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Design of Power Intelligent Control DCS Module Based on Improved PID

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Abstract

Distributed control system (DCS) is the core of power system control. A power intelligent control DCS module based on improved PID is studied to realize the output gain control of power electrical system and improve the control efficiency of power electrical system. Combined with integrated DSP information processing chip, a design of power intelligent control DCS module based on output power amplification and regulation is proposed. The overall model of DCS power control system is designed, and the DCS power control frequency doubling gain amplifier is constructed. The signal anti-interference design adopts cascade filter and output power amplification adjustment method to obtain the reset circuit of DCS controller. The output power amplification and adjustment algorithm are designed to equalize the gain distribution to improve the power control performance of DCS. The test results show that the output gain of intelligent power control is large, the adaptive performance is good, and the output stability is strong.

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References

  1. Tu, B., Chuai, R., Xu, H.: Outlier detection based on K-mean distance outlier factor for gait signal. Inf. Control 48(1), 16–21 (2019)

    Google Scholar 

  2. Wei, X.S., Luo, J.H., Wu, J.: Selective convolutional descriptor aggregation for fine-grained image retrieval. IEEE Trans. Image Process. 26(6), 2868–2881 (2017)

    Article  MathSciNet  Google Scholar 

  3. Liu, Q., Guan, W., Li, S., Wang, F.: Indoor WiFi-PDR fusion location algorithm based on extended kalman filter. Comput. Eng. 45(4), 66–71 (2019)

    Google Scholar 

  4. Wang, Z., Huang, M., et al.: Integrated algorithm based on density peaks and density-based clustering. J. Comput. Appl. 39(2), 398–402 (2019)

    Google Scholar 

  5. He, H., Tan, Y.: Automatic pattern recognition of ECG signals using entropy-based adaptive dimensionality reduction and clustering. Appl. Soft Comput. 55, 238–252 (2017)

    Article  Google Scholar 

  6. Zhu, Y., Zhu, X., Wang, J.: Time series motif discovery algorithm based on subsequence full join and maximum clique. J. Comput. Appl. 39(2), 414–420 (2019)

    Google Scholar 

  7. Sun, X., Li, X.G., Li, J.F., et al.: Review on deep learning based image super-resolution restoration algorithms. Acta Automatica Sinica 43(5), 697–709 (2017)

    MATH  Google Scholar 

  8. Yan, S., Xu, D., Zhang, B., et al.: Graph embedding and extensions. a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)

    Article  Google Scholar 

  9. Li, B., Wang, C., Huang, D.S.: Supervised feature extraction based on orthogonal discriminant projection. Neurocomputing 73(1), 191–196 (2009)

    Article  Google Scholar 

  10. Hou, C., Nie, F., Li, X., et al.: Joint embedding learning and sparse regression. a framework for unsupervised feature selection. IEEE Trans. Cybern. 44(6), 793–804 (2014)

    Article  Google Scholar 

  11. Liu, J., Luo, X.: Short-term optimal environmental economic hydrothermal scheduling based on handling complicated constraints of multi-chain cascaded hydropower station. Proc. CSEE 32(14), 27–35 (2012)

    Google Scholar 

  12. Ding, Q., Lu, W., Xu, C., et al.: Passivity-based control of a three-phase shunt hybrid active power filter. J. Electr. Mach. Control 18(05), 1–6 (2014)

    Google Scholar 

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Correspondence to Chao Song .

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Song, C. (2019). Design of Power Intelligent Control DCS Module Based on Improved PID. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_53

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  • DOI: https://doi.org/10.1007/978-3-030-36402-1_53

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-36401-4

  • Online ISBN: 978-3-030-36402-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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