Adaptive multi mode power control of a direct drive PM wind generation

IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL 1, NO 4, DECEMBER 2013 217 Adaptive Multi-mode Power Control of a Direct-Drive PM Wind Generation System in a Microgrid Lijun He, Student Member, IEEE, Yongdong Li, Member, IEEE, and Ronald G Harley, Fellow IEEE Abstract— This paper presents an adaptive multi-mode power control algorithm for the converter of a direct-drive permanent magnetic wind generation system in a microgrid The strategy is implemented in a field-oriented control machine drive system, with a two-level IGBT full-power rated ac–dc machine-side converter shown in Fig The objective of this paper control is to operate the wind generation 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 faster, more robust, and adaptive to changes of the environment than the conventional variable-step hill climbing search algorithm, while the non-MPPT module has a fast dynamic response to the change of the power command and the environment, and an accurate steady-state response as well Moreover, the result shows that the control strategy can automatically switch between the MPPT and non-MPPT modes Index Terms— Direct-drive, field-oriented control (FOC), maximum power point tracking (MPPT), microgrid, non-MPPT, permanent magnetic synchronous generator (PMSG) I I NTRODUCTION T HE stochastic characteristic of wind can be mitigated by combining wind turbines and storage systems in a microgrid connected to the utility While in a microgrid, the wind turbine generator is required to work in both maximum power point tracking (MPPT) and fixed power point tracking (non-MPPT) modes Conventional MPPT strategies are broadly classified into the following types According to the aerodynamic characteristic of the turbine blades, which varies from one turbine design to another, there is a unique optimal tip speed ratio (TSR) λopt for various wind speeds to capture the maximum power Pwind_opt The first type of MPPT is TSR method in [1] The basic idea is to control the generator torque by controlling the machineside converter (MSC) to ensure an optimal rotor speed ωm_opt , Manuscript received June 1, 2013; revised September 23, 2013; accepted September 25, 2013 Date of publication September 30, 2013; date of current version October 29, 2013 Recommended for publication by Associate Editor W Gao L He is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA (e-mail: lhe30@gatech.edu) Y Li is with the Department of Electrical Engineering, Tsinghua University, Beijing 100084, China (e-mail: liyd@mail.tsinghua.edu.cn) R G Harley is with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA, and also with the University of KwaZulu-Natal, Durban 4041, South Africa (e-mail: rharley@gatech.edu) Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org Digital Object Identifier 10.1109/JESTPE.2013.2284015 Fig Topology of PMSG system with MSC which is obtained from the given unique λopt and wind speed v This type of MPPT scheme requires an anemometer which, however, is inaccurate due to the turbulence disturbance of the turbine blades and the variation of the wind speed along the length of the blade [2] The other two types of MPPT are the power signal feedback (PSF) method in [1] and the optimal torque (OT) method in [3]; they eliminate the need for anemometers, but still require the parameter values of specific wind turbines Moreover, all the above three types of MPPT only maximize the captured mechanical wind power Pwind , but not the output electric power Po from the converters The relationship of Po and Pwind is defined in (1), where ηg and ηc 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 electric power Po [4] Po = ηg ηc Pwind (1) The more universal MPPT strategy is the hill climbing search (HCS) [1] It is particular advantageous since it not only eliminates the need for an anemometer used in calculating λopt in the TSR method, but also eliminates the need for specific wind turbine parameters used in the TSR, PSF, or OT methods 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 speed changes [4] The variable-step HCS in [5] can solve the first tradeoff effectively, but worsens the latter [4] By far, the most effective MPPT algorithm is reported in [4]; once the wind speed change is detected, the algorithm switches from the variable-step HCS to PSF mode to avoid unexpected large oscillations in the output power Po Although this algorithm is based on an uncontrolled rectifier + a dc/dc converter topology, the most