Updated overview of research in control, power electronics, renewable energy and smart grid integration

An Overview of Research Activities in

C ONTROL AND S MART G RID I NTEGRATION

Qing-Chang Zhong
Q.Zhong@Sheffield.ac.uk

Chair in Control and Systems Engineering
Dept. of Automatic Control and Systems Engineering
The University of Shefﬁeld
United Kingdom

Outline of the talk

A little bit about myself
Activities in process control
Activities in control theory
Activities in power and energy systems
Some sample platform technologies
Applications in wind power, HEV and high-speed trains

Q.-C. Z HONG : A N OVERVIEW OF R ESEARCH ACTIVITIES IN C ONTROL AND S MART G RID I NTEGRATION – p. 2/44

A little bit about myself
1990, started working in the area of control after receiving the first degree
1997, MSc in Control Theory & Eng. from Hunan University
2000, PhD in Control Theory & Eng. from Shanghai Jiaotong University
2004, PhD in Control & Power from Imperial College, awarded the Best Thesis Prize
2006, first research monograph Robust Control of Time-delay Systems published by
Springer-Verlag London.
2007, Director of EPSRC-funded Network for New Academics in Control Engineering,
currently more than 170 members, joined UKACC in Oct 2010 as a Group Member with
support from UKACC.
2009, Senior Research Fellow of Royal Academy of Engineering /Leverhulme Trust
2010, Fellow of IET
2010, Professor in Control Engineering, Loughborough University
2010, research monograph Control of Integral Processes with Dead Time by
Springer-Verlag
2012, Chair in Control and Systems Engineering, The University of Sheffield
2012, research monograph Control of Power Inverters in Renewable Energy and Smart
Grid Integration to be published by Wiley-IEEE Press

Evolution of my research activities

Research activities
Power & Energy Systems
Robust Control Theory
& Time-Delay Systems

Process Control

1998 2001 2004 2007 2010 2013
Year

Wide spectrum of expertise Research philosophy

From hardware to software Focused and thorough research
From applied to theoretical Holistic approach: Down to details but keep
the big picture in mind
From control to power
Looking for solutions and problems as well
Cover many application areas
Looking for hidden links

Activities in process control
Control of integral processes with dead-time: A research monograph, Control of Integral
Processes with Dead Time, jointly with Antonio Visioli from Italy, appeared in 2010.

Advances in Industrial Control
Disturbance observer-based control strategy

Dead-beat response

Stability region on the control parameter space Antonio Visioli
Qing-Chang Zhong
Achievable speciﬁcations etc

Practical experience with a production line
1 Control of
Integral Processes
16 reactors, controlled by 3 industrial computers
with Dead Time
Effective object code > 100 KB (Intel 8086 assembler)

Analogue control variables and measurements etc.

Continuous Stirred Tank Reactor (CSTR) System


Activities in control theory
Robust control of time-delay systems (frequency-domain approaches): Solved a series of
fundamental problems in this area:

Projections

J-spectral factorisation

Delay-type Nehari problem

Standard H ∞ problem of single-delay systems

Unified Smith predictor

Realisation of distributed delays in controllers
Infinite-dimensional systems: applied the generic theory
of infinite-dimensional systems to time-delay systems
and solved problems about feedback stabilizability,
approximate controllability, passivity etc
Uncertainty and disturbance estimator (UDE)-based
robust control: can be applied to linear or nonlinear,
time-varying or time-invariant systems with or
without delays; attracted several Indian groups.


