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Multi-objective Oriented Search Algorithm for Multi-objective Reactive Power Optimization

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Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence (ICIC 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5755))

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Abstract

This paper presents a novel algorithm, multi-objective oriented search algorithm (MOOSA), to deal with the problem of multi-objective reactive power optimization in power system. The multi-objective oriented search algorithm has strong ability to search optimal solutions and well-distributed solutions in Pareto front. The results show that the proposed algorithm is able to balance the multi objects in multi-objective reactive power optimization through the simulations on IEEE 30-bus testing system. The paper concludes that MOOSA is an effective tool to handle the problem of multi-objective reactive power optimization.

Project Supported by National Natural Science Foundation of China (60870004).

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References

  1. Momoh, J.A., Adapa, R., El-Hawary, M.E.: A Review of Selected Optimal Power Flow Literature to 1993. I. Nonlinear and Quadratic Programming Approaches. IEEE Trans on Power Systems 14, 96–104 (1999)

    Article  Google Scholar 

  2. Momoh, J.A., El-Hawary, M.E., Adapa, R.: A Review of Selected Optimal Power Flow Literature to 1993. II. Newton, Linear Programming and Interior Point Methods. IEEE Trans on Power Systems 14, 105–111 (1999)

    Article  Google Scholar 

  3. Liu, M.B., Yang, Y.: Optimal Reactive Power Planning Incorporating Steady State Voltage Stability Constraints. Automation of Electric Power Systems 29, 21–25 (2005)

    Google Scholar 

  4. Zhang, W.J., Ye, J.F., Liang, W.J.: Multi-objective Reactive Power Optimization Based on Improved Genetic Algorithm. Power System Technology 28, 67–71 (2004)

    MATH  Google Scholar 

  5. Lou, S.H., Li, Y., Wu, Y.W.: Multi-objective Reactive Power Optimization Using Quantum Genetic Algorithm. High Voltage Engineering 31, 69–83 ( 2005)

    Google Scholar 

  6. Feng, S.G., Ai, Q.: Application of Fast and Elitist Non-dominated Sorting Generic Algorithm in Multi-objective Research Power Optimization. Transactions of China Electrotechnical Society 28, 146–151 (2007)

    Google Scholar 

  7. Liu, F., Yan, W., Yu, D.C.: A Hybrid Strategy Based on GA and IPM for Optimal Reactive Power Flow. Proceedings of the CSEE 25, 67–72 (2005) (in Chinese)

    Google Scholar 

  8. Shigang, F., Qian, A.: Multi-objective Reactive Power Optimization Using SPEA2. High Voltage Engineering 33, 115–119 (2007)

    Google Scholar 

  9. Lou, S.H., Wu, Y.W., Xiong, X.Y.: Mutative Scale Chaos Optimization Algorithm for Reactive Power Optimization of Power System. Power System Technology. 29, 20–24 (2005)

    Google Scholar 

  10. Xiong, H.G., Cheng, H.Z., Hu, Z.C.: A Hybrid Algorithm Based on Chaos Optimization Algorithm and Immune Algorithm for Multi-objective Optimal Reactive Power Flow. Power System Technology 31, 33–37 (2007) (in Chinese)

    Google Scholar 

  11. Liu, J., Li, D., Gao, L.Q.: Vector Evaluated Adaptive Particle Swarm Optimization Algorithm for Multi-objective Reactive Power Optimization. Proceedings of the CSEE 28, 22–28 (2008)

    Google Scholar 

  12. Lou, S.H., Wu, Y.W., Xiong, X.Y.: Application of Multi-Objective Particle Swarm Optimization Algorithm in Power System Reactive Power Optimization Based on Evaluation of Distance in Fitness Space. Power System Technology 31, 41–46 (2007)

    Google Scholar 

  13. Zhang, X.X., Chen, W.R., Dai, C.H.: Application of Oriented Search Algorithm in Reactive Power Optimization. In: The Third International Conference Electric Utility Deregulation and Restructuring and Power Technologies, pp. 2856–2861 (2008)

    Google Scholar 

  14. Huang, V.L., Suganthan, P.N., Qiin, A.K.: Multi-objective Differential Evolution with External Archive and Harmonic Distance-based Diversity Measure. Technical Report, Nanyang Technological University (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical Engineering, Southwest Jiaotong University, Chengdu, 610031, China

    Xuexia Zhang & Weirong Chen

Authors
  1. Xuexia Zhang
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  2. Weirong Chen
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, China

    De-Shuang Huang

  2. Graduate School of Electrical Engineering, University of Ulsan, Korea, San 29, Mugeo-Dong, Nam-Ku, 680 - 749, Ulsan, Korea

    Kang-Hyun Jo

  3. School of Electrical Engineering, University of Ulsan, Ulsan, South Korea

    Hong-Hee Lee

  4. School of Electrical Engineering, University of Ulsan, South Korea

    Hee-Jun Kang

  5. e.B.I.S. s.r.l. (electronic Business in Security), Spin-Off of Polytechnic of Bari, Str. Prov. per Casamassima Km., 3, 70010, Valenzano, (BA), Italy

    Vitoantonio Bevilacqua

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© 2009 Springer-Verlag Berlin Heidelberg

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Zhang, X., Chen, W. (2009). Multi-objective Oriented Search Algorithm for Multi-objective Reactive Power Optimization. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_25

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  • DOI: https://doi.org/10.1007/978-3-642-04020-7_25

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04019-1

  • Online ISBN: 978-3-642-04020-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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