The Effect of Control Strategy on Tidal Stream Turbine Performance in Laboratory and Field Experiments

Abstract

The first aim of the research presented here is to examine the effect of turbine control by comparing a passive open-loop control strategy with a constant rotational speed proportional–integral–derivative (PID) feedback loop control applied to the same experimental turbine. The second aim is to evaluate the effect of unsteady inflow on turbine performance by comparing results from a towing-tank, in the absence of turbulence, with results from the identical machine in a tidal test site. The results will also inform the reader of: (i) the challenges of testing tidal turbines in unsteady tidal flow conditions in comparison to the controlled laboratory environment; (ii) calibration of acoustic Doppler flow measurement instruments; (iii) characterising the inflow to a turbine and identifying the uncertainties from unsteady inflow conditions by adaptation of the International Electrotechnical Commission technical specification (IEC TS): 62600-200. The research shows that maintaining a constant rotational speed with a control strategy yields a 13.7% higher peak power performance curve in the unsteady flow environment, in comparison to an open-loop control strategy. The research also shows an 8.0% higher peak power performance in the lab compared to the field, demonstrating the effect of unsteady flow conditions on power performance. The research highlights the importance of a tidal turbines control strategy when designing experiments.

Full text

energies
Article
The Effect of Control Strategy on Tidal Stream
Turbine Performance in Laboratory and
Field Experiments
Carwyn Frost 1, *ID , Ian Benson 1, Penny Jeffcoate 2, Björn Elsäßer 3ID and Trevor Whittaker 1
1School of Natural and Built Environment, Queens University Belfast, David Keir Building,
Queen’s University, Belfast BT7 1NN, UK; ian.benson@qub.ac.uk (I.B.); t.whittaker@qub.ac.uk (T.W.)
2Sustainable Marine Energy, Edinburgh EH6 6QW, UK; penny.jeffcoate@sustainablemarine.com
3
Danish Hydraulics Institute (DHI), Ports and Offshore Technology, Agern Allé5, 2970 Hørsholm, Danmark;
bje@dhigroup.com
*Correspondence: c.frost@qub.ac.uk; Tel.: +44-289-097-4012
Received: 9 May 2018; Accepted: 5 June 2018; Published: 12 June 2018


Abstract:
The first aim of the research presented here is to examine the effect of turbine
control by comparing a passive open-loop control strategy with a constant rotational speed
proportional–integral–derivative (PID) feedback loop control applied to the same experimental
turbine. The second aim is to evaluate the effect of unsteady inflow on turbine performance by
comparing results from a towing-tank, in the absence of turbulence, with results from the identical
machine in a tidal test site. The results will also inform the reader of: (i) the challenges of testing tidal
turbines in unsteady tidal flow conditions in comparison to the controlled laboratory environment;
(ii) calibration of acoustic Doppler flow measurement instruments; (iii) characterising the inflow to
a turbine and identifying the uncertainties from unsteady inflow conditions by adaptation of the
International Electrotechnical Commission technical specification (IEC TS): 62600-200. The research
shows that maintaining a constant rotational speed with a control strategy yields a 13.7% higher peak
power performance curve in the unsteady flow environment, in comparison to an open-loop control
strategy. The research also shows an 8.0% higher peak power performance in the lab compared to
the field, demonstrating the effect of unsteady flow conditions on power performance. The research
highlights the importance of a tidal turbines control strategy when designing experiments.
Keywords: tidal energy; experimental testing; acoustic Doppler profiler; Strangford Lough
1. Introduction
The economic feasibility of offshore wind energy has reached unprecedentedly low strike prices in
the recent Contracts for Difference (CfD) auction in the UK at £57.50/MWh in 2022/23, dropping 43%
since 2012 [
1
]. The offshore renewable energy sector must drive to deliver other significant technologies
that can achieve competitive levelised cost of electricity (LCOE). To deliver further cost reductions
in an economically sustainable manner, developers must improve their technical understanding of a
technology and gain experience of its performance in the real marine environment.
Even though numerical models and various design codes and methods for rotational power
generators such as wind turbines and hydropower turbines are well established and in general
yield excellent results, these models still require validation through experimental testing [
2
,
3
].
It is noted from this model scale testing that Reynolds independence is a key consideration for
non-dimensional comparison.
To date there is limited published research on the experimental testing of prototype devices in the
marine environment. However, Gaurier et al. [
4
] published a study comparing the test results from the
Energies 2018,11, 1533; doi:10.3390/en11061533 www.mdpi.com/journal/energies

