Search Results (939)

The complexity of voltage stability assessment in modern smart grids has increased significantly with the growing penetration of renewable energy sources and the dynamic nature of load variations. Although standard analytical methods are accurate, they are computationally expensive and unsuitable for real-time applications. This paper proposes a hybrid analytical–machine learning framework for efficient voltage stability assessment and classification. The proposed approach consists of two stages. First, a power flow analysis is performed to compute the Fast Voltage Stability Index (FVSI) and quantify the proximity of the system operating conditions to voltage instability. Then, the FVSI values are converted into binary stability labels to formulate a supervised classification problem. In the second stage, the Extreme Gradient Boosting (XGBoost) algorithm is employed to learn the relationship between system operating variables and the corresponding stability states. The performance of the proposed method is evaluated on the IEEE 30-bus system and compared with that of conventional machine learning and deep learning models, such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Deep Neural Networks (DNNs). The simulation results show that the XGBoost-based framework outperforms the benchmark models in terms of classification accuracy, robustness, and computational efficiency. The proposed method provides a fast, reliable, and interpretable solution for real-time voltage stability monitoring. Therefore, it is suitable for modern smart grid applications. Full article

The growing share of photovoltaic generation in power grids intensifies the need for converter architectures capable of combining efficient energy conversion, DC-bus stability, and ancillary service provision at the grid coupling point. This paper presents the modeling, implementation, and simulation-based evaluation of a 91 kWp three-phase photovoltaic (PV) system integrated with a battery energy storage system (BESS), developed in the PLECS environment. The proposed architecture comprises three interleaved Boost stages for maximum power point tracking (MPPT), a DC bus regulated at 600 V, three independent bidirectional buck–boost converters for LiFePO₄ bank management, and a two-level three-phase voltage source inverter (VSI) with an LC output filter. The control is organized in cascade voltage–current loops for the DC–DC stages and in vector control within the synchronous reference frame (SRF) for the inverter, with synchronization via SRF-PLL. A C-Script supervisory block integrates the Perturb and Observe (P&O) MPPT algorithm, independent state of charge (SOC) estimation per bank via coulomb counting, and Volt–VAR reactive power reference generation with a dead band of 0.90–1.10 pu. Five scenarios are analyzed for validation: DC-bus regulation under irradiance transients; reactive power support during undervoltage and overvoltage events (0.80–0.85 pu and 1.15–1.20 pu); BESS operation as an active DC-link support element; and PV curtailment with fully charged banks. All five scenarios were additionally corroborated on a Typhoon HIL402 Pro 2 hardware-in-the-loop platform, reproducing the PLECS waveforms within the amplitude and timing resolution of the oscilloscope captures. Across all scenarios, the DC bus is held within $\pm 15$ V (2.5%) of the 600 V reference, with the worst-case transient recovering in 80–100 ms; under a sustained 9 s bidirectional disturbance, redirecting PV surplus to BESS charging in both the undervoltage and overvoltage segments—with no externally imposed active-current limit—keeps the current-vector magnitude $\sqrt{i_d^2+i_q^2}$ below the 335 A rating throughout (≈271 A and ≈242 A, respectively), while the available reactive margin $Q_{disp}$ reaches ≈78–80 kVAr in both segments and the bank SOC advances by ≈$0.03$ pu; and supervisory curtailment under a sustained overvoltage ride-through with a saturated bank keeps the per-bank SOC dispersion within $4\times {10}^{-5}$ pu while expanding the available reactive margin $Q_{disp}$ from ≈50 to ≈90 kVAr. Full article

Proton exchange membrane fuel cell (PEMFC) systems typically generate low-voltage and high-current DC power, requiring a step-up converter interface for electric vehicle (EV) and DC microgrid applications. In addition, excessive current ripple and rapid transient loading conditions may increase electrical and thermal stress within the fuel cell stack. Consequently, both converter topology and control strategy play important roles in maintaining stable system operation and favorable PEMFC operating conditions. This paper presents a differential flatness-based nonlinear control strategy for a PEMFC-fed multiphase interleaved boost converter. The proposed control structure combines inner-loop inductor current regulation with outer-loop DC bus energy regulation. This configuration achieves stable voltage control, balanced phase-current sharing, and reduced fuel cell current ripple during transient operating conditions. A two-phase interleaved boost converter prototype was experimentally implemented using a 2.5 kW PEMFC platform and a dSPACE DS1202 MicroLabBox real-time controller. Experimental tests under steady-state and dynamic loading conditions were conducted to evaluate DC bus voltage regulation, transient response, current-sharing capability, and robustness against load disturbances. The experimental results demonstrated that the proposed nonlinear controller achieved faster transient voltage recovery and smaller DC bus voltage deviation compared with a conventional PI-based control approach. In addition, the interleaved converter structure reduced input current ripple at the PEMFC output terminals during dynamic operation. Overall, the results indicate that the proposed control strategy is suitable for PEMFC-powered EV and DC microgrid applications requiring stable DC bus regulation and fast dynamic power control. Experimental results demonstrate that the proposed controller reduces the DC bus voltage recovery time from approximately 150 ms to 50 ms, corresponding to a 66.7% improvement over a conventionally tuned PI controller. In addition, the maximum DC bus voltage deviation is reduced from approximately 1.0 V to 0.5 V while maintaining balanced phase-current sharing with less than 3% mismatch throughout the tested operating conditions. Full article

