Abstract—This paper presents an adaptive power control algorithm for a Direct-drive PM wind generation system in a micro grid. The strategy is based on the vector control algorithm for a two-level IGBT full power converter. The object of this novel control is to operate the wind generation, with a hybrid DC-AC link topology, either in Maximum Power Point Tracking (MPPT) mode, or non-MPPT mode with enhanced tracking performance. The simulation proves that the proposed MPPT algorithm is much faster, more robust and adaptive to changes of the environment than the conventional variable-step Hill Climbing Search (HCS) algorithm, while the non-MPPT module has a fast response to the change of the power demand and the environment, and accurate steady state response as well. Moreover, the result shows that the control strategy can switch between the MPPT and non-MPPT automatically.
Index Terms—Direct-drive, micro grid, maximum power point tracking (MPPT), non-MPPT, permanent magnetic synchronous generator (PMSG), Vector Control (VC)
I. INTRODUCTION The stochastic characteristics of the wind can be mitigated
by combining wind turbines and energy storage systems in a micro grid connected to the utility. While in a micro grid, the generator is required to work in both maximum power point tracking (MPPT) and fixed power point tracking (non-MPPT) modes.
There are several types of MPPT strategies. According to the aero-dynamic characteristic of the turbine blades, which varies from one turbine design to another, there is a unique optimal tip speed ratio �opt for various wind speeds to capture the maximum power Pmax. The first type of MPPT is the tip speed ratio (TSR) method in [1]. The basic idea is to calculate the optimal rotor speed �m with the given unique �opt and wind speed v. This requires an anemometer which, however, is inaccurate due to the turbulence disturbance of the wind turbine blades and the variation of the wind speed along the length of the blade [2]. The power signal feedback (PSF) in [1] and the optimal torque (OT) method in [3] do not need the anemometer, but still require the parameters of specific wind turbines. Moreover, all above three methods maximize the captured mechanical wind power Pwind, not the output electric power Po. The relationship of Po and Pwind is defined in (1), where �g and �c are the generator and converter efficiencies respectively, and vary with rotor speed. Therefore, even when the optimal Pwind is obtained, it cannot guarantee the optimal output electric power Po [4].
�� � ���������������������������������������������������� �� The more universal MPPT strategy is the hill climbing
search (HCS) in [1]. It eliminates the need for an anemometer utilized in calculating �opt in TSR, and specific wind turbine parameters utilized in the TSR, PSF or OT. More importantly, the objective function is the output electric power Po, instead of the captured mechanical power Pwind. However, HCS has to make a tradeoff between the step size and tracking speed, as well as a tradeoff between the perturbation direction and tracking ability during wind changes [4]. The variable-step HCS in [5] can solve the first tradeoff effectively, but worsen the latter [4]. By far, the most effective algorithm is in [4], where there are three modes in the tracking algorithm. Once the wind speed change is detected, it switches from the variable-step HCS to the PSF mode to avoid large oscillations and lose tracking. However, this algorithm is based on an “uncontrolled rectifier and a DC/DC converter” topology, while the most widely used topology is a two-level IGBT full power converter. The above review indicates the need for a novel MPPT, based on a machine-side two-level IGBT converter (MSC), controlled to adapt during wind speed changes.
The proposed new method has all the advantages of the MPPT in [4], especially an excellent tracking ability during wind changes. It is based on the vector control algorithm for the IGBT MSC, with full control of active and reactive power to improve power quality and inject reactive power into the gird when low voltage ride through (LVRT) is required. Furthermore, considering the wind generators working in a micro grid with energy storage system of limited-capacity, the proposed power control method has a specially designed non-MPPT mode to operate, whenever the wind energy exceeds the limited capability of the storage system, while the micro grid is working in an island condition. Section II introduces the MPPT’s algorithm; section III introduces the non-MPPT’s algorithm; sections IV and V present the simulation and experimental results; section VI is the conclusion.
II. THE NOVEL MPPT ALGORITHM
A. General MPPT Control Diagram The topology of the DDPMSG system with machine-side
converter (MSC) appears in Fig. 1. The machine’s math model in synchronous dq coordinate is described in (2) and (3) [6].