Algebraic Riccati Equations
The well-known algebraic Riccati equation (ARE)

A∗ X + XA + XRX + E = 0
can be represented as

W1 W U U1  
A R
X H X
H= .
- +
−E −A∗
Y1 (=0) Y V V1=0

Assume that U1 is nonsingular and V1 = 0. The solution is obtained
when Y1 = 0 while changing X. The transfer matrix from U1 to W1 is

I
AX = I 0 H .
X

J-spectral factorisation
J-spectral factorisation is deﬁned as

Λ(s) = W ∼ (s)JW (s),
where the J-spectral factor W (s) is bistable and Λ(s)
∼ . T
is a para-Hermitian matrix: Λ(s) = Λ (s) = Λ (−s).
Assume that Λ, having no poles or zeros on the jω-axis
including ∞, is realised as

Hp BΛ
Λ= = D + CΛ (sI − Hp )−1 BΛ (1)
CΛ D

and denote the A-matrix of Λ−1 as Hz , i.e.,
Hz = Hp − BΛ D−1 CΛ .

Theorem Λ admits a J-spectral factorisation if and
only if there exists a nonsingular matrix ∆ such that

Ap 0 Az ?
∆−1 Hp ∆ = −
p , ∆−1 Hz ∆ = −
? A+ 0 Az
+
p p
where Az
− and A− are stable, and and are anti- Az
+ A+
stable. If this condition is satisﬁed, then a J−spectral
factor is formulated as
   
I
 I 0 ∆−1 Hp ∆   I 0 ∆−1 BΛ 
0
 
 
W =

  ,

 −∗ I 
 Jp,q DW CΛ ∆   DW 
0

∗
where DW is a nonsingular solution of DW Jp,q DW = D.

∞
The standard H problem of
single-delay systems
Given a γ > 0, ﬁnd a proper controller K such that the
closed-loop system is internally stable and

Fl (P, Ke−sh) ∞
< γ.

'
z '
w
P u
'1
y e−sh I ' u

E K


Simplifying the problem
z'
u
@' 1
@ '
u
−sh
e I

Cr (P ) K
T
E
w y

z' @' 1
@ u @ 'z1
@
u
@' 1
@ '
u
−sh
e I

Cr (P ) Gα Cr (Gβ ) K
T
E E wE
1
w y y

Delay-free problem 1-block delay problem

Gα is the controller generator of the delay-free pro-
. −1
blem. Gβ is deﬁned such that Cr (Gβ ) = Gα . Gα and
Cr (Gβ ) are all bistable.

Solution to the problem
Solvability ⇐⇒ :
H0 ∈ dom(Ric) and X = Ric(H0 ) ≥ 0;
J0 ∈ dom(Ric) and Y = Ric(J0 ) ≥ 0;
ρ(XY ) < γ 2 ;
γ > γh , where γh = max{γ : det Σ22 = 0}.
u
' @'
@

B2 − Σ12 Σ−1 C1 Σ−∗ B1
∗
 
c A + B2 C1 22 22

Q V −1 = C1 I 0 
Z V −1
−γ −2 B1 Σ∗ − C2 Σ∗
∗
21 22 0 I
T
-
E c
h E
y

Implementation of the controller
As seen above, the control laws associated with delay systems
normally include a distributed delay like
¢ h
v(t) = eAζ Bu(t − ζ)dζ,
0

or in the s-domain, Z(s) = (I − e−(sI−A)h ) · (sI − A)−1 .
The implementation of Z is not trivial because A
1
may be unstable. This problem had confused the 10

delay community for several years and was pro- 0
10

Approximation error
posed as an open problem in Automatica in 2003.
−1
N=1
It was reported that the quadrature implementation 10
might cause instability however accurate the imple- −2 N=5
10
mentation is.
−3
10 N=20
My investigation shows that: −4
10
The quadrature approximation error converges to 0 −2
10 10
−1
10
0
10
1
10
2
10
3

Frequency (rad/sec)
in the sense of H ∞ -norm.

Rational implementation
xN x N −1 x2 x1 ub
Π … Π Π Φ −1 B
u
vr … Π = ( sI − A + Φ ) −1 Φ

Π = (sI − A + Φ)−1 Φ,
¡ h
Φ=( N
0 e−Aζ dζ)−1 .


