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Research on Optimal Location and Paramater of TCSC Based on MPSO Technique

Author: WangYanPeng
Tutor: CaiXingGuo
School: Harbin Institute of Technology
Course: Electrical Engineering
Keywords: Transmission capacity Continuous trend Sensitivity method Improved Particle Swarm Population entropy
CLC: TM761
Type: Master's thesis
Year: 2009
Downloads: 34
Quote: 0
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Abstract


Controlled series compensation (TCSC) can effectively improve the system 's transmission capacity , reactive compensation system to improve system stability and power quality , but if the the TCSC device location select improper , may result in reduced transmission capacity , the stability of the system worse , its capacity improper selection will result in the waste of the investment cost , therefore reasonable to select the of TCSC installation location and installed capacity is very important . This paper presents a new method - the \group improved particle swarm optimization algorithm to determine the the TCSC final number of installation , installation location and installation capacity . This article uses two methods to determine installed in the system of the TCSC sensitive branch group is a sensitivity method , i.e. the computing system's transmission capacity on the branch reactance sensitivity , according to the value of the sensitivity of the line sort , taking into account the sensitivity method not very accurate , higher sensitivity front of m slip composition sensitive branch group ; another new method of determining the installation location - simple particle swarm optimization , taking into account the TCSC is usually installed in the transmission line is longer larger transmission capacity on the line , and in particle swarm TCSC location dimension is only one dimension of the initial particle swarm populations can generally reflect the branch installed TCSC to select the initial population calculated the optimal front m constituted sensitive branch group . Determine sensitive branch group , the first branch of the sensitive branch group coding , then take advantage of the improved particle swarm optimization TCSC final number of installation , installation location and installed capacity . For traditional particle swarm algorithm is easy to fall into local optimum cellular automata theory and the theory of population entropy introduced to improve the traditional particle swarm optimization , the use of the population entropy change to control flying speed of particles , so that the performance of the particle swarm has been some increase . Through simulation to verify the feasibility and effectiveness of the proposed algorithm . TCSC expensive, and how to make use of the the TCSC device to improve system transmission capacity , increase revenue , and concern for researchers , in the case of considering TCSC cost TCSC location and constant volume , a brief analysis .

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CLC: > Industrial Technology > Electrotechnical > Transmission and distribution engineering, power network and power system > Power system automation > Automatically adjusts
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