Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

Anang Tjahjono; Ony Asraul Qudsi; Novie Ayub Windarko; Dimas Okky Anggriawan; Ardyono Priyadi; Mauridhi Hery Purnomo

doi:10.1109/MICEEI.2014.7067301

Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

Anang Tjahjono
, Ony Asraul Qudsi
, Novie Ayub Windarko
, Dimas Okky Anggriawan
, Ardyono Priyadi
, Mauridhi Hery Purnomo

Electronic Engineering Polytechnic Institute of Surabaya
Institut Teknologi Sepuluh Nopember

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

16 Citations (Scopus)

Abstract

This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC-DC boost converter is connected between the PV module and the load. Duty cycle of DC-DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m² and 1000W/m², respectively.

Original language	English
Title of host publication	Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014
Editors	Elyas Palantei
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	14-19
Number of pages	6
ISBN (Electronic)	9781479967261
DOIs	https://doi.org/10.1109/MICEEI.2014.7067301
Publication status	Published - 24 Mar 2014
Event	2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014 - Makassar, Indonesia Duration: 26 Nov 2014 → 30 Nov 2014

Publication series

Name	Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014

Conference

Conference	2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014
Country/Territory	Indonesia
City	Makassar
Period	26/11/14 → 30/11/14

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

ANFIS
DC-DC boost converter
MPPT
Photovoltaic module

Access to Document

10.1109/MICEEI.2014.7067301

Cite this

Tjahjono, A., Qudsi, O. A., Windarko, N. A., Anggriawan, D. O., Priyadi, A., & Purnomo, M. H. (2014). Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System. In E. Palantei (Ed.), Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014 (pp. 14-19). Article 7067301 (Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MICEEI.2014.7067301

Tjahjono, Anang ; Qudsi, Ony Asraul ; Windarko, Novie Ayub et al. / Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System. Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014. editor / Elyas Palantei. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 14-19 (Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014).

@inproceedings{34aad8f4ac9041e8ac20bdf61c6b612c,

title = "Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System",

abstract = "This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC-DC boost converter is connected between the PV module and the load. Duty cycle of DC-DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P\&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m2 and 1000W/m2, respectively.",

keywords = "ANFIS, DC-DC boost converter, MPPT, Photovoltaic module",

author = "Anang Tjahjono and Qudsi, \{Ony Asraul\} and Windarko, \{Novie Ayub\} and Anggriawan, \{Dimas Okky\} and Ardyono Priyadi and Purnomo, \{Mauridhi Hery\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014 ; Conference date: 26-11-2014 Through 30-11-2014",

year = "2014",

month = mar,

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doi = "10.1109/MICEEI.2014.7067301",

language = "English",

series = "Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014",

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

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}

Tjahjono, A, Qudsi, OA, Windarko, NA, Anggriawan, DO, Priyadi, A & Purnomo, MH 2014, Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System. in E Palantei (ed.), Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014., 7067301, Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014, Institute of Electrical and Electronics Engineers Inc., pp. 14-19, 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014, Makassar, Indonesia, 26/11/14. https://doi.org/10.1109/MICEEI.2014.7067301

Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System. / Tjahjono, Anang; Qudsi, Ony Asraul; Windarko, Novie Ayub et al.
Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014. ed. / Elyas Palantei. Institute of Electrical and Electronics Engineers Inc., 2014. p. 14-19 7067301 (Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014).

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

TY - GEN

T1 - Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

AU - Tjahjono, Anang

AU - Qudsi, Ony Asraul

AU - Windarko, Novie Ayub

AU - Anggriawan, Dimas Okky

AU - Priyadi, Ardyono

AU - Purnomo, Mauridhi Hery

PY - 2014/3/24

Y1 - 2014/3/24

N2 - This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC-DC boost converter is connected between the PV module and the load. Duty cycle of DC-DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m2 and 1000W/m2, respectively.

AB - This paper proposes an intelligent control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) for maximum power point tracking (MPPT) of PV module. The method is verified under several irradiance and temperature conditions. DC-DC boost converter is connected between the PV module and the load. Duty cycle of DC-DC boost converter is controlled by ANFIS in order to obtain the MPPT. The ANFIS directly takes operating power and voltage level as input. The proposed system is developed under Simulink-Matlab and the system of PV is simulated in PSIM to verify the effectiveness of method. The results show the proposed method can obtain the highest output power than Fuzzy Logic (FL) and Perturbation and Observation (P&O) method i.e., 30.893 and 42.973 for irradiance is 750W/m2 and 1000W/m2, respectively.

KW - ANFIS

KW - DC-DC boost converter

KW - MPPT

KW - Photovoltaic module

UR - https://www.scopus.com/pages/publications/84946688327

U2 - 10.1109/MICEEI.2014.7067301

DO - 10.1109/MICEEI.2014.7067301

M3 - Conference contribution

AN - SCOPUS:84946688327

T3 - Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014

SP - 14

EP - 19

BT - Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014

A2 - Palantei, Elyas

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014

Y2 - 26 November 2014 through 30 November 2014

ER -

Tjahjono A, Qudsi OA, Windarko NA, Anggriawan DO, Priyadi A , Purnomo MH. Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System. In Palantei E, editor, Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 14-19. 7067301. (Proceeding - 2014 Makassar International Conference on Electrical Engineering and Informatics, MICEEI 2014). doi: 10.1109/MICEEI.2014.7067301

Photovoltaic module and maximum power point tracking modelling using Adaptive Neuro-Fuzzy Inference System

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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