Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems

Mostafa Ahmed; Mohamed Abdelrahem; Ralph Kennel; Christoph M. Hackl

doi:10.1109/ISIE45063.2020.9152256

Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems

Mostafa Ahmed
, Mohamed Abdelrahem
, Ralph Kennel
, Christoph M. Hackl

Chair of High-Power Converter Systems

Technical University of Munich
Munich University of Applied Sciences

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

10 Scopus citations

Abstract

The variability of photovoltaic (PV) energy because of atmospheric conditions dependency necessitates employing a maximum power point tracking (MPPT) technique in the installed PV systems. So in this paper, finite control set model predictive control (FCS-MPC) is used to extract the maximum power from the PV source using a boost converter as interfacing circuit. The proposed technique combines FCS-MPC with an extended Kalman filter (EKF) to reduce the number of required sensors. The EKF is used to estimate both of the PV current and the capacitor voltage. This eliminates two sensors circuits from the PV system, which decreases the system cost. The proposed technique is validated by simulation results under different operating conditions.

Original language	English
Title of host publication	2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1039-1044
Number of pages	6
ISBN (Electronic)	9781728156354
DOIs	https://doi.org/10.1109/ISIE45063.2020.9152256
State	Published - Jun 2020
Event	29th IEEE International Symposium on Industrial Electronics, ISIE 2020 - Delft, Netherlands Duration: 17 Jun 2020 → 19 Jun 2020

Publication series

Name	IEEE International Symposium on Industrial Electronics
Volume	2020-June

Conference

Conference	29th IEEE International Symposium on Industrial Electronics, ISIE 2020
Country/Territory	Netherlands
City	Delft
Period	17/06/20 → 19/06/20

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

EKF
FCS-MPC
MPPT
PV systems
Sensorless control
boost converter.

Access to Document

10.1109/ISIE45063.2020.9152256

Cite this

Ahmed, M., Abdelrahem, M., Kennel, R., & Hackl, C. M. (2020). Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings (pp. 1039-1044). Article 9152256 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE45063.2020.9152256

Ahmed, Mostafa ; Abdelrahem, Mohamed ; Kennel, Ralph et al. / Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems. 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 1039-1044 (IEEE International Symposium on Industrial Electronics).

@inproceedings{78f9fbb6d6804a3b8dc69f8201a501ef,

title = "Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems",

abstract = "The variability of photovoltaic (PV) energy because of atmospheric conditions dependency necessitates employing a maximum power point tracking (MPPT) technique in the installed PV systems. So in this paper, finite control set model predictive control (FCS-MPC) is used to extract the maximum power from the PV source using a boost converter as interfacing circuit. The proposed technique combines FCS-MPC with an extended Kalman filter (EKF) to reduce the number of required sensors. The EKF is used to estimate both of the PV current and the capacitor voltage. This eliminates two sensors circuits from the PV system, which decreases the system cost. The proposed technique is validated by simulation results under different operating conditions.",

keywords = "EKF, FCS-MPC, MPPT, PV systems, Sensorless control, boost converter.",

author = "Mostafa Ahmed and Mohamed Abdelrahem and Ralph Kennel and Hackl, \{Christoph M.\}",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 29th IEEE International Symposium on Industrial Electronics, ISIE 2020 ; Conference date: 17-06-2020 Through 19-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ISIE45063.2020.9152256",

language = "English",

series = "IEEE International Symposium on Industrial Electronics",

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

pages = "1039--1044",

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Ahmed, M, Abdelrahem, M, Kennel, R & Hackl, CM 2020, Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems. in 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings., 9152256, IEEE International Symposium on Industrial Electronics, vol. 2020-June, Institute of Electrical and Electronics Engineers Inc., pp. 1039-1044, 29th IEEE International Symposium on Industrial Electronics, ISIE 2020, Delft, Netherlands, 17/06/20. https://doi.org/10.1109/ISIE45063.2020.9152256

Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems. / Ahmed, Mostafa; Abdelrahem, Mohamed; Kennel, Ralph et al.
2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2020. p. 1039-1044 9152256 (IEEE International Symposium on Industrial Electronics; Vol. 2020-June).

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

TY - GEN

T1 - Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems

AU - Ahmed, Mostafa

AU - Abdelrahem, Mohamed

AU - Kennel, Ralph

AU - Hackl, Christoph M.

PY - 2020/6

Y1 - 2020/6

N2 - The variability of photovoltaic (PV) energy because of atmospheric conditions dependency necessitates employing a maximum power point tracking (MPPT) technique in the installed PV systems. So in this paper, finite control set model predictive control (FCS-MPC) is used to extract the maximum power from the PV source using a boost converter as interfacing circuit. The proposed technique combines FCS-MPC with an extended Kalman filter (EKF) to reduce the number of required sensors. The EKF is used to estimate both of the PV current and the capacitor voltage. This eliminates two sensors circuits from the PV system, which decreases the system cost. The proposed technique is validated by simulation results under different operating conditions.

AB - The variability of photovoltaic (PV) energy because of atmospheric conditions dependency necessitates employing a maximum power point tracking (MPPT) technique in the installed PV systems. So in this paper, finite control set model predictive control (FCS-MPC) is used to extract the maximum power from the PV source using a boost converter as interfacing circuit. The proposed technique combines FCS-MPC with an extended Kalman filter (EKF) to reduce the number of required sensors. The EKF is used to estimate both of the PV current and the capacitor voltage. This eliminates two sensors circuits from the PV system, which decreases the system cost. The proposed technique is validated by simulation results under different operating conditions.

KW - EKF

KW - FCS-MPC

KW - MPPT

KW - PV systems

KW - Sensorless control

KW - boost converter.

UR - https://www.scopus.com/pages/publications/85089475646

U2 - 10.1109/ISIE45063.2020.9152256

DO - 10.1109/ISIE45063.2020.9152256

M3 - Conference contribution

AN - SCOPUS:85089475646

T3 - IEEE International Symposium on Industrial Electronics

SP - 1039

EP - 1044

BT - 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 29th IEEE International Symposium on Industrial Electronics, ISIE 2020

Y2 - 17 June 2020 through 19 June 2020

ER -

Ahmed M, Abdelrahem M, Kennel R, Hackl CM. Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems. In 2020 IEEE 29th International Symposium on Industrial Electronics, ISIE 2020 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2020. p. 1039-1044. 9152256. (IEEE International Symposium on Industrial Electronics). doi: 10.1109/ISIE45063.2020.9152256

Maximum Power Point Tracking Based Model Predictive Control and Extended Kalman Filter Using Single Voltage Sensor for PV Systems

Abstract

Publication series

Conference

UN SDGs

Keywords

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