Performance enhancement of CLLC resonant converter for the hybrid AC-DC microgrid application using AI algorithm based two-level optimal design technique

• Main Author: Krishna Swamy, Shravan Kumar
• Other Authors: Jack Zhang Xin
• Format: Theses and Dissertations
• Language: English
• Published: 2019
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: http://hdl.handle.net/10356/78865
• Institution: Nanyang Technological University

To enhance the power conversion efficiency of high-power density CLLC resonant converter, popularly used in two-way direction hybrid AC-DC microgrid application as DC transformer to interlink DC and AC buses, based on Artificial Intelligence (AI) optimal design technique. CLLC converter works on open loop action for which, the duty cycle and the switching frequencies are fixed, because the DC and AC bus voltages are monitored and regulated by Energy Management System (EMS). Therefore, in AC-DC hybrid microgrid applications, the primary and major concern for the proposed converter is power conversion efficiency, not the voltage control. The dissertation work deals with the optimization of overall-power-loss and its relevant magnetic design of the proposed converter by using two-level optimization design technique based on AI algorithm. In the level-I optimization, the overall-powerloss of converter including the switching loss, driving loss, resonant capacitors power loss, transformer core loss and transformer copper loss are optimized to derive the optimal design parameters including leakage inductance and , resonant capacitance and and mutual inductance based on proposed algorithm. In the level-II optimization, magnetic design of planar transformer is executed to attain the AI optimal design. The planar transformer leakage inductances are considered as resonant inductances for the magnetic design of proposed converter. The optimal design parameters , and are derived by regulating proper space between transformer primary and secondary windings ( ) and the airgap thickness ( ). The equations of and are derived to achieve the optimal parameters , and . The proposed optimal design techniques and the derived equations of and are verified by simulation and experimental.