Journal of Applied Science and Engineering

Published by Tamkang University Press

1.30

Impact Factor

1.60

CiteScore

Longhai Fu  

Department of Electrical and Electronic Engineering, Yantai Vocational College, Yantai 262670, China


 

Received: August 18, 2022
Accepted: November 3, 2022
Publication Date: March 9, 2023

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.6180/jase.202311_26(11).0004  


ABSTRACT


In order to accurately compensate the harmonics and reactive power of the active power filter, a parallel type active filter detection algorithm based on adaptive inverse control is proposed to control and follow the DC capacitor voltage, obtain the command signal needed by the active filter, and accomplish the purpose of harmonic control and reactive power compensation. The simulation results show that the grid current after compensation is similar to the standard sinusoidal wave shape, indicating that the algorithm has better compensation performance and following performance; after compensation, the total current distortion rate is reduced by 3%; when the harmonic component of the supply voltage is high, the harmonic current of the supply current is controlled within the specified range, indicating that the active filter can effectively compensate for harmonics, verifying the adaptive inverse control for the active filter The effectiveness of the adaptive inverse control on the active filter is verified. Comparing the low-pass filter with the adaptive inverse control algorithm, the fundamental current obtained by the latter reaches the steady state 0.015s faster than the former, indicating that the response speed is faster than that of the normal low filter using the adaptive inverse control algorithm. It shows that the shunt-type active filter based on adaptive inverse control has the characteristics of fast dynamic response and good follow-through.


