In this paper we propose a floating-point genetic algorithm (GA) for optimal design of a reconfigurable antenna array by varying only the phase excitations of the radiators while fixing the common amplitude distributi...
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ISBN:
(纸本)078039433X
In this paper we propose a floating-point genetic algorithm (GA) for optimal design of a reconfigurable antenna array by varying only the phase excitations of the radiators while fixing the common amplitude distribution constant. This common amplitude distribution is jointly optimized with phases to generate dual beam pattern antenna array. Results show a good agreement between the desired and GA synthesized pattern. Results are very encouraging even without minimizing the amplitude excitation dynamic range ratio (DRR).
This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding...
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This paper proposes a floating-point genetic algorithm (FPGA) to solve the unit commitment problem (UCP). Based on the characteristics of typical load demand, a floating-point chromosome representation and an encoding-decoding scheme are designed to reduce the complexities in handling the minimum up/down time limits. Strategic parameters of the FPGA are characterized in detail, i.e., the evaluation function and its constraints, population size, operation styles of selection, crossover operation and probability, mutation operation and probability. A dynamic combination scheme of genetic operators is formulated to explore and exploit the FPGA in the non-convex solution space and multimodal objective function. Experiment results show that the FPGA is a more effective technique among the various styles of geneticalgorithms, which can be applied to the practical scheduling tasks in utility power systems. (C)] 2006 Elsevier B.V. All rights reserved.
Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reli...
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Standalone hybrid remote area power systems, also known as microgrids (MGs), can provide reasonably priced electricity in geographically isolated and the edge of grid locations for their operators. To achieve the reliable operation of MGs, whilst consuming minimal fossil fuels and maximising the penetration of renewables, the voltage and frequency should be maintained within acceptable limits. This can be accomplished by solving an optimisation problem. floating-point genetic algorithm (FP-GA) is a heuristic technique that has a proven track record of effectively identifying the optimal solutions. However, in addition to needing appropriate operators, the solver needs a fitness function to yield the most optimal control variables. In this study, a suitable fitness function is formulated, by including the operational, interruption and technical costs, which are then solved with an FP-GA, with different combinations of operators. The developed fitness function and the considered operators are tested for the non-linear optimisation problem of a 38-bus MG. Detailed discussions are provided on the impact, which different operators have upon the outcomes of the fitness function.
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