Novel Adaptive Power Control of a Direct-Drive PM Wind Generation System in a Micro
Grid Lijun He(1), Yongdong Li(2), Member, IEEE, and Ronald Harley(1), Fellow IEEE
(1) Georgia Institute of Technology Atlanta, GA, USA (2) Dept. Electrical Engineering, Tsinghua University, Beijing, China
ud, uq: the d, q axis voltages of the generator id, iq: the d, q axis currents of the generator p: pole- pairs of the generator �d, �q: the d, q axis stator flux linkages �r : rotor flux linkage �: rotor electric speed in rad/s Tem, Tl: electromagnetic torque and load torque Ld, Lq: the d, q axis synchronous inductance r: the d, q axis stator resistance For the widely used surface-mount PMSG, Ld = Lq.
Therefore, Tem = p�riq and Tem is purely a function of iq; while id only affects the excitation. This results in id and iq completely decoupled and easy to control. The general MPPT control block diagram, based on the vector control algorithm is illustrated in Fig. 2 and details appear in II-B. To get unity power factor for the generator terminals, the inner d-axis current loop has command id
* = 0. The MPPT block inputs are measured rotor speed �m_back
and electromagnetic power Pback, calculated using (4); the output is the mechanical rotor speed reference �m_ref [5]. �,-�. � "#$�$/,-�. �� %�����$/,-�.�������������������� 0��
θ
*di
vα vβαβ dq
dcomvqcomv
*qi
, ,a b ci i i
di qi
θ_m backω
Fig. 2. General MPPT control based on vector control algorithm
B. The Novel Adaptive MPPT Methodology Instead of purely using HCS in traditional MPPT, this
adaptive MPPT method switches between multi-operational modes, based on the wind speed change detection; with these specially-designed modes, this method is especially adaptive to environment changes, and presents a significantly enhanced tracking performance. The flow chart is shown in Fig. 3.
Fig. 3. Flow chart for the novel adaptive MPPT algorithm, for vector control
There are three operating modes for each control period, as classified in [4]. However, all the criteria and methodology in this paper are based on the vector control algorithm for IGBT MSC, instead of the DC/DC converter as in [4]: 1) No Wind Speed Change
Mode1: When the tracking point is far from the MPP, the variable-step HCS is applied to increase the tracking speed. 12�$/�#3 4�1 � 567�,-�. 4�6��������������������������� 8�
The sign is determined by the signs of ��m_back(n) and �Pback(n), as in Fig. 3.
Mode 0: When the tracking gets close to the MPP, the ��m_ref(n) in (5) is negligibly small, and the period of switching frequency will cause some deviation for �m_back(n), hence the object now is to keep the tracking result unchanged.
When �Pback(n) < a (a is a relatively small tolerance), �$/�#3 4� 9 ��$/�#3 4 � ������������������������������� :� 2) Wind Speed Change
Mode 2: When a wind speed change is detected, the object now is to avoid the oscillation caused by the HCS, therefore the approximate optimal power curve Pwind_opt = kp_opt·�m_opt
3 is
used, until there is no apparent wind speed change. Unlike traditional PSF, every time switching from mode 0 to mode 2, this kp_opt is updated and stored using the variable-speed HCS searching result in mode 0. Therefore, kp_opt no longer depends on specific wind turbine parameters. The mode 2 detection is,
When (m�Pback(n) �Pback(n-1)) not (�Pback(n) < a), (m is a relatively small ratio),
The anemometer cannot measure wind speed accurately. On the other hand, when there is a sudden wind speed change, the captured wind power curve Pwind-�m changes from curve 1 to curve 2 in Fig. 4; meanwhile, due to the large inertia of the turbine, the rotor speed �m_back remains the same, therefore the captured wind power Pwind jumps from point A to B instantaneously, which results in an apparent change for measured �Pback(n) as well. This sudden increase of �Pback(n) can be selected as the flag to switch from mode 1 to mode 2. The criterion is shown in the Mode 2 description in II-B.
Fig. 4. Operation point jumps from A to B with a sudden wind speed change
The above criterion is only applied to the detection of a significant wind speed change. Actually, for a relatively small wind speed change, the variable-step HCS will not lead to much oscillation and mode 0 itself tolerates the input deviation. Moreover, mode 2 should be switched from mode 1 only, since in mode 0, the absolute �Pback(n) is too small, and the high relative ratio of �Pback(n) and �Pback(n-1) is more likely to be the result of disturbing signals.
III. THE NOVEL NON-MPPT ALGORITHM
A. Power Control Principles of PMSG in Micro Grid The increased penetration of renewable generation, the
long-distance transmission and the island condition of the micro grid, results in a weak grid connection [7].
