PV Maximum Power-Point Tracking by Using Artificial Neural Network

Nur Alfiani Shoffiana; Imam Abadi; Nabilah Kartika Pertiwi; Ayu Anisa Damayanti; Ayu Dian Hartati; Mia Dwi Susanti; Selfi Stendafity

doi:10.1109/ICAMIMIA60881.2023.10427794

PV Maximum Power-Point Tracking by Using Artificial Neural Network

Nur Alfiani Shoffiana^*
, Suyanto
, Imam Abadi
, Nabilah Kartika Pertiwi
, Ayu Anisa Damayanti
, Ayu Dian Hartati
, Mia Dwi Susanti
, Selfi Stendafity

^*Corresponding author for this work

Department of Engineering Physics
Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

The utilization of solar panels as an effort to implement new and renewable energy still has challenges in optimizing the performance of solar panels to produce electrical power efficiently. Therefore, a careful planning and operational process is needed to maximize the performance of solar panels. One of the important steps to plan carefully the process of installing solar panels is to estimate the production of electrical power generated by solar panels. This research was conducted to design a maximum voltage estimation system on solar panels by considering solar radiation and air temperature. The Mean Squared Error (MSE) parameter is used as a performance parameter of the artificial neural network used. The results showed a significant relationship between solar radiation, air temperature and voltage on solar panels made from polycrystalline. Testing and evaluation of the estimation system is done using independent data that has never been seen before. The MSE result in the test is 9.9602e-12 which has met the MSE target set in this study.

Original language	English
Title of host publication	2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	859-863
Number of pages	5
ISBN (Electronic)	9798350309225
DOIs	https://doi.org/10.1109/ICAMIMIA60881.2023.10427794
Publication status	Published - 2023
Event	2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Lombok, Indonesia Duration: 14 Nov 2023 → 15 Nov 2023

Publication series

Name	2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings

Conference

Conference	2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023
Country/Territory	Indonesia
City	Lombok
Period	14/11/23 → 15/11/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Maximum Voltage
Mean Squared Error (MSE)
Solar Panel

Access to Document

10.1109/ICAMIMIA60881.2023.10427794

Cite this

Shoffiana, N. A., Suyanto , Abadi, I., Pertiwi, N. K., Damayanti, A. A., Hartati, A. D., Susanti, M. D., & Stendafity, S. (2023). PV Maximum Power-Point Tracking by Using Artificial Neural Network. In 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings (pp. 859-863). (2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAMIMIA60881.2023.10427794

Shoffiana, Nur Alfiani ; Suyanto, ; Abadi, Imam et al. / PV Maximum Power-Point Tracking by Using Artificial Neural Network. 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 859-863 (2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings).

@inproceedings{e7f60d25d3594de19f6656e8e738a6d1,

title = "PV Maximum Power-Point Tracking by Using Artificial Neural Network",

abstract = "The utilization of solar panels as an effort to implement new and renewable energy still has challenges in optimizing the performance of solar panels to produce electrical power efficiently. Therefore, a careful planning and operational process is needed to maximize the performance of solar panels. One of the important steps to plan carefully the process of installing solar panels is to estimate the production of electrical power generated by solar panels. This research was conducted to design a maximum voltage estimation system on solar panels by considering solar radiation and air temperature. The Mean Squared Error (MSE) parameter is used as a performance parameter of the artificial neural network used. The results showed a significant relationship between solar radiation, air temperature and voltage on solar panels made from polycrystalline. Testing and evaluation of the estimation system is done using independent data that has never been seen before. The MSE result in the test is 9.9602e-12 which has met the MSE target set in this study.",

keywords = "Maximum Voltage, Mean Squared Error (MSE), Solar Panel",

author = "Shoffiana, \{Nur Alfiani\} and Suyanto and Imam Abadi and Pertiwi, \{Nabilah Kartika\} and Damayanti, \{Ayu Anisa\} and Hartati, \{Ayu Dian\} and Susanti, \{Mia Dwi\} and Selfi Stendafity",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 ; Conference date: 14-11-2023 Through 15-11-2023",