Feedback stabilisation of delay systems
The feedback stabilizability of the state–input delay
system
x(t) = A0 x(t) + A1 x(t − r) + P u(t) + P1 u(t − r)
˙

is equivalent to the condition

Rank (P + e−rλi P1 )∗ · ϕi = di , i = 1, 2, · · · , l.

where λi ∈ {λ1 , λ2 , · · · , λl } = {λ ∈ C : det ∆(λ) =
0 and Reλ ≥ 0} with ∆(λ) := λI − A0 − A1 e−rλ .
The dimension of Ker∆(λi )∗ is di and the basis of
Ker∆(λi )∗ is ϕi , ϕi , · · · , ϕi i for i = 1, 2, · · · , l .
1 2 d


UDE-based Robust Control
The Uncertainty and Disturbance Estimator (UDE) is a strategy to estimate the
uncertainties and disturbances in a system. The controller is designed so that
the state of the system tracks the state of the reference model chosen, with all
the uncertainties and disturbances estimated with an estimator, called UDE. It
can be applied to linear or nonlinear, time-invariant or time-varying systems
with or without state delays.
The resulting control law for a nonlinear system

u(t) = b+ (−(g1 (t) + ε(g2 (t) + g3 (t))) + Am xm (t) + Bm c(t))
¢ t
+ 1
+b (I − (Am + K)T ) e(t) − (Am + K) e(t)dt
T 0

The simpliﬁed nonlinear control law consists of three terms. The ﬁrst term
cancels all the known system dynamics, while the second term introduces the
desired dynamics given by the reference model and the last term performs a PI
control action.

The two-degree-of-freedom nature
If the system is linear without delay, then

X(s) = Hm (s)C(s) + Hd (s)Ud (s)

with
Hm (s) = (sI − Am )−1 Bm , Hd (s) = (sI − (Am + K))−1 ·(1 − Gf (s)) .

0dB

H f ( jω )

H ki ( jω )
− 20 log ω ki

20 log δ

H di ( jω )

ω ki ω
ωf


Application to Continuous Stirred Tank Reactors
(CSTR)
 
1 x2 (t)  1
x1 (t) = − x1 (t) + Da (1 − x1 (t)) × exp 
˙ x2 (t)
+ − 1 x1 (t − τ ),
λ 1+ λ
γ0

 
1 x2 (t)
 + 1 − 1 x2 (t−τ )+βu(t),
x2 (t) = −
˙ +β x2 (t)+HDa (1−x1 (t))×exp  x (t)
λ 1+ 2 λ
γ 0

where x1 (t) is the reactor conversion rate and x2 (t) is the dimensionless temperature.

Conversion
0.9

Rate
0.5
Steady-state states: x1 and x2/10

0.8 Setpoint
x1
State
0.7 x2/10 0
0 5 10 15 20
0.6

Temperature
5
0.5 Setpoint
0.4 State
0
0.3 0 5 10 15 20
60
0.2
Control
40

Effort
0.1 20
0
0
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2 0 5 10 15 20
Steady-state input: u Time [sec]

Steady-state operating points Change of operating points


Activities in power and energy systems
Sample platform technologies

Provision of a neutral line
Power quality improvement
Synchronverters: Grid-friendly inverters
Parallel operation of inverters
C-inverters
Active capacitors
Harmonic droop controller
Sinusoid-locked loops
AC Ward Leonard drive systems

Applications

Wind power
Hybrid electric vehicles
High-speed trains


Neutral line provision
Vave
0.2V/div

iN
50A/div

iL
50A/div

ic
20A/div

0.17 0.18 0.19 0.20 0.21 0.22 0.23 0.24 0.25 0.26 0.27
Time (sec)

Proposed a topology and control algorithms to provide a stable balanced
neutral line for inverters.

This decouples its control from that of the inverter;
It enables independent phase control for inverters;
Can be used for multi-level inverters as well.