In the conventional topology, the DDPMSG is connected through an AC-DC-AC converter to a weak utility in Fig. 5, the DDPMSG should act as a voltage source to regulate the DC-link voltage, by adding a third voltage loop to the regular double-loop speed control, and the power generated is determined by the DC link voltage, instead of MPPT [7].
+
−dcV
Fig. 5. Conventional topology with wind turbine directly connected to utility
However, for the micro grid with a hybrid DC-AC link, all distributed generators and storage systems are connected to the DC bus, shown in Fig. 6. Therefore within the capacity of the storage system, MPPT can still be implemented for the
power control strategy even in the island condition; unless one particular condition happens, classified in Fig. 7.
From Fig. 7, when and only when the micro grid is working as an island with a local load that is too small, should the DDPMSG use the non-MPPT to reduce its power injection to the DC link, and protect the storage system from over charging.
Moreover, unlike the conventional topology in Fig. 5, as an islanded micro grid, since all distributed generators and loads are connected at the point of common coupling (PCC), only one device can act as the constant voltage source. For this study, the storage system is selected to regulate DC voltage in both MPPT and non-MPPT modes, therefore the MSC still acts as a current source in the non-MPPT mode to inject the reference fix power into the DC link. Hence, a power feedback signal is added in Fig. 8 as a third control loop.
θ
*di
vα vβαβ dq
dcomvqcomv
*qi
, ,a b ci i i
di qi
θ_m backω
Fig. 8. General non-MPPT control based on vector control algorithm In addition, for a given output power demand Pref, there are
two operation points, B1 and B2 [7], shown in Fig. 9.
50 100 150 200 250 300 350 400
00
00
00
00
00
rotor speed �m back
win
d po
wer
Pw
ind Curve 1
Curve 2
A
B
DC link−
mω Fig. 9. Two operating points in the non-MPPT mode and power flow
The power output Po is calculated as follows [7], ignoring
the losses: ��� � �-�� � ��` abc�c��-�� � �$( ��$�� ����������������� d� Once the command switches from MPPT Pmax to non-
MPPT Pref in Fig. 8, the captured wind power Pwind will decrease to prevent overcharging. It requires the rotor speed �m to decrease, if the target point is B1. That indicates the kinetic energy decreases and Pacc < 0. Therefore, from (8), the final output electric power Po will increase in contrary to our expectation, especially considering the large wind turbine inertia. This results in the storage system over charging even more at the very beginning, and maybe followed by oscillations, instability and damage to storage devices. The same analysis can be applied to target point B2, and Pacc this time, however, contributes to the stability of the system; therefore, a simple PI can be implemented for the power loop.
Apart from the difference in transient performance, other metrics are taken into consideration in Table I.
TABLE I
COMPARISON OF TWO OPERATION POINTS B1, B2 B1 B2I
Loss Less more Speed range [�min , �opt] [�opt , �max] Setting time Long Short
Control difficult easy From the above, although the control for B2 is easier, it has
more losses and a maximum speed limit. In reality, the power control in this sector includes the pitch angle control as well. Therefore, in this paper, only the rotor speed control of operating point B1 is considered.
B. The Novel Adaptive Non-MPPT Methodology From the analysis in section III-A, a third power loop
should be added to regulate the speed reference, and an adaptive non-MPPT power control algorithm is in need to eliminate oscillation and transient instability for the target point B1.
Equation (4) indicates that the outer power loop is coupled with the inner current loop; the easiest way to decouple the two loops is to force the third loop to respond much slower by setting kp = 0 (the proportional gain in power PI controller). The complete controller block diagram is depicted in Fig. 8, and the power PI controller with kp = 0 is in (9). �$/�#3 � < e�#���� �� � < e&��#3 � �,-�.'��
f ��$/�#3�� � <&��#3 � �,-�.'������������������������������������� g�
For a traditional controller, as time goes by, Perror goes down and the acceleration ��hi/jkl�> decreases; however, if ki (the integral gain in the power PI controller in (9)) can be modified to increase, it can help maintain the acceleration to a relative high level to speed up the tracking. That is the basic idea of the first stage of the adaptive PI. There are typically two ways to design the increasing ki in (10) and (11). 1) Exponential Increment (IC) Adaptive PI Controller: </-�-=> 4� � &� � </-�-=>/��#� m n�op�'</-�-=>/�>������� �q� 2) Constant Increment (IC) Adaptive PI Controller: </-�-=> 4� � &� � </-�-=>/��#� m 4 � ��'</-�-=>/�>��� ���
As time goes by, Perror is decreasing, if Perror falls within a small range, the controller switches to the second stage automatically, where ki is now fixed sufficiently large to boost the speed loop reference �m_ref. The flow chart for the non- MPPT adaptive PI controller, with the first stage using (10) is shown in Fig. 10.
