research-article

Power Quality Comprehensive Evaluation of DC Distribution Network Based on Maximizing Deviation and Fuzzy Matter-Element Model

Authors:

Zhiguo WangAuthors Info & Claims

EEET '18: Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology

Pages 22 - 26

https://doi.org/10.1145/3277453.3277458

Published: 19 September 2018 Publication History

EEET '18: Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology

Power Quality Comprehensive Evaluation of DC Distribution Network Based on Maximizing Deviation and Fuzzy Matter-Element Model

Pages 22 - 26

Abstract
References

Abstract

At present, DC supply technology has become a research hotspot again. And the wide application of all-controlling power electronics devices also makes DC distribution system usher in a new opportunity for development. Through the analysis of the possible power quality problems existing in DC power distribution network, a set of comprehensive evaluating method for DC power quality is proposed in this paper. To avoid the excessive subjectivity or objectivity of weight coefficients, the combination weighting method based on maximizing deviation is adopted to allocate the subjective and objective weight proportion fairly, thus making the decision of comprehensive weights more accurate. Fuzzy matter-element model is built to evaluate power quality of DC distribution network in order to solve the problems in DC power quality evaluation process, such as fuzziness and uncertainty. Finally, numerical examples show that the proposed comprehensive evaluation model can be used to evaluate DC power quality with feasible and practicability results.

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Cited By

Yan GZhang HWang QWang LWang L(2024)Power Quality Comprehensive Evaluation Method Based on Game Theory Combination Weighting and QUALIFLEX AlgorithmThe Proceedings of the 11th Frontier Academic Forum of Electrical Engineering (FAFEE2024)10.1007/978-981-97-8828-6_33(302-311)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-97-8828-6_33
Li HYi BLi QMing JZhao Z(2020)Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural NetworkIEEE Access10.1109/ACCESS.2020.29745718(34339-34349)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2974571

Index Terms

Power Quality Comprehensive Evaluation of DC Distribution Network Based on Maximizing Deviation and Fuzzy Matter-Element Model
1. Hardware
  1. Power and energy
    1. Energy distribution
      1. Smart grid

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EEET '18: Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology

September 2018

246 pages

ISBN:9781450365413

DOI:10.1145/3277453

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 19 September 2018

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EEET '18

EEET '18: 2018 International Conference on Electronics and Electrical Engineering Technology

September 19 - 21, 2018

Tianjin, China

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Cited By

Yan GZhang HWang QWang LWang L(2024)Power Quality Comprehensive Evaluation Method Based on Game Theory Combination Weighting and QUALIFLEX AlgorithmThe Proceedings of the 11th Frontier Academic Forum of Electrical Engineering (FAFEE2024)10.1007/978-981-97-8828-6_33(302-311)Online publication date: 30-Nov-2024
https://doi.org/10.1007/978-981-97-8828-6_33
Li HYi BLi QMing JZhao Z(2020)Evaluation of DC Power Quality Based on Empirical Mode Decomposition and One-Dimensional Convolutional Neural NetworkIEEE Access10.1109/ACCESS.2020.29745718(34339-34349)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2974571

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