Modified Adaptive Differential Evolution for Optimal Reconfiguration and Distributed Generator Allocation in Distribution Network

• Main Author: Netravati Gundalli
• Other Authors: Cheng-Chien Kuo
• Format: Others
• Language: en_US
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/545a4x

碩士 === 國立臺灣科技大學 === 電機工程系 === 106 === Reconfiguration is an indispensable method for loss reduction in power distribution systems and is also used to restore loads in out-of-service areas in case of a fault. Power loss in an electrical distribution network is unavoidable. In order to have an efficient and economical operation, the power loss can be minimized up to some level. This thesis presents an efficient way of solving distribution system reconfiguration (DSR). The objective of the thesis is to minimize the active/real power loss, bus voltage profile improvement and boost capacity of the system by simultaneous reconfiguration and DG allocation in radial distribution networks. Usually, the distribution network is a closed loop even though the operation is radial with the opening of a unique sectionalizing switch that disconnects a branch/line in the loop. This process of reconfiguration is done in a way such that system loss is minimized. The additional and effective way of reducing power loss is done by distributed generators (DGs) to the system buses. A great number of researchers have been proposed for distributed generation (DG) placement in distribution networks to minimize the power loss. Very few researchers have been done for reconfiguration in parallel with the DG installation for the minimization of system power loss. In this thesis work, linear population size reduction technique of success history based adaptive differential evolution (L-SHADE) is used for the simultaneous reconfiguration and DG allocation. The results obtained from the application of the proposed method on two well-known distribution networks such as IEEE 33-bus and IEEE 69-bus radial distribution system. The simulation results demonstrate that the L-SHADE method is able to find highly competitive results when compared with the other literature.