Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller

Samhar Saeed Shukir

doi:doi:10.11648/j.ijem.20210501.11

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Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller

Samhar Saeed Shukir

Published in International Journal of Engineering Management (Volume 5, Issue 1)

Received: 26 March 2021 Accepted: 12 April 2021 Published: 26 April 2021

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Abstract

The Dynamic Voltage Restorer (DVR) is one of the most efficient and effective custom power devices in protecting the sensitive equipment against voltage sag and voltage harmonics due to; lower cost, smaller size and dynamic response. The inverter is the core of the DVR and it directly affects the performance of the DVR, incorrect injection or delay in the process would be dangerous to sensitive loads. The major functions of the DVR controller are, detection of voltage disturbances events in the system, calculation of the compensating voltage and generation the reference signal for the PWM to trigger the voltage source inverter. PI controller and fuzzy logic controller has been compared with the proposed fuzzy neural optimized fuzzy logic controller in correcting the sag problems and mitigating the harmonics distortion with linear and non-linear loads. Fuzzy Neural optimized Fuzzy Logic controller is the most efficient in improving the performance of the Dynamic Voltage Restorer in compensating any kind of voltage variations and reducing the voltage Total Harmonic Distortion (THD) by enhancing an injection capability of the DVR which is highly influenced by a control algorithm employed. The system is simulated in MATLAB and results confirm the validity and feasibility.

Published in	International Journal of Engineering Management (Volume 5, Issue 1)
DOI	10.11648/j.ijem.20210501.11
Page(s)	1-11
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Dynamic Voltage Restorer, Fuzzy Logic Controller, Fuzzy Neural Optimized Fuzzy Logic Controller, Sag Correction and Harmonics Mitigation

References

[1]	Newman, MJ, Holmes, DG, Nielsen, JG & Blaabjerg, F 2005, 'A dynamic voltage restorer (DVR) with selective harmonic compensation at medium voltage level', IEEE Transaction on Industry Applications, vol. 41, pp. 1744-1753.
[2]	Nielsen, JG & Blaabjerg, F 2005, ' A detailed comparison of system topologies for dynamic voltage restorer', IEEE Transactions on Industrial Applications, vol. 41, no. 5, pp. 1272-1280.
[3]	Salimin, RH & Rahim, MSA 2011, 'Simulation analysis of DVR performance for voltage sag mitigation', Proceedings of IEEE Power Engineering and Optimization Conference (PEOCO), pp. 261-266.
[4]	F. A. L. Jowder, "Design and analysis of dynamic voltage restorer for deep voltage sag and harmonic compensation", IET Gener. Transm. Distrib., vol. 3, no. 6, pp. 547-560, 2009.
[5]	M. N. Tandjaoui, et al., "Sensitive Loads Voltage Improvement Using Dynamic Voltage Restorer," International Conference on Electrical Engineering and Informatics, 2011. Conference publication. IEEE Xplore digital library.
[6]	C. Fitzer, A. Anulampalam, M. Barnes, and R. Zurowski "Mitigation of Saturation in Dynamic Voltage Restorer Connection Transformers ", IEEE Transactions on Power Electronics, Volume: 17, Issue: 6, Nov. 2002, pp. 1058–1066.
[7]	J. G. Nielsen, F. Blaabjerg, N. Mohan, "Control strategies for dynamic voltage restorer compensating voltage sags with phase jump", Proc. IEEE/APEC'01 Conference, vol. 2, pp. 1267-1273, 2001.
[8]	J. Klapper, J. T. Frankle, Phase-Locked and Frequency-Feedback Systems, New York:Academic Press, 1972.
[9]	J. G. Nielsen, Design and Control of a Dynamic Voltage Restorer, 2002.
[10]	S. Aboulem, E. M. Boufounas, I. Boumhidi, "Optimal tracking and robust intelligent based PI power controller of the wind turbine systems", 2017 Intelligent Systems and Computer Vision (ISCV), pp. 1-7, 2017.
[11]	S. Nayak, S. Gurunath, N. Rajasekar, "Advanced single-phase inverse park PLL with tuning of PI controller for improving stability of grid utility using soft computing technique", 2016 Online International Conference on Green Engineering and Technologies (IC-GET), pp. 1-5, 2016.
[12]	H. A. Kazem, "Harmonic Mitigation Techniques Applied to Power Distribution Networks", Advances in Power Electronics, pp. 10, Jan. 2013.
[13]	D. Chen, H. C. He, and H. Wang, "Fuzzy control technique based on continuous t-norm and s-norm," Control Theory and Applications, vol 18, no. 5, pp. 717-721, 2001.
[14]	W. X. Zhang, G. X. Liang, Fuzzy control and system, Xi'an: Xi'an Jiaotong University Press, 1998, pp. 72-78.
[15]	C. Benachaiba, B. Ferdi, "Voltage quality improvement using DVR", Electrical Power Quality and Utilization Journal, vol. XIV, no. 1, pp. 39-45, 2008.
[16]	Jang JSR (1993) ANFIS: adaptive network-based fuzzy inference systems. IEEE Trans Sys Man Cybern 23: 665-685.
[17]	Hung T. Nguyen, Nadipuram R. Prasad Carol L. Walker, Elbert A. Walker. 'A First Course in FUZZY and NEURAL CONTROL', printed in the United States of America 1234567890 printed on acid -free paper; chapter. 2; pp. 88-90.

