Earticle

초록

영어

Allocation of optimum active power is a backbone of power system generation planning and its high impact contribution is the need of current electrical utilities and power engineers need to browse this area in short and long term planning scenarios. Power demand requirements mapped to economic feasible solutions matching voltage profile, power demand, minimization of losses, voltage stability and improve the capacity of the system is the need of the hour. Modern techniques based on evolutionary computing, artificial intelligence, search method find their objectives in the area of economic load dispatch planning to reach global optimal solution for this multi-decision, multi-objective combinatorial problem subjected to different constraints. Many algorithms suffer from global convergence problem. To vanish this drawback, neuro inspired genetic hybrid algorithm (NIGHA) has been proposed in this paper to solve economic dispatch problem. Unlike other algorithms, NIGHA utilizes the weights of Neural Network to explore information and knowledge to train GA parameters to search for feasible region where optimal global solution converges. The suggested technique is tested on IEEE 25 bus system. Test results are compared with other techniques presented in literature. Proposed technique has outperformed other methods in terms of cost, computation time.

목차

Abstract
 1. Introduction
 2. Problem Formulation
 3. Proposed Technique
  3.1. Genetic Algorithm
  3.2. Flow Chart of GA
  3.3. Artificial Neural Network
  3.4. NIGHA
 4. Active Power Dispatch using NIGHA
 5. Simulation Results
 6. Conclusion
 7. Future Scope
 References

키워드

저자

• Navpreet Singh Tung [ Assistant Professor, Department of Electrical Engineering, Bhutta Group of Institutions, Ludhiana India ]
• Prof. (Dr). Sandeep Chakravorty [ Dean and Professor, Department of Electrical Engineering, Baddi University, Baddi, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605