A micro-grid is a localized energy system that typically operates as part of a larger, wide-area synchronous grid but can function independently when necessary. It comprises energy generators, loads, storage units, and control systems, all highly integrated and manageable. This study presents the design and implementation of a 60,000-watt solar photovoltaic (PV) microgrid incorporating an advanced fault detection and localization mechanism, aimed at addressing the limitations of conventional reactive fault systems. These traditional systems often respond only after fault currents surpass the tolerance thresholds of grid components, leading to reduced efficiency, equipment damage, or total system failure. To mitigate these issues, a DC micro-grid consisting of six solar PV arrays was modeled using Proteus 8.15 Professional and Siemens TIA Portal. Each array comprised 32 units of 400W, 12V panels arranged in an 8x4 configuration, delivering 72V per array. The PV arrays were individually connected through dedicated contactors (MCB1–6). Fault detection and isolation were achieved using smart electronics, specifically Arduino Nano microcontrollers integrated with WCS1600 current sensors capable of sensing up to 500A. The system efficiently identified and isolated faults occurring within any array. During testing, no faults were flagged for current values of 72.33A, 90.42A, 123.69A, 117.15A, 172.02A, and 199.09A, as they remained within the safe 200A threshold. However, overcurrent values recorded at PV arrays 3, 4, 5, and 6 (235.09A, 307.43A, 412.72A, and 209.09A, respectively) due to simulation of fault (short circuit, load-related faults, battery system faults, DC bus fault or converter and distribution fault) were promptly detected, and the affected arrays were disconnected to protect the system. Compared to previous research, this approach leveraging a hybrid of Arduino Microcontroller and Siemens S7-1200 PLC (CPU1214CDC/DC/DC) demonstrated improved efficiency and reliability in proactive fault detection and localization. Ultimately, the study successfully developed a programmable, feedback-enabled microgrid system capable of anticipating and mitigating faults before component tolerance limits are breached.