Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System
Abstract
:1. Introduction
- ✓
- A new DVC control scheme is proposed and designed for the DFIG-based DRWP system.
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- The proposed DVC control scheme with fuzzy PWM technique and neural algorithms is a robust strategy compared to classical DVC control with PI controllers and improves the dynamic performances of the DFIG-based DRWP system.
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- The proposed method is simple and therefore easier to implement compared to the traditional technique. In addition, the proposed method reduces the reactive and active power ripples and minimizes the total harmonic distortion value of the stator current of DFIG compared to other reference strategies proposed in the literature.
2. Related Work
3. Material and Method
3.1. Fuzzy PWM Technique
3.2. DRWP Model
- PM: Main turbine power.
- PA: Auxiliary turbine power.
- PT: Total aerodynamic power.
- TM: Main turbine torque.
- TA: Auxiliary turbine torque.
- TT: Total aerodynamic torque.
3.3. DVC Method
3.4. Neural DVC Method with FPWM Technique
4. Results
5. Conclusions
- A new four-level FPWM technique was presented and confirmed with numerical simulation.
- A new DVC control scheme based on neural network and four-level FPWM technique was presented and confirmed with numerical simulation.
- Minimizes the reactive power, stator current, electromagnetic torque, and active power ripples.
- A robust control scheme was proposed.
- Reduce the harmonic distortion of stator current.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
List of Symbols: | |
ϕr, ϕr* | Actual and reference rotor flux |
Te, Te* | Actual and reference torques |
ωn, ωr | Nominal and rotor speeds |
θr | Rotor flux angle |
p | Generator pole pairs |
λ | Tip speed ration |
Vm | Wind speed |
Pn | Nominal Power |
Vs, Is | Vectors of the stator voltage and current |
Vra,b,c, Ira,b,c | Rotor voltage and current in abc frame |
Vα,β, Iα,β | Voltage and current in αβ frame |
Rs, Rr | Stator and rotor resistances |
ϕαs, ϕβs | Stator flux components in αβ frame |
Ω | Ohm (unit) |
Ki, Kp | Integral and proportional gains |
Lr, Ls, Lm | Rotor, stator, and mutual inductances |
wb | Weber (unit) |
Hz | Hertz (unit) |
MW | Migawatt (Unit) |
mH | Millihenry (unit) |
N.m | Newton-meter (Unit) |
List of Acronyms: | |
DVC | Direct vector control |
PI | Proportional integral |
FLC | fuzzy logic controller |
FNN | Feedforward neural networks |
DPC | Direct power control |
SMC | Sliding mode control |
IVC | Indirect vector control |
VAWT | Vertical axis wind turbine |
HAWT | Horizontal axis wind turbine |
THD | Total harmonic distortion |
PWM | Pulse width modulation |
FOC | Field-oriented control |
DRWT | Dual-rotor wind turbine |
FSMC | Fuzzy sliding mode control |
TSMC | Terminal sliding mode controller |
SVM | Space vector modulation |
IP | Integral-proportional |
DFIG | doubly-fed induction generator |
FPWM | Fuzzy Pulse width modulation |
DRWP | Dual-rotor wind power |
NDVC | Neural direct vector control |
MRSMC | Multi-resonant-based sliding mode controller |
ISMC | Integral sliding mode controller. |
Appendix A
References | Study Nature | Control | Controller | Type of Generator | Complexity | Current Oscillations | Reference Tracking | Dynamic Responses |
---|---|---|---|---|---|---|---|---|
Ref. [7] | Simulation | FOC | PI | DFIG | Low | High | ++ | + |
Ref. [8] | Simulation | FOC | FOPI | DFIG | High | Low | +++ | ++ |
Ref. [9] | Simulation | Nonlinear control | Fuzzy SOSMC | DFIG | High | Neglected | +++ | +++ |
