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1. Optimal-Frequency Critical Soft Switching Method of Synchronous DC/DC Converter Based on Model Predictive Control

   https://doi.org/10.1109/APEC.2019.8721886  

   Zhou, Liwei; Preindl, Matthias (March 2019, IEEE Applied Power Electronic Conference and Exposition (APEC))
2. A Nonisolated Common-Ground High Step-Up Soft-Switching DC–DC Converter With Single Active Switch

   https://doi.org/10.1109/TIE.2022.3198262  

   Abbasian, Sohrab; Farsijani, Mohammad; Tavakoli Bina, Mohammad; Shahirinia, Amir (June 2023, IEEE Transactions on Industrial Electronics)
3. An interleaved non‐isolated high gain soft switching DC–DC converter with small input current ripple

   https://doi.org/10.1049/pel2.12425  

   Abbasian, Sohrab; Farsijani, Mohammad; Tavakoli Bina, Mohammad; Abrishamifar, Adib; Hosseini, Arya; Shahirinia, Amir (April 2023, IET Power Electronics)
4. AC-Informed DC Optimal Transmission Switching Problems via Parameter Optimization

   https://doi.org/10.1109/TPWRS.2025.3576102  

   Taheri, Babak; Molzahn, Daniel K (November 2025, IEEE Transactions on Power Systems)

   Optimal Transmission Switching (OTS) problems minimize operational costs while treating both the transmission line energization statuses and generator setpoints as decision variables. The combination of nonlinearities from an AC power flow model and discrete variables associated with line statuses makes AC-OTS a computationally challenging Mixed-Integer Nonlinear Program (MINLP). To address these challenges, the DC power flow approximation is often used to obtain a DC-OTS formulation expressed as a Mixed-Integer Linear Program (MILP). However, this approximation often leads to suboptimal or infeasible switching decisions when evaluated with an AC power flow model. This paper proposes an enhanced DC-OTS formulation that leverages techniques for training machine learning models to optimize the DC power flow model's parameters. By optimally selecting parameter values that align flows in the DC power flow model with apparent power flows—incorporating both real and reactive components—from AC Optimal Power Flow (OPF) solutions, our method more accurately captures line congestion behavior. Integrating these optimized parameters into the DC-OTS formulation significantly improves the accuracy of switching decisions and reduces discrepancies between DC-OTS and AC-OTS solutions. We compare our optimized DC-OTS model against traditional OTS approaches, including DC-OTS, Linear Programming AC (LPAC)-OTS, and Quadratic Convex (QC)-OTS. Numeric results show that switching decisions from our model yield better performance when evaluated using an AC power flow model, with up to 44% cost reductions in some cases. 

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5. Variable-Frequency Explicit Model Predictive Control of Wide Band Gap DC/DC Converter with Critical Soft Switching

   https://doi.org/10.1109/APEC.2019.8721846  

   Zhou, Liwei; Preindl, Matthias (March 2019, IEEE Applied Power Electronic Conference and Exposition (APEC))