Search Results (219)

Accurate and timely detection of islanding events is essential for ensuring the stability and safety of hybrid power systems with high penetration of distributed energy resources. Traditional islanding detection methods often face challenges related to detection speed, false alarms, and robustness under dynamic operating conditions. This paper proposes a Robust Random Vector Functional Link Network (RVFLN)-based detection framework that leverages engineered features extracted from voltage, current, and power signals in a hybrid microgrid. The proposed method integrates statistical, spectral, and spatiotemporal features—including the Dynamic Harmonic Profile (DHP), which tracks rapid harmonic distortions during disconnection, the Sub-band Energy Ratio (SBER), which quantifies the redistribution of signal energy across frequency bands, and the Islanding Anomaly Index (IAI), which measures multivariate deviations in system behavior—capturing both transient and steady-state characteristics. A real-time digital simulator (RTDS) is used to model diverse scenarios including grid-connected operation, islanding at the Point of Common Coupling (PCC), synchronous converter islanding, and fault events. The RVFLN is trained and validated using this high-fidelity data, enabling robust classification of operational states. Results demonstrate that the RVFLN achieves high accuracy (up to 98.5%), low detection latency (average 0.05 s), and superior performance across precision, recall, and F1 score compared to conventional classifiers such as Random Forest, SVM, and k-NN. The proposed approach ensures reliable real-time islanding detection, making it a strong candidate for deployment in intelligent protection and monitoring systems in modern power networks. Full article

This study proposes a current control strategy to enhance the control stability of an interior permanent magnet synchronous motor (IPMSM) under transient conditions, such as rapid acceleration or deceleration in electric vehicle (EV) applications. Conventional current control methods provide optimal steady-state current references corresponding to torque commands using a lookup table (LUT)-based approach. However, during transitions between these reference points, particularly in the field-weakening region at high speeds, the voltage limit may be exceeded. When the voltage limit is exceeded, unstable overmodulation states may occur, degrading stability and resulting in overshoot of the inverter input current. Although ramp generators are commonly employed to interpolate between current references, a fixed ramp slope may fail to ensure a sufficient voltage margin during rapid transients. In this study, a method is proposed to dynamically adjust the rate of change of the d-axis current reference in real time based on the difference between the inverter output voltage and its voltage limit. By enabling timely field-weakening before rapid changes in speed or q-axis current, the proposed strategy maintains control stability within the voltage limit. The effectiveness of the proposed method was verified through simulations based on real vehicle driving profiles and dynamometer experiments using a 38 kW class IPMSM for a hybrid electric vehicle (HEV), demonstrating reduced input DC current overshoot, improved voltage stability, and enhanced torque tracking performance under high-speed transient conditions. Full article

The increasing demand for high-performance power converters for electric vehicle (EV) applications places a significant emphasis on developing effective and robust control strategies for DC-DC converter operation. This paper deals with the development, simulation, and experimental validation of an adaptive Lyapunov-type Nonlinear Sliding Mode Control (L-SMC) strategy for a DC–DC boost converter, addressing significant uncertainties caused by large variations in system parameters (R and L) and ensuring the tracking of a voltage reference. The proposed control strategy employs the Lyapunov stability theory to build an adaptive law to update the parameters of the sliding surface so the system can achieve global asymptotic stability in the presence of uncertainty in inductance, capacitance, load resistance, and input voltage. The nonlinear sliding manifold is also considered, which contributes to a more robust and faster convergence in the controller. In addition, a logic optimization technique was implemented that minimizes switching (chattering) operations significantly, and as a result of this, increases ease of implementation. The proposed L-SMC is validated through both simulation and experimental tests under various conditions, including abrupt increases in input voltage and load disturbances. Simulation results demonstrate that, whether under nominal parameters (R = 320 Ω, L = 2.7 mH) or with parameter variations, the voltage overshoot in all cases remains below 0.5%, while the steady-state error stays under 0.4 V except during the startup, which is a transitional phase lasting a very short time. The current responds smoothly to voltage reference and parameter variations, with very insignificant chattering and overshoot. The current remains stable and constant, with a noticeable presence of a peak with each change in the reference voltage, accompanied by relatively small chattering. The simulation and experimental results demonstrate that adaptive L-SMC achieves accurate voltage regulation, a rapid transient response, and reduces chattering, and the simulation and experimental testing show that the proposed controller has a significantly lower steady-state error, which ensures precise and stable voltage regulation with time. Additionally, the system converges faster for the proposed controller at conversion and is stabilized quickly to the adaptation reference state after the drastic and dynamic change in either the input voltage or load, thus minimizing the settling time. The proposed control approach also contributes to saving energy for the application at hand, all in consideration of minimizing losses. Full article

