3324IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Using_artificial_neural_networks_and_sliding_mode_control__the_performance_of_hybrid_renewable_energy_sources_connected_to_the_grid_is_improved_ijariie23234.pdf (Accessed : )
IEEE MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI, "Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved," International Journal Of Advance Research And Innovative Ideas In Education, vol. 10, no. 2, pp. 3305-3324, Mar-App 2024. [Online]. Available: https://ijariie.com/AdminUploadPdf/Using_artificial_neural_networks_and_sliding_mode_control__the_performance_of_hybrid_renewable_energy_sources_connected_to_the_grid_is_improved_ijariie23234.pdf [Accessed : ].
Turabian MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. "Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 10 number 2 ().
Vancouver MOHAMMED YASEEN, TELLA DEEPA, ALAGALA KEVIN, DONKA ANAND, YAMALI SRINIVASU, and CH.VISHNU CHAKRAVARTHI. Using artificial neural networks and sliding mode control, the performance of hybrid renewable energy sources connected to the grid is improved. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2024 [Cited : ]; 10(2) : 3305-3324. Available from: https://ijariie.com/AdminUploadPdf/Using_artificial_neural_networks_and_sliding_mode_control__the_performance_of_hybrid_renewable_energy_sources_connected_to_the_grid_is_improved_ijariie23234.pdf
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