Online Reactive Power Suppression Strategy for Triple Active Bridge Converter Based on Dynamic Fundamental Voltage-Balance Modulation

The triple active bridge (TAB) converter presents a significant challenge for efficiency optimization due to its complex model resulting from power coupling between ports and multiple control degrees of freedom. Existing high-precision optimization strategies are often based on complex models and modulation methods, typically relying on offline optimization and online look-up tables during operation, making them difficult to apply widely in practical engineering scenarios. To address these challenges, an online reactive power suppression strategy is proposed, which reduces conduction losses by suppressing reactive power during operation. First, the total reactive power model is established, and a dynamic fundamental voltage-balance modulation (DFVM) strategy is employed to automatically suppress fundamental reactive power. Moreover, the zero-voltage-switching range can also be expanded. To further optimize the total reactive power, an additional degree of freedom (the shared phase-shift coefficient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d_{\text{CP}}$</tex-math></inline-formula>) is introduced into the control strategy, which can be dynamically adjusted based on operating conditions. Furthermore, by analyzing the slopes of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d_{\text{CP}}$</tex-math></inline-formula> and the outer phase shifts in relation to output power and reactive power, an incremental balance-based iterative optimization method was proposed. This method enables adaptive online calculation of the optimal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$d_{\text{CP}}$</tex-math></inline-formula>, thereby enhancing the applicability of the control algorithm. Finally, a 3.3-kW prototype was developed to validate the proposed strategy, achieving a peak efficiency of 95.3%. Under light-load conditions, efficiency improved by 5.13% compared to existing online strategies.