The paper presents an analytical study and small-signal modeling of a non-inverting two-switch buck–boost converter based on the LM5118 controller, accounting for parasitic elements. The converter dynamics were analyzed using simulations in the PLECS^® 5.0.2 environment. Furthermore, the steady-state and dynamic characteristics of the LM5118-based two-switch buck–boost converter were examined using the PSpice for TI^® simulator for different input voltages and load conditions. The obtained simulation results show close correspondence with the analytical analysis, confirming the validity of the proposed approach. Full article

Modeling and stability analysis of a PV–energy storage AC/DC integrated three-port grid-connected power electronic device is investigated in this paper for low-voltage single-phase renewable energy applications. The device consists of a PV Boost converter port, a battery-side bidirectional DC-DC converter port, and a single-phase full-bridge grid-connected inverter. To analyze the coupling-induced stability characteristics of this multi-converter system, mathematical models of the PV array, battery, and AC/DC integrated device are established. Considering the periodic time-varying nature introduced by the single-phase grid voltage, a phase-angle-based simplified discrete model is developed to transform the system into a discrete model evaluated at a fixed grid-voltage phase angle. Based on this model, eigenvalue sensitivity, eigenvalue trajectories, and bifurcation diagrams are used to identify the influence of key control parameters on system stability. The results show that excessive proportional gains in the inverter current loop and energy storage control loop reduce the stability margin and may lead to period-doubling bifurcation, Hopf bifurcation, or unstable grid current operation. The period-doubling and Hopf stability boundaries are identified at k_p4 ≈ 1.40 and k_p2 ≈ 1.75, respectively. In simulation, the grid-current THD increases from 2.07% to 3.60% as k_p4 rises from 1.3 to 1.6 and from 2.07% to 2.27% as k_p2 rises from 1.7 to 1.8. MATLAB/Simulink simulations and hardware-in-the-loop experiments further verify that the identified stability boundaries are consistent with the degradation of grid current quality and the increase in total harmonic distortion. The proposed modeling and analysis method provides a reference for parameter tuning and stable operation of single-phase PV–energy storage three-port grid-connected devices. Full article

The intensive development of electric vehicle (EV) technology, renewable energy systems, and stationary energy storage solutions has amplified the demand for advanced Battery Management Systems (BMS). The imbalance in cells within lithium-ion battery packs, due to manufacturing tolerances, varying aging, and thermal gradients, reduces available capacity, cycle life, and can cause thermal runaway. Active charge equalization with DC–DC converters has become a recent research focus among various balancing techniques because it has a better capability of redistributing energy. This paper gives a detailed study of converter-based cell-balancing topologies with a specific focus on the bidirectional flyback converter and the interleaved two-phase variant. Non-isolated topologies (buck–boost, Cuk converter topology, interleaved buck–boost) and isolated topologies (flyback, push–pull, dual-active bridge, LLC resonant) are compared concerning functional efficiency, component reduction, galvanic isolation, scalability, and bidirectional capability. The concept of soft-switching, including zero-voltage switching (ZVS) and zero-current switching (ZCS), and their circuit realizations are discussed. Advanced control models and artificial intelligence (AI) for the estimation of state-of-charge (SoC) and real-time optimization are mentioned. Thermal issues, scalability, reliability, and wide-bandgap semiconductor devices (SiC/GaN) are discussed. Full article

Both temperature and solar radiation cause variations in photovoltaic output power. Nevertheless, each variation has a maximum power point, which represents the photovoltaic output’s maximum efficiency. Photovoltaic power must be managed at the highest point in order to achieve optimal efficiency. This can be accomplished by employing a converter to regulate the photovoltaic output voltage at the maximum power. This study proposes a quadratic boost converter (QBC) to control photovoltaic output power by using the Deep Recurrent Neural Network (DRNN) algorithm. The goal of the DRNN is to decrease ripple at the maximum point and speed up the time to reach the maximum power point. The QBC is designed to obtain a higher DC output voltage than a regular boost converter, so it can eliminate the use of a step-up transformer if the photovoltaic is connected to an inverter. The proposed method is applied to a 50 Wp solar panel with an Arduino microcontroller as the controller device. The experimental results demonstrate that the DRNN algorithm-based QBC effectively controls the solar panel output power at the maximum point with a smoother ripple and a faster response. The QBC is also able to produce higher-voltage output according to its characteristics. Full article