widely used topology for the MSC is a two-level IGBT converter, as shown in Fig 2168-6777 © 2013 IEEE 218 IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL 1, NO 4, DECEMBER 2013 In addition, since the wind generator we studied is working in a microgrid with a limited-capacity storage system, a specially designed non-MPPT mode is required, so as to take over when the wind energy exceeds the limited capability of the storage system, while the microgrid is islanded This paper proposes a novel adaptive multi-mode power control scheme, specifically applied to the direct-drive permanent magnet synchronous generator (DDPMSG) in a microgrid, as shown in Fig 1, with a widely used fieldoriented controlled (FOC) MSC This controller can operate in either MPPT or non-MPPT conditions and automatically switches from one to the other While in MPPT mode, this controller is faster and more robust than the conventional HCS method, but more importantly, it provides an excellent tracking ability during wind speed changes; while in the non-MPPT mode, there are three power control algorithms proposed, that not only provide accurate steady-state power tracking, but also fast dynamic response to the change of the power command and environment Sections II and III introduce the proposed MPPT and non-MPPT algorithm, respectively; Sections IV and V present the simulation and experimental results; Section VI gives the conclusion Fig General MPPT control based on FOC scheme Fig Flow chart for the novel MPPT algorithm, on a FOC scheme II N OVEL MPPT A LGORITHM A General MPPT Control Diagram The topology of the DDPMSG with MSC appears in Fig The machine’s mathematical model in the synchronous dq reference frame is described in (2) and (3) [6] ⎧ dψd ⎪ ⎪Ud = ri d + dt − ψq ω ⎪ ⎨U = ri + dψq + ψ ω q q d dt (2) ⎪ψd = L d i d + ψr ⎪ ⎪ ⎩ ψq = L q i q Tem = p ψd i q − ψq i d ω J dω dt = p Tem − Tl − B p (3) where (in physical units) u d , u q are the d and q axis voltages of the stator, i d and i q are the d and q axis currents of the stator, p is pole pair of the generator, ψd and ψq are the d and q axis stator flux linkages, ψr is the rotor flux linkage, ω is the rotor electric speed in radians/second, Tem and Tl are the electromagnetic and load torques, L d and L q are the d and q axis synchronous inductances, and r is the d and q axis stator resistance For the widely used surface-mount PMSG, L d = L q Therefore, Tem = pψr i q is purely a function of i q ; while i d only affects the excitation This results in i d and i q being decoupled and easy to control individually The general MPPT control block diagram for the MSC implemented in a typical FOC drive system is shown in Fig and the details appear in Section II-B To achieve the maximum torque per ampere at the stator terminals, i d∗ = The inputs for the MPPT controller are measured rotor speed ωm and electromagnetic power Pem , obtained from (4); the output from the MPPT controller is the mechanical rotor speed reference ωm_ref [5] Pem = Tem ωm = pψr i q ωm (4) B Novel Adaptive MPPT Methodology Instead of purely implementing conventional HCS [1], as mentioned in Section I, this proposed MPPT method switches between multioperational modes, based on the detection of wind power change These specially designed modes bring the benefit of adaptive tracking to environmental changes The one-step flow chart appears in Fig There are three operating modes for each MPPT control period, as classified in [4] However, all the criteria and methodologies in this paper are only applied to an FOC drive system with an IGBT MSC, not the dc/dc converter in [4] HE et al.: ADAPTIVE MULTI-MODE POWER CONTROL Fig change 219 Operating point jumps from M to N with a sudden wind speed Fig Conventional topology with wind turbine directly connected to utility 1) No Wind Speed Change Mode 1: At the nth step, if the tracking point is far from the MPP, the variable-step HCS is applied as in (5) to increase the tracking speed (C is a constant ratio) The sign of ωm_ref is determined by the signs of ωm (n) and Pem (n) in Fig ωm_ref (n) = C | Pem (n)| (5) Mode 0: The tracking approaches the MPP, ωm_ref (n) in (5) is negligibly small and of the same order of magnitude as the speed deviation caused by the switching of IGBTs; hence, the object now is to keep the tracking result unchanged and stable When Pem (n) < a (a is a relatively small tolerance) ωm_ref (n) = ωm ref (n − 1) (6) 2) Wind Speed Change Mode 2: When a significant wind speed change is detected, the primary challenge now is to