To eliminate the dependence of the induction motor (IM) flux-oriented control system on position sensors, IM sensorless control based on a full-order state observer is studied in this paper. First, according to the IM rotor flux linkage models of current and voltage, the speed of the full-order state observer for IM and the solution for the feedback matrix are designed. Then, to simplify the expression of the feedback matrix and improve the stability of the observer for high-speed operation, a novel solving method of the feedback matrix by left-shifting the poles of the observer is proposed, and the terms containing rotor speed are simplified. On this basis, a speed estimation method based on the current error and Lyapunov theory is proposed. For low-speed operation, the feedback matrix parameter design method is proposed based on the stability conditions of d–q axis current error model. Finally, the feasibility and effectiveness of the proposed full-order state observer are verified by simulation and experiment. Since the improved full-order state observer can provide accurate speed feedback and rotor flux position for flux-oriented vector control systems, the IM drive system exhibits good steady-state and dynamic performance. Full article

In some areas, such as Gansu in China and Texas in the USA, lots of wind power bases are located far away from load centers. Transmitting large amounts of wind power to load centers through long transmission lines will lead to wind turbines being integrated into a weak grid, which decreases the stability limits of wind turbines. To solve this problem, this study investigates the stability limits of a Doubly Fed Induction Generator (DFIG) connected to a weak grid in a DC-link voltage control timescale. To start with, a model of the DFIG in a DC-link voltage control timescale is presented for stability limit analysis, which facilitates profound physical understanding. Through steady-state stability analysis based on sensitivity evaluation, it is found that the critical factor restricting the stability limit of the DFIG connected to a weak grid is ∂P_e/∂ (−i_rd), changing from positive to negative. As ∂P_e/∂ (−i_rd) reaches zero, the system reaches its stability limit. Furthermore, by considering control loop dynamics and grid strength, the stability limit of the DFIG is investigated based on eigenvalue analysis with multiple physical scenarios. The results of root locus analysis show that, when the DFIG is connected to an extremely weak grid, reducing the bandwidth of the PLL or increasing the bandwidth of the AVC with equal damping can increase the stability limit. The aforesaid theoretical analysis is verified through both time domain simulation and physical experiments. Full article

Aiming at the transient synchronization instability problem of grid-forming energy storage under a fault in the grid-connected inverter, this paper proposes an adaptive transient synchronization support strategy for grid-forming energy storage facing inverter faults. First, the equal area rule is employed to analyze the transient response mechanism of the grid-forming energy storage grid-connected inverter under faults, revealing the negative coupling relationship between active power output and transient stability, as well as the positive coupling relationship between reactive power output and transient stability. Based on this, through the analysis of the dynamic characteristics of the fault overcurrent, the negative correlation between the fault inrush current and impedance and the positive correlations among the fault steady-state current, active power, and voltage at the point of common coupling are identified. Then, a variable proportional–integral controller is designed to adaptively correct the active power reference value command, and the active power during the fault is gradually restored via the frequency feedback mechanism. Meanwhile, the reactive power reference value is dynamically adjusted according to the voltage at the point of common coupling to effectively support the voltage. Finally, the effectiveness of the proposed strategy is verified in MATLAB/Simulink. Full article