Keywords: Adaptive; Inverse control; Active filter


REFERENCES


  1. [1] A. Zoghbi and D. Berkani, (2021) “Performance improvement of the shunt active power filter using a novel adaptive filtering approach" Turkish Journal of Electrical Engineering and Computer Sciences 29(1): 203–222. DOI: 10.3906/ELK-2005-193.
  2. [2] K. Mohanaprasad, A. Singh, K. Sinha, and T. Ketkar, (2019) “Noise reduction in speech signals using adaptive independent component analysis (ICA) for hands free communication devices" International Journal of Speech Technology 22(1): 169–177. DOI: 10.1007/s10772-019-09595-9.
  3. [3] J. Wang, J. Xue, J. Lu, and X. Qiu, (2019) “A switching strategy of the frequency-domain adaptive algorithm for active noise control" The Journal of the Acoustical Society of America 146(2): 1045–1050. DOI: 10.1121/1.5120260.
  4. [4] S. Hou, J. Fei, C. Chen, and Y. Chu, (2019) “Finite-time adaptive fuzzy-neural-network control of active power filter" IEEE Transactions on Power Electronics 34(10): 10298–10313. DOI: 10.1109/TPEL.2019.2893618.
  5. [5] X. Min, Y. Li, and S. Tong, (2020) “Adaptive fuzzy output feedback inverse optimal control for vehicle active suspension systems" Neurocomputing 403: 257–267. DOI: 10.1016/j.neucom.2020.04.096.
  6. [6] M. Fallah and B. Moetakef-Imani, (2019) “Adaptive inverse control of chatter vibrations in internal turning operations" Mechanical Systems and Signal Processing 129: 91–111. DOI: 10.1016/j.ymssp.2019.04.007.
  7. [7] X. Zhang, S. Su, G. Zhu, and Y. Peng, (2019) “Adaptive discrete-time estimated inverse control for piezoelectric positioning stage" IEEE Access 7: 155120–155129. DOI: 10.1109/ACCESS.2019.2948495.
  8. [8] X. Liu, M. Huang, R. Xiong, J. Shan, and X. Mao, (2018) “Adaptive inverse control of piezoelectric actuators based on segment similarity" IEEE Transactions on Industrial Electronics 66(7): 5403–5411. DOI: 10.1109/TIE.2018.2868011.
  9. [9] J. Fei and T. Wang, (2019) “Adaptive fuzzy-neuralnetwork based on RBFNN control for active power filter" International Journal of Machine Learning and Cybernetics 10(5): 1139–1150. DOI: 10.1007/s13042-018-0792-y.
  10. [10] P. K. Ray and S. D. Swain, (2020) “Performance enhancement of shunt active power filter with the application of an adaptive controller" IET Generation, Transmission & Distribution 14(20): 4444–4451. DOI: 10.1049/ietgtd.2020.0334.
  11. [11] G. Veerasamy, R. Kannan, R. Siddharthan, G. Muralidharan, V. Sivanandam, and R. Amirtharajan, (2022) “Integration of genetic algorithm tuned adaptive fading memory Kalman filter with model predictive controller for active fault-tolerant control of cement kiln under sensor faults with inaccurate noise covariance" Mathematics and Computers in Simulation 191: 256–277. DOI: 10.1016/j.matcom.2021.07.023.
  12. [12] P. Santiprapan, A. Booranawong, K. Areerak, and H. Saito, (2020) “Adaptive repetitive controller for an active power filter in three-phase four-wire systems" IET Power Electronics 13(13): 2756–2766. DOI: 10.1049/iet-pel.2019.1401.
  13. [13] T. L. Molloy, J. Inga, M. Flad, J. J. Ford, T. Perez, and S. Hohmann, (2019) “Inverse open-loop noncooperative differential games and inverse optimal control" IEEE Transactions on Automatic Control 65(2): 897–904. DOI: 10.1109/TAC.2019.2921835.
  14. [14] M. J. da Silva, S. C. Ferreira, J. P. da Silva, M. G. dos Santos, A. L. Paganotti, and L. M. Barbosa, (2020) “Equivalency between adaptive notch filter PLL and inverse park PLL by modeling and parameter adjustment" IEEE Latin America Transactions 18(12): 2112–2121. DOI: 10.1109/TLA.2020.9400439.
  15. [15] J. Liu, B. Qiao, X. Zhang, R. Yan, and X. Chen, (2019) “Adaptive vibration control on electrohydraulic shaking table system with an expanded frequency range: theory analysis and experimental study" Mechanical Systems and Signal Processing 132: 122–137. DOI: 10.1016/j.ymssp.2019.06.024.
  16. [16] Y. Pu, C. Yao, X. Li, and Z. Liu, (2022) “Adaptive active vibration control for piezoelectric smart structure with online hysteresis identification and compensation" Journal of Vibration and Control 28(5-6): 626–636. DOI: 10.1177/1077546320980574.
  17. [17] K. Okano, T. Itatsu, N. Sasaoka, and Y. Itoh, (2020) “Auxiliary-noise power-scheduling method for online secondary path modeling in pre-inverse active noise control system" IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences 103(3): 582–588. DOI: 10.1587/transfun.2019EAP1120.
  18. [18] W. Xu, (2021) “Adaptive probability hypothesis density filter for multi-target tracking with unknown measurement noise statistics" Measurement and Control 54(3-4): 279–291. DOI: 10.1177/0020294021992800.
  19. [19] Y. Fang, J. Fei, and T. Wang, (2020) “Adaptive backstepping fuzzy neural controller based on fuzzy sliding mode of active power filter" IEEE Access 8: 96027–96035. DOI: 10.1109/ACCESS.2020.2995755.
  20. [20] D. Shi, W.-S. Gan, B. Lam, S. Wen, and X. Shen, (2021) “Optimal output-constrained active noise control based on inverse adaptive modeling leak factor estimate" IEEE/ACM Transactions on Audio, Speech, and Language Processing 29: 1256–1269. DOI: 10.1109/TASLP.2021.3065730.
  21. [21] H.-S. Yan and C. Zhang, (2020) “Inverse control of single-input/single-output nonlinear time-varying systems with noise disturbances by multi-dimensional Taylor network" Transactions of the Institute of Measurement and Control 42(13): 2450–2464. DOI: 10.1177/0142331220915349.