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Subject: search.filters.subject.Ant colony optimisation

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    Voltage Stability Enhancement by Computational Intelligence Methods
    (2018-01-01)
    Nakawiro, Worawat
    This chapter discusses the concepts of optimal power flow (OPF) and associated computational intelligence (CI) methods, namely artificial neural network (ANN) and ant colony optimisation (ACO). It presents an application of CI methods to solve voltage stability constrained OPF (VSCOPF) for preventive and corrective control actions. The chapter also presents two examples that apply CI techniques to determine optimal preventive and corrective control measures for enhancing voltage stability of the IEEE 30-bus test system. ACO is used to determine the optimal control action that will guarantee minimum voltage stability margin (VSM). The change in VSM due to load curtailment at effective locations is approximated by the sensitivity method in order to save computing time. Simulation results show the capability of the proposed method to improve VSM in normal operation and to restore stable operation in critical situations.
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    Ant colony optimisation for economic dispatch problem with non-smooth cost functions
    (2010-06-01)
    Pothiya, Saravuth
    ;
    Ngamroo, Issarachai
    ;
    Kongprawechnon, Waree
    This paper presents a novel and efficient optimisation approach based on the ant colony optimisation (ACO) for solving the economic dispatch (ED) problem with non-smooth cost functions. In order to improve the performance of ACO algorithm, three additional techniques, i.e. priority list, variable reduction, and zoom feature are presented. To show its efficiency and effectiveness, the proposed ACO is applied to two types of ED problems with non-smooth cost functions. Firstly, the ED problem with valve-point loading effects consists of 13 and 40 generating units. Secondly, the ED problem considering the multiple fuels consists of 10 units. Additionally, the results of the proposed ACO are compared with those of the conventional heuristic approaches. The experimental results show that the proposed ACO approach is comparatively capable of obtaining higher quality solution and faster computational time. © 2009 Elsevier Ltd. All rights reserved.