Fig. 10. Flow chart for non-MPPT exponential IC adaptive PI controller
IV. SIMULATION RESULT The proposed power controller performance is evaluated in
Simulink and results are shown: A. MPPT simulation; B. Non-MPPT simulation.
Various wind curves Pwind-�m under different wind speeds are generated using (12) and (13) with two degrees of freedom h, b, plotted in Fig. 11. "$ � r� �+:�q �$ � b�s � �qt m u������������������������ � � �$ � "$�$����������������������������������������������� �+� when� �vi��hi � q, Pm = Pmax, �m = �m_opt, where
The MPPT has already reached its steady state optimal
MPP speed �back = 245.5 rad/s, under the wind power curve (h = 180, b = 0.91) at 1 s. There is a sudden wind speed increase and the wind power curve changes to (h = 200, b = 1.0) curve. It takes 2 s for a traditional variable-step HCS to track the next MPP and the optimal speed is 260.3 rad/s, while the adaptive MPPT only needs 0.04 s, 2% time of the traditional method, to get the steady state value 261.5 rad/s. Therefore, in this case, the proposed method needs less tracking time and results in more accurate steady state response (Theoretically, the final MPP speed is 261.7 rad/s, point A in Fig. 11; due to the preset tolerance in mode 0, 20 W, the actual tracking point only falls into the vicinity of A in Fig. 11, instead of the exact point A). 2) A Sudden Wind Speed Decrease in Steady State
Fig. 13. Results when wind power curve has a sudden decrease at t =1 s
TABLE III MEASUREMENT
MPPT Setting time (s) Steady state optimal speed (rad/s)
In this case, the MPPT has reached its steady state optimal
�back =270.5 rad/s at 1 s, around point A in Fig. 11, under the wind power curve (h = 200, b = 1.0) at 1 s. There is a sudden wind speed increase, and the wind power curve changes to (h = 180, b = 0.91) curve. The setting time for the traditional variable-step HCS and proposed adaptive HCS are the same, but the adaptive method settles at 246.5 rad/s (close to point B of 245 rad/s in Fig. 11), while the variable-step HCS settles at a more inaccurate value of 222.0 rad/s. 3) A Sudden Wind Speed Increase, before the Reach of Steady State
Fig. 14. Results when wind power curve has a sudden increase at t = 0.1 s
TABLE IV MEASUREMENT
MPPT Setting time (s) Steady state optimal speed (rad/s)
In this case, the wind curve increases from (h = 100, b =
0.63) to (h = 200, b = 1.0), at time 0.1 s in Fig. 14, before the MPP speed for the current curve is reached at C of 181.3 rad/s in Fig. 11. The variable-step HCS takes 0.28 s to reach the steady state �back = 277.5 rad/s, while the adaptive method needs only 0.23 s to reach the steady state, at �back = 266.7 rad/s, much closer to theoretical MPP speed of 261.7 rad/s. The adaptive method under this condition needs less setting time, and settles at a value closer to the theoretical MPP. 4) A Sudden Wind Speed Decrease, before the Reach of Steady State
Fig. 15. Results when wind power curve has a sudden increase at t = 0.14 s
50 100 150 200 250 300 350 400
500
1000
1500
2000
2500
X: 261.7Y: 2341
rotor speed wm(rad/s)
Cap
ture
d w
ind
pow
er P
win
d (W
)
X: 245Y: 1969
X: 181.3Y: 933.1
h=200,b=1.0
h=180,b=0.91•h=100,b=0.63
0 0.5 1 1.5 2 2.5 3-300
-250
-200
-150
-100
-50
0
time t(s)
wba
ck(r
ad/s
)
Variable-step HCSAdaptive HCS
0 0.5 1 1.5 2 2.5 3-300
-250
-200
-150
-100
-50
0
time t(s)
wba
ck(r
ad/s
)
Adaptive HCSVariable-step HCS
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-300
-250
-200
-150
-100
-50
0
time t(s)
wba
ck(r
ad/s
)
Adaptive HCSVariable-stepHCS
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-300
-250
-200
-150
-100
-50
0
time t(s)
wba
ck(r
ad/s
)
Adaptive HCSVariable-step HCS
A
B
C
TABLE V MEASUREMENT
MPPT Setting time (s) Steady state optimal speed (rad/s)
In this case, the wind curve decreases from (h = 200, b =
1.0) to (h = 100, b = 0.63) at 0.14 s in Fig. 14, before the MPP speed for the original curve, has been reached, at point A in Fig. 11. The variable-step HCS results in great oscillations and takes 0.4 s to reach the steady state value of 136 rad/s, while the adaptive method needs 50% time to reach the steady state of 191.5 rad/s, much closer to the theoretical MPP 181.3 rad/s.