The integration of renewable energy sources (RESs) into power systems, particularly in microgrids, is becoming a prominent trend aimed at reducing dependence on traditional energy sources. Replacing conventional synchronous generators with grid-connected RESs through power electronic converters has significantly reduced the inertia of microgrids. This reduction negatively impacts the dynamics and operational performance of microgrids when confronted with uncertainties, posing challenges to frequency and voltage stability, especially in a standalone operating mode. To address this issue, this research proposes enhancing microgrid stability through frequency control based on virtual inertia (VI). Additionally, the Iterative Learning Control (ILC) method is employed, leveraging iterative learning strategies to improve the quality of output response control. Accordingly, the ILC-VI control method is introduced, integrating the iterative learning mechanism into the virtual inertia controller to simultaneously enhance the system’s inertia and damping coefficient, thereby improving frequency stability under varying operating conditions. The effectiveness of the ILC-VI method is evaluated in comparison with the conventional VI (C-VI) control method through simulations conducted on the MATLAB/Simulink platform. Simulation results demonstrate that the ILC-VI method significantly reduces the frequency nadir, the rate of change of frequency (RoCoF), and steady-state error across iterations, while also enhancing the system’s robustness against substantial variations from renewable energy sources. Furthermore, this study analyzes the effects of varying virtual inertia values, shedding light on their role in influencing response quality and convergence speed. This research underscores the potential of the ILC-VI control method in providing effective support for low-inertia microgrids. Full article

This article proposes a nonlinear control strategy to address the voltage instability issue caused by the boost converter with an uncertain constant power load (CPL). This strategy combines a passivity-based controller (PBC) with a virtual DC motor controller (VDCM). Initially, a PBC is designed for the boost converter, which enhances the robustness of the converter with CPL perturbations in the DC bus voltage. To overcome the limitations of PBC, including steady-state errors resulting from variations in load or input voltage, the VDCM is incorporated, simulating the characteristics of a DC motor. This addition improves the system’s inertia and damping, making it more stable and significantly enhancing its dynamic performance. The efficacy and stability analysis of the proposed control strategy is validated through both simulation and experimentation. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

This paper proposes a novel integer-mixed probabilistic optimal power flow (IM-POPF) strategy for frequency regulation in islanded microgrids under uncertain operating conditions. Existing load-shedding approaches face critical limitations: continuous frameworks fail to reflect the discrete nature of actual load disconnections, while deterministic models inadequately capture the stochastic behavior of renewable generation and load variations. The proposed approach formulates load shedding as an integer optimization problem where variables are categorized as integer (load disconnection decisions at specific nodes) and continuous (voltages, power generation, and steady-state frequency), better reflecting practical power system operations. The key innovation combines integer load-shedding optimization with efficient uncertainty propagation through Unscented Transformation, eliminating the computational burden of Monte Carlo simulations while maintaining accuracy. Load and renewable uncertainties are modeled as normally distributed variables, and probabilistic constraints ensure operational limits compliance with predefined confidence levels. The methodology integrates Differential Evolution metaheuristics with Unscented Transformation for uncertainty propagation, requiring only 137 deterministic evaluations compared to 5000 for Monte Carlo methods. Validation on an IEEE 33-bus radial distribution system configured as an islanded microgrid demonstrates significant advantages over conventional approaches. Results show 36.5-fold computational efficiency improvement while achieving 95.28% confidence level compliance for frequency limits, compared to only 50% for deterministic methods. The integer formulation requires minimal additional load shedding (21.265%) compared to continuous approaches (20.682%), while better aligning with the discrete nature of real-world operational decisions. The proposed IM-POPF framework successfully minimizes total load shedding while maintaining frequency stability under uncertain conditions, providing a computationally efficient solution for real-time microgrid operation. Full article

Green hydrogen production from water electrolysis (WE) is one of the most promising technologies to realize a decarbonized future and efficiently utilize intermittent renewable energy. Among the various WE technologies, the emerging anion exchange membrane (AEMWE) technology shows the greatest potential for producing green hydrogen at a competitive price. To achieve this goal, simple methods for the large-scale synthesis of efficient and low-cost electrocatalysts are needed. This paper proposes a very simple and scalable process for the synthesis of nanostructured NiCo- and NiFe-based electrode materials for a zero-gap AEMWE full cell. For the preparation of the cell anode, oxides with different Ni molar fractions (0.50 or 0.85) are synthesized by the sol–gel method, followed by calcination in air at different temperatures (400 or 800 °C). To fabricate the cell cathode, the oxides are reduced in a H₂/Ar atmosphere. Electrochemical testing reveals that phase purity and average crystal size significantly influence cell performance. Highly pure and finely grained electrocatalysts yield higher current densities at lower overpotentials. The best performing membrane electrode assembly exhibits a current density of 1 A cm⁻² at 2.15 V during a steady-state 150 h long stability test with 1 M KOH recirculating through the cell, the lowest series resistance at any cell potential (1.8 or 2.0 V), and the highest current density at the cut-off voltage (2.2 V) both at the beginning (1 A cm⁻²) and end of tests (1.78 A cm⁻²). The presented results pave the way to obtain, via simple and scalable techniques, cost-effective catalysts for the production of green hydrogen aimed at a wider market penetration by AEMWE. Full article