avoid the oscillation caused by the variable-step HCS, therefore the approximate optimal power curve Pwind_opt = k p_opt · is used, until there is no apparent wind speed change ωm_opt Unlike traditional PSF, every time a switching takes place from Mode to 2, this k p_opt is updated and stored using the variable-speed HCS searching result in mode 0, as shown in Fig Therefore, k p_opt no longer depends on specific wind turbine parameters The mode detection criterion is stated as follows When (m Pem (n) ≥ Pem (n−1)) not ( Pem (n) < a), (m is a relatively small ratio) ωm_ref (n) = Pem (n) k p_opt (7) C Wind Speed Change Detection As illustrated in Section I, the anemometer cannot give accurate wind speed values On the other hand, when there is a sudden wind speed change, the captured wind power curve Pwind − ωm changes from curve to in Fig 4; meanwhile, due to the large inertia of the turbine, the rotor speed ωm remains the same, therefore the captured wind power Pwind jumps from point M to N instantaneously, which results in an apparent change for the measured Pem (n) This sudden change can be selected as the flag to switch from mode to The criterion is described in Section II-B The above criterion is only applied to the detection of a significant wind speed change For an insignificant wind speed change, the variable-step HCS will not lead to much oscillation and mode itself tolerates the input deviation Moreover, the Fig A hybrid DC-AC link micro grid topology Fig Power control mode for PMSG in microgrid Pem (n) in mode should exceed the preset small tolerance a in mode 0, otherwise the high ratio of Pem (n) and Pem (n − 1) is more likely to be the result of disturbance signals, rather than the actual wind speed change III N OVEL N ON -MPPT A LGORITHM A Power Control Principles in Microgrid The increased penetration of renewable generation, longdistance transmission, and the island condition of a microgrid results in a possible 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, therefore the generator should act as a voltage source to regulate the dc-link voltage by adding a third voltage loop to the regular speed control; the power generated is determined by the dc link voltage, instead of MPPT [7] In a microgrid with a hybrid dc–ac link, as shown in Fig 6, however, all distributed generators and storage systems are connected to the dc bus Therefore, within the capacity of the storage system, MPPT can still be implemented for power control strategy even in the island condition, unless one particular condition happens, as shown in Fig From Fig 7, when and only when the microgrid is working as an island with a local load, which is too small, should the DDPMSG use the non-MPPT to reduce its power injection to the dc bus, and protect the storage system from overcharging Moreover, unlike the conventional topology in Fig 5, in an islanded microgrid, since all distributed generators and loads are connected at the point of common coupling, only one device can act as the constant voltage source For this paper, the storage system is selected to regulate the dc bus voltage 220 IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL 1, NO 4, DECEMBER 2013 TABLE I C OMPARISON OF T WO O PERATING P OINTS B1 AND B2 implemented for the power loop Apart from the difference in transient performance, other metrics are cocnsidered in Table I Pwind − Pacc = Po where dωm (8) dt From this table, although the control for B2 is easier, it has more losses and a maximum speed limit In reality, the power control for B2 includes pitch angle control; therefore, only rotor speed control for operating point B1 is considered here Pacc = ωm J B Novel Adaptive Non-MPPT Methodology Fig General non-MPPT control based on FOC scheme Fig Two operating points in the non-MPPT mode and power flow under all conditions; therefore, the MSC still acts as a current source in the non-MPPT mode, to inject the reference fixed amount of power into the dc bus Hence, a PSF is added in Fig as a third control loop Once the command switches from MPPT to non-MPPT, the control strategy will switch from that shown in Fig to that shown in Fig As plotted in the left bottom part of Fig 9, the captured wind power Pwind will decrease from the maximum point Pwind_opt to the fixed amount of power Pref , so as to prevent overcharging of the dc link capacitor For the given command Pref , there are two operating points, B1 and B2 , on the mechanical wind power curve Pwind − ωm in Fig 9, and the actual output electric power Po is calculated, as