Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems

Mahinsasa Narayana; Keith Sunderland; Ghanim Putrus; Michael Conlon

doi:10.1109/UPEC.2018.8541911

Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems

Mahinsasa Narayana
, Keith Sunderland
, Ghanim Putrus
, Michael Conlon

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

Abstract

Wind Energy Conversion Systems (WECS) cannot react instantaneously to wind speed variations. Therefore, the performance of the system and the energy conversion capability of the rotor - particularly for urban locations, characterized as having an erratic wind resource - are negatively affected. This study investigates the use of a time series technique as a means to improve the performance and conversion efficiency of wind turbines. In this regard, simulation of the wind sensor maximum power point tracking (MPPT) method (ideal situation) through MATLAB-Simulink™ is considered. Analysis of the Adaptive Linear Prediction (ALP) technique as a real time control reference to improve optimal control of wind turbines suggests that predictive techniques such as ALP can offer increased energy yield opportunities. Indeed, the energy capture technique investigated in this study, suggested that an energy yield increase in excess of 5% is achievable.

Original language	English
Title of host publication	Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781538629109
DOIs	https://doi.org/10.1109/UPEC.2018.8541911
Publication status	Published - 20 Nov 2018
Externally published	Yes
Event	53rd International Universities Power Engineering Conference, UPEC 2018 - Glasgow, United Kingdom Duration: 4 Sep 2018 → 7 Sep 2018

Publication series

Name	Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018

Conference

Conference	53rd International Universities Power Engineering Conference, UPEC 2018
Country/Territory	United Kingdom
City	Glasgow
Period	4/09/18 → 7/09/18

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Linear adaptive prediction
Power mapping technique
Wind energy conversion systems
Wind speed estimation
Wind turbine

Access to Document

10.1109/UPEC.2018.8541911

Cite this

Narayana, M., Sunderland, K., Putrus, G., & Conlon, M. (2018). Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems. In Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018 Article 8541911 (Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UPEC.2018.8541911

Narayana, Mahinsasa ; Sunderland, Keith ; Putrus, Ghanim et al. / Maximum Power Point Tracking : Adaptation Linear Prediction for Wind Energy Conversion Systems. Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. (Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018).

@inproceedings{d74acb30669c47fcacd51f061ef06aa1,

title = "Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems",

abstract = "Wind Energy Conversion Systems (WECS) cannot react instantaneously to wind speed variations. Therefore, the performance of the system and the energy conversion capability of the rotor - particularly for urban locations, characterized as having an erratic wind resource - are negatively affected. This study investigates the use of a time series technique as a means to improve the performance and conversion efficiency of wind turbines. In this regard, simulation of the wind sensor maximum power point tracking (MPPT) method (ideal situation) through MATLAB-Simulink{\texttrademark} is considered. Analysis of the Adaptive Linear Prediction (ALP) technique as a real time control reference to improve optimal control of wind turbines suggests that predictive techniques such as ALP can offer increased energy yield opportunities. Indeed, the energy capture technique investigated in this study, suggested that an energy yield increase in excess of 5\% is achievable.",

keywords = "Linear adaptive prediction, Power mapping technique, Wind energy conversion systems, Wind speed estimation, Wind turbine",

author = "Mahinsasa Narayana and Keith Sunderland and Ghanim Putrus and Michael Conlon",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 53rd International Universities Power Engineering Conference, UPEC 2018 ; Conference date: 04-09-2018 Through 07-09-2018",

year = "2018",

month = nov,

day = "20",

doi = "10.1109/UPEC.2018.8541911",

language = "English",

series = "Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018",

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

booktitle = "Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018",

address = "United States",

}

Narayana, M, Sunderland, K, Putrus, G & Conlon, M 2018, Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems. in Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018., 8541911, Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018, Institute of Electrical and Electronics Engineers Inc., 53rd International Universities Power Engineering Conference, UPEC 2018, Glasgow, United Kingdom, 4/09/18. https://doi.org/10.1109/UPEC.2018.8541911

Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems. / Narayana, Mahinsasa; Sunderland, Keith; Putrus, Ghanim et al.
Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. 8541911 (Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018).

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

TY - GEN

T1 - Maximum Power Point Tracking

T2 - 53rd International Universities Power Engineering Conference, UPEC 2018

AU - Narayana, Mahinsasa

AU - Sunderland, Keith

AU - Putrus, Ghanim

AU - Conlon, Michael

PY - 2018/11/20

Y1 - 2018/11/20

N2 - Wind Energy Conversion Systems (WECS) cannot react instantaneously to wind speed variations. Therefore, the performance of the system and the energy conversion capability of the rotor - particularly for urban locations, characterized as having an erratic wind resource - are negatively affected. This study investigates the use of a time series technique as a means to improve the performance and conversion efficiency of wind turbines. In this regard, simulation of the wind sensor maximum power point tracking (MPPT) method (ideal situation) through MATLAB-Simulink™ is considered. Analysis of the Adaptive Linear Prediction (ALP) technique as a real time control reference to improve optimal control of wind turbines suggests that predictive techniques such as ALP can offer increased energy yield opportunities. Indeed, the energy capture technique investigated in this study, suggested that an energy yield increase in excess of 5% is achievable.

AB - Wind Energy Conversion Systems (WECS) cannot react instantaneously to wind speed variations. Therefore, the performance of the system and the energy conversion capability of the rotor - particularly for urban locations, characterized as having an erratic wind resource - are negatively affected. This study investigates the use of a time series technique as a means to improve the performance and conversion efficiency of wind turbines. In this regard, simulation of the wind sensor maximum power point tracking (MPPT) method (ideal situation) through MATLAB-Simulink™ is considered. Analysis of the Adaptive Linear Prediction (ALP) technique as a real time control reference to improve optimal control of wind turbines suggests that predictive techniques such as ALP can offer increased energy yield opportunities. Indeed, the energy capture technique investigated in this study, suggested that an energy yield increase in excess of 5% is achievable.

KW - Linear adaptive prediction

KW - Power mapping technique

KW - Wind energy conversion systems

KW - Wind speed estimation

KW - Wind turbine

UR - https://www.scopus.com/pages/publications/85059933948

U2 - 10.1109/UPEC.2018.8541911

DO - 10.1109/UPEC.2018.8541911

M3 - Conference contribution

AN - SCOPUS:85059933948

T3 - Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018

BT - Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 4 September 2018 through 7 September 2018

ER -

Narayana M, Sunderland K, Putrus G, Conlon M. Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems. In Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. 8541911. (Proceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018). doi: 10.1109/UPEC.2018.8541911

Maximum Power Point Tracking: Adaptation Linear Prediction for Wind Energy Conversion Systems

Abstract

Publication series

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UN SDGs

Keywords

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