The interleaved parallel Buck–Boost converter can reduce output voltage ripple and has been widely used in engineering practice. The application of fractional-order theory has a significant influence on model accuracy and power converter performance. Based on fractional calculus theory and the state space averaging method, this paper establishes a fractional-order mathematical model of the CCM interleaved parallel Buck–Boost converter. The steady-state operating point and ripple characteristics of the converter under the Caputo fractional-order definition are analyzed and compared with those under other fractional-order definitions. Fractional-order energy storage elements are constructed, and a fractional-order circuit simulation model of the converter is established for comparative simulation analysis. Finally, experiments are carried out to verify the effectiveness of the theoretical analysis. Full article

This paper proposes a new hybrid switched inductor and switched capacitor (HSISC) buck–boost DC–DC converter. For the duty ratio above 28%, the proposed converter operates as a boost converter; otherwise, it acts as a buck converter. Compared with conventional buck–boost converters, incorporating the hybrid switched inductor and switched capacitor (HSISC), the network yields a substantial voltage gain at lower duty ratios. Being a non-isolated topology, high-frequency transformers and the associated issues are absent. Additionally, the proposed topology has the merits of continuous input current, making it suitable for renewable energy integration and vehicle-to-grid (V2G) applications, a wide range of duty ratio for boost operation, and ease of control as there are only two modes of operation with switches operating in a complementary manner. Operational analysis for the two modes, necessary mathematical derivations for component design, and a steady-state analysis of the converter are reported. The experimental findings for the converter, which were conducted at a duty ratio of 0.05 to 0.5 at a switching frequency of 10 kHz, are reported. The presented results provide proof-of-concept validation based on analytical and simulation studies, demonstrating the feasibility and operational characteristics of the proposed converter. Full article

As a critical device for ensuring stable operation in power substations, an Uninterruptible Power Supply (UPS) plays a vital role in maintaining a continuous power supply, improving power quality, and enhancing system reliability. However, startup inrush current and backflow ripple voltage significantly impact the stability of DC power supply equipment. This paper proposes a DC-DC circuit-reuse method with double-line frequency ripple suppression for substation UPS systems. This approach utilizes a boost converter to transfer the double-line frequency pulsation from the DC input to the bus capacitor side, thereby reducing the double-line frequency components at the input. Notably, this Boost converter simultaneously serves as part of the power factor correction (PFC) circuit connecting the grid and DC bus, achieving functional integration that significantly reduces system costs. A 3 kVA UPS experimental platform was constructed to validate the proposed method. The test results demonstrate that while the bus voltage exhibits slight pulsation at double-line frequency, the input current transforms into an approximate DC quantity. This second-harmonic ripple suppression method effectively improves key performance indicators: Startup inrush current decreases by 38% compared to conventional designs, and backflow ripple voltage amplitude is suppressed below 0.5% of the nominal DC voltage. The proposed topology demonstrates superior compatibility with existing substation DC systems and provides a cost-optimized solution for power quality enhancement in critical infrastructure applications. Full article

Maximum power point tracking (MPPT) is essential for photovoltaic (PV) efficiency under dynamic environments. This paper presents a comparative analysis of three MPPT algorithms: incremental conductance (INC), Q-Learning (QL) reinforcement learning, and Transformer-inspired Machine Learning (TML) applied to a photovoltaic (PV) array configured in a 1S×3P with a DC-DC boost converter designed for a 48 V DC output. Simulations were performed under four scenarios in MATLAB/Simulink: standard test conditions (STC), irradiance variation, temperature variation, and combined wide-range variations. At STC, all three exceed 99% tracking efficiency, with QL achieving the highest efficiency, 99.914%; TML having the best output voltage regulation (48.071 V); and INC converging fastest (5.4 ms). Under dynamic irradiance variations, QL attained the highest average tracking efficiency (91.85%) and average output power (481.4 W), whereas INC converged within 36.9 ms. Under temperature variations, TML achieved the highest average tracking efficiency (97.448%) and average power output (549.30 W), while INC maintained the fastest convergence rate (68.6 ms). With wide-range combined variation, QL achieves the highest average tracking efficiency (91.32%) and output power (469.9 W), outperforming TML (84.06%) and INC (57.39%) by 7.3 and 33.9 percentage points, respectively; INC converges fastest (33.1 ms) but delivers 64.1% less output power than QL. The simulation results demonstrate that artificial intelligence-driven algorithms can significantly improve maximum power point tracking (MPPT) under dynamic conditions. To establish real-world viability, future work requires hardware-in-the-loop (HIL) testing and experimental validation. Full article