shown in (8) [7], ignoring losses If the target point is B1 , this operation requires the rotor speed ωm to decrease; therefore, the kinetic energy decreases and Pacc < From (8), the final output electric power Po will somehow increase in contrary to our expectation, especially considering the large turbine inertia This results in the storage system overcharging the dc link capacitor even more at the very beginning, and thus even possibly followed by oscillations, instability, and damage to storage devices The same analysis can be applied to target point B2 , but Pacc this time, however, contributes to the stability of the system; therefore, a simple PI controller can be 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 preferably required to eliminate oscillation and transient instability for the target B1 Equation (4) shows Pem is a function of i q and ωm ; thus, the outer power loop is coupled with the current loop and the speed loop The bandwidth of the speed loop is much lower; thus, the output of the non-MPPT controller in the third loop, the resultant speed reference ωm_ref , should respond slowly, by setting the proportional gain for the non-MPPT PI controller as k p = The complete controller block diagram is in Fig 8; the non-MPPT controller with k p = is in (9) ωm_ref = ki Perror dt = ki (Pref − Pem ) dt dωm_ref = ki (Pref − Pem ) (9) dt For a traditional controller, as time goes by, the error Perror decreases and the acceleration (dωm_ref )/dt decreases; however, if ki [the integral gain in the controller in (9)] can be increased, it can help to maintain the acceleration to a relative high level to speed up the tracking That is the basic idea of the stage I of the adaptive PI controller There are typically two ways to design ki , denoted in (10) and (11), where ki_coe , ki_init , and r are constants and the sign of ki_coe is determined in Fig 10 1) Exponential Increment (IC) Adaptive PI Controller → ki (n) = ± ki_coe · r n−1 ki_init (10) 2) Constant Increment (IC) Adaptive PI Controller ki (n) = ± ki_coe · (n − 1) ki_init (11) As time goes by, Perror is decreasing; once Perror falls within a small range (−b, b), where b is a small constant, the controller switches to stage II automatically, where ki is HE et al.: ADAPTIVE MULTI-MODE POWER CONTROL 221 TABLE II PARAMETER S ET FOR THE PM M ACHINE IN S IMULINK TABLE III R ESULTS OF A S UDDEN I NCREASE I N W IND S PEED AT S TEADY S TATE Fig 10 Flow chart for non-MPPT exponential IC adaptive PI controller Fig 12 Results when wind power curve has a sudden increase at t = s bus voltage is set as 600 V, which is high enough to support the generator within the operating speed range, using space vector pulsewidth modulation A MPPT Simulation Fig 11 and C Various Pwind − ωm wind curves with Pwind_opt s at point A, B, now fixed at a sufficiently large value of ki_const to boost the speed loop reference The flow chart for the adaptive nonMPPT controller, with stage I using (10) is shown in Fig 10, where ωm_ref_adapt and ωm_ref_const are the speed references for stages I and II, respectively, and Ts is the MPPT control period IV S IMULATION R ESULTS The proposed sliding mode power controller performance is evaluated in Simulink and results are shown in the following Various wind curves Pwind − ωm under different wind speeds are plotted in Fig 11 The blue dashed curve is denoted as wind curve I, whereass the green dotted curve and red solid curve are denoted as wind curve II and wind curve III, respectively The parameter set for the PM machine is listed in Table II, as specified in the Simulink PMSM model The dc bus is kept constant by an ideal battery model and connected to the machine via a three-phase two-level IGBT/diode MSC The dc 1) Sudden Wind Speed Increase at Steady State: As shown in Fig 12, the MPPT has already reached its steady-state optimal speed 245.5 rad/s on wind curve II at s There is a sudden wind speed increase and the Pwind −ωm curve changes to wind curve III As shown in Table III, it takes s for the variable-step HCS to track the next MPP and the optimal speed is 260.3 rad/s, while the proposed method only needs 0.04 s, 2% time of the traditional method, to reach the steady-state value of 261.5 rad/s Therefore, the proposed method needs less tracking time and results in more accurate steady-state response (theoretically, the MPP speed for wind curve III is 261.7 rad/s, point A in Fig 11; due to the preset tolerance in mode 0: a = 20 W, the actual tracking point will only fall within the vicinity