Energies 2018,11, 1533 2 of 16
same turbine undertaken in different lab facilities. They conclude that turbulence characteristics in
lab facilities need to be described more adequately to better assess performance results. Furthermore,
Mycek et al. studied the effect of turbulence intensity on a model turbine in a circulating flume.
The range of turbulence intensities used (3% and 15%) may be typical of what is found at a tidal test
site and the results show that this change in intensity has a near negligible effect on the time averaged
performance of the turbine [5].
To support the industry in understanding the challenges of tidal turbine performance assessment a
series of studies have been undertaken by Queen’s University Belfast at a significant scale in tank, lake and
tidal environments. The experimental campaigns are described in Table 1and related publications are cited.
Table 1. Tidal Turbine Testing (TTT) project history.
Project Date Experiment Description Publications
TTT 2013–2014 ◦Tandem pushing tests in Montgomery Lake
◦Moored tandem tests in Strangford Narrows [6–8]
TTT 2 2014–2015
◦MaRINET (Marine Renewables Infrastructure Network)
transnational access to Consiglio Nazionale delle
Ricerche-Istituto nazionale per studi ed esperienze di
architettura navale (CNR-INSEAN) for turbine tests
◦MaRINET transnational access to Strangford Narrows for
SCHOTTEL Hydro STG 50 turbine (SCHOTTEL,
Spay, Germany) tests
[9–12]
TTT 3 2015–2017 ◦Moored single turbine testing in Strangford Narrows
◦Towing tank testing in CNR-INSEAN with Wave and
Yaw Angles
[13]
From this previous research and literature review it has been reported that experimental testing
in an unsteady marine environment results in power performance disparities when compared with
steady flow testing. Jeffcoate et al. [
7
] reports a 24% reduction in C
P
between steady and unsteady
flows. Meanwhile, Starzmann et al. [
11
] report comparable power performance between steady and
unsteady flows (only a 5% change in C
P
); however, the thrust performance was higher in the unsteady
conditions relative to the steady conditions. In addition to this previous work, applicable studies
have been conducted by other groups at a similar scale. The work of Forbush et al. [
14
] reports an
8% increase in performance with turbulent flow; however, the cross-flow tidal turbine used in this
research sets it apart as a unique result, which may be specific to the rotor type. Blackmore et al. [
15
]
performed repeat experiments in a circulating flume, the inflow characteristics were varied using a
grid. The inflow velocity was maintained, while the Turbulence Intensity (TI) and length scale varied.
The findings showed a 10% reduction in C
P
with an increase in TI, conversely an increase in length
scale resulted in an increase in CP.
In all field experiment cases, the cause of power disparity between lab and field is partly attributed
to uncertainty in the experiments. Further consideration of the source of uncertainty has identified
two critical contributing factors:
i.
A crucial factor in the accuracy of performance measurements arises from the control strategy
imposed on the tidal turbine. The capability of the control system to keep the rotor operating
near its optimum Tip Speed Ratio (TSR), when driven by an unsteady and non-uniform
inflow velocity, and avoiding ‘stall’, is a key element in a successful commercial turbine.
Similarly, the control strategy adopted during an experiment can increase the error bounds
in derived performance indicators, such as C
P
. Control strategies used in previous tidal
turbine performance testing vary. For devices at low Technology Readiness Level (TRL)
scales [
16
], such as those used in laboratory scale experiments, an open-loop control may
suffice [
9
]. However, more commonly, a closed-loop, proportional–integral–derivative (PID)

Energies 2018,11, 1533 3 of 16
feedback control strategy is found [
17
,
18
] in higher TRL devices. It is recognised that in
order to develop the industry, advanced control strategies require development and testing
in highly parameterised conditions. Efforts are being made in this field in both research and
industry. The use of overspeed, pitch or stall control strategies with peak power tracking, or
surface mapping algorithms for condition monitoring purposes are under development [
19
,
20
].
Furthermore, future options may include the possibility of feed-forward algorithms such as
those being trialled in the wind sector [21].
ii.
The second source of increased uncertainty comes from the increased variability of the inflow
velocity. This is of particularly significance since the power density scales with the cube of the
inflow velocity. Furthermore, both genuine variability and sampling errors are compounded
in real velocimetry data. Separating and quantifying their effects requires care in calibration
of instruments and data analysis. In order to promote consistent best practice in the power
performance testing of Tidal Energy Converters (TECs), Johnstone et al. published best practices
for the wave and tidal sector [
22
] which specifies the requirements for clear uncertainty analysis.
Further to this the IEC (Geneva, Switzerland) published a Technical Specification IEC/TS
62600-200 [
23
]. The specification provides the methodology for determining an average value
for velocity at a site, enabling the time average performance of a turbine to be captured and
reported to a common standard. The IEC specification has been used in other research projects
and across the industry [10,13,24] and will be used to guide the data analysis in this paper.
In summary, the literature review has highlighted experiments where turbine testing in the
lab has been compared to the field. The control strategy and inflow measurements are significant
parameters that must reflect the device TRL and testing environment. In order to inform the sector of
the significance of these parameters, the TTT turbine will be deployed in the lab and field, the derived
performance results will be compared for two control strategies, and in a steady and unsteady inflow
environment. The lab experiments will be used as an opportunity to calibrate and derive uncertainty
metrics for the inflow, this will then be applied to the field experiments and performances compared.
2. Experimental Setup
Under the TTT 3 project, both the laboratory and field tests were conducted using the same
instrument, maintaining continuity between experiments. This was achieved by using the same turbine,
velocity instruments and data acquisition system. The laboratory experiments were undertaken in the
controlled steady environment of the towing tank facility CNR-INSEAN, Italy. The field test experiments
were performed in the uncontrolled, unsteady environment of the tidal test site in Strangford Narrows,
Northern Ireland (Lat.: 54.381801◦, Long.: −5.556743◦).
The 1.5 m diameter turbine is pictured during operations in Strangford Lough in 2016 (Figure 1);
the device dimensions can be found in the supplementary appendix of previous work [
6
] and remain
consistent in this work. The rotor blades are an Eppler E387 airfoil geometry, each blade is 0.575 m long
and features a pitch distribution similar to industrial wind turbine blades, with a root pitch angle of
32
◦
which recedes to 13
◦
at the tip of the blade. As this is not a commercial blade and is not designed to
be scaled the significance of Reynolds independency is limited.
Figure 1. Tidal Turbine Testing (TTT) device and rotor.

Energies 2018,11, 1533 4 of 16
2.1. Flow Instrumentation
Acoustic Doppler profilers (ADPs) are often used for tidal flow resource assessment, and are
the given method for characterising inflow conditions by the IEC [
23
]. The Nortek 2 MHz
‘Aquadopp’ used in the experiments was the primary instrument for determining inflow velocity in
these experiments.
The experimental setup shown in this section relates to all work as part of the TTT 3 project.
Please refer to referenced papers for further details on previous experimental setups [
6
,
9
].
The supporting gantry also hosts a Nortek ‘Vector’ Acoustic Doppler Velocimetry (ADV) with the
sample volume located at the apex of swept area at 16 Hz, this provided insight into the inflow
turbulence. The deployment parameters for both instruments are given below. In accordance with
the IEC standards for power performance assessment, the inflow measurements were taken at an
upstream distance of between 2–5 equivalent diameters from the plane of rotation [23].
The geometrical setup can be seen in Figure 2and is detailed in Table 2, where the parameter
D2 has two values: one for the towing tank and one for Strangford Lough tests, respectively.
This difference was due to the location of suitable mounting points in the towing carriage.
Figure 2.
Turbine, Acoustic Doppler profiler (ADP) and Acoustic Doppler Velocimetry (ADV)
deployment Configuration.