This paper proposes an Active Gate Driver (AGD) for a DC–DC boost converter based on Silicon Carbide (SiC) power devices, in which surge voltage and ringing arise from their fast-switching characteristics. In this work, a practical and simple 4-bit logic AGD was proposed to adjust the gate resistance during the Miller interval of the SiC MOSFET. This helps suppress these effects and lowers the surge voltage and ringing stress on the power device. Experimental results demonstrate that the voltage overshoot decreases from 52 V to 22 V, corresponding to a reduction from 52% to 22% under a 100 V output condition, while the peak drain–source voltage decreases from 152 V to 122 V. The turn-off energy increases from 60.2 µJ to 76.9 µJ due to the slightly reduced switching speed. This trade-off represents the improvement in the comparison between transient suppression and switching loss. In addition, the voltage ringing is significantly attenuated. Although the modified switching strategy slightly increases switching loss, it effectively improves waveform quality and reduces voltage stress. These results confirm that the proposed AGD provides a simple and effective solution for improving the switching robustness and reliability of SiC-based DC–DC boost converters. Full article

In high-step-up ratio converters for portable battery-powered devices, MOSFET switching loss limits efficiency and thermal design. This paper evaluates a multi-stage adaptive gate driver (MS-AGD) after transfer from a 900 V SiC MOSFET high-step-up converter to a 25 V Si MOSFET transformer-based boost circuit. The MS-AGD detects the Miller plateau by differential sensing and controls gate current in four stages through cascode current mirrors. The target-platform comparison combines measured switching waveforms with a temperature-based $\zeta$ coefficient and an apparent $R_{on-effective}$ indicator under a fixed device, load, fixture, pulse sequence, and thermal path. Total switching energy is not determined directly. Tests at 15 frequency points from 23.26 to 125 kHz show that drain-source voltage reaches its valley in about 500 ns with MS-AGD rather than about 1300–1450 ns with fixed-resistor drive and that the MOSFET package-temperature rise is reduced at all tested points by about 25% on average. The fitted apparent thermal-electrical indicator is also lower. These mutually consistent waveform and thermal results indirectly support a reduced turn-on switching-loss contribution while avoiding interpretation of $\zeta$ or apparent $R_{on-effective}$ as direct measurements of total switching loss or instantaneous channel resistance. Full article

This study addresses the power dispatching of a DC-coupled three-phase grid-connected photovoltaic (PV) and energy storage-integrated system by proposing a peak shaving and valley-filling control architecture based on time-of-use (TOU) pricing. This research involves achieving maximum power-point tracking (MPPT) for PVMAs using a boost converter combined with the perturb and observe (P&O) method. A lithium-iron phosphate battery pack is integrated into the DC link via a bidirectional buck-boost converter, where charging and discharging control is executed according to peak and off-peak periods to regulate and stabilize the DC link voltage. Furthermore, bidirectional power flow control for peak and off-peak electricity consumption is realized using hysteresis current control and sinusoidal pulse-width modulation (SPWM) technologies within a smart inverter. By integrating the aforementioned power control architecture, the grid system can store energy from the utility during off-peak hours and release the stored energy during peak hours to reduce the load demand on the utility side. Initially, a simulation environment was established using Matlab/Simulink (2024b version) software, followed by control verification of the proposed system on a physical platform. The simulation and experimental results confirm that the integrated control architecture can precisely control the system’s DC link voltage at 800 V and stabilize the grid-connected AC voltage at an effective value (RMS) of 380 V. Moreover, under conditions of peak/off-peak switching and load variations, the system effectively demonstrates its stability and efficacy in performing valley filling and peak shaving. The proposed strategy achieves a power factor above 0.99 and a total harmonic distortion (THD) below 5%, regulates the DC-link voltage at 800 V with a steady-state error within 1.75%, and prevents up to 66.4 kWh of over-contract energy consumption per day under a 35 kW contract capacity, thereby contributing to sustainable energy management and economic savings. Full article

This paper presents a Finite Control Set-Model Predictive Control (FCS-MPC) strategy for a modified single-phase 17-level Double Flying-Capacitor Multilevel (DFCM) converter. The proposed approach integrates current regulation, capacitor voltage balancing, switching frequency reduction, delay compensation, and FPGA-based real-time implementation within a unified predictive control framework. A multi-objective cost function exploits the converter’s redundant switching states to achieve accurate control while reducing computational burden. Additionally, the converter topology provides voltage-boosting capability without requiring an additional DC-DC stage. The proposed controller was validated through offline and Hardware-in-the-Loop (HIL) simulations. Simulation results demonstrate robust operation, effective capacitor voltage balancing, and excellent current quality, achieving a THD of 0.7%. Full article