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- W4385882893 abstract "In line with sustainable future energy applications, the present study aims to model and control a super-lift Luo power converter and to convert a low direct current input voltage to a high direct current output voltage. To this end, using Matlab/Simulink environment, a super-lift luo power converter and PD-type-2 fuzzy-neural network (PD-T2FNN) were designed. A PD-T2FNN was used to analyze the super-lift performance of luo power converter against different operational conditions. Different operational conditions were considered as ramp set-point reference voltage change, step set-point input voltage change and internal disturbance (load) change. Performances of the PD-T2FNN was evaluated in terms of settling time, overshoot, undershoot and recovery time. The simulation findings clearly demonstrated that PD-T2FNN displayed an effective performance in terms of its durability against the above-mentioned operational conditions. It can be thus concluded that the super-lift luo power converter offers a feasible system for intelligent and sustainable future energy applications such as fuel cell and photovoltaic systems with a very low input voltage." @default.
- W4385882893 created "2023-08-17" @default.
- W4385882893 creator A5009957216 @default.
- W4385882893 date "2023-01-01" @default.
- W4385882893 modified "2023-10-14" @default.
- W4385882893 title "PD-Type-2 Fuzzy Neural Network Based Control of a Super-Lift Luo Converter Designed for Sustainable Future Energy Applications" @default.
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- W4385882893 doi "https://doi.org/10.1007/978-3-031-39774-5_62" @default.
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