The above four cases prove that no matter how and when wind speed changes, the proposed MPPT method always has less setting time, less oscillation, and a more accurate steady state response than the traditional variable-step HCS. It is also pointed out that due to the preset tolerance a = 20 W in mode 0, mentioned in section II-B, as well as the power losses between the captured mechanical wind power Pwind and output electric power, the tracking results fall into the vicinity of the theoretical MPP speed, instead of the exact point.
B. Non-MPPT Simulation The generated wind curves used to demonstrate the effect
of proposed non-MPPT controller are plotted in Fig. 15.
Fig. 16. Generated wind curves for non-MPPT simulation 1) Switching from MPPT to Non-MPPT
The result of different non-MPPT controllers with a sudden command to switch from MPPT to non-MPPT is in Fig. 17.
Fig. 17. Results when switching from MPPT to Non-MPPT at t =0.5 s
TABLE VI MEASUREMENT
Non-MPPT controller Setting time(s) Steady state ripple (%)
Exponential IC adaptive PI 0.0777 2.3 Constant IC adaptive PI 0.0955 2.3
Traditional PI 0.140 2.3
In Fig. 17, the command switches from MPPT to non-MPPT with Pref = 1500 W at 0.5 s. The exponential IC and constant IC adaptive PI controllers perform significantly better than the traditional PI controller, reducing the overshoot greatly and the setting time to 30-40%. The exponential IC adaptive PI controller performs even better than the constant IC adaptive PI, with setting time 0.0777 s less than 0.0955 s in Table VI. All the three controllers give accurate steady state tracking results, at point B in Fig. 16, and the whole system can switch from MPPT to non-MPPT mode automatically. 2) A Sudden Wind Speed Change
Fig. 18. Results with a sudden wind speed change at t = 0.5 s
Fig. 18 shows that when there is an arbitrary wind speed deviation at 0.5 s, the wind power curve changes from (h = 200, b = 1.0) to (h = 180, b = 0.91); the exponential IC adaptive PI power controller, under non-MPPT command, takes only 0.03 s to get back to the reference power 1500 W, from point B to C in Fig. 16. 3) A Sudden Command Change
Fig. 19. Results with a sudden output power command at t = 0.5 s
This result is carried out with the exponential IC adaptive
controller, and the result in Fig. 19 shows that this adaptive non-MPPT controller tracks the accurate operating point within 0.02 s, following a sudden command change from 1500 W to 2000 W, from point B to point A in Fig. 16, under the wind curve (h = 200, b = 1.0) at t = 0.5 s.
The above simulation results validates the proposed controller mitigates oscillation and reduces setting time at generator terminals on a large scale, in both MPPT and non-MPPT mode. Therefore, the high order harmonics of the generated power is greatly reduced. The storage system in a
50 100 150 200 250 300 350 400
500
1000
1500
2000
2500
X: 200Y: 2000
rotor speed wm(rad/s)
capt
ured
win
d po
wer
(w)
X: 165.8Y: 1500
X: 157.8Y: 1500
h=180,b=0.91•h=100,b=0.63
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-300
-250
-200
-150
-100
-50
0
time t(s)
wba
ck(rad/
s)
Exp IC Adaptive PITraditional PIConst IC Adaptive PI
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-180
-160
-140
-120
-100
-80
-60
-40
-20
0
time t(s)
wba
ck(rad
/s)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1-500
0
500
1000
1500
2000
time (s)
Pba
ck(w
)
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9-250
-200
-150
-100
-50
0
time t(s)
wba
ck(rad
/s)
A
B C
micro grid brings the benefit of mitigating the stochastic characteristics of the wind, sudden command change or load change on the utility-level, but its performance depends on how good the storage system’s frequency response is. If the power high frequency components cannot be fully absorbed by the storage system, it will result in the fluctuation of the DC voltage and, to make matters worse, the high frequency components will be injected into the utility when grid-connected, hence the storage system cannot effectively mitigate the stochastic characteristics of the wind as one might expect.
