State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When c...State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When compared with ANFIS controllers,traditional controllers such as PI and PID show challenges.They may not sufficiently react to changing conditions or non-linearity’s and use fixed gain values requiring hand tuning.By means of learning,ANFIS controllers can thus dynamically change their parameters,so providing enhanced accuracy and flexibility in real-time control.The main objectives are to control the DC link voltage,lower total harmonic distortion(THD),and lower the errors.The Synchronous Reference Frame(SRF)transformation changes three-phase AC into a two-axis(d-q)system,making it easier to control active and reactive power separately.We developed a thorough Simulink model in MATLAB 2023a to model the bidirectional off-board fast charger at a power level of 60 kW.After validation,a 5-kW hardware prototype was built in the lab.The main platform is an AC-DC converter,followed by a DC-DC converter.A programmable DC power supply,Chroma 62050H-600S,connected to the DC-DC converter,mimics the dynamic characteristics of a battery.The control algorithm,deployed on a Spartan-6 LX9 FPGA,manages both voltage and current,maintaining a stable DC link voltage of 800 V.The results obtained indicate that the ANFIS controller outperforms a conventional PI controller when handling dynamic load variations.展开更多
基金the financial support provided by the Royal Academy of Engineering,UK(Project Reference No:TSP-2526-7102),which enabled the successful execution of this research work.
文摘State this study looks at how well a three-phase bidirectional converter works for Vehicle-to-Grid(V2G)services by using both Adaptive Neuro-Fuzzy Inference System(ANFIS)and Proportional-Integral(PI)controllers.When compared with ANFIS controllers,traditional controllers such as PI and PID show challenges.They may not sufficiently react to changing conditions or non-linearity’s and use fixed gain values requiring hand tuning.By means of learning,ANFIS controllers can thus dynamically change their parameters,so providing enhanced accuracy and flexibility in real-time control.The main objectives are to control the DC link voltage,lower total harmonic distortion(THD),and lower the errors.The Synchronous Reference Frame(SRF)transformation changes three-phase AC into a two-axis(d-q)system,making it easier to control active and reactive power separately.We developed a thorough Simulink model in MATLAB 2023a to model the bidirectional off-board fast charger at a power level of 60 kW.After validation,a 5-kW hardware prototype was built in the lab.The main platform is an AC-DC converter,followed by a DC-DC converter.A programmable DC power supply,Chroma 62050H-600S,connected to the DC-DC converter,mimics the dynamic characteristics of a battery.The control algorithm,deployed on a Spartan-6 LX9 FPGA,manages both voltage and current,maintaining a stable DC link voltage of 800 V.The results obtained indicate that the ANFIS controller outperforms a conventional PI controller when handling dynamic load variations.