Energies 2018,11, 1533 5 of 16
Table 2.
Turbine, Acoustic Doppler profiler (ADP) and Acoustic Doppler Velocimetry (ADV)
deployment parameters.
Parameter ADP ADV
Strangford CNR-INSEAN Strangford CNR-INSEAN
Distance D1 (m) 0.30 m 0.30 m 0.86 m 0.86 m
Distance D2 (m) 2.99 m 3.80 m 3.10 m 3.7 m
Distance D3 (m) 1.75 m 1.75 m 1.75 m 1.75 m
Power high high high high
Transmit length N/A N/A 8 mm 8 mm
Number of cells 20 20 N/A N/A
Cell Size (m) 0.25 m 0.25 m N/A N/A
Blanking Distance (m) 0.25 m 0.25 m N/A N/A
Co-ordinate System Beam Beam Beam Beam
Sample Frequency
(Hz) 1 Hz 1 Hz 16 Hz 16 Hz
Sample Period (s) 120–600 s 90–140 s 120–600 s 90–140 s
2.2. Flow Instrumentation Validation
Many ADP instruments have previously been independently calibrated and validated to verify the
quality of factory settings. Shih et al. [
25
] demonstrated the close agreement between calibrations for
two different ADP suppliers, RDI (Poway, CA, USA) & SonTek (San Diego, CA, USA) in a towing tank
facility in 2000. More recently, Oberg et al. [
26
] performed similar experiments with the most recent
instrument firmware, again using RDI and SonTek instruments. Little work has been found which
independently calibrates the Nortek Aquadopp ADP used in this experiment; however, Elsäβer et al.
showed discrepancies in time averaged velocities of up to 0.19 ms
−1
between two instruments
collocated in the field [
27
]. In order to ensure instrument accuracy, the ADP (2 MHz Aquadopp)
was calibrated during the towing tests under conditions as close as were possible to the field. Seeding
of the towing tank was required to improve the Signal to Noise Ratio (SNR).
To validate this ADP calibration against another independent instrument while working in the
field, the Nortek Vector ADV was likewise calibrated during the tank work. 16 Hz Vector data
was averaged over 1-s bins to match the ADP (2 MHz Aquadopp) data bins. All the calibration
results are given in Table 3and show the deviation of the instruments from their factory calibration.
Each calibration equations were derived for U, V and W from the towing tests for the Aquadopp (ADP)
and Vector (ADV) instruments. The linear equations demonstrate the precision and bias drift since
the previous calibration (in mm/s) as yielded by the Nortek transform matrices for each instrument.
Note the signs are chosen to make the output components have the same axis convention for both
instruments. U is the main direction of inflow, along the turbine axis.
Table 3. Deviation of Nortek instruments from factory calibration.
Nortek Transform Matrix Output ADP—Aquadopp ADV—Vector
x U = −1.0124x + 4.97 U = −1.0042x + 6.4
y V = −1.0124y + 0 V = +1.0042y + 0
z W = −1.0124z + 0 W = −1.0042z + 0
Bias in U (1 σ)−0.03% +0.05%
Precision in U (1 σ)±0.6% ±0.9%
In summary, the calibration performed in the towing tank agrees closely with the factory
calibration, showing less than a percentage difference in bias and precision. The same individual
instruments, deployment parameters and locations were used during both the laboratory and fieldwork
testing. Further validation of the results has been undertaken using the fieldwork data, further detail

Energies 2018,11, 1533 6 of 16
can be found in Appendix A. This provides confidence in instrument accuracy and draws attention to
the high spatial variability in tidal flows, as discussed by Elsäβer et al. [27].
2.3. Control Strategy
The control of wind turbines is an established and mature area of technology. The control strategy
involves multiple inputs and outputs [
28
] and more than one control loop, often extending to variables
intended to optimise the performance of an array and its grid interface.
In contrast, the original TTT 2 experimental design was an open-loop load control, as shown
in Figure 3. Control was provided by a binary array of resistors (5 values, each the double of the
previous: 10.25 ohms, 20.5 ohms etc.). Thus, 32 load values could be placed on the DC-bus by switching
combinations in and out. Electromechanical contactors select the load on command from the central
Data Acquisition System (DAQ), based on a National Instruments Compact Rio (cRio) running Labview
and further custom-made interface electronics and cabling.
Figure 3. TTT 2 open-loop control and binary load.
In developing the control system, a single control loop was included with only one process
variable as the input (the rotor shaft speed) and one output (the electrical demand placed on the
alternator). A review of Proportional–Integral–Derivative (PID) control theory, developed in the mid
20th century, is presented by Bennet [
29
]. In order to incorporate a closed-loop PID load control,
a linear regulator was used to place an electronic load on the turbine at the DC output terminals.
The upgraded PID control loop is shown in Figure 4. An absolute rotary encoder was included to
provide higher resolution of shaft speed for the PID loop.
The PID control system was run as a Labview virtual instrument in the DAQ programme,
which was written specifically for the project. Most of the programme’s other features were identical
for both TTT 2 (open-loop) and TTT 3 (PID; see Table 1for project details). To control the turbine for
the TTT 3 system, the user selects a chosen shaft speed (set point) and the system then attempts to
maintain that speed independently of flow features in the field, or of towing speed in the laboratory.
The PID parameters used were identical in both the field and towing tank; all the results under PID
control were collected with the parameters set as in Table 4.

Energies 2018,11, 1533 7 of 16
Figure 4. TTT 3 closed-loop Proportional–Integral–Derivative (PID) control and programmable load.
Table 4. Proportional-Integral-Differential parameters.
Symbol Description Value
Kc Proportional constant 2.2 (no dimensions)
Ti
Integration time constant
0.003 (min)
Td
Differential time constant
0.001 (min)
The proportional constant, Kc or gain was either positive or negative, depending on the rotational
direction of the driveshaft. This also applied to the set RPM (Rotations per Minute) value in the
control system. The tuning of the loop parameters was done in the field with the Zeigler and Nichols
‘ultimate cycling’ method [
30
], followed by some minor optimisation by trial and error. It is worth
noting that the ultimate cycling method gave adequate robustness and precision of control for our
purposes and was only slightly adjusted with subsequent trial and error.
3. Non-Dimensional Performance Characteristics
As previously noted, the DAQ features a National Instrument Compact Rio, the cRio synchronises
the various instrument and control data streams into a common format, timestamps them and outputs
them as a data file for post-processing.
To compare and analyse the performance of the turbine between the various experimental
campaigns, the non-dimensional performance characteristics will be derived. These are shown in
Equations (1) and (2) and provide the Tip Speed Ratio (TSR) and Coefficient of Power (CP).
TSRi=ωi×r
Ui
(1)
CP,i=Pi
1
2×ρ×A×Ui3(2)
where the rotational speed of the turbine
ω
was in rad/s, the turbine radius, r= 0.75 m, water density,
ρ=
1025
kg/m3
and the turbine area,
A=
1.767
m2
. The mean power,
P
was derived from the
product of the rotational speed and mechanical torque measurements averaged over the period of the