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APA Style

Samhar Saeed Shukir. (2021). Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. International Journal of Engineering Management, 5(1), 1-11. https://doi.org/10.11648/j.ijem.20210501.11

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ACS Style

Samhar Saeed Shukir. Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. Int. J. Eng. Manag. 2021, 5(1), 1-11. doi: 10.11648/j.ijem.20210501.11

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AMA Style

Samhar Saeed Shukir. Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. Int J Eng Manag. 2021;5(1):1-11. doi: 10.11648/j.ijem.20210501.11

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@article{10.11648/j.ijem.20210501.11,
  author = {Samhar Saeed Shukir},
  title = {Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller},
  journal = {International Journal of Engineering Management},
  volume = {5},
  number = {1},
  pages = {1-11},
  doi = {10.11648/j.ijem.20210501.11},
  url = {https://doi.org/10.11648/j.ijem.20210501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijem.20210501.11},
  abstract = {The Dynamic Voltage Restorer (DVR) is one of the most efficient and effective custom power devices in protecting the sensitive equipment against voltage sag and voltage harmonics due to; lower cost, smaller size and dynamic response. The inverter is the core of the DVR and it directly affects the performance of the DVR, incorrect injection or delay in the process would be dangerous to sensitive loads. The major functions of the DVR controller are, detection of voltage disturbances events in the system, calculation of the compensating voltage and generation the reference signal for the PWM to trigger the voltage source inverter. PI controller and fuzzy logic controller has been compared with the proposed fuzzy neural optimized fuzzy logic controller in correcting the sag problems and mitigating the harmonics distortion with linear and non-linear loads. Fuzzy Neural optimized Fuzzy Logic controller is the most efficient in improving the performance of the Dynamic Voltage Restorer in compensating any kind of voltage variations and reducing the voltage Total Harmonic Distortion (THD) by enhancing an injection capability of the DVR which is highly influenced by a control algorithm employed. The system is simulated in MATLAB and results confirm the validity and feasibility.},
 year = {2021}
}

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TY  - JOUR
T1  - Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller
AU  - Samhar Saeed Shukir
Y1  - 2021/04/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ijem.20210501.11
DO  - 10.11648/j.ijem.20210501.11
T2  - International Journal of Engineering Management
JF  - International Journal of Engineering Management
JO  - International Journal of Engineering Management
SP  - 1
EP  - 11
PB  - Science Publishing Group
SN  - 2640-1568
UR  - https://doi.org/10.11648/j.ijem.20210501.11
AB  - The Dynamic Voltage Restorer (DVR) is one of the most efficient and effective custom power devices in protecting the sensitive equipment against voltage sag and voltage harmonics due to; lower cost, smaller size and dynamic response. The inverter is the core of the DVR and it directly affects the performance of the DVR, incorrect injection or delay in the process would be dangerous to sensitive loads. The major functions of the DVR controller are, detection of voltage disturbances events in the system, calculation of the compensating voltage and generation the reference signal for the PWM to trigger the voltage source inverter. PI controller and fuzzy logic controller has been compared with the proposed fuzzy neural optimized fuzzy logic controller in correcting the sag problems and mitigating the harmonics distortion with linear and non-linear loads. Fuzzy Neural optimized Fuzzy Logic controller is the most efficient in improving the performance of the Dynamic Voltage Restorer in compensating any kind of voltage variations and reducing the voltage Total Harmonic Distortion (THD) by enhancing an injection capability of the DVR which is highly influenced by a control algorithm employed. The system is simulated in MATLAB and results confirm the validity and feasibility.
VL  - 5
IS  - 1
ER  -

