Hybrid swarm evolutionary programming for optimal distributed generation in distribution system
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 111
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شناسه ملی سند علمی:
JR_IJNAA-12-0_080
تاریخ نمایه سازی: 11 آذر 1401
چکیده مقاله:
This paper discusses the incorporation of the Hybrid Swarm Evolutionary Programming (SEP) for optimal distributed generation (DG) in the distribution system. High load demand will result in unstable control power distribution due to power transmission loss. The compensation process can be implemented by installing a compensation device to prevent this from happening. It is required the optimal sizing and location for the devices to achieve the objective and this can be done by using an optimization technique. Thus, this project aims to develop a hybrid computational intelligence technique is called hybrid SEP for loss minimization. The proposed method embedded the element of Particle Swarm Optimization (PSO) into traditional Evolutionary Programming (EP) to improve precision of traditional EP algorithm. The purpose of this study is to investigate the maximum benefits of DG integration to be gained. The proposed techniques are validated on IEEE-۶۹ bus radial system with multiple units of DG. The results showed that the most effective type of DG inject to IEEE-۶۹ bus radial system is with DG Type III, with ۹۵% of active power loss reduction and the best on voltage profile improvement. Hybrid SEP shows superior to EP in term of loss minimization.
کلیدواژه ها:
نویسندگان
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Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia
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Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia
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Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia
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School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia
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Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia
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Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis, Perlis, Malaysia