Energies 2018,11, 1533 8 of 16
test window. Both non-dimensional performance indicators require a value for the inflow velocity
to the turbine (
Ui
). To derive the mean current velocity, the method of bins, employed by the IEC
62600:200 [
23
], was used. This method has been outlined in previous work; however, the method
has since been developed by the authors to account for bias introduced by the Doppler noise and
thus determine the associated uncertainty in the inflow velocity. Further detail on the method and its
development can be found in Appendix Band previous research [13].
4. Results
The following section is presented in two subsections, time series results and derived performance
(time averaged) results. In each subsection, the comparison between field and lab results will be made
and a comparison of the two turbine control strategies (open loop and PID control).
4.1. Time Series Results
Figure 5is a time series plot comparing the experimental data in the field (Strangford Lough)
and the lab (CNR-INSEAN); the two experimental setups feature the PID controller and were conducted
under the TTT 3 experiments in 2016/17. Turbine rotational speed, fluid inflow velocity and turbine
output power are plotted as time series. Figure 5a shows the rotational velocity from the PID controller
was set to 50 RPM for the field and lab. The stability in both sets of results are good with standard
deviations of 2
σRPM
= 1.57 and 0.45 respectively for the field and lab. Subplot (b) shows the relative
velocity of the water passing the turbine. One of the challenges of ADP and ADV deployments in
towing tanks is maintaining a sufficient level of seeding in the water to suit the acoustic reflection and
achieve strong SNR, this is not an issue in the field. For the lab velocity, the carriage encoder was used
to derive the velocity and subsequent performance characteristics. The ADP results from the field
show high flow variation (2
σVel
= 0.18) and this instability is carried through to the mechanical power
of the turbine (see Figure 5c, while the steady conditions in the lab result in a steady power output.
Figure 5.
Time series plot of (
a
) turbine rotational speed; (
b
) inflow velocity; and (
c
) mechanical power.
(d) Tip Speed Ratio and (e) Coefficient of Mechanical Power using PID controller in the lab and field.
The derived non-dimensional performance characteristics are shown in subplots (d) and (e).
For the field results it is clear the TSR fluctuates with the inverse of the inflow velocity as expected.
More significantly, the slight difference in TSR results in a significant difference in the C
P
. The lab
results have a stable TSR and C
P
with 2
σTSR
= 0.16 and 2
σCP
= 0.03, while the fluctuating field results
have lower mean value and higher standard deviation (2σTSR = 0.70 and 2σCP = 0.07).

Energies 2018,11, 1533 9 of 16
Figure 6shows the time series results from the open loop controller and PID controller from Strangford
Lough field campaigns in TTT and TTT 3. Time series were selected which had the same time-averaged
velocity and the time series (and thus average) extends to circa four minutes in both cases. The TTT
deployment in 2013 used an open loop control strategy setting a constant demand torque to the alternator,
while the TTT 3 deployment in 2016 used the PID (closed loop) control strategy with a set RPM, fluctuating
the demand torque to maintain the constant RPM. Note the PID Control case exhibits the same results as
shown in Figure 5. The open-loop control RPM time series (Figure 6a) has a higher standard deviation
(2
σRPM
= 6.37) than the equivalent closed-loop control time series (2
σRPM
= 1.49). However, maintaining a
constant RPM does not result in a constant power output due to the fluctuations in the inflow velocity
as shown in Figure 6b. The inflow velocity for both experiments is derived from the IEC method of bins
using the ADP data, as described previously. The mechanical power time series, Figure 6c, shows little
difference in signal fluctuation between the two control methods, as confirmed by the standard deviations
(2
σPower
= 110.58 and 109.88 respectively for open and PID control). The cause of the fluctuations has now
been isolated to the inflow velocity fluctuations, as opposed to the type of control strategy, and can be
considered independent of fluctuations in RPM. The further significance of these two control strategies
can be seen in the derived performance characteristics. Figure 6d,e show the TSR and C
P
it is clear
that while the PID experiences a more stable TSR value (2
σTSR
= 1.02 and 0.70 Open and PID control
respectively) the C
P
for both control strategies have similar fluctuations (2
σCP
= 0.06 and 0.07 Open and
PID control respectively).
Figure 6.
Strangford Lough time series plot of (
a
) turbine rotational speed; (
b
) inflow velocity; and (
c
)
mechanical power. (d) Tip Speed Ratio (e) Coefficient of Mechanical Power.
4.2. Derived Performance Results
The power performance characteristics of the Eppler rotor using the same experimental setup and
PID control settings in CNR-INSEAN and Strangford Lough are shown in Figure 7. The varying inflow
experienced at Strangford Lough has been plotted in the figure using 0.1 ms
−1
velocity bins associated
with each of the time averaged reading (this is in accordance with the IEC standard [
23
]). Two trends are
noted in to the uncertainty bounds; firstly, with increasing TSR, the uncertainty increases. This indicates
the uncertainty from rotational speed,
ω
and inflow velocity
Ui
are the dominating sources. Secondly,
the uncertainty bounds for low velocity bins (U
0.8
–U
1.0
) are comparably greater than higher velocity

