Development of hybrid constrained genetic algorithm and particle swarm optimisation algorithm for load flow

Pao Yue-kong Library Electronic Theses Database

Development of hybrid constrained genetic algorithm and particle swarm optimisation algorithm for load flow

 

Author: Ting, Tiew-on
Title: Development of hybrid constrained genetic algorithm and particle swarm optimisation algorithm for load flow
Degree: Ph.D.
Year: 2008
Subject: Hong Kong Polytechnic University -- Dissertations.
Electric power systems -- Load dispatching.
Swarm intelligence.
Department: Dept. of Electrical Engineering
Pages: xviii, 130 leaves : ill. ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2239597
URI: http://theses.lib.polyu.edu.hk/handle/200/2666
Abstract: The calculation of active and reactive power flow in an electric network to ensure satisfactory voltage profile and loading of transmission circuits is of utmost importance in power system planning, operation and control. Conventional methods such as the Newton-Raphson and the fast decoupled load flow methods have been widely used by the power utilities. However, when a power system becomes highly stressed, it will be difficult for conventional methods to converge. Furthermore, as more and more non-linear devices, for instance Flexible AC transmission system (FACTS) devices, are used in power transmission networks, conventional load flow methods may have difficulties in solving the load flow problem and hence it is also difficult to use these methods to determine the maximum loading points and to assess the static voltage stability of a power system. To overcome the above mentioned problems, this thesis is devoted to the development of alternative approach in dealing with the load flow problem based on evolutionary computation. In particular, this thesis reports work on the formation of a hybrid algorithm comprising of the constrained genetic algorithm and Particle Swarm Optimisation. Based on the virtual population concept embedded in a constrained genetic algorithm for load flow previously developed, the Particle Swarm Optimisation method is utilised as an efficient means to generate high quality candidate solutions in the virtual population during the optimisation process in seeking for the load flow solution. An experimental approach is reported in the thesis on finding the best parameter settings for use in the Particle Swarm Optimisation part of the hybrid algorithm. The performance of the developed algorithm is demonstrated using the IEEE 30-, 57- and 118-bus systems. The results in finding the maximum loading points using the new hybrid method are presented and discussed. The use of the hybrid algorithm in determining the Type-1 load flow solutions for voltage stability assessment is also demonstrated and described in the thesis. This thesis also develops a stochastic method for determining the maximum loading point of power system using the developed hybrid constrained genetic algorithm and Particle Swarm Optimisation and a strategy in starting the search for the maximum loading point in the infeasible operation region of the power system. The new approach is applied to IEEE 14-, 30- and 57-bus test systems and the results are presented. The new hybrid algorithm developed in the thesis is found to be powerful in solving the load flow problem for heavy-loaded systems and is efficient in locating the Type-I load flow solutions and determining the maximum loading point of a power system.

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