In the quest for sustainable energy sources, wind generators (WGs) have emerged as a promising solution to provide power for remote and grid-connected applications within IoT-enabled smart power systems. This research underscores the significance of wind energy as a valuable and sustainable energy source in the context of IoT-enabled smart power systems and delves into optimizing its utilization. Specifically, The authors introduce a novel approach to address the limitations of conventional hill climbing search (HCS) methods by incorporating a modified fuzzy logic-based HCS algorithm. The results unequivocally demonstrate that this proposed algorithm significantly enhances efficiency, consistently achieving an impressive efficiency rating of approximately 95% across diverse wind conditions. These findings mark a significant stride towards realizing the full potential of wind energy as a reliable and sustainable energy source within the context of IoT-connected smart power systems, paving the way for a greener and smarter energy future.

This work presents a dual-output Boost–SEPIC converter developed for compact and efficient auxiliary power management in electric vehicles. The converter independently regulates two output voltages that may be higher, lower, or equal to the input, while its hybrid topology reduces component count and enhances power density. The study includes steady-state analysis, small-signal modelling, and derivation of key transfer functions to support controller design. A digital current-programmed predictive control strategy is implemented to achieve accurate current tracking, low ripple, and reliable response under dynamic load conditions. Simulation and experimental results validate the robustness of the proposed system, demonstrating stable dual-output regulation, improved efficiency, and reduced stress on the energy storage unit. These contributions collectively highlight the converter’s novelty as a compact and intelligent power solution for next-generation electric mobility platforms.

DC microgrids integrating renewable energy sources such as solar and wind require robust control strategies to ensure stable voltage regulation under varying operating conditions. This paper proposes an Adaptive Fuzzy PID (AFPID) controller for voltage regulation in a solar-based DC microgrid and compares its performance with a conventional Fuzzy PID (FPID) controller. The proposed control strategy is implemented in the DC–DC converter interfaced with a photovoltaic (PV) system operating under maximum power point tracking (MPPT). The effectiveness of the AFPID controller is evaluated based on key dynamic performance indices including settling time, steady-state error, overshoot, and oscillation magnitude. Simulation results obtained using MATLAB/Simulink demonstrate that the AFPID controller significantly improves system performance. Specifically, the settling time is reduced from 3.0 s (FPID) to 1.5 s (AFPID), representing a 50% improvement. The steady-state error decreases from 0.015 to 0.008, while the overshoot is reduced from 4.5% to 1.8%. These results confirm enhanced transient response and improved voltage stability.

The growing use of solar photovoltaic systems needs dependable multiport DC-DC converters that can perform at high levels. The performance of converters declines when semiconductor components experience open-circuit or short-circuit faults because these faults require immediate detection to prevent total system failure. A unified system for current control and fault detection based on diode current as a shared state element for output control and fault detection has been developed in this research paper for solar-powered multiport DC-DC converters. The predictive control law establishes duty cycle estimation through charge-second balance principles, which allow voltage regulation to function properly during standard operations without needing extra sensor equipment. The analysis through frequency-domain methods shows the closed-loop system maintains stable operation because it runs with a 1.318 kHz bandwidth and positive phase margin, which allows for quick system response and protection against disturbances. The diagnostic algorithm proposed in this study measures diode current during multiple switching periods to investigate conduction properties, which help identify normal functioning versus OC and SC faults. The experimental tests on a laboratory prototype running at 20 kHz show that the system can detect faults within a time period shorter than one-fourth of the switching cycle, which enables quick fault management and improved system security. The proposed method achieves multiple benefits by lowering both sensor requirements and system processing demands while providing quick fault detection, accurate fault identification, and consistent output control for renewable energy systems and critical power electronic systems. The effectiveness of the proposed method is validated through both simulation and experiment using a 200 W laboratory prototype, where accurate fault detection and control performance are achieved.

It is necessary to design, new varieties of voltage source inverter (VSI) structure to ensure the better power quality and maintain equilibrium between electrical distribution system (EDS) and consumer with economic pricing. As a main part of operation, deep belief learning network (DBLN) algorithm is implemented for switching ON/OFF the VSI. In order to build a high-accuracy evaluation model, a VSI based distribution static compensator (DSTATCOM) is considered as the reference model. Afterwards, the inductor & capacitor are successfully identified by mathematical analysis &incorporated in the EDS. Based on the input supply current harmonics reduction, power factor (PF) improvement in the input, potential regulation & constant DC potential capacitor of both structures, the evaluation outcomes are obtained. For a healthy three-phase three-wire (3P3W) EDS, these above essential performance indicators of the DSTATCOM are analyzed as per IEEE-519-2017 and EN-50160 standard. The overall EDS with DSTATCOM are designed using MATLAB/ Simulink. Simulation as well as experimental results using d-SPACE-1104 validated the effectiveness, accuracy, superiority & feasibility of the projected topology for the EDS.