Power quality improvement
Power quality is a very important problem for renewable energy and
distributed generation.
Transformer

DC power Inverter LC
source bridge filter

ia ib ic uga ugb ugc

u’ga Phase-lead
u’gb low-pass
PWM u’gc filter
modulation
u’
u’ga
+ PLL
+
+
u’gb
+
+ u’gc
u + θ
-
e + iref
Internal model M -
dq
Id*
and stabilizing +
- Iq*
compensator C + abc

Current controller
3
#1:1

2 #1:2
Current [A]

1

0
The recorded current THD
-1 was 0.99%, while the grid
-2
voltage THD was 2.21%.
-3
0.00 0.01 0.02 0.03 0.04 0.05
Time Z HONG : A N OVERVIEW OF R ESEARCH ACTIVITIES IN C ONTROL AND S MART G RID I NTEGRATION – p. 21/44
Q.-C.
[sec]

Synchronverters:
Grid-friendly inverters
Synchronverters are inverters that are mathematically
equivalent to the conventional synchronous generators and
thus are grid-friendly.
Can be used for STATCOMs, HVDC, grid connection of
renewable energy, distributed generation and electric
vehicles etc.
Can automatically change the energy ﬂow between the AC
bus and the DC bus.

y
ˆ
ˆ

P (W) and Q (Var)
P
Q
©

Time (Second)

The basic idea
(θ = 0 )

Rotor field axis
Dp
Rs , L

Rotation -
Tm 1 θ& 1 θ
Js s
M
-
M

Te
N Eqn. (7)
Field voltage
Q Eqn. (8)
Rs , L Rs , L Eqn. (9) e
Mf if i

M

The basic idea is to adopt the mathematical model of a synchronous generator
as the core of the controller. What’s left is for the inverter to reproduce e
at its terminals. Control strategies developed for conventional synchronous
generators can be used for inverters.

Dp θr
&
-
Reset θg

Pset p Tm 1 θ& 1 θ
θ&
n Js s
-
θc

Fromto the power part
Te
Eqn. (7)
Q Eqn. (8)
PWM
Eqn. (9)
e generation
- Mf if
Qset 1 i
Ks

Dq
- Amplitude v fb
vm detection
vr
Four control parameters
No conventional PI control
No dq transformation etc
Frequency control, voltage control, real power control and reactive
power control are packed in one controller

Parallel operation of inverters
S1 = P1 + jQ1 S 2 = P2 + jQ2
Vo ∠0 o
Ro1 Ro 2
~ E ∠δ
1 1 Z E 2 ∠δ 2 ~

E*
Conventional droop controller Ei - Pi vo
ni

vr
Ei = E ∗ − ni Pi ,
1 Qi i
ωi = ω ∗ + mi Qi , mi
s
ω it+δ i
ω*
Limitations:

Ei should be the same
The per-unit output impedance should be the same
} =⇒ Not robust at all !
Fundamental trade-off between the power sharing accuracy and the voltage drop

Robust droop controller (Patent pending)
E*

Ke
-
RMS

Ei 1 - ni Pi
s vo
vri

1 Qi i
mi
s
ω it+δ i
ω*

Accurate sharing of both real power and reactive power
Excellent voltage regulation
Low THD
Fast response

Experimental results

Reactive Power [Var]
28 2
24 P1 P2 0 Q1 Q2
Real Power [W]

20 −2
16 −4
12
8 −6
4 −8
0 −10
−4 −12
0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12
Time [s] Time [s]

28 24

Output Voltage [V]
24 E1 E2 16 vo
Voltage [V]

20 8
16
0
12
8 −8
4 −16
0 −24
0 1 2 3 4 5 6 7 8 9 10 11 12 7 7.01 7.02 7.03 7.04 7.05 7.06
Time [s] Time [s]

4 10
i1 i2 9

THD of vo [%]
2 8
Current [A]

7
6
0 5
4
−2 3
2
1
−4 0
7 7.01 7.02 7.03 7.04 7.05 7.06 0 1 2 3 4 5 6 7 8 9 10 11 12
Time [s] Time [s]


C-inverters
The output impedance of an inverter is normally inductive and can be
made resistive. Is it possible to make it capacitive? Yes, and it turns out
to be better than the other ones. Such inverters are called C-inverters.
This has ﬁlled up a gap in the theory.

