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A block diagram of small signal DC-DC zeta converter with PWM closed-loop control model III. PROPOSED METHOD
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DC-DC zeta converter is one type of fourth-order DC-DC converter. The converter is composed of two inductors and two capacitors which can be operated in step down or step up mode. Until now, researchers are still a bit of attention on DC-DC zeta converter compared with the other fourth-order DC-DC converters i.e. sepic and cuk converter. The dynami...
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Citations
... Interval type-2 fuzzy logic (IT2FL) was used to adjust the crossover probability (p cross ) and the mutation probability (p mut ) in order to improve the performance of genetic algorithm. IT2FL has been widely applied in many fields, and the results are very good [4][5][6][7]. ...
Many countries have been triggered to provide a new energy policy which promotes renewable energy applications be-cause of public awareness to reduce the global warming and rising in fuel prices. Renewable energy sources such as solar energy are green and promising energy in the future for widespread use. Combining renewable energy sources with battery makes electricity supply more economical and reliable to meet all possible load level. This paper proposed a new hybrid method to optimize Photovoltaic (PV)-Battery systems. The proposed method was named Interval type-2 fuzzy adaptive genetic algorithm (IT2FAGA). Genetic algorithm (GA) is one of modern optimization techniques that has been successfully applied in various areas of power systems. To enhance the ability of GA to prevent trapping in local optima and increase convergence in a global optima, the crossover probability (pcross) and the mutation probability (pmut), parameters in GA, are tuned using interval type-2 fuzzy logic (IT2FL). Objective function used in this paper was the annual cost of sytem (ACS) consisting of the annual capital cost (ACC), annual replacement cost (ARC), annual operation cost maintenance (AOM). The proposed method was also compared to fuzzy adaptive genetic algorithm (FGA) and standard genetic algorithm (SGA). Simulation results indicated that the proposed method had a better perfomance in minimizing the objective function than the other two methods