Energies 2018,11, 1533 10 of 16
bins (U
1.1
–U
1.4
) at the same or similar TSR values. This may be due to the Signal to Noise Ratio (SNR)
of the instrument being poor at these lower velocities, increasing uncertainty. The two curves show very
close agreement at low TSR; however, as the power curves reach their peak, there is an 8.0% difference
in C
P
and a 4.5% difference in the TSR for peak C
P
. The separation between the curves is maintained
as the turbine approaches freewheeling. The uncertainty bounds in the results overlap, but there is
also a number of data points well below the performance curve. This deficit in power performance
is due to the unsteady inflow parameters and this outcome agrees with Starzmann et al. [
11
] and is
also comparable with results from the flume experiments by Blackmore et al. [
15
]. This shows that
turbine power performance is adversely affected by unsteady inflow. Further consideration of the
inflow turbulence metrics and their significance on hydrodynamic performance must be considered.
Figure 7. Mechanical Power performance for Eppler Rotor in the lab and field using PID Controller.
In its nature, a tidal test site will have a significantly varying inflow, this can be seen by the
data points corresponding to the average velocity over which they were taken. Table 5further shows
this variation in velocities in accordance with the IEC standards for power performance of a tidal
turbine [23]. Each data set in the velocity bins comes from a time averaged result of at least 2 min.
Table 5. Tabulated results for Strangford Lough.
Velocity Bin
(ms−1)
Mean Current
Velocity, (Ui)
Mean Power
Output, (Pi)
Mean SD of Power
Output, ( ´
SDP)
Number of
Data Sets, n
0.65–0.75 0.720 37.972 7.088 3
0.75–0.85 0.802 100.929 45.999 6
0.85–0.95 0.922 113.202 59.554 4
0.95–1.05 1.012 193.498 94.951 13
1.05–1.15 1.100 215.158 91.195 16
1.15–1.25 1.183 264.867 78.834 8
1.25–1.35 1.254 83.518 15.557 2
In Figure 8, the average mechanical power performance of the PID control system can be seen to
have a higher performance curve than the equivalent open loop control system. At peak performance,
the PID control turbine is 13.7% more efficient than the open-loop control turbine.

Energies 2018,11, 1533 11 of 16
Figure 8.
Mechanical Power Performance of turbine in Strangford Lough using two control strategies.
In addition to the increase in performance, the inclusion of the closed-loop control system improves
the distribution of the data points across the C
P
-TSR curve. The open-loop control results have greater
scatter, resulting in a comparatively poorer fit (R
2
= 0.84 and 0.96 for Open and PID Control respectively,
Degrees of Freedom = 4). Furthermore, on the right-hand side of the curve, the closed-loop control system
has reached a higher TSR, providing a fuller picture of the Eppler rotor performance curve, this is due to
the control system having a higher variability in resistance, thus getting closer to freewheeling.
5. Discussion
The comparison of performance in turbine response during experiments in the lab and field show
that the performance characteristics do change in the presence of unsteady inflow conditions. While there
is a performance drop in C
P
between the lines of best fit of the steady lab conditions and the unsteady field
conditions, the uncertainty bounds of the data points overlap suggesting the difference is with the region
of uncertainty. In order to improve these uncertainty bounds, it is suggested that increasing the number
of samples/sample rate or increasing the number of bins in the projected area of the rotor is required.
These options are limited when using an ADP due to their correlation with the Doppler noise source.
This highlights the importance of instrument selection and setup for deriving the inflow performance.
The method used to account for uncertainties shows robustness in its application in the field
and lab. The use of ADPs to measure inflow in the field is appropriate in high flow environments,
when characterising full-scale turbine performance, in accordance with the IEC standard. However,
awareness of the ADPs’ contribution to uncertainty in the derived performance characteristics
is important. Due to the difficulty in maintaining sufficient seeding material in towing tanks,
the appropriateness of ADPs remains a challenge; however, as shown in steady flow conditions,
the carriage velocity is sufficiently accurate. Alternatively, Particle Image Velocimetry (PIV)
measurements would be the favoured method in towing tanks.
The comparison of the PID controller with a set RPM and open-loop controller in unsteady inflow
conditions (Strangford Lough) showed a 13.7% difference in peak C
P
. The reasons for this distinction
in performance are shown in the time series results. Figure 6d shows that restricting the RPM with the
PID controller reduces the fluctuation in the TSR over the period of the experiment. As the open-loop
control experiences greater fluctuations in TSR, during experiments near peak performance the turbine
will be operating at suboptimal TSR in the C
P
-TSR curve. This accounts for the reduction in scatter and
higher performance at optimal TSR. However, the PID control with set RPM still results in a fluctuating
TSR. In a commercial system where characterising the performance curve isn’t the objective, a more
appropriate control strategy would use a set TSR, or peak power tracking. This may require knowledge
of both the turbine rotational speed and the inflow velocity. This is a consideration for further work.

Energies 2018,11, 1533 12 of 16
6. Conclusions
The experimental work presented in this paper successfully completed two experimental campaigns
in CNR-INSEAN, Italy and Strangford Lough, Northern Ireland. The continuity of experimental equipment
between the two sites provided the opportunity to investigate ADP and ADV calibration agreement with
factory settings. It was found that both instruments closely agreed with the factory transformation matrices.
The research has furthered the development of the IEC bin method [
23
] by the inclusion of a
Doppler noise bias correction factor and a method for calculating uncertainties in the derived performance
characteristics, with particular attention to uncertainties associated with inflow characterisation.
The work has shown that the effect of unsteady inflow on the derived turbine power performance
has a slight detrimental impact of circa 8%. This is in line with the findings of Starzmann et al. [
11
]
and Blackmore et al. [
15
] and can be considered within the experimental accuracy. To clarify this further
will require narrowing the confidence intervals and possible methods of doing this have been suggested.
The two control types, representative of typical strategies in experimental and prototype devices,
have shown that a PID feedback controller with set RPM helps achieve a distinctly higher performance
curve than the open loop control system, when applied to the turbine operating in unsteady flow
conditions such as Strangford Lough. This is critical to consider in experimental design. The fluctuating
TSR, as a result of unsteady flow and constrained RPM, remains a weakness in the system if in the
presence of large inflow fluctuations. For this reason, it is recognised as a suitable control strategy
for characterising a turbine in unsteady flow conditions. Continued development of the PID control
is an area of further work, using peak power tracking algorithms and a pre-defined power curve.
The inclusion of these strategies will bring the device further in-line with existing industry strategies
and make experimental output more relatable to the sector.
Author Contributions:
Conceptualization, C.F., I.B., P.J., B.E. and T.W.; Data curation, C.F., I.B. and P.J.; Formal
analysis, C.F. and I.B.; Funding acquisition, P.J. and B.E.; Investigation, C.F. and I.B.; Methodology, C.F., I.B., P.J.
and B.E.; Project administration, C.F., B.E. and T.W.; Software, C.F. and I.B.; Supervision, B.E. and T.W.; Validation,
I.B.; Visualization, C.F. and I.B.; Writing–original draft, C.F.; Writing–review & editing, I.B., P.J., B.E. and T.W.
Funding:
This research was funded by Invest Northern Ireland, through the Centre for Advanced Sustainable
Energy (CASE).
Acknowledgments:
The authors would like to thank Francesco Salvatore, Luigi Fabbri and all the other
CNR-INSEAN staff who supported the work. The authors would also like to thank Cuan Boake of Applied
Renewables Research Ltd., Ralf Starzmann of Schottel Hydro and Graeme Mackie of Oceanflow Energy.
Conflicts of Interest:
The authors declare no conflict of interest. The funding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
Nomenclature
Parameter Symbol
Area of turbine (m2)A
Velocity Bin Identifier i
Time Instant Identifier j
Depth Profile Bin Identifier k
Number of samples n
Density (kg/m3)ρ
Extracted Power P
Extracted Torque Q
Turbine Radius (m) r
Sample Identifier s
Extracted Thrust T
Mean Velocity (m/s) U
Rotational Speed (rad/s) ω

