Wind Farm Coordinated Control and Optimisation Tanvir Ahmad

272 Pages·2017·24.75 MB·English

Durham E-Theses
Wind Farm Coordinated Control and Optimisation
AHMAD, TANVIR
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Wind Farm Coordinated Control
and Optimisation
Tanvir Ahmad
School of Engineering & Computing Sciences
Durham University
This dissertation is submitted for the degree of
Doctor of Philosophy
Monday 6th November, 2017
Abstract
This thesis develops and implements computationally efficient and accurate wind
farm coordinated control strategies increasing energy per area by mitigating wake
losses. Simulations with data from the Brazos, Le Sole de Moulin Vieux (SMV) and
Lillgrund wind farms show an increase of up to 8% in farm production and up to
6% in efficiency. A live field implementation of coordinated control strategies show
that curtailing upstream turbine by up to 17% in full or near-full wake conditions can
increase downstream turbine’s production by up to 11%. To the best knowledge of
the author, this is the first practical implementation of Light Detection And Ranging
(LiDAR) based coordinated control strategies in an operating wind farm.
With coordinated control, upstream turbines are curtailed using coefficient of power
or yaw offsets in such a way that the decrease in upstream turbines’ production is
less than the increase in downstream turbines’ production resulting in net gain. This
optimum curtailment is achieved with on-line coordinated control which requires an
accurate and fast processing wind deficit model and an optimiser which achieves the
desired results with high processing speed using minimum overheads.
Performance evaluation of carefully selected optimisers was undertaken using an
objective function developed for increasing farm production based on coordinated
control. This evaluation concluded that Particle Swarm Optimisation (PSO) is the
most suitable optimiser for on-line coordinated control due to its high processing speed,
computational efficiency and solution quality.
The standard Jensen model was used as a starting point for developing a fast
processing and accurate wind deficit model referred to as the Turbulence Intensity
based Jensen Model (TI-JM), taking wake added turbulence intensity and deep array
effect into consideration. The TI-JM uses free-stream and wake-added turbulence
intensities for predicting effective values of wake decay coefficients deep inside the farm.
This model is validated using WindPRO and data from three wind farms case studies
as benchmarks.
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A methodology for assessing the impact of wakes on farm production is developed.
This methodology visualises wake effects (in 360°) by calculating power production
using data from the wind farms (case-studies). The wake affected wind conditions are
further analysed by calculating relative efficiency.
The innovative coordinated control strategies are evaluated using data from the
wind farms case studies and WindPRO as benchmarks. A live field implementation
of coordinated control strategies demonstrated that the production of downstream
turbines can be increased by curtailing upstream turbines. This field setup consisted
of two operating wind turbines equipped with modern LiDAR. Analyses of the high
frequency real time data were performed comparing field results with simulations. It
was found that simulations are in good agreement (within a range of 1.5%) with field
results.
Declaration
The work in this thesis is based on research carried out by Tanvir Ahmad under the
supervision of Dr. Peter Matthews and Dr. Behzad Kazemtabrizi within the School
of Engineering and Computing Sciences at the University of Durham in the United
Kingdom. No part of this thesis has been submitted elsewhere for any other degree or
qualification. It all my own work unless referenced to the contrary in the text.
Copyright © 2017 by Tanvir Ahmad
"The copyright of this thesis rests with the author. No quotations from it should be
published without the author’s prior written consent and information derived from it
should be acknowledged
Tanvir Ahmad
Monday 6th November, 2017

Dedication
I would like to dedicate this thesis to my loving family; especially to my parents who
taught me to dream big and to work hard for achieving those dreams.
Thank you all for encouraging and believing in me.
Acknowledgements
This thesis would not have been completed if it was not for the assistance of the many
people, who have helped me throughout the three years since November, 2013. I would
like to take this opportunity to thank them all. Specifically:
• My PhD supervisors; Dr. Peter Matthews and Dr. Behzad Kazemtabrizi, for
their continuous support, guidance and positive words throughout the duration
of this research.
• The Commonwealth Scholarship Commission (CSC) UK for funding this work.
(Scholarship reference no. PKCS-2013-384).
• The International Office Durham University, for funding my field visit to Maïa Eo-
lis (now Engie Green) France through the Erasmus + Staff Mobility Programme.
• The St Aidan’s College Durham University, for the Clark Travel Award for
supporting my work in Pakistan.
• Maïa Eolis (now Engie Green) France, for providing valuable data and providing
an opportunity to work on the French National Project SMARTEOLE (ANR-14-
CE05-0034).
• Dr. Nicolas Girard, Dr. Olivier Coupiac, Arthur Petit, Dr. Sophie Guignard
(all from Maïa Eolis (now Engine Green)) for sharing their invaluable insights,
perspectives and experiences.
• Pawel Gancarski, administrator Windbench: V&V Repositories for Wind Energy,
for providing useful information about the Lillgrund wind farm.
• DNVGL: Garrad Hassan & Partners Limited, for their support and free license
for the research version of WindFarmer software.
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• Dr. Christopher Crabtree for his support and assistance over the period of this
research.
• Christopher Smith for always giving me directions making this journey easy for
me.
• Mrs Sharne Procter, the Director International Office Durham University, for her
guidance, support and kind words making my stay in Durham very special.
• Hunza Zainab and Ifrah Idrees, the two interns from Lahore University of Man-
agement Sciences (LUMS) Pakistan, for their contribution towards this research
and the International Office Durham University for funding this internship.
• ThecolleaguesIhavemetaroundtheworldthroughoutmytravelsandthroughout
my time at Durham; for making the PhD tolerable.
• The people at Durham; Donatella Zappala, Jamie Godwin, Bindi Chen, Baseer
Ullah, Adnan Cheema and the many others for their support and joys in the
department.
• My family, for always believing in me. Without there support, this would not
have been possible and I would not be where I am today.
List of publications
1. Tanvir Ahmad, Olivier Coupiac, Arthur Petit, Sophie Guignard, Nicolas Girard,
Behzad Kazemtabrizi, and Peter C. Matthews. Field Implementation and Trial of
Coordinated Control of Wind Farms. IEEE Transactions on Sustainable Energy,
2017 (Approved)
2. Tanvir Ahmad, Peter C. Matthews, Behzad Kazemtabrizi, and Olivier Coupiac.
Intelligent and fast processing wind farm controller for production maximisation.
Journal of Renewable Energy, 2017 (under review)
Conference Papers
1. Tanvir Ahmad, Nicolas Girard, Behzad Kazemtabrizi, and Peter Matthews.
Analysis of two onshore wind farms with a dynamic farm controller. In European
Wind Energy Association (EWEA) Annual Conference, Paris France, November,
2015
2. T. Ahmad, P. Matthews, B. Kazemtabrizi. PSO based wind farm controller. In
The 11th edition of the International Conference on Evolutionary and Deter-
ministic Methods for Design, Optimization and Control with Applications to
Industrial and Societal Problems, EUROGEN-2015 Glasgow, UK, 560 2015, pp.
277 - 283
3. Tanvir Ahmad, Peter Matthews, Behzad Kazemtabrizi and Christopher J. Smith.
Dynamic Wind Farm Controller. In The 11th European Association of Wind
Energy (EAWE) PhD Seminar on Wind Energy in Europe, Stuttgart Germany,
September 2015 (Session Award Winner)
4. Tanvir Ahmad, Peter Matthews, and Behzad Kazemtabrizi. Wake Flow Model
for Wind Farm Control. In The 10th European Association of Wind Energy