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Author Information

Samhar Saeed Shukir

Electrical Department, Technical Institute- Kut, Middle Technical University, Baghdad, Iraq

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Plain Text BibTeX RIS

APA Style

Samhar Saeed Shukir. (2021). Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. International Journal of Engineering Management, 5(1), 1-11. https://doi.org/10.11648/j.ijem.20210501.11

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ACS Style

Samhar Saeed Shukir. Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. Int. J. Eng. Manag. 2021, 5(1), 1-11. doi: 10.11648/j.ijem.20210501.11

Copy | Download

AMA Style

Samhar Saeed Shukir. Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller. Int J Eng Manag. 2021;5(1):1-11. doi: 10.11648/j.ijem.20210501.11

Copy | Download

@article{10.11648/j.ijem.20210501.11,
  author = {Samhar Saeed Shukir},
  title = {Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller},
  journal = {International Journal of Engineering Management},
  volume = {5},
  number = {1},
  pages = {1-11},
  doi = {10.11648/j.ijem.20210501.11},
  url = {https://doi.org/10.11648/j.ijem.20210501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijem.20210501.11},
  abstract = {The Dynamic Voltage Restorer (DVR) is one of the most efficient and effective custom power devices in protecting the sensitive equipment against voltage sag and voltage harmonics due to; lower cost, smaller size and dynamic response. The inverter is the core of the DVR and it directly affects the performance of the DVR, incorrect injection or delay in the process would be dangerous to sensitive loads. The major functions of the DVR controller are, detection of voltage disturbances events in the system, calculation of the compensating voltage and generation the reference signal for the PWM to trigger the voltage source inverter. PI controller and fuzzy logic controller has been compared with the proposed fuzzy neural optimized fuzzy logic controller in correcting the sag problems and mitigating the harmonics distortion with linear and non-linear loads. Fuzzy Neural optimized Fuzzy Logic controller is the most efficient in improving the performance of the Dynamic Voltage Restorer in compensating any kind of voltage variations and reducing the voltage Total Harmonic Distortion (THD) by enhancing an injection capability of the DVR which is highly influenced by a control algorithm employed. The system is simulated in MATLAB and results confirm the validity and feasibility.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Comparison the Performance of the Dynamic Voltage Restorer Based on PI, Fuzzy Logic, and Fuzzy Neural Controller
AU  - Samhar Saeed Shukir
Y1  - 2021/04/26
PY  - 2021
N1  - https://doi.org/10.11648/j.ijem.20210501.11
DO  - 10.11648/j.ijem.20210501.11
T2  - International Journal of Engineering Management
JF  - International Journal of Engineering Management
JO  - International Journal of Engineering Management
SP  - 1
EP  - 11
PB  - Science Publishing Group
SN  - 2640-1568
UR  - https://doi.org/10.11648/j.ijem.20210501.11
AB  - The Dynamic Voltage Restorer (DVR) is one of the most efficient and effective custom power devices in protecting the sensitive equipment against voltage sag and voltage harmonics due to; lower cost, smaller size and dynamic response. The inverter is the core of the DVR and it directly affects the performance of the DVR, incorrect injection or delay in the process would be dangerous to sensitive loads. The major functions of the DVR controller are, detection of voltage disturbances events in the system, calculation of the compensating voltage and generation the reference signal for the PWM to trigger the voltage source inverter. PI controller and fuzzy logic controller has been compared with the proposed fuzzy neural optimized fuzzy logic controller in correcting the sag problems and mitigating the harmonics distortion with linear and non-linear loads. Fuzzy Neural optimized Fuzzy Logic controller is the most efficient in improving the performance of the Dynamic Voltage Restorer in compensating any kind of voltage variations and reducing the voltage Total Harmonic Distortion (THD) by enhancing an injection capability of the DVR which is highly influenced by a control algorithm employed. The system is simulated in MATLAB and results confirm the validity and feasibility.
VL  - 5
IS  - 1
ER  -

Copy | Download