As a conclusion, with this proposed power control method, the mitigation of the overshoot and oscillation of generator output power not only contributes to the overall input–output efficiency and reliability [4], but also greatly limits the injected high frequency power and reduces the frequency response requirements for the storage system. Generally, as pointed out in [8], in a hybrid storage system, the super capacitor acts as a fast-dynamic storage unit and is controlled to absorb high frequency power; the battery acts as a long-term storage unit to absorb low frequency power for a long time. Considering the fact that the super capacitor is much more expensive than the battery, the proposed power controller greatly reduces the capacity of the super capacitor required, so that the total cost of the hybrid storage system is reduced.
V. EXPERIMENTAL RESULT
A. Experimental Platform The test bed is shown in Fig. 20, with the parameters for
the PMSG and primer in Table VII. The primer is controlled by a servo motor driver; the IGBT MSC is controlled by DSP 28335. The battery (one cell is 12 V, 65 AH and six cells serially connected), is controlled as a voltage source with Udc_ref = 150 V, with the control strategy in Fig.21 [8]. The sliding rheostat acts as the DC load, with Rmax 220 �.
Fig. 20. DDPMSG hardware platform
TABLE VII PMSG PARAMETERS
Stator resistance Rs = 0.7 �
Stator inductance Ls = 0.00408 H
d-axis inductance Ld = 0.00408 H
q-axis inductance Lq = 0.00408 H
Rotor flux linkage r = 0.2180 Wb
Pole pairs p = 4
Rater speed n = 1500 rpm
Rated power Pn =2000 W
Rated voltage Un = 220 V
Rated current In = 7.5 A
Inertia J = 0.00072 kg·m2
Rated torque Tn = 14.33 N·m
Fig. 21. Constant DC voltage control strategy for a battery unit
B. Experimental Results 1) Non-MPPT Steady State Result
The non-MPPT power tracking steady state result is in Fig. 22, where CH1 is DC voltage, CH3 is phase A generator current, CH4 is DC current; the output power Pout = CH1mCH4. It is clearly to see that at steady state, the DC bus voltage is set at 150 V, and the output current has few harmonics. 2) Non-MPPT with Sudden Power Command Changes
The non-MPPT tracking results with power demand changes is in Figs. 23-25. CH1, CH3, CH4 is defined the same as in Fig. 22. The cursor readings for two power commands are shown in Fig. 24. The generator speed waveform (red), as well as the current on d, q axis, id (green), iq (blue) is shown in Fig. 25.
Since the power command is doubled, the measured output power increases from 66.181 W to 134.15 W, in Fig. 24.
Moreover, since the input primer torque is constant, the amplitude of the generator current in CH3 remains unchanged, with only the frequency, as well the rotor speed doubled, to inject more active power. The high frequency components of the overshoot can be absorbed by super capacitors in the future work.
Fig. 22. Steady state of non-MPPT waveforms with zoomed in below
Fig. 23. Non-MPPT waveforms with sudden changes for power demands
Fig.24.two curser readings for different power commands
Fig. 25. Speed waveforms, for different power comman
VI. CONCLUSION This paper proposed a novel adaptive
algorithm for DDPMSG in micro grid wtopology, based on the vector control algorilevel IGBT full-power MSC. The simulation results prove both the MPPT mode and non-Msignificant advantages over traditional cadaptive, robust and accurate, especially undchange of the wind speed and the power demmodes can switch automatically under differe
With this novel adaptive power controllthe stochastic impact of the wind energy caon the utility-level and micro grid-level as wthe efficiency and reliability of the whole systhe frequency requirements and cost of thetherefore making the wind energy not only friendly, but also grid-friendly.
VII. REFERENCES [1] I. K. Buehring and L. L. Freris, "Control polic
conversion systems," IEE Proceedings, PartTransmission and Distribution, vol. 128, pp. 253-2
[2] Kathryn E. Johnson and Lucy Y. Pao, "A tutorialcontrol of wind turbines and wind farms," in ProConference, 2009.
