A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers
Abstract
:1. Introduction
- A MPDPC with the Bayesian estimation for PWM rectifiers in the aircraft ACVF power system is presented, which has not been fully investigated in existing literature for PWM rectifier’s parameter estimation. The probability distribution function of estimated parameters is presented through the analysis of variation for inductance’s value caused by different factors such as temperature. The Bayesian estimation algorithm integrated with MPDPC is derived in detail.
- The performance of the Bayesian estimation is compared with LSE according to the converging rate, stability of estimated parameters by digital simulation. For the Bayesian methods with MPDPC, the result of the estimation is also compared by using the input current signal with white noise. The digital simulation results validate the robust of Bayesian estimation to disturbance from the input signals. From the numbers of samples for the input measuring signal, The Bayesian estimation methods do not need more samples which would greatly reduce the computational complexity.
- The proposed Bayesian estimation method is compared with the LSE method and traditional MPDPC in experiments. The advantage of estimation methods is verified by digital simulation and experimental results. The several performance indexes are listed in following Tables to be discussed, the final conclusion can be obtained to test the feasibility of the proposed method.
- The Hard In Loop (HIL) and experimental testing rig was built to verify the theoretical analysis under the aircraft ACVF Power system.
2. Traditional Model Predictive Control Algorithm
3. Online Parameter Estimation Algorithm
4. Simulation and Experiment Results
4.1. Digital Simulation
4.2. Hard-in-Loop Simulation
5. Experimental Verification
6. Conclusions
Author Contributions
Conflicts of Interest
Nomenclature
A,B | Parameters matrix dimension 3 × 3. |
Y | Parameters matrix dimension (k − 1) × 1. |
Parameters matrix dimension (k − 1) × 3. | |
Estimated parameters matrix (3 × 1) and error matrix [(k − 1) × 1]. | |
Estimated parameters. | |
Grid voltage vector . | |
Grid current vector . | |
Voltage vector of PWM rectifiers. | |
Grid filter inductance. | |
Grid filter resistance. | |
Grid axis voltage. | |
Grid axis current. | |
rectifiers dc link voltage. | |
Grid active and reactive powers. | |
Grid active and reactive reference powers | |
Switching state of the upper switch at each leg. | |
Sampling time. | |
AC | Alternating Current |
ACVF | Alternating Current Variable Frequency |
ATRU | Auto-Transformer Rectifiers Unit |
ESR | Equivalent Series Resistance |
EKF | Extended Kalman Filter |
FCS-MPC | Finite Control Set-Model Predictive Control |
IGBT | Insulated Gate Bipolar Transistor |
LSE | Least Square Estimation |
TRU | Transformer rectifiers Unit |
THD | Total Harmonic Distortion |
MPC | Model Predictive Control |
MPDPC | Model Predictive Direct Power Control |
PWM | Pulse Width Modulation |
UPS | Uninterruptable Power Supply |
Appendix A
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Parameters | Symbol | Value |
---|---|---|
Input voltage RMS/V | 115 | |
Fundamental frequency/Hz | 400 | |
Sampling period/μs | 20 | |
Output voltage/V | 350 | |
Input inductance/mH | 5 | |
Input resistance/Ω | 0.01 | |
Capacitance/μF | 940 | |
DC load/Ω | 61.25 |
Schemes | Traditional MPDPC | MPDPC with LSE | MPDPC with Bayesian Estimation | |
---|---|---|---|---|
Performance | ||||
Operation time of the algorithm | 10.8 uS | 15.2 uS | 16 uS | |
THD of line current | 8.6% | 6.7% | 6.8% | |
Power factor | 0.92 | 0.98 | 0.99 | |
Converging rate for the estimated value | no | low | fast | |
Stability for the estimated value | no | low | high |
Value | Results from the Paper [15] | Results from This Paper | |
---|---|---|---|
Parameters | |||
Sampling frequency | 20 KHz | 100 KHz | |
Main AC Frequency | 60 Hz | 360–800 Hz | |
Number of sampling points in one cycle | 333 | 125–270 | |
DC link voltage | 260 V | 350 V | |
Rated Power | 680 W | 1.2 KW | |
THD of AC current | 2.34% | 4.68% | |
Converging time to the true value | 10 ms | 6.7 ms |
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Lei, T.; Tan, W.; Chen, G.; Kong, D. A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers. Energies 2018, 11, 2490. https://doi.org/10.3390/en11092490
Lei T, Tan W, Chen G, Kong D. A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers. Energies. 2018; 11(9):2490. https://doi.org/10.3390/en11092490
Chicago/Turabian StyleLei, Tao, Weiwei Tan, Guangsi Chen, and Delin Kong. 2018. "A Novel Robust Model Predictive Controller for Aerospace Three-Phase PWM Rectifiers" Energies 11, no. 9: 2490. https://doi.org/10.3390/en11092490