Browsing by Author "Komolafe, Olusola"

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    Multiobjective Optimal Power Flow Using Hybrid Evolutionary Algorithm
    (IEEE, 2010-01) Alawode, Kehinde; Komolafe, Olusola; Abimbola, Jubril
    This paper solves the environmental/ economic dispatch power system problem using the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and its hybrid with a Convergence Accelerator Operator (CAO), called the NSGA-II/CAO. These multiobjective evolutionary algorithms were applied to the standard IEEE 30-bus six-generator test system. Several optimization runs were carried out on different cases of problem complexity. Different quality measure which compare the performance of the two solution techniques were considered. The results demonstrated that the inclusion of the CAO in the original NSGA-II improves its convergence while preserving the diversity properties of the solution set.
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    Reliability investigation of the Nigerian Electric Power Authority transmission network in a deregulated environment
    (International journal of Electronic and Electrical Engineering, 2003-11-12) Komolafe, Olusola; Momoh, A.; Omoigui, Michael
    The paper presents the findings of reliability investigation carried out on the 330 kV high voltage transmission network of the Nigerian Electric Power Authority (NEPA). The system made up of three hydro generating stations in the north and four thermal stations in the south is loosely tied together by long transmission lines that are basically radial. The system was represented by a 24-bus, 39-transmission line and seven generating stations that made up the grid. The base load was taken as 1776 MW representing approximately 30% of installed capacity of about 6000 MW. Taking into consideration all available VAr injections and voltage control equipment the load was increased by 10% consecutively and the system generating voltages and. available VAr were controlled to make sure that all bus voltages were within tolerance of +5% and -10% of rated values The results show that the system was able to cope with the load demand without problems up to 3500 MW. Although some stations experienced low voltages, these were corrected when the generator voltages were adjusted via reactive power injection. But by the time the load was increased to 3800 MW, all the available VAr injection had been utilized and most stations were at their upper voltage limits. The system response for load above 4160 MW was completely unacceptable. Suggestions are given that will enable the network to cope should the installed generating capacity be increased in order to meet the increasing load demand.
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    Solving Multi-Objective Economic Dispatch Problem Via Semidefinite Programming
    (IEEE, 2013-08) Abimbola, Jubril; Alawode, Kehinde; Komolafe, Olusola
    This paper presents a solution for multi-objective economic dispatch problem with transmission losses semidefinite programming (SDP) formulation. The vector objective is reduced to an equivalent scalar objective through the weighted sum method. The resulting optimization problem is formulated as a convex optimization via SDP relaxation. The convex optimization problem was solved to obtain Pareto-optimal solutions. The diversity of the solution set was improved by a nonlinear selection of the weight factor. Simulations were performed on IEEE 30-bus, 57-bus, and 118-bus test systems to investigate the effectiveness of the proposed approach. Solutions were compared to those from one of the well-known evolutionary methods. Results show that SDP has an inherently good convergence property and a lower but comparable diversity property.
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    Open Access
    Solving Multi-Objective Economic Dispatch Problem Via Semidefinite Programming
    (IEEE, 2013-08) Abimbola, Jubril; Alawode, Kehinde; Komolafe, Olusola
    This paper presents a solution for multi-objective economic dispatch problem with transmission losses semidefinite programming (SDP) formulation. The vector objective is reduced to an equivalent scalar objective through the weighted sum method. The resulting optimization problem is formulated as a convex optimization via SDP relaxation. The convex optimization problem was solved to obtain Pareto-optimal solutions. The diversity of the solution set was improved by a nonlinear selection of the weight factor. Simulations were performed on IEEE 30-bus, 57-bus, and 118-bus test systems to investigate the effectiveness of the proposed approach. Solutions were compared to those from one of the well-known evolutionary methods. Results show that SDP has an inherently good convergence property and a lower but comparable diversity property