Energies 2018,11, 1533 13 of 16
Standard Deviation σ
Two Standard Deviation (95% confidence interval) 2σ
Appendix A
ADP Towing Tank Calibration Results
To test the calibration equations shown in Section 2.2, the disagreement between Vector and Aquadopp
velocity measurements in Strangford Lough were examined over 30 runs. These measurements were completely
independently of all those used for the calibration. The inflow velocity in the Strangford was not necessarily well
aligned with the turbine axis during all tests, so the magnitudes and the yaw angles for the inflow velocities were
analysed. Poorer agreement between the two instruments was found at yaw angles of 15 degrees or more.
In Table A1, the variable Sexpresses the disagreement between concurrent Vector and Aquadopp velocity
measurements, as a percentage. The values predicted using the calibration data alone, as well as those obtained
from the validation data, are tabulated.
Table A1. Precision and bias of ADP and ADV.
Variable S(Percentage Disagreement
between Vector and Aquadopp)
Prediction from
Calibration
Whole
Validation Dataset
Validation for Yaw
within ±15 Degrees
Bias = Mean(S)−0.08% +1.03% +0.27%
Precision = Standard Deviation(S)±1.1% ±2.53% ±2.02%
The Aquadopp is evidently a very good unbiased estimator of mean inflow velocity for yaw angles under
15 degrees.
The validation data utilises effectively 1.7 Aquadopp depth cells, where 5.7 are used over the full swept area.
This means that the precision in the inflow velocity expressed as a standard deviation is expected to be very close
to 1% when averaged over the swept area.
Appendix B
B.1. Deriving Single Measurement for Inflow Velocity
The IEC 62600:200 technical specification sets out the approach for determining the power performance of a
device using ADP inflow data. The ‘method of bins’ enables the velocities in the shear profile across the projected
area of the rotor to be represented as an area weighted and power averaged single point measurement.
Figure A1 illustrates this method, the projected area of the turbine is slices into a series of sections representing
the depth bins of the ADP. Meanwhile, the tubular sections represent each sample, or time. For the set-up in the
TTT 3 project, each depth bin is 0.25 m deep and each time period is 1 s. Each section has an area A
K
and a velocity
measurement Ui,j,k,n (see nomenclature for definitions).
The IEC approach to obtaining a single value for the flow across the swept area of the turbine during a given
test case follows this sequence of equations [
23
]. Firstly, the power-weighted and area average for the projected
area of the turbine for each period is calculated in Equation (A1), and illustrated by the thicker lines in Figure A1.
ˆ
Ui,j,n="1
A×
s
∑
k=1
U3
i,j,k,n×Ak#1
3
(A1)
The equation uses the instantaneous velocity measurement in each of profiler depth bins (
Ui,j,k,n
), which was
cubed and weighted by the area of the depth bin (A
k
). The sum of these is then divided by the total swept area (A)
and cube rooted. This provides the power-weighted current velocity ( ˆ
Ui,j,n) for each period.
Figure A1. Power-weighted current velocity calculation illustration.