Ensuring the fire resistance and thermal stability of power cables is crucial for their reliable performance in fire environments, essential for sustainable power distribution, and allowing for more time to extinguish fires and for evacuation. This study utilises numerical simulation to analyse the thermal behaviour of fire-resistant medium-voltage cable, focusing on the impact of conductor radius and material properties under external heat flux. A heat transfer model of cables with conductor radii of 3 mm, 5 mm, and 7 mm under a localised external heat flux of 750 °C was developed. The results show that smaller conductors stabilise faster (reaching the steady state at 45 min for 3 mm vs. 79 min for 7 mm) but experience higher thermal stress, with conductor temperatures peaking at 692.5 °C. Larger conductors enhance axial heat conduction, reduce steady-state temperature by up to 25%, and improve heat dissipation by over 360%. The 5 mm conductor radius provided balanced performance, lowering the temperature by 65 °C compared to 3 mm, although it remained 20.1% hotter than the 7 mm. The ceramic layer played a crucial role in reducing heat flux in the heat source section. Optimised polyethylene insulation and ceramic material improved heat retention and surface temperature control in non-heat source sections. Also, thermal resistance analysis decreased from 1.00 K/W (3 mm) to 0.65 K/W (7 mm). Among material properties, increasing ceramic thermal conductivity had a more significant impact on reducing core temperature than improving insulation. These findings provide practical recommendations for optimising conductor geometry and material properties for more fire-resistant cables. Full article

The present study investigates the control strategy of a novel doubled-fed induction generator (DFIG) with a series grid-side converter (SGSC) during grid faults. The rotor-side inverter is subject to a control strategy derived from the Model Predictive Current Control (MPCC) theory, which is implemented during periods of fault occurrence; for the series grid-side converter, the positive and negative sequence component control is implemented during both steady state and fault periods to enhance system stability and performance. The proposed coordinated control strategy is implemented on a doubly fed turbine with SGSC, while taking into account different degrees of symmetric and asymmetric faults to further evaluate the efficacy of the proposed method. The results of the simulations demonstrate the efficacy of the model-predictive current control scheme applied to the rotor-side converter under conditions of asymmetric faults. This enables the suppression of a range of phenomena, including rotor overcurrent, stator overcurrent, and overvoltage, electromagnetic torque ripple, and DC bus voltage during low-voltage ride-through (LVRT), among others. The present study confirms the viability of implementing positive and negative sequences of voltage separation control in the SGSC during both grid faults and steady state. This approach is expected to minimize the switching of SGSC control strategies and thereby reduce output power fluctuations. The Rotor Side Converter (RSC) and SGSC can perform coordinated control during faults, and the proposed method is able to improve low-voltage ride-through performance compared to existing methods, thereby preventing damage to the converter under multiple fault conditions. Full article

This paper introduces an enhanced approach for optimizing the flux-weakening performance of a non-salient permanent magnet synchronous machine (PMSM), by incorporating the maximum torque per voltage (MTPV) region into a conventional voltage magnitude feedback control strategy. The MTPV control strategy is initially optimized for steady-state performance by incorporating the effect of resistance, which plays a crucial role in small power motors. To maintain stability and good dynamics in the flux-weakening region, a current command feedback MTPV controller is utilized, as opposed to a voltage command feedback approach. Additionally, to address stability concerns in the MTPV region, a feedback type proportional-integral (PI) MTPV controller is designed and implemented. The stability in both the over-modulation and various flux-weakening regions is further enhanced using a voltage vector modifier (VVM). Therefore, the proposed feedback-based flux-weakening control enhances system steady-state performance, dynamic response, and stability across both linear and over modulation regions under various flux-weakening conditions, making it suitable for general-purpose applications. The effectiveness of the proposed method is validated through experimental results. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article