Earticle

초록

영어

There are a large amount of advantages to make efficient load phase balancing, such as loss minimization, energy restoration, security, reliability and voltage balance. Optimal load phase balance is obtained by solving the load re-distribution problem as a combinatorial optimization problem. This enables the best switching option that gives a balanced load arrangement among the phases and minimizes power loss to be arrived at. In this paper, adding decaying self-feedback continuous neural network (ADSCHNN) is applied to realize phase swapping for load re-arrangement in the low voltage circuit of the distribution network. The network energy function of the ADSCHNN is constructed for objective function that defines the load phase balancing problem. The ADSCHNN is applied to solve the problem when load is represented in terms of current flow at the connection points, and when load is defined in terms of the real power. The results obtained using ADSCHNN are compared with those from a heuristic algorithm, and from fuzzy logic expert system. Simulations results on real practical data show that the ADSCHNN is very effective and outperforms other known algorithms in terms of the maximum difference of the phase currents or powers.

목차

Abstract
 1. Introduction
 2. Formatting Your Paper
 3. Adding Decaying Self-feedback Continuous Hopfield Neural Network (ADSCHNN)
 4. Problem Analysis and Energy Function Construction
  4.1. Load Balancing Problem Analysis
  4.2. Energy Function Construction for the ADSCHNN
 5. Simulation Results
  5.1. Current Loads
  5.2. Power Loads
 6. Conclusion
 Acknowledgements
 References

키워드

저자

• Chun-guo Fei [ College of Aeronautical Automation, Civil Aviation University of China, China ]
• Rui Wang [ College of Aeronautical Automation, Civil Aviation University of China, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605