Implementation
Optimal design to minimise the voltage THD
Parallel operation
6
4
2

The gain factor
0
Optimal capacitance to eliminate the −2 Original inductor
−4
h-th harmonic voltage: −6 3rd only
1 −8
Co = (hω ∗ )2 L −10
3rd and 5th
−12
5th only
−14
1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7
ω/ω*

C-inverters R-inverters
28 28
24 P1 P2 24 P1 P2
20 20

P [W]

P [W]
16 16
12 12
8 8
4 4
0 0
0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12
Time [s] Time [s]
4 4
2 Q1 Q2 2 Q1 Q2
Q [Var]

Q [Var]
0 0
−2 −2
−4 −4
−6 −6
−8 −8
0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12
Time [s] Time [s]
THD of vo (%)

THD of vo (%)
30 30
25 25
20 20
15 15
10 10
5 5
0 0
0 1 2 3 4 5 6 7 8 9 10 11 12 0 1 2 3 4 5 6 7 8 9 10 11 12
Time [s] Time [s]
20 20
10 10
vo [V]

vo [V]

0 0
−10 −10
−20 −20
7 7.01 7.02 7.03 7.04 7.05 7.06 7 7.01 7.02 7.03 7.04 7.05 7.06
Time [s] Time [s]

Active capacitors
Capacitors are fundamental building blocks for electronic and electrical cir-
cuits. A capacitor can be built via putting two conducting plates together,
separated with an electric insulator. A control strategy has been proposed to
implement capacitors with inverters.
60

More accurate 40

Magnitude (dB)
20
More stable, e.g. w.r.t temperature 0 Ro=0.0Ω, no KR
−20 Ro=0.0Ω, with KR
Controllable frequency characteristics
−40 Ro=0.2Ω, with KR
Changing the way how active power −60
90
ﬁlters (APF) are controlled
45

Phase (deg)
0

−45

−90
−1 0 1 2 3 4
10 10 10 10 10 10
Frequency (rad/sec)

6 10 i v
i v 8
4 6
2 4
2

i, v

i, v
0 0
−2
−2 −4
−4 −6
−8
−6 −10
0 0.01 0.02 0.03 0.04 0 0.01 0.02 0.03 0.04
Time [s] Time [s]

10 16
8 i v 12 i v
6 8
4
2 4
i, v

i, v
0 0
−2 −4
−4 −8
−6
−8 −12
−10 −16
0 0.01 0.02 0.03 0.04 0 0.01 0.02 0.03 0.04
Time [s] Time [s]

20 i v 20 i v
16 16
12 12
8 8
4 4
i, v

i, v
0 0
−4 −4
−8 −8
−12 −12
−16 −16
−20 −20
0 0.01 0.02 0.03 0.04 0 0.01 0.02 0.03 0.04
Time [s] Time [s]


Harmonic droop controller
Zo Load/grid
i

~ v o1
i1 ih

…
~ vr
vo ↓ … ↓ …
~ voh

…
(a) One circuit including all harmonics
S h = Ph + Qh
ih voh = 0 if vrh is the same as
Z o ( jhω * ) the voltage dropped on the
~ ~ ↓ output impedance Zo by
v rh voh ih the harmonic current com-
(b) The circuit at the h-th harmonic ponent ih .
frequency Q.-C. Z :A O HONG N VERVIEW OF R ESEARCH ACTIVITIES IN C ONTROL AND S MART G RID I NTEGRATION – p. 32/44

Without With 3rd and 5th harmonics droop controller
6 6
i1 i2 i1 i2
Current [A]