Ref. [10] | Simulation | Amplitude control | PI | DFIG | Low | Low | ++ | ++ |
Ref. [11] | Simulation | DPC | SOSMC | DFIG | High | Neglected | +++ | ++++ |
Ref. [12] | Simulation | FOC | PI | DFIG | Low | High | ++ | + |
Ref. [13] | Simulation | DPC | Synergetic STA | DFIG | Medium | Low | +++ | ++++ |
Ref. [14] | Simulation | Nonlinear control | TSMC | DFIG | High | Neglected | +++ | +++ |
Ref. [15] | Simulation | Fractional-order command | FOPI | DFIG | Medium | Low | +++ | ++ |
Ref. [16] | Simulation | Nonlinear control | Backstepping control | DFIG | High | Neglected | +++ | +++ |
Ref. [17] | Simulation | DPC | FGA | DFIG | Medium | Low | ++ | +++ |
Ref. [18] | Simulation | DPC | Backstepping control | DFIG | High | Neglected | +++ | ++++ |
Ref. [19] | Simulation | Nonlinear control | Feedback linearization control | DFIG | High | Neglected | +++ | ++ |
Ref. [20] | Simulation | DPC | VOC | DFIG | Medium | Low | ++ | +++ |
Ref. [21] | Simulation | SDC | SDC | DFIG | Medium | Low | ++ | +++ |
Ref. [22] | Simulation | DPC | MPC | DFIG | Medium | Low | +++ | +++ |
Ref. [23] | Simulation | DPC | SOSMC | DFIG | High | Low | ++++ | ++++ |
Ref. [24] | Simulation | DPC | DC voltage regulation | DFIG | Medium | Low | +++ | ++ |
Ref. [25] | Simulation | FOC | STFOTSMC | DFIG | High | Low | +++ | +++ |
Ref. [26] | Simulation | DPC | Adaptive High SMC | DFIG | High | Low | +++ | ++++ |
Ref. [27] | Simulation | DPC | Extended power theory | DFIG | Medium | High | +++ | ++ |
Ref. [28] | Simulation | FOC | Full-order adaptive observer | DFIG | High | Low | ++ | ++ |
Ref. [29] | Experimental | Nonlinear control | Feedback linearization | DFIG | High | Low | +++ | +++ |
Ref. [30] | Simulation | Nonlinear control | Fuzzy SOSMC | DFIM | High | Neglected | ++++ | ++++ |
Ref. [31] | Simulation | SOSMC | MOO | DFIG | Medium | Low | ++ | +++ |
Ref. [32] | Simulation | DTC | Hysteresis comparator | DFIG | Medium | High | ++ | ++ |
Ref. [33] | Simulation | DPC | SMC | DFIG | Medium | Neglected | ++++ | +++ |
Ref. [34] | Simulation | Nonlinear control | SMC | DFIG | High | Neglected | ++ | +++ |
Ref. [35] | Experimental | Nonlinear control | Fuzzy fractional order robust command | DFIG | High | Neglected | +++ | ++++ |
Ref. [36] | Simulation | DPC | MPS | DFIG | Medium | Low | ++ | +++ |
Ref. [37] | Simulation | DPC | Neural controller | DFIG | Medium | Low | ++++ | +++ |
Ref. [38] | Simulation | FOC | PI | DFIG | Low | High | ++ | + |
Ref. [39] | Simulation | DTC | Resonant current control | DFIG | Medium | High | +++ | ++ |
Ref. [40] | Simulation | DTC | PI | DFIG | Low | Low | +++ | ++ |
Ref. [41] | Simulation | FOC | PI | DFIG | Low | High | ++ | + |
Ref. [42] | Simulation | FOC | Neuro-fuzzy | DFIG | Medium | Low | ++ | ++ |
Ref. [43] | Simulation | DVC | Fuzzy SMC | DFIG | Medium | Neglected | ++++ | +++ |
Ref. [44] | Simulation | FOC | Neural SOSMC | DFIG | High | Low | ++ | +++ |
Ref. [45] | Simulation | DVC | PI | DFIG | Low | High | ++ | + |
Ref. [46] | Experimental | IVC | PI | DFIG | Low | High | ++ | + |
Ref. [47] | Simulation | DPC | Neuro-fuzzy | DFIG | Medium | Low | +++ | +++ |
Ref. [48] | Experimental | IVC | Hysteresis rotor current controller | DFIG | Medium | Low | ++ | ++ |
Appendix B. Design of the Fuzzy Logic Controller
e | NB | NM | NS | EZ | PS | PM | PB |
---|---|---|---|---|---|---|---|
∆e | |||||||
PB | EZ | PS | PM | PB | PB | PB | PB |
EZ | NB | NM | NS | EZ | PS | PM | PB |
NM | NB | NB | NB | NM | NS | EZ | PS |
NB | NB | NB | NB | NB | NM | NS | EZ |
PM | NS | EZ | PS | PM | PB | PB | PB |
PS | NM | NS | EZ | PS | PM | PB | PB |
NS | NB | NB | NM | NS | EZ | PS | PM |