year = "2023",

doi = "10.1109/ICAMIMIA60881.2023.10427794",

language = "English",

series = "2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "859--863",

booktitle = "2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings",

address = "United States",

}

Shoffiana, NA, Suyanto, , Abadi, I, Pertiwi, NK, Damayanti, AA, Hartati, AD, Susanti, MD & Stendafity, S 2023, PV Maximum Power-Point Tracking by Using Artificial Neural Network. in 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings. 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 859-863, 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023, Lombok, Indonesia, 14/11/23. https://doi.org/10.1109/ICAMIMIA60881.2023.10427794

PV Maximum Power-Point Tracking by Using Artificial Neural Network. / Shoffiana, Nur Alfiani; Suyanto, ; Abadi, Imam et al.
2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. p. 859-863 (2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - PV Maximum Power-Point Tracking by Using Artificial Neural Network

AU - Shoffiana, Nur Alfiani

AU - Suyanto,

AU - Abadi, Imam

AU - Pertiwi, Nabilah Kartika

AU - Damayanti, Ayu Anisa

AU - Hartati, Ayu Dian

AU - Susanti, Mia Dwi

AU - Stendafity, Selfi

PY - 2023

Y1 - 2023

N2 - The utilization of solar panels as an effort to implement new and renewable energy still has challenges in optimizing the performance of solar panels to produce electrical power efficiently. Therefore, a careful planning and operational process is needed to maximize the performance of solar panels. One of the important steps to plan carefully the process of installing solar panels is to estimate the production of electrical power generated by solar panels. This research was conducted to design a maximum voltage estimation system on solar panels by considering solar radiation and air temperature. The Mean Squared Error (MSE) parameter is used as a performance parameter of the artificial neural network used. The results showed a significant relationship between solar radiation, air temperature and voltage on solar panels made from polycrystalline. Testing and evaluation of the estimation system is done using independent data that has never been seen before. The MSE result in the test is 9.9602e-12 which has met the MSE target set in this study.

AB - The utilization of solar panels as an effort to implement new and renewable energy still has challenges in optimizing the performance of solar panels to produce electrical power efficiently. Therefore, a careful planning and operational process is needed to maximize the performance of solar panels. One of the important steps to plan carefully the process of installing solar panels is to estimate the production of electrical power generated by solar panels. This research was conducted to design a maximum voltage estimation system on solar panels by considering solar radiation and air temperature. The Mean Squared Error (MSE) parameter is used as a performance parameter of the artificial neural network used. The results showed a significant relationship between solar radiation, air temperature and voltage on solar panels made from polycrystalline. Testing and evaluation of the estimation system is done using independent data that has never been seen before. The MSE result in the test is 9.9602e-12 which has met the MSE target set in this study.

KW - Maximum Voltage

KW - Mean Squared Error (MSE)

KW - Solar Panel

UR - https://www.scopus.com/pages/publications/85186536615

U2 - 10.1109/ICAMIMIA60881.2023.10427794

DO - 10.1109/ICAMIMIA60881.2023.10427794

M3 - Conference contribution

AN - SCOPUS:85186536615

T3 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings

SP - 859

EP - 863

BT - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023

Y2 - 14 November 2023 through 15 November 2023

ER -

Shoffiana NA, Suyanto , Abadi I, Pertiwi NK, Damayanti AA, Hartati AD et al. PV Maximum Power-Point Tracking by Using Artificial Neural Network. In 2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. p. 859-863. (2023 International Conference on Advanced Mechatronics, Intelligent Manufacture and Industrial Automation, ICAMIMIA 2023 - Proceedings). doi: 10.1109/ICAMIMIA60881.2023.10427794

PV Maximum Power-Point Tracking by Using Artificial Neural Network

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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