Current [A]
4 4
2 2
0 0
−2 −2
−4 −4
7 7.01 7.02 7.03 7.04 7.05 7.06 7 7.01 7.02 7.03 7.04 7.05 7.06
Time [s] Time [s]
(a) Currents
20 20
10 10
vo [V]

vo [V]
0 0
−10 −10
−20 −20
7 7.01 7.02 7.03 7.04 7.05 7.06 7 7.01 7.02 7.03 7.04 7.05 7.06
Time [s] Time [s]
(b) Output voltage
20 20
16 16
Mag (%)

THD=15.92% Mag (%) THD=8.57%
12 12
8 8
4 4
0 0
1 3 5 7 9 11 13 15 17 19 1 3 5 7 9 11 13 15 17 19
Harmonic order Harmonic order
(c) Harmonic voltage components
Q.-C. Z HONG : A N OVERVIEW
R A COF ESEARCH CTIVITIES IN ONTROL AND S MART G RID I NTEGRATION – p. 33/44

Sinusoid-locked loops
v = vm sin θ v i Xs e = E sin θ

~ SSM model ~

When there is no power exchanged with the grid, the voltage e is the same as the terminal voltage
v. That is, they have

the same frequency

the same phase

the same amplitude


Tracking the grid voltage
70 70 70
60 60 60
50 50 50

f [Hz]

f [Hz]

f [Hz]
40 40 40
30 30 30
20 20 20
10 10 10
0 0 0
0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2
Time [s] Time [s] Time [s]

(b) Frequency tracking

30 30 30
25 25 25
20 20 20

E [V]

E [V]

E [V]
15 15 15
10 10 10
5 5 5
0 0 0
0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2

(c) Detection of the voltage amplitude

30 30 30

15 15 15
e [V]

e [V]

e [V]
0 0 0

−15 −15 −15

−30 −30 −30
0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2

(d) Voltage tracking

10 10 10
8 8 8
THD [%]

THD [%]

THD [%]
6 6 6
4 4 4
2 2 2
0 0 0
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(e) THD of e

8 8 8

6 6 6
θ [rad]

θ [rad]

θ [rad]
4 4 4

2 2 2

0 0 0
0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2 0 0.04 0.08 0.12 0.16 0.2

(e) Phase tracking


Tracking a voltage with a
varying frequency
With the proposed SLL With the SOGI-based PLL With the STA
70 70 70
f fv f fv f fv
Frequency [Hz]

Frequency [Hz]

Frequency [Hz]
60 60 60

50 50 50

40 40 40

30 30 30
0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10

(a) Frequency tracking
45 45 45
E vm E vm E vm
Amplitude [V]

Amplitude [V]

Amplitude [V]
35 35 35

25 25 25

15 15 15
0 2 4 6 8 10 0 2 4 6 8 10 0 2 4 6 8 10

(b) Amplitude tracking


Tracking a square wave 40
With the proposed SLL
40
With the SOGI-based PLL
40
With the STA

20 20 20

v [V]

v [V]

v [V]
0 0 0

−20 −20 −20

−40 −40 −40
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(a) Input signal

100 100 100
f fv f fv f fv

Frequency [Hz]

Frequency [Hz]

Frequency [Hz]
80 80 80
60 60 60
40 40 40
20 20 20
0 0 0
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(b) Frequency tracking

50 50 50
E vm E vm E vm
Amplitude [V]

Amplitude [V]

Amplitude [V]
45 45 45

40 40 40

35 35 35

30 30 30
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(c) Amplitude tracking

60 60 60

30 30 30
e [V]

e [V]

e [V]
0 0 0

−30 −30 −30

−60 −60 −60
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(d) Recovered voltage e

15 15 15
THD [%]

THD [%]

THD [%]
10 10 10

5 5 5

0 0 0
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(e) THD of e

8 8 8
θe v θe v θe v
6 6 6
θ [rad]

θ [rad]

θ [rad]

4 4 4

2 2 2

0 0 0
0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2 0.1 0.12 0.14 0.16 0.18 0.2

