The rapid proliferation of single-phase non-linear loads, such as LED lighting and IT equipment, in modern Smart Buildings has introduced significant power quality challenges in low-voltage electrical installations. A critical but often underestimated consequence is the severe overloading of the neutral conductor caused by triplen harmonics (particularly the 3rd harmonic), which sum algebraically even in balanced three-phase systems. This paper analyzes the electrical and thermal impact of these distortions using a detailed MATLAB/Simulink model of a 400/230 V (3P + N) network. The simulation results demonstrate that under highly distorted conditions (Scenario S3), the neutral current can reach 180% of the nominal phase current (18 A vs. 10 A). Furthermore, the Joule losses analysis reveals a thermal stress more than three times higher on the neutral conductor (peak ~65 W) compared to the phase conductor (~20 W), challenging the traditional design practice of neutral undersizing. To address these safety issues, this study proposes a novel neutral-to-phase current ratio index (k_N) and a proactive decision matrix for Building Management Systems (BMS). Unlike traditional mitigation strategies that rely on static hardware oversizing, passive filters, or specialized transformers, the proposed approach offers a dynamic, cost-effective, and software-driven solution that can be easily integrated into the existing automation infrastructure of modern Smart Buildings. The model identifies a critical tipping point at a 3rd harmonic content of 35.3%, where k_N ≥ 1. By continuously monitoring the k_N parameter, the proposed algorithm enables a transition from passive protection to active power management, triggering automated responses to prevent insulation degradation and mitigate fire hazards.

The transition toward active distribution networks requires advanced control solutions capable of handling the rapid dynamics of distributed energy resources. This paper proposes a low-cost, intelligent IoT architecture designed for the real-time optimization and analysis of energy systems within low-voltage networks. Unlike centralized monitoring approaches constrained by communication latency, the proposed solution leverages Intelligent Edge Processing (IEP) implemented on ESP32 embedded nodes to optimize data flow and decision-making. This architecture executes stability assessments directly at the network edge, calculating critical analysis indicators such as the Voltage Deviation Index (VDI) and Rate of Change of Frequency (RoCoF). The system was validated on the CIGRE European LV benchmark under severe stress scenarios, including rapid solar transients and voltage sags. The results demonstrate that the proposed architecture effectively coordinates storage interventions, ensuring voltage recovery within 300 ms and maintaining power quality within EN 50160 limits even during severe voltage sags. The study validates the feasibility of using industrial IoT edge computing as a resilient, non-wire alternative for modernizing complex energy systems.

This paper proposes an automation solution applied to a 315 kVA transformer station—a benchmark unit in distribution networks—to address the challenges posed by the large-scale integration of renewable energy sources. The core issue addressed is the uncontrolled rise of the voltage profile at consumer terminals during periods of peak solar radiation. The technical solution involves implementing an automated tap-changer control system (Digital OLTC), which utilizes the existing regulation taps to maintain voltage within normative limits. This study demonstrates how the digitalization of classical 315 kVA infrastructure transforms a passive component into an active, intelligent node, essential for the stability of Smart Grids.

Open-pit lignite mines contain high-power converter-fed drives whose operating state strongly affects current harmonics and internal power-quality conditions. The primary objective of this study is to experimentally characterize load-dependent voltage and current harmonic regimes in an operating open-pit lignite mining electrical system and to use a simplified MATLAB/Simulink R2021b inverter-motor model only as a trend-interpretation tool. In situ measurements were obtained on the E05 SRS 1300-26/3.5 bucket-wheel excavator and associated conveyor systems. The measurements show low voltage distortion at the monitored internal points (the Total Harmonic Distortion of Voltage (THD_U) below 1.5% in the reported cases) and strongly load-dependent current distortion, including high relative the Total Harmonic Distortion of Current (THD_I) during low-current operation. The simulation reproduces selected qualitative drive-side trends but does not represent the upstream network, feeder impedances, transformer behavior, capacitor-bank interaction, or background supply distortion; it is therefore not presented as an independently validated network model. A separate Battery Energy Storage System (BESS) calculation is retained only as an illustrative resilience scenario based on an assumed four-hour duty profile and is not used as evidence for the measured harmonic findings. The main contribution is an experimentally supported case study of load-dependent harmonic behavior in an operating lignite-mine electrical system, complemented by simulation-assisted physical interpretation and clearly separated prospective modernization implications.

Hybrid PV–BESS microgrids represent a promising solution for enhancing energy resilience; however, the implementation of black-start functions in such architectures remains a complex challenge, particularly in the presence of medium-voltage transformers and grid-following photovoltaic sources. This paper proposes and evaluates, through MATLAB/Simulink simulation, a black-start strategy for a hybrid microgrid derived from the architecture of a real 31.59 MWp photovoltaic power plant connected through a 20 kV collector network. The strategy is based on a BESS operated in grid-forming mode, responsible for voltage and frequency formation, controlled energization of the internal infrastructure, and support of the initial restoration stages, while the photovoltaic groups are synchronized at a later stage in order to reduce the energy burden imposed on the storage system. The methodology combines controlled voltage ramping for transformer inrush current mitigation, hybrid PV–BESS restoration, robustness evaluation under irradiance variation, sequential load restoration, and staged energization of the internal stations TS1–TS5. The results show that the soft-start strategy reduces the maximum instantaneous current of transformer TS2 from 188.3620 A to 0.3515 A and the maximum current of the BESS source from 4729.2 A to 57.7929 A. In the hybrid scenario, after photovoltaic source synchronization at 7 s, PV power rises to 2.75 MW, while the power supplied by the BESS decreases from 3.00 MW to 0.25 MW. The analysis of irradiance variation confirms the increased energy dependence on the BESS under reduced solar resource conditions, while sequential load restoration demonstrates the feasibility of controlled reconnection up to 30.25 MW. Overall, the study confirms that the proposed architecture and staged control logic provide a feasible and transferable framework for enhancing the resilience of large-scale photovoltaic power plants based on hybrid PV–BESS microgrids.