## 1 Introduction  
Solar energy generation has recently emerged as one of the most promising renewable resources [1]. The electric power grid is increasingly affected by the safe and reliable operation of rapidly growing solar energy production [2]. During a grid fault, electric power grid rules require grid-connected photovoltaic power plants (PVPPs) to have low voltage ride-through (LVRT) capabilities [3]. LVRT capability protects PV panels and maintains connectivity during grid faults [4]. Each country's transmission system operator (TSO) has different standards for electric power grid codes. A country will require greater reactive power support during a grid fault if its grid codes are weak. After a short-term problem, many units will be disconnected, the frequency and voltage stability of the electric power system will decrease, and even the interruption of electric power may occur in some locations [5]. Therefore, several techniques have been proposed in the literature to improve the LVRT capabilities of grid-connected PVPPs. The most common methods for improving LVRT involve adding more devices, advanced control strategies, or altering the inverter topology. In [6], a phase-locked loop (PLL) technique is frequently implemented to track the frequency and phase of a three-phase grid in real time. Superior disturbance rejection capabilities are possessed by the PLL. In [7], the power quality and voltage control approach is employed to improve the LVRT mode while adhering to grid code requirements. In [8], the multi-objective control approach is applied to increase LVRT capability by eliminating oscillations in the power, effectively reducing harmonics in the current injected into the grid. In [9], a novel decentralized inverter control algorithm is implemented to minimize LVRT's harmful effects by greatly reducing system losses. In [10], the recurrent wavelet fuzzy neural network technique is employed to increase the LVRT capability of the PVPP system. In [11], multimode inverter control and current control approaches are presented to reduce the negative impacts of rapid load reduction and grid faults. In addition to these control schemes, a hybrid passivity-based control strategy [12], multi-frequency PR control [13], and power park modules [14] have also been proposed in the literature. A three-phase cascaded H-bridge is employed to enhance the PVPP system's LVRT capability [15; 16; 17]. Traditional control methods, such as PLL, provide limited performance while being low-cost. A decentralized inverter control algorithm has performance limitations in large systems, whereas it has a fast response time with local control. The multimode inverter control strategy adapts to different modes, but its instability during transitions may cause problems. The recurrent wavelet fuzzy neural network and multi-objective control methods provide multi-parameter optimization, but they have high complexity and cost. The proposed DC chopper and CBFCL, based on the machine learning (ML) method, have significant advantages in terms of LVRT capability, speed, and flexibility compared to other control methods in the literature. In addition, the proposed system offers a simpler and more efficient solution, reduces transition delays with predictive modelling, has flexibility and stability due to adaptive control, and offers an optimum solution at a medium cost. The DC chopper approach is frequently employed in the literature to protect PV and wind systems from the negative impacts of LVRT [18; 19]. Later research improved the DC chopper method and presented modified DC chopper approaches [20; 21]. In [22], the DC-link crowbar is proposed to enhance the LVRT capability of the grid-connected PV system. A proposed control method prevented the AC overcurrent at the PCC and the DC-link overvoltage. In [23], the authors propose a controllable crowbar using self-hailing for a hybrid generation system. The controllable crowbar compares to the traditional crowbar in terms of dynamic performance.