(f) Phase tracking

AC Ward Leonard drive systems
Extended the concept of Ward Leonard drive systems to AC machines.
Inverter

Load Variable
Prime speed
mover Load
Prime
VDC SG SM/IM
Constant Variable mover
speed speed
Variable
speed

Fixed field
Controllable field Fixed field

(a) Conventional (DC) Ward Leonard drive systems (b) AC Ward Leonard drive systems

Potential application areas:
High-speed train drive systems
Ship drive systems

Wind turbine control
Power Processing Unit

Tr Tg us UDC u
Wind Aerodynamics Drive-train Generator Rotor-side Grid-side
v (Rotor blades) Converter Converter Grid

ωr ωg is IDC i

Pitch/yaw/stall/brake Control ? Control Energy Control
Control Storage
System

Control

The wind turbine, patented and donated by Nheolis, France, was installed on the EEE building at Liverpool.

HEV driver model
Joint work with Dr Shen et al at Ricardo UK.
Regenerative braking is an important feature of HEVs. According to the EU regulations, if the
travel of brake pedal is used to derive the regenerative braking torque then more stringent brake
safety requirements need to be met. The use of the engine or vehicle speed inevitably introduces
a discontinuous powertrain torque when the acceleration pedal is released or when the brake pedal
is applied, which causes oscillations in the torque. A rule-based driver model with look-ahead
information is proposed and tested in an HIL system consisting of an HCU and a vehicle model.
BMS Battery

150 EUDC Reference Pedals
Vehicle Speed

Transmission
MCU ISG

Final Drive
Speeds

HCU
100 ECE ECE ECE

Clutch
(km/h)

ECE Driver Vehicle
50
EMS ICE
0
0 200 400 600 800 1000 1200
1
gas>0, brake<0

TCU
Pedal position

0.5
Vehicle Speeds
0

−0.5 Control flow Information flow Power flow
0 200 400 600 800 1000 1200 Vehicle Systems
Blue−engine, Red−ISG

200 Model in dSPACE
ISG torque ro restart engine
Torque (Nm)

100

0

−100
0 200 400 600 800 1000 1200
4000 Engine idle stop
Restart engine to 3rd Performance
Engine Speed

Shift to 2nd to 5th
monitor
(rpm)

2000

to 4th
0
0 200 400 600 800 1000 1200
0.66
State of Charge

0.64

0.62

0.6
0 200 400 600 800 1000 1200 HCU strategy
Time (sec) In control Unit


HEV powertrain

EPSRC grant with the total funding of £3.5M (to be started early next year)
WP2.1: Power electronics and energy management (£571K)

Traction power systems for
high-speed trains
Compensation of negative-sequence currents
Compensation of reactive power
Reducing harmonic currents
Capacity reduction of the traction transformer

A
20
B
16 iA iB iC
C
12
iB iC iA
8
B A
C 4

i [A]
ib b c a ia
0
−4
Section
iL iL −8
insulator
−12
−16
−20
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
Time/s

(simulation results)

Power electronics lab
Local AC Bus Grid

Converter Converter Converter
A B C

DC Bus
• Converters can be connected to either the local AC
bus or the grid
• Load can be connected to either the local AC bus or
the grid as well
• For LV batteries, a DC/DC converter is needed

(being built)


Current funding: ~£1.5M
EPSRC: £180K to foster long-term “best-with-best” international collaboration with top
researchers in the US.
EPSRC: £571K, HEV, two postdocs (not started yet)
EPSRC KTA: £126K, EV charging systems, one postdoc
EPSRC KTA: £120K, synchronverter, one postdoc
EPSRC, TSB and Power Systems Warehouse (KTP): £181K, one RA
EPSRC: EP/H004424/1, £68K, airport operations, one PhD student
EPSRC and Add2: DHPA Award, £90K, wind power, one PhD student
EPSRC and Nheolis: DHPA Award, £90K, HIL, one PhD student


Updated overview of research in control, power electronics, renewable energy and smart grid integration

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