of A in Fig 11, instead of the exact point) 2) Sudden Wind Speed Decrease at Steady State: As shown in Fig 13, the MPPT has reached its steady-state optimal speed 270.5 rad/s at s, around point A in Fig 11 At s, there is a sudden wind speed decrease, and the Pwind −ωm curve changes from the wind curve III to II As shown in Table IV, the settling time for the variable-step HCS and proposed method is the same, but the adaptive method settles at 246.5 rad/s (closer to point B of 245 rad/s in Fig 11), while the conventional method settles at a more inaccurate value of 222.0 rad/s 222 Fig 13 IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL 1, NO 4, DECEMBER 2013 Results when wind power curve has a sudden decrease at t = s Fig 15 Results when wind power curve has a sudden increase at t = 0.14 s TABLE VI R ESULTS OF A S UDDEN D ECREASE IN W IND S PEED B EFORE S TEADY S TATE Fig 14 Results when wind power curve has a sudden increase at t = 0.1 s TABLE IV R ESULTS OF A S UDDEN D ECREASE IN W IND S PEED AT S TEADY S TATE Fig 16 TABLE V R ESULTS OF A S UDDEN I NCREASE IN W IND S PEED B EFORE S TEADY S TATE 3) Sudden Wind Speed Increase Before Steady State: In this case, the wind curve increases from the wind curve I to III at 0.1 s in Fig 14, before the MPP speed of 181.3 rad/s for the present wind curve I is reached, denoted as point C in Fig 11 As shown in Table V, the variable-step HCS takes 0.28 s to reach the steady state 277.5 rad/s, while the adaptive method needs 0.23 s to reach the steady state, at 266.7 rad/s, which is also closer to the theoretical MPP speed of 261.7 rad/s The adaptive method under this condition needs less setting time and tracks a value closer to the theoretical MPP 4) Sudden Wind Speed Decrease Before Steady State: In this case, the wind curve decreases from the wind curve III to I at 0.14 s in Fig 14, before the MPP speed for the original curve has been reached, point A in Fig 11 As shown in Table VI, the variable-step HCS results in oscillations and takes 0.4 s to reach the steady-state value of 136 rad/s, while the adaptive method needs only 50% of this time to reach the steady state of 191.5 rad/s, also closer to the theoretical MPP value of 181.3 rad/s Wind curves for non-MPPT simulation 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 steadystate 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 Po , the tracking results will only fall into the vicinity of the theoretical MPP, instead of the exact point B Non-MPPT Simulation The wind curve II and III in Fig 11 are used to demonstrate the effect of the proposed non-MPPT controller, with a few operation points denoted in Fig 16 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 shown in Fig 17 In Fig 17, the command switches from MPPT to non-MPPT with Pref = 1500 W at 0.5 s The exponential and constant IC adaptive PI controllers perform significantly better than the traditional PI controller, greatly reducing the overshoot 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 VII All the three controllers give accurate steadystate tracking results for point B in Fig 16, and the whole HE et al.: ADAPTIVE MULTI-MODE POWER CONTROL Fig 17 223 Fig 19 Results with a sudden output power command at t = 0.5 s Fig 20 DDPMSG hardware platform Results when switching from MPPT to non-MPPT at t = 0.5 s TABLE VII R ESULTS OF S WITCHING FROM MPPT TO N ON -MPPT Fig 18 Results with a sudden wind speed change at t = 0.5 s system can automatically switch from MPPT to non-MPPT mode 2) Sudden Wind Speed Change: Fig 18 shows that when there is a wind speed change at 0.5 s, the wind power curve changes from wind curve III to II; the exponential increment (IC) adaptive PI power controller, under non-MPPT command, takes only 0.03 s to get back to the reference power of 1500 W, from point B to C in Fig 16 3) Sudden Command Change: This result is carried out with the exponential IC adaptive controller 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 to 2000 W, from point B to A in Fig 16, along the wind curve wind curve III at t = 0.5 s The above simulation results validate that the proposed controller remains closed-loop stable and that it mitigates oscillation and greatly reduces settling time at the generator terminals, in both MPPT and non-MPPT modes As known, the storage system in a microgrid brings the benefit of mitigating the stochastic characteristics of the wind, sudden command change or load change on the utility level, but its performance strongly depends on how good the frequency response