Or method | Max |
And method | Min |
FIS type | Mamdani |
Defuzzification | Centroid |
Implication | Min |
Aggregation | Max |
Appendix C. Design of the Feedforward Neural Network
Parameters | Values |
---|---|
Performances | Mean Squared Error (mse) |
Training | Levenberg-Marquardt algorithm (trainlm) |
TrainParam.show | 50 |
TrainParam.Lr | 0.05 |
Neurons of output layer | 1 |
TrainParam.goal | 0 |
TrainParam.mu | 0.8 |
Neurons of input layer | 1 |
Coeff. of acceleration of convergence (mc) | 0.9 |
Derivative | Default (default deriv) |
Number of hidden layers | 1 |
Functions of activation | Tensing, Purling, trainlm |
Number of output layer | 1 |
TrainParam.eposh | 250 |
Number of input layer | 1 |
Neurons of hidden layer | 8 |
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Response Time | |||
---|---|---|---|
Torque | Active Power | Reactive Power | |
DVC-PWM | 0.036 s | 0.036 s | 0.014 s |
NDVC-FPWM | 1.78 ms | 1.78 ms | 8.5 ms |
Criteria | Control Techniques | |
---|---|---|
DVC | NDVC-FPWM | |
Reactive and active power tracking | Well | Excellent |
Dynamic response (s) | Medium | Fast |
Reduce active and reactive power ripples | Acceptable | Excellent |
THD (%) | 1.65 | 0.13 |
Settling time (ms) | High | Medium |
Active power ripple (W) | Around 2000 | Around 800 |
Sensitivity to parameter change | High | Medium |
Overshoot (%) | Remarkable ≈ 12% | Neglected |
Simplicity of converter and filter design | Simple | Simple |
Torque: ripple (N.m) | Around 170 | Around 50 |
Rise Time (s) | High | Medium |
Reactive power ripple (VAR) | Around 1700 | Around 980 |
Simplicity of calculations | Simple | Rather complicated |
Stator current: ripple (A) | Around 100 | Around 15 |
Quality of stator current | Acceptable | Excellent |
Improvement of transient performance | Good | Excellent |
Response Time | ||||
---|---|---|---|---|
Torque | Reactive Power | Active Power | ||
Proposed technique: NDVC-FPWM | 1.78 ms | 8.5 ms | 1.78 ms | |
Ref. [60] | Neuro-second order sliding mode control (NSOSMC) | 5 ms | 9 ms | 5 ms |
Direct power control (DPC) | 18 ms | 17 ms | 18 ms |
Reference | Strategy | THD (%) |
---|---|---|
Ref. [61] | DTC control | 2.57 |
Ref. [47] | FOC control | 3.7 |
Ref. [55] | Classical DTC method | 6.70 |
Fuzzy DTC method | 2.40 | |
Ref. [62] | DPC with PI controllers | 0.43 |
Ref. [63] | 12 sectors DPC(12-DPC) control | 0.40 |
Ref. [64] | Fuzzy SMC method | 1.15 |
Ref. [65] | SOSMC method | 3.13 |
Ref. [66] | DPC control with STA controller | 1.66 |
Ref. [67] | DVC control with synergetic sliding mode controller | 0.50 |
Ref. [68] | Integral sliding mode control (ISMC) | 9.71 |
Multi-resonant-based sliding mode controller (MRSMC) | 3.14 | |
Ref. [69] | DPC with terminal synergetic controller | 0.25 |
Ref. [70] | Two-level DTC control | 8.75 |
Three-level DTC control | 1.57 | |
Ref. [71] | Intelligent super twisting sliding mode controller | 0.52 |
Proposed strategy | Traditional DVC control | 1.65 |
NDVC-FPWM control | 0.13 |
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Benbouhenni, H.; Bizon, N. Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System. Mathematics 2021, 9, 2403. https://doi.org/10.3390/math9192403
Benbouhenni H, Bizon N. Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System. Mathematics. 2021; 9(19):2403. https://doi.org/10.3390/math9192403
Chicago/Turabian StyleBenbouhenni, Habib, and Nicu Bizon. 2021. "Advanced Direct Vector Control Method for Optimizing the Operation of a Double-Powered Induction Generator-Based Dual-Rotor Wind Turbine System" Mathematics 9, no. 19: 2403. https://doi.org/10.3390/math9192403