# Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm
###### Abstract  
The primary function of hybrid direct current circuit breakers (HCBs) is to quickly interrupt fault currents to protect high-voltage direct current (HVDC) systems. To enhance the reliability and stability of HVDC systems, optimal design of HCBs is required to minimize the peak fault current, interruption time, and recovery time. Therefore, this study develops a multi-objective genetic algorithm (MOGA)-based optimization model to identify the optimal parameters for HCBs. The MOGA model consists of three objective functions that provide trade-offs among reductions in the peak fault current, the interruption time, and the recovery time. The proposed algorithm is verified with a novel HCB topology using inverse current injection techniques. The performance of the HCB topology with the optimal parameters is validated in the MATLAB/Simulink environment. In addition, a comparison study between the optimal design of an HCB using the proposed algorithm and a typical HCB model is presented in this study to show the effectiveness of the proposed optimization method. Our simulation results show that the optimal parameter design of HCBs significantly reduces the magnitude of the peak fault current and operating time, thus maintaining the safe and stable operation of the entire system. high-voltage direct current (HVDC) grid; hybrid circuit breaker (HCB); multi-objective genetic algorithm (MOGA) +
Footnote †: journal: Applied Soft Matter  
inductors, and capacitors. Since they can increase peak fault currents, the interruption time, and the recovery time, these elements should be optimized to enhance the overall performance of the HCBs. Several ways of optimizing the parameters of DCCB topologies have been investigated [22; 23; 24; 25]. An optimization algorithm was proposed in [22] that ensured that fault currents could be interrupted when the derivatives of injected currents were lower than a critical value. However, the proposed algorithm in [22] was limited to mechanical DCCBs and was not able to be applied in HCBs. This is because HCBs include switching components, such as semiconductors and thyristors, so it is difficult to obtain exact models of HCBs to identify their optimal design. To overcome this problem, genetic algorithms (GAs) employing the heuristic search and optimization technique have been used for the design of HCBs. In [23], a GA was used to find the breaking time and the commutation capacitor voltage. The optimal design of DCCBs can ensure that commutation branches can successfully transfer main currents to the commutation branches. A multi-objective optimization model was proposed in [24] that used a GA to minimize the commutation circuit parameters and reduce the development time. Another GA-based algorithm was proposed in [25] for the optimal design of HCBs. This algorithm aimed to minimize the energy capacity of the commutation capacitor and the parameters of the thyristors, such as voltage and current. Most previous studies on the optimal design of HCBs have focused on minimizing the commutation branches, such as the energy capacity of capacitors, the capacitor voltage, and the thyristor current or voltage. However, many other parameters that are also important, including peak fault current, interruption time, and recovery time, have not received enough attention. Reducing the magnitude of peak fault currents can avoid the damage that is caused by extremely high fault currents in systems, while reducing the interruption time significantly improves system stability and quickly isolates fault areas. In addition, the recovery time, which is the time that is required for the HCB to become ready for the next interruption, should also be considered to avoid the long-term interruption of power to connected AC grids. To achieve these goals, this study developed a multi-objective optimization model that uses a GA to minimize the peak fault current, interruption time, and recovery time. The GA can handle both discrete and continuous variables and can also solve complex optimization problems [26].