Fuzzy logic feed-forward impedance shaping of DAB converter in DC microgrid with CPL load

Gupta, Ravishankar; Singh, Navdeep

doi:10.1007/s43236-023-00641-z

Cited by 2 publications

(2 citation statements)

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“…As impedance mismatch is the primary cause of system instability [9], it may be prevented by modifying terminal impedances [10]. Various techniques have been proposed to modify Z out (s) or Z in (s) to satisfy stability criteria.…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy logic is robust in handling uncertainties and vague information, making it suitable for systems with imprecise data. However, interoperability can be challenging and its performance is highly dependent on the quality of the rule base [10]. ANFIS combines the strengths of fuzzy logic and neural networks, offering adaptability, learning Interpretability can be challenging, limited in handling complex dynamics [10] Backstepping Well-suited for non-linear systems, Disturbance rejection Design complexity increases with system complexity [20] Sliding mode control Robustness to disturbance and uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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ANFIS-based FOPID controller to enhanced stability of cascaded converter for electric vehicle (EV) charger

Gupta,

Singh

2024

Eng. Res. Express

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In an electric vehicle (EV) charger system, a two-stage conversion process is integrated and cascaded with a voltage source converter (VSC) and a Dual active bridge converter (DAB). These impedances VSC outputs impedance (ZoutVSC(s)) and DAB have input impedance (ZinDAB(s)) interact, leading to voltage fluctuation and power loss. To address the cascaded converter's impedance interaction, the modified controller that combines an adaptive Neuro-fuzzy inference system (ANFIS) based fractional order proportional integral derivative (FOPID) controller is used with effective active damping (AD). The modified controller (ANFIS+FOPID+AD) uses ANFIS’s skills to capture and predict the system's nonlinear behaviour, using its trained data to guide the FOPID’s parameter. The presence of AD ensures the sorting out of impedance interaction problems. Compared to the PID controller, the FOPID controller includes two more degrees of freedom and offers superior adaptability and performance. The effectiveness of the modified controller is tested via frequency response analysis and time domain simulations. Time domain simulations underscore the advantage of the modified controller (ANFIS+FOPID+AD), revealing a remarkable 30% settling time and a 25% overshoot compared to the (FOPID+AD) controller. It is better regarding flexibility, faster response time, and improved system stability. The system performance has been validated and compared by simulation process and HIL technique OPAL RT OP44512.

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Section: Introductionmentioning

confidence: 99%