Energies 2018,11, 1533 14 of 16
It is at this point that the adaptation of the IEC ‘method of bins’ is introduced. All ADP measurements are
subject to uncertainty from Doppler noise. The IEC method requires each 1 s sample at each depth cell to be
cubed, as shown in Equation (A1), thus cubing the measurement error from Doppler noise. To correct for this,
the Doppler noise bias correction method has been developed. To demonstrate the analytical derivation for the
Doppler noise bias correction; let us take mas the mean and
σ
as the standard deviation of a normal variate.
The variable qis introduced, which is related to the variance of the distribution as shown in Equation (A2).
σ2=m2q(A2)
In the case of power measurements derived from ADP data, it is the distribution of the cube of samples,
(X
k
)
3
, which have been obtained from the normal distribution N(m,
σ
) that is important. The effect of cubing a
measurement with an included sampling error was first formally examined by Haldane in statistical biology [
31
].
Haldane showed that the mean of this resulting distribution exceeds m
3
by the ratio R, as follows in Equation (A3).
R=(1+3q)= (X3
k)/m3(A3)
The values of qand m, as defined above, are readily obtainable from the ADP data. We have called the ratio
Rthe ‘Doppler noise bias’, and Rcan be applied as a correction wherever the cube of a noisy velocity signal is
sought and the ratio of mand
σ
is known. For the derivation of the power-weighted, area-averaged velocity,
as previously described in Equation (A1), this can now be re-written to include the bias correction factor derived,
as shown in Equation (A3). The outcome of this is an unbiased velocity measurement and reduced uncertainty in
the propagation of the performance metrics.
ˆ
Ui,j,n="1
A×
s
∑
k=1
U3
i,j,k,n×Ak
Ri,k,n#1
3
(A4)
The datasets were averaged over periods between 2 and 10 min for the Strangford Lough testing.
For CNR-INSEAN, due to the limited length of the tanks, the maximum averaging period was approximately 90 s;
however, given the controlled nature of the experiments in the laboratory this was not considered to be an issue.
The mean velocity for the data set (
Ui,n
) is calculated from the power weighted values
ˆ
Ui,j,n
over the time period
from j= 1 to j= L, the length of the run is in seconds. Lastly, the average for all the velocities recorded in the given
current velocity bin is calculated. The velocity bin increments were set to 0.10 ms
−1
and only flood phase of the
tide is considered. These steps are described and equations defined in previous work [13,23].
The turbine’s instrumentation as described earlier includes a torque sensor and rotational encoder on the
driveshaft behind the rotor. These outputs provide the mechanical power (P,W) of the rotor in advance of
drivetrain losses. Power is calculated using Equation (A5).
´
Pi=1
Ni
Ni
∑
n=1
´
Qi,n×´
ωi,n(A5)
The same velocity bin increments apply here also. When sampled over the same time period as the ADP data,
the turbine data has more sample points (n), as it is sampled at 16 Hz as opposed to the 1 Hz sample frequency of
the ADP. The mechanical power performance can then be calculated using the non-dimensional performance
characteristic, C
P
as previously described in Equations (1) and (2) inserting Equations (A4) and (A5). The water
density was set to 1000 kg·m−3and 1025 kg·m−3for laboratory and field data respectively.
B.2. Propagation of Uncertainty
Understanding of the propagation of instrument uncertainties is crucial to determining the confidence
intervals of derived performance characteristics. Similar studies into the propagation of uncertainty have been
conducted in this area before [
32
]. The previous work showed the significance of uncertainties in the torque,
thrust and bending moments when propagated to derive the power performance coefficient. The propagation of
inflow uncertainties was not specifically considered by Doman et al., due to the close control afforded by towing
tank experiments. For experimental set-ups in real tidal flows, this exception can no longer be made. Therefore,
the following section concentrates on the propagation of the velocity uncertainty from the ADP data, through the
method of bins, used by the IEC technical specification (TS62600:200).
To ascertain the uncertainty of the derived performance indicators of a tidal turbine, the uncertainty of each
variable in the derived performance indicator must be pooled. Equations (A6)–(A8) shows the propagation of
uncertainty equation associated with each of the performance indicators.
σTSR =TSR ×sσω
´
ωi2
+σr
r2+σ´
Ui
´
Ui2
(A6)
σCP=CP×sσQ
´
Qi2
+σω
´
ωi2
+σρ
ρ2
+σA
A2+3×σ´
Ui
´
Ui2
(A7)

Energies 2018,11, 1533 15 of 16
σCT=CT×sσT
´
Ti2
+σρ
ρ2
+σA
A2+2×σ´
Ui
´
Ui2
(A8)
As Equations (A7) and (A8) show, the propagated uncertainty is most sensitive to uncertainties in the
inflow velocity. The uncertainty variables are derived from the Root Mean Squared (RMS) of bias and precision
uncertainties, as shown in other work [
13
,
32
]. This is the case for all uncertainty parameters derived, with the
exception of the inflow velocity uncertainty. The exception to the inflow velocity is due to the correction applied
for Doppler noise bias in Equation (A6). This correction accounts for the bias uncertainty, leaving only the
precision uncertainty, which is calculated in Equation (A9)
σ´
Ui=σ
√s/√n(A9)
References
1.
Department for Buisness, Energy & Industrial Stragety. Contracts for Difference Second Allocation Round
Results. 2017. Available online: https://www.gov.uk/government/publications/contracts-for-difference-
cfd-second-allocation-round-results (accessed on 20 January 2018).
2.
Mason-Jones, A.; O’Doherty, D.M.; Morris, C.E.; O’Doherty, T.; Byrne, C.B.; Prickett, P.W.; Grosvenor, R.I.;
Owen, I.; Tedds, S.; Poole, R.J. Non-dimensional scaling of tidal stream turbines. Energy
2012
,44, 820–829.
[CrossRef]
3.
Clarke, J.A.; Connor, G.; Grant, A.D.; Johnstone, C.M. Design and testing of a contra-rotating tidal current
turbine. Proc. Inst. Mech. Eng. Part A J. Power Energy 2007,221, 171–179. [CrossRef]
4.
Gaurier, B.; Germain, G.; Facq, J.V.; Johnstone, C.M.; Grant, A.D.; Day, A.H.; Nixon, E.; Di Felice, F.;
Costanzo, M. Tidal energy “Round Robin” tests comparisons between towing tank and circulating tank
results. Int. J. Mar. Energy 2015,12, 87–109. [CrossRef]
5.
Mycek, P.; Gaurier, B.; Germain, G.; Pinon, G.; Rivoalen, E. Experimental study of the turbulence intensity
effects on marine current turbines behaviour. Part I: One single turbine. Renew Energy
2014
,66, 729–746.
[CrossRef]
6.
Jeffcoate, P.; Whittaker, T.; Boake, C.; Elsaesser, B. Field tests of multiple 1/10 scale tidal turbines in steady
flows. Renew. Energy 2016,87, 240–252. [CrossRef]
7.
Jeffcoate, P.; Elsaesser, B.; Whittaker, T.; Boake, C. Testing Tidal Turbines—Part 1: Steady Towing Tests vs.
Tidal Mooring Tests. In Proceedings of the ASRANet International Conference on Offshore Renewable
Energy, Glasgow, UK, September 2014; Volume 68, pp. 55–87. Available online: https://pure.qub.ac.uk/
portal/files/11366206/ASRANet_2014_PJeffcoate.pdf (accessed on 20 January 2018).
8.
Atcheson, M.; MacKinnon, P.; Elsaesser, B. A large scale model experimental study of a tidal turbine in
uniform steady flow. Ocean Eng. 2015,110, 51–61. [CrossRef]
9.
Jeffcoate, P.; Salvatore, F.; Boake, C.; Elsaesser, B.; Vallerano, V. Effect of Submergence on Tidal Turbine
Performance. In Proceedings of the 11th European Wave and Tidal Energy Conference, Nantes, France,
6–11 September 2015; pp. 3–9.
10.
Jeffcoate, P.; Starzmann, R.; Elsaesser, B.; Scholl, S.; Bischoff, S. Field measurements of a full scale tidal
turbine. Int. J. Mar. Energy 2015,12, 3–20. [CrossRef]
11.
Starzmann, R.; Jeffcoate, P.; Scholl, S.; Bischof, S.; Elsaesser, B. Field testing a full-scale tidal turbine Part 1:
Power Performance Assessment. In Proceedings of the 11th European Wave and Tidal Energy Conference,
Nantes, France, 6–11 September 2015; pp. 1–7.
12.
Schmitt, P.; Elsaesser, B.; Bischof, S.; Starzmann, R. Field testing a full-scale tidal turbine Part 2: In-line
Wake Effects. In Proceedings of the 11th European Wave and Tidal Energy Conference, Nantes, France,
6–11 September 2015; pp. 1–7.
13.
Frost, C.; Benson, I.; Elsäßer, B.; Starzmann, R.; Whittaker, T. Mitigating Uncertainty in Tidal Turbine
Performance Characteristics from Experimental Testing. In Proceedings of the European Wave & Tidal
Energy Conference, Cork, Ireland, 27 August 2017.
14.
Forbush, D.; Polagye, B.; Thomson, J.; Kilcher, L.; Donegan, J.; McEntee, J. Performance characterization of a
cross-flow hydrokinetic turbine in sheared inflow. Int. J. Mar. Energy 2016,16, 150–161. [CrossRef]
15.
Blackmore, T.; Myers, L.E.; Bahaj, A.S. Effects of turbulence on tidal turbines: Implications to performance,
blade loads, and condition monitoring. Int. J. Mar. Energy 2016,14, 1–26. [CrossRef]

