# An Improved Deadbeat Control Strategy Based on Repetitive Prediction Against Grid Frequency Fluctuation for Active Power Filter BTech EEE Academic projects

ABSTRACT:

In order to improve the harmonic compensation performance of active power filter (APF) in distribution network, based on deadbeat control theory, the command current prediction algorithm and current tracking control strategy are optimized in this article. Firstly, the command current repetitive prediction in abc coordinate system is transferred to dq for improving its accuracy in lead compensation, and the equivalent for fractional delay beat is achieved by Lagrange Interpolation Polynomial to solve the problem of inaccurate prediction caused by grid frequency fluctuation. Then, considering the inherent half-sampling-period delay of sinusoidal PWM (SPWM), an improved deadbeat control strategy for current tracking is proposed by estimating the output current of next sampling period. Because the output current in next sampling period is replaced by that in current sampling period with traditional deadbeat control strategy, this estimation could make up for the defect of low control precision caused by that replacement. After that, adding error repetitive correction into the improved deadbeat control channel to reduce the periodic tracking error of output current. Finally, the stability and accuracy of the improved control system are analyzed theoretically, and its feasibility and effectiveness are verified by the simulation and hardware-in-the-loop (HIL) experiments.

KEYWORDS:

1. Deadbeat control
2. Frequency fluctuation
3. Harmonic compensation
4. Lagrange interpolation polynomial
5. Error repetitive correction

SOFTWARE: MATLAB/SIMULINK

BLOCK DIAGRAM:

Figure 1. Three-Phase Apf Topology And Overall Control Structure.

EXPECTED SIMULATION REUSLTS:

Figure 2. Predicted Command Current With Traditional Or Proposed Prediction Algorithm At Different Grid Frequency. (A) Grid Frequency of 50hz. (B) Grid Frequency Of 50.5hz. (C) Grid Frequency Of 49.5hz.

Figure 3. Simulation Results For The Improved Deadbeat Control Strategy With Traditional Or Proposed Repetitive Prediction. (A) With Traditional Prediction At Grid Frequency Of 50.5hz. (B) With Proposed Prediction At Grid Frequency Of 50.5hz (C) With Traditional Prediction At Grid Frequency Of 49.5hz. (D) With Proposed Prediction At Grid Frequency Of 49.5hz.

Figure 4. Power Grid Voltage And Nonlinear Load Current. (A) Power Grid

Voltage. (B) Nonlinear Load Current.

Figure 5. Grid-Side Current And Output Current Of Phase A With Traditional Or Improved Deadbeat Control Strategy. (A) Traditional Deadbeat. (B) Improved Deadbeat (Krc D 0).

Figure 6. Grid-Side Current And Output Current Of Phase A With Different Value Of Krc. (A) Krc D 0:15. (B) Krc D 0:30. (C) Krc D 0:45.

CONCLUSION:

In order to improve the harmonic compensation performance of APF, command current prediction algorithm and dead-beat control strategy for current tracking are optimized in this article. The feasibility and effectiveness of the proposed method are verified by theoretical analysis, simulation, and experiment. The conclusions are as follows:

(1) The accuracy of command current prediction is the pre- requisite for optimizing the current tracking control strategy. Compared with the traditional command current repetitive prediction algorithm, the proposed one exhibits higher prediction accuracy and stronger adaptability to the fluctuation of grid frequency.

(2) Compared with the traditional deadbeat control, because the APF output current in the next sampling period has been estimated, the effective controlled frequency band of the control system is enlarged on the premise of ensuring system stability.

(3) When current tracking error repetitive correction is added into the improved deadbeat control channel, the periodic tracking error could be reduced to some extent, and the control accuracy is increased as well.

(4) The simulation and experiment results demonstrate that the proposed control method has a fine steady-state performance to grid frequency fluctuation and a satisfactory dynamic response to the sudden change of load current.

REFERENCES:

[1] Y. Fang, J. Fei, and T. Wang, “Adaptive backstepping fuzzy neural controller based on fuzzy sliding mode of active power filter,” IEEE Access, vol. 8, pp. 96027_96035, Jun. 2020.

[2] J. Chen, H. Shao, Y. Cheng, X. Wang, G. Li, and C. Sun, “Harmonic circulation and DC voltage instability mechanism of parallel-SVG system,” IET Renew. Power Gener., vol. 14, no. 5, pp. 793_802, Apr. 2020.

[3] J. Fei and Y. Chu, “Double hidden layer output feedback neural adaptive global sliding mode control of active power filter,” IEEE Trans. Power Electron., vol. 35, no. 3, pp. 3069_3084, Mar. 2020.

[4] W. U. K. Tareen and S. Mekhielf, “Three-phase transformerless shunt active power filter with reduced switch count for harmonic compensation in grid-connected applications,” IEEE Trans. Power Electron., vol. 33, no. 6, pp. 4868_4881, Jun. 2018.

[5] Z.-X. Zou, K. Zhou, Z. Wang, and M. Cheng, “Frequency-adaptive fractional-order repetitive control of shunt active power filters,” IEEE Trans. Ind. Electron., vol. 62, no. 3, pp. 1659_1668, Mar. 2015.