of the storage system is If the high-frequency power components cannot be fully absorbed by the storage system, it will result in fluctuation of the dc bus voltage and, to make matters worse, these high-frequency components will be injected back into the utility when grid connected; hence, the storage system cannot effectively mitigate the stochastic characteristics of the distributed generation system as one might expect As a conclusion, this proposed power control method mitigates the overshoot and oscillation of the generator output power This not only contributes to the overall input–output efficiency and reliability [4], but also greatly limits the injected high-frequency power components and thereby reduces the frequency response requirements for the storage system As pointed out in [8], in a typical hybrid storage system, the super capacitor acts as a fast-dynamic storage unit and is controlled to absorb high-frequency power; while 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 required capacity of the super capacitor, so that the total cost of the hybrid storage system is reduced V E XPERIMENTAL R ESULTS A Experimental Platform Tso demonstrate that the power control is practically achievable, with the dc bus voltage in the microgrid stabilized by the battery’s voltage loop control, several tests are carried out on the test bed, as shown in Fig 20 The parameters for the 224 IEEE JOURNAL OF EMERGING AND SELECTED TOPICS IN POWER ELECTRONICS, VOL 1, NO 4, DECEMBER 2013 TABLE VIII PMSG PARAMETERS Fig 22 Non-MPPT waveforms with sudden changes for power commands Fig 21 Steady state of non-MPPT waveforms PMSG and prime mover are presented in Table VIII The prime mover, which emulates a wind turbine, is controlled by a servo motor driver; the IGBT MSC is controlled by a DSP 28 335 The battery (one cell is 12 V, 65 AH; six cells are serially connected) is controlled as a voltage source with a dc bus reference Udc_ref = 150 V, using the control strategy in [8] The sliding rheostat acts as the dc load, with maximum resistance Rmax ≈ 220 B Experimental Results 1) Non-MPPT Steady State: The non-MPPT power tracking steady-state result appears in Fig 21, where CH1 is dc bus voltage v dc , CH3 is phase a generator current i a , CH4 is dc current i dc ; the output power Pout = v dc · i dc = CH1 · CH4 At the steady state, the dc bus voltage remains constant and the output power is constant, which shows that the proposed power controller in the non-MPPT mode is closed-loop stable in the steady state 2) Non-MPPT With Sudden Power Command Changes: The non-MPPT tracking results with power command changes appears in Figs 22 and 23 In Fig 22, CH1, CH3, CH4, and Pout have the same definitions as in Fig 21 The generator speed waveform ωm and the currents on d and q axes, i d , and i q are shown in Fig 23 In Fig 22(a), since the power command is doubled, the measured output power first increases from 66.2 to 134.2 W After a while, according to another power command change, the output power steps down, back to the nominal value Moreover, since the input prime mover torque is constant, the amplitude of the generator current in CH3 i sa in Fig 22(b) and the generator currents in d and q axes in Fig 23, i d and i q , remain unchanged at different power commands in steady state; only the current frequency as well as the rotor speed is doubled to inject more active power in Fig 22(b) or reduced to 50% to inject less power The above results show that the non-MPPT power controller is still able to track the desired power while the command is varying and that Fig 23 Speed waveform, with power command reduced by 50% the closed loop control remains stable at both power levels The high-frequency components of the overshoot will be absorbed by super capacitors in the future work VI C ONCLUSION This paper proposed a novel adaptive multi-mode power control algorithm for DDPMSG in a microgrid with a hybrid dc–ac link topology, implemented in a typical FOC drive system with a two-level IGBT MSC The simulation and experimental results prove that both the MPPT and non-MPPT modes have significant advantages over traditional controllers; they are more adaptive, robust, and accurate, especially during unexpected changes of wind speed and power command; the two modes can slide automatically under different conditions With this novel adaptive power controller in a microgrid, the stochastic impact of the wind energy can be limited both on the utility level and microgrid level It will enhance the efficiency