[3] Morimoto S., Nakayama H., Sanada M., Takeda Ymaximization control for variable-speed wind genIPMSG," IEEE Transactions on Industry Applica2005.
[4] Syed Muhammad Raza Kazmi, Hiroki Goto, Hai-Ichinokura, "A novel algorithm for fast and efficmaximum power point tracking in wind energy cIEEE Transactions on Industrial Electronics, voJan. 2011.
[5] Jia Yaoqin, Yang Zhongqing, Cao Binggang, "A point tracking control scheme for wind generation,2002.
[6] Zedong Zheng, "Research of PMSM High PerfMechanical Sensorless Operation," Ph.D. dissertaEngineering, Tsinghua University, Beijing, China,
[7] Xibo Yuan, Fei Wang, Boroyevich, D., Yongdonglink Voltage Control of a Full Power ConverterOperating in Weak-Grid Systems," Power Transactions on , vol.24, no.9, pp.2178-2192, Sept
[8] Bo Dong, Yongdong Li, and Zhixue Zheng, "Conbus voltage in islanded operation of microgrDeregulation and Restructuring and Power Techn4th International Conference on, pp.1671-1674, Ju
ds
e power control with DC-AC link ithm for the two-and experimental
MPPT mode have controllers, more
der the unexpected mand; and the two ent conditions. ler in micro-grid, n be limited both well as enhancing stem and reducing e storage system, environmentally-
cies for wind energy t C - Generation,
261, Sept. 1981. l on the dynamics and oc. American Control
Y., "Sensorless output neration system using ations, vol. 41, no. 1,
formance Control and ation, Dept. Electrical 2008.
g Li, Burgos, R., "DC-r for Wind Generator
Electronics, IEEE t. 2009. ntrol strategies of DC-rid," Electric Utility ologies (DRPT), 2011
uly 2011.
VIII. BIOGRAP
Lijun He (S’12) electrical engineeriBeijing, China, in student in the schoEngineering, GeorAtlanta.
Her research incontrol, and renewa
Yongdong Li (M’08from Harbin Institutin 1982, and the Mthe Department of National PolytechnFrance, in 1984 and
He was a Profes1996, and Invited PPolytechnique de Tcoauthored more th
papers, and two monographs on digital conconverter. His research interests include powtopologies, machine control and wind power g
Dr. Li is a Senior Member of the ChinaVice Chairman of the China Power Electronicthe Electrical Automation Committee of Chin
Ronald G. Harleythe M.Sc.Eng. degengineering from Pretoria, South Adegree from Londo1969.
He is currentlySchool of ElectricGeorgia Institute ohas coauthored mor
PHIES received the B.S. degree in
ing from Tsinghua University, 2011, and is currently a PhD
ool of Electrical and Computer rgia Institute of Technology,
nterests include electric machine able energy.
8) received the B.S.E.E. degree te of Technology, Harbin, China,
M.S.E.E. and Ph.D. degrees from Electrical Engineering, Institute
nique de Toulouse, Toulouse, 1987, respectively.
ssor of Tsinghua University since rofessor of the Institute National Toulouse. He has authored or han 200 conference and journal ntrol of ac motor and multilevel wer electronics, power converter generation. a Electro-Technique Society, the cs Society, and Vice Chairman of na Automation Association.
y (M’77–SM’86–F’92) received gree (cum laude) in electrical
the University of Pretoria, frica, in 1965, and the Ph.D. on University, London, U.K., in
y a Regents’ Professor with the cal and Computer Engineering, of Technology, Atlanta, GA, He re than 500 papers and 6 patents.
![Page 1: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/1.jpg)
![Page 2: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/2.jpg)
![Page 3: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/3.jpg)
![Page 4: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/4.jpg)
![Page 5: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/5.jpg)
![Page 6: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/6.jpg)
![Page 7: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/7.jpg)
![Page 8: [IEEE 2012 IEEE Power Electronics and Machines in Wind Applications (PEMWA) - Denver, CO, USA (2012.07.16-2012.07.18)] 2012 IEEE Power Electronics and Machines in Wind Applications](https://reader031.documents.pub/reader031/viewer/2022020609/575082561a28abf34f98e278/html5/thumbnails/8.jpg)
