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Optimal Reconfiguration and Capacitor Allocation in Radial Distribution Systems Using the Hybrid Shuffled Frog Leaping Algorithm in the Fuzzy Framework | ||
| Journal of Operation and Automation in Power Engineering | ||
| مقاله 5، دوره 3، شماره 1، شهریور 2015، صفحه 56-70 اصل مقاله (833.36 K) | ||
| نوع مقاله: Research paper | ||
| نویسندگان | ||
| Mostafa Sedighizadeh* ؛ Mahdi mahmoodi | ||
| Shahid Beheshti University | ||
| چکیده | ||
| In distribution systems, in order to diminish power losses and keep voltage profiles within acceptable limits, network reconfiguration and capacitor placement are commonly used. In this paper, the Hybrid Shuffled Frog Leaping Algorithm (HSFLA) is used to optimize balanced and unbalanced radial distribution systems by means of a network reconfiguration and capacitor placement. High accuracy and fast convergence are the highlighted points of the proposed approach because of solving the multi-objective reconfiguration and capacitor placement in fuzzy frame work. These objectives are the minimization of total network real power losses, the minimization of buses voltage violation, and load balancing in the feeders. Each objective is transferred into fuzzy domain using membership function and fuzzified separately. Then, the overall fuzzy satisfaction function is formed and considered as a fitness function. To gain the optimal solution, the value of this function will be maximized. In the literature, several reconfiguration and capacitor placement methods have been investigated, which are implemented separately. However, there are few studies which simultaneously apply these two strategies. The proposed algorithm has been implemented in three IEEE test systems (two balanced and one unbalanced systems). Numerical results obtained by simulation show that the performance of the HSFLA algorithm is much higher than several other meta-heuristic algorithms. | ||
| کلیدواژهها | ||
| Artificial Intelligence؛ Optimal Reconfiguration and Capacitor Placement؛ Shuffled Frog Leaping Algorithm (SFLA)؛ Multi-objective optimization؛ Distribution Systems | ||
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