and reliability of the whole system and reduce the frequency requirements and cost of the storage system, therefore making the wind energy not only environmentally friendly, but also grid friendly HE et al.: ADAPTIVE MULTI-MODE POWER CONTROL R EFERENCES [1] I K Buehring and L L Freris, “Control policies for wind energy conversion systems,” IEE Proc., C, Generat., Transmiss Distrib., vol 128, no 5, pp 253–261, Sep 1981 [2] L Y Pao and K E Johnson, “A tutorial on the dynamics and control of wind turbines and wind farms,” in Proc Amer Control Conf., St Louis, MO, USA, Jun 2009, pp 2076–2089 [3] S Morimoto, H Nakayama, M Sanada, and Y Takeda, “Sensorless output maximization control for variable-speed wind generation system using IPMSG,” IEEE Trans Ind Appl., vol 41, no 1, pp 60–67, Jan 2005 [4] S M R Kazmi, H Goto, H.-J Guo, and O Ichinokura, “A novel algorithm for fast and efficient speed-sensorless maximum power point tracking in wind energy conversion systems,” IEEE Trans Ind Electron., vol 58, no 1, pp 29–36, Jan 2011 [5] Y Jia, Z Yang, and B Cao, “A new maximum power point tracking control scheme for wind generation,” in Proc PowerCon, Kunming, China, Oct 2002, pp 144–148 [6] Z Zheng, “Research of PMSM high performance control and mechanical sensorless operation,” Ph.D dissertation, Dept Electr Eng., Tsinghua Univ., Beijing, China, 2008 [7] X Yuan, F Wang, D Boroyevich, Y Li, and R Burgos, “DC-link voltage control of a full power converter for wind generator operating in weak-grid systems,” IEEE Trans Power Electron., vol 24, no 9, pp 2178–2192, Sep 2009 [8] B Dong, Y Li, and Z Zheng, “Control strategies of DC-bus voltage in islanded operation of microgrid,” in Proc 4th Int Conf Electr Utility DRPT, Weihai, China, Jul 2011, pp 1671–1674 Lijun He (S’12) received the B.S degree in electrical engineering from Tsinghua University, Beijing, China, in 2011 She is currently working toward the Ph.D degree with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA Her current research interests include control, design and condition monitoring of electric machines, and renewable energy 225 Yongdong Li (M’08) received the B.S.E.E degree from the Harbin Institute of Technology, Harbin, China, in 1982, and the M.S.E.E and Ph.D degrees from the Department of Electrical Engineering, Institute National Polytechnique de Toulouse, Toulouse, France, in 1984 and 1987, respectively He has been a Professor with Tsinghua University, Beijing, China, since 1996, and an Invited Professor with Institute National Polytechnique de Toulouse, Toulouse He has authored or co-authored more than 200 conference and journal papers, and two monographs on digital control of ac motor and multilevel converter His current research interests include power electronics, power converter topologies, machine control, and wind power generation Dr Li is a Senior Member of the China Electro-Technique Society, the Vice Chairman of the China Power Electronics Society, and a Vice Chairman of the Electrical Automation Committee of China Automation Association Ronald G Harley (M’77–SM’86–F’92) received the M.Sc.Eng degree (cum laude) in electrical engineering from the University of Pretoria, Pretoria, South Africa, in 1965, and the Ph.D degree from the University of London, London, U.K., in 1969 He was appointed to the Chair of electrical machines and power systems with the University of Natal, Durban, South Africa, in 1971 He is currently a Regents’ Professor and Duke Power Company Distinguished Professor with the School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA He has published over 600 papers in the field and presented several key-note papers He holds six U.S patents His current research interests include the dynamic behavior and condition monitoring of electric machines, motor drives, power systems, and their components and controlling them by the use of power electronics and intelligent control algorithms, as well as the design of electric machines Dr Harley is a fellow of the Institution of Engineering and Technology, U.K., and the Royal Society in South Africa, and a member of the Academy of Science in South Africa He received the Cyril Veinott Electromechanical Energy Conversion Award from the IEEE Power Engineering Society for outstanding contributions to the field of electromechanical energy conversion in 2005 and the IEEE Richard H Kaufmann Field Award with a citation For contributions to monitoring, control and optimization of electrical processes including electrical machines and power networks in 2009