Fast Optimal Network Reconfiguration With Guided Initialization Based on a Simplified Network Approach

被引:42
作者
Al Samman, Mohammad [1 ]
Mokhlis, Hazlie [1 ]
Mansor, Nurulafiqah Nadzirah [1 ]
Mohamad, Hasmaini [2 ]
Suyono, Hadi [3 ]
Sapari, Norazliani Md. [4 ]
机构
[1] Univ Malaya, Dept Elect Engn, Fac Engn, Kuala Lumpur 50603, Malaysia
[2] Univ Teknol MARA, Fac Elect Engn, Shah Alam 40450, Malaysia
[3] Brawijaya Univ, Dept Elect Engn, Fac Engn, Malang 65145, Indonesia
[4] Management & Sci Univ, Fac Informat Sci & Engn, Dept Engn & Technol, Shah Alam 40100, Malaysia
关键词
Distribution system; firefly algorithm; network reconfiguration; POWER LOSS MINIMIZATION; DISTRIBUTION-SYSTEMS; GENETIC ALGORITHM; LOSS REDUCTION; RELIABILITY; ENHANCEMENT; IMPROVEMENT; POPULATION; LOSSES;
D O I
10.1109/ACCESS.2020.2964848
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Optimal Network Reconfiguration (NR) is a well-accepted approach to minimize power loss and enhance voltage profile in the Electrical Distribution Networks (EDN). Since the NR problem contains huge combinational search space, most researchers consider the meta-heuristic techniques to attain NR solution. However, these meta-heuristic techniques do not guarantee to obtain the optimal solution besides they require large processing time to converge. This is mainly due to (1) random initialization and updating of population and (2) the continuous verification of population during the search process. With the aim of reducing the computational time and improving the consistency in obtaining the optimal solution as well as minimizing power loss and enhancing the voltage profile of the EDN, this work proposes a new method based on two-stage optimizations. The proposed method introduces an approach to simplify the network into simplified network graph. Then, this approach is utilized for guided initializations and generations of the population and for the proper population's codification. The proposed method is implemented using the firefly algorithm and verified on 33-bus and 118-bus test systems. The results show the ability of the proposed method to obtain the optimal solution within fast computational time and with superior consistency compared to the conventional methods.
引用
收藏
页码:11948 / 11963
页数:16
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