A hybrid algorithm based optimal placement of DG units for loss reduction in the distribution system

被引:77
|
作者
Suresh, M. C., V [1 ]
Edward, J. Belwin [2 ]
机构
[1] VIT Vellore, Dept EEE, Sch Elect Engn, Vellore, Tamil Nadu, India
[2] VIT Univ, Sch Elect Engn, Dept EEE, Vellore, Tamil Nadu, India
关键词
Cuckoo Search (CS); Distribution networks; Distribution generation; Grasshopper Optimization Algorithm (GOA); Loss reduction; OPTIMAL NETWORK RECONFIGURATION; GENERATION PLACEMENT; OPTIMIZATION; INTEGRATION; ALLOCATION; LOAD; MODELS;
D O I
10.1016/j.asoc.2020.106191
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Distributed generation (DG) has been utilized in some electric power networks. Power loss reduction, environmental friendliness, voltage improvement, postponement of system upgrading, and increasing reliability are some advantages of DG-unit application This paper uses a hybrid technique to optimize the position and size of DG units to reduce losses in the distribution system. The hybrid technique is the joined execution of both the Grasshopper Optimization Algorithm (GOA) and Cuckoo Search (CS) technique. Here, the GOA optimization behavior is upgraded by utilizing the CS technique. Here, the perfect position of the DG unit is settled with respect to the power loss, line power flow and voltage profile using the proposed system. For improving the dynamic execution, the limit of DG is directed by the proposed technique with respect to the cost work. The motivation behind the proposed system is to produce optimal capacity to lessen the aggregate power loss and enhance the voltage profiles of power distribution networks. The proposed hybrid technique is executed in MATLAB/Simulink working platform and the dynamic dependability execution is tested and considered with IEEE 33-bus distribution networks and IEEE 69-bus system. The stability by diminishing loss of the distribution system is investigated by executed different load state of the system. The execution of the proposed system is analyzed and compared with different existing techniques. (C) 2020 Elsevier B.V. All rights reserved.
引用
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页数:15
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