Energies 2018,11, 1533 16 of 16
16.
European Commission Technology readiness levels (TRL). Horizon 2020 General Annexes; European Commission:
Brussels, Belgium, 2015; p. 4995.
17.
Mason-Jones, A.; O’Doherty, D.M.; Morris, C.E.; O’Doherty, T. Influence of a velocity profile & support
structure on tidal stream turbine performance. Renew. Energy 2013,52, 23–30. [CrossRef]
18.
Payne, G.S.; Stallard, T.; Martinez, R. Design and manufacture of a bed supported tidal turbine model for
blade and shaft load measurement in turbulent flow and waves. Renew. Energy
2017
,107, 312–326. [CrossRef]
19.
Harrold, M.J. Experimental and Numerical Assessment of a Tidal Turbine Control Strategy, University
of Strathclyde, 2016. Available online: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.723008
(accessed on 20 January 2018).
20.
Allmark, M.; Grosvenor, R.; Prickett, P. An approach to the characterisation of the performance of a tidal
stream turbine. Renew. Energy 2017,111, 849–860. [CrossRef]
21.
Schlipf, D.; Pao, L.Y.; Cheng, P.W. Comparison of Feedforward and Model Predictive Control of Wind
Turbines Using LIDAR. In Proceedings of the 2012 IEEE 51st IEEE Conference on Decision and Control
(CDC), Maui, HI, USA, 10–13 December 2012; pp. 3050–3055.
22.
Johnstone, C.M.; McCombes, T.; Bahaj, A.S.; Myers, L.E.; Holmes, B.; Koefoed, J.P.; Bittencourt, C. EquiMar:
Development of Best Practices for the Engineering Performance Appraisal of Wwave and Tidal Energy
Converters. In Proceedings of the 9th European Wave and Tidal Energy Conference, Southampton, UK,
5–9 September 2011.
23.
IEC/TS 62600-200 Power Performance Assessment of Electricity Producing Tidal Energy Converters
Commissioned by: IEC. 2011. Available online: https://webstore.iec.ch/publication/7242 (accessed on
20 January 2018).
24.
Clark, T.; Black, K.; Ibrahim, J.; Minns, N.; Fisher, S.; Roc, T.; Hernon, J.; White, R. MRCF-TiME-KS9b
Turbulence: Best Practices for Data Processing, Classification and Characterisation of Turbulent Flows.
Ocean Array Systems: Edinburgh, UK. 2015. Available online: http://www.oceanarraysystems.com/
publications (accessed on 20 January 2018).
25.
Shih, H.H.; Payton, C.; Sprenke, J.; Mero, T. Towing Basin Speed Calibration of Acoustic Doppler Current
Profiling Instruments. In Joint Conference on Water Resource Engineering and Water Resources Planning and
Management 2000; American Society of Civil Engineers: Reston, VA, USA, 2000; pp. 1–10.
26.
Oberg, K.; Mueller, D.S. Validation of Streamflow Measurements Made with Acoustic Doppler Current
Profilers. J. Hydraul. Eng. 2007,133, 1421–1432. [CrossRef]
27.
Elsaesser, B.; Torrens-Spence, H.; Schmitt, P.; Kregting, L. Comparison of Four Acoustic Doppler Current
Profilers in a High Flow Tidal Environment—Queen’s University Belfast Research Portal—Research Directory
& Institutional Repository for QUB. In Proceedings of the 3rd Asian Wave and Tidal Energy Conference,
Singapore, 24–28 October 2016.
28. Pao, L.Y.; Johnson, K.E. Control of Wind Turbines. IEEE Control Syst. 2011,31, 44–62. [CrossRef]
29. Bennett, S. The past of pid controllers. Annu. Rev. Control 2001,25, 43–53. [CrossRef]
30.
Ziegler, J.G.; Nichols, N.B. Optimum Settings for Automatic Controllers. J. Dyn. Syst. Meas. Control
1993
,
115, 220. [CrossRef]
31.
Haldane, J.B.S. Moments of the distributions of power and products of normal variates. Biometrika
1942
,
32, 226–242. [CrossRef]
32.
Doman, D.A.; Murray, R.E.; Pegg, M.J.; Gracie, K.; Johnstone, C.M.; Nevalainen, T. Tow-tank testing of a
1/20th scale horizontal axis tidal turbine with uncertainty analysis. Int. J. Mar. Energy
2015
,11, 105–119.
[CrossRef]
©
2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).