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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한전기학회 Journal of Electrical Engineering & Technology Journal of Electrical Engineering & Technology Vol.12 No.1
- 발행연도
- 2017.1
- 수록면
- 161 - 172 (12page)
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초록· 키워드
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In the paper, a hybrid technique is proposed for detecting the location and capacity of distributed generation (DG) sources like wind and photovoltaic (PV) in power system. The novelty of the proposed method is the combined performance of both the Biography Based Optimization (BBO) and Particle Swarm Optimization (PSO) techniques. The mentioned techniques are the optimization techniques, which are used for optimizing the optimum location and capacity of the DG sources for radial distribution network. Initially, the Artificial Neural Network (ANN) is applied to obtain the available capacity of DG sources like wind and PV for 24 hours. The BBO algorithm requires radial distribution network voltage, real and power loss for determining the optimum location and capacity of the DG. Here, the BBO input parameters are classified into sub parameters and allowed as the PSO algorithm optimization process. The PSO synthesis the problem and develops the sub solution with the help of sub parameters. The BBO migration and mutation process is applied for the sub solution of PSO for identifying the optimum location and capacity of DG. For the analysis of the proposed method, the test case is considered. The IEEE standard bench mark 33 bus system is utilized for analyzing the effectiveness of the proposed method. Then the proposed technique is implemented in the MATLAB/simulink platform and the effectiveness is analyzed by comparing it with the BBO and PSO techniques. The comparison results demonstrate the superiority of the proposed approach and confirm its potential to solve the problem
목차
Abstract
1. Introduction
2. Recent Related Works
3. Problem Formulation
4. Hybrid Technique for Optimum Location and Capacity of DG
5. Results and Discussion
6. Conclusion
References
참고문헌 (33)
참고문헌 신청
Duong Quoc Hung, N Mithulananthan and R. C. Bansal, “Analytical strategies for renewable distri- buted generation integration considering energy loss minimization”, Applied Energy, Vol. 105, pp. 75-85, 2013
Sh Abdi and K Afshar, “Application of IPSO-Monte Carlo for optimal distributed generation allocation and sizing”, Electrical Power and Energy Systems, Vol. 44, pp. 786-797, 2013
Wen-Shan Tan, Mohammad Yusri Hassann, Md Shah Majid and Hasimah Abdul Rahman, “Optimal distributed renewable generation planning: A review of different approaches”, Renewable and Sustainable Energy Reviews, Vol. 18, pp. 626-645, 2013
Carmen L T Borges and Djalma M Falca o, “Optimal distributed generation allocation for reliability, losses, and voltage improvement”, Electrical Power and Energy Systems, Vol. 28, pp. 413-420, 2006
Rene Prenc, Davor skrlec and Vitomir Komen, “Distributed generation allocation based on average daily load and power production curves”, Electrical Power and Energy Systems, Vol. 53, pp. 612-622, 2013
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UCI(KEPA) : I410-ECN-0101-2017-560-002013414