Search Results (1,283)

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

Under the “dual carbon” goals and rapid clean energy development, power grids face challenges including rapid load growth, uneven power flow distribution, and limited transmission capacity. This paper proposes a novel FACTS device with fault tolerance and switchable topology that maintains power flow control over multiple lines during N-1 faults, enhancing grid safety and economy. The paper establishes a steady-state mathematical model based on additional virtual nodes and provides power flow calculation methods to accurately reflect the device’s control characteristics. An entropy-weighted TOPSIS method was employed to establish a quantitative evaluation system for assessing the grid performance improvement after FACTS device integration. To address interaction issues among multiple flexible devices, an optimization planning model considering th3e coordinated effects of UPFC and VSC-HVDC was constructed. Multi-objective particle swarm optimization obtained Pareto solution sets, combined with the evaluation system, to determine the optimal configuration schemes. Considering wind power uncertainty and fault risks, we propose a system-level coordinated operation strategy. This strategy constructs probabilistic risk indicators and introduces topology switching control constraints. Using particle swarm optimization, it achieves a balance between safety and economic objectives. Simulation results in the Jiangsu power grid scenarios demonstrated significant advantages in enhancing the transmission capacity, optimizing the power flow distribution, and ensuring system security. Full article

Island electrical networks often face stability and resilience issues due to their weakly meshed structure, which lowers system inertia and compromises supply continuity. This challenge is further intensified by the increasing integration of renewable energy sources, promoted by decarbonization goals, whose intermittent and variable nature complicates grid stability management. To address this, Red Eléctrica de España—the transmission system operator of Spain—has planned several improvements in the Canary Islands, including the installation of new wind farms and a second transmission circuit on the island of La Palma. This new infrastructure will complement the existing one and ensure system stability in the event of N-1 contingencies. This article evaluates the stability of the island’s electrical network through dynamic simulations conducted in PSS^®E, analyzing four distinct fault scenarios across three different grid configurations (current, short-term upgrade and long-term upgrade with wind integration). Generator models are based on standard dynamic parameters (WECC) and calibrated load factors using real data from the day of peak demand in 2021. Results confirm that the planned developments ensure stable system operation under severe contingencies, while the integration of wind power leads to a 33% reduction in diesel generation, contributing to improved environmental and operational performance. 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 large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

In transient stability assessment (TSA) of power systems, the extreme scarcity of unstable scenario samples often leads to misjudgments of fault risks by assessment models, and this issue is particularly pronounced in new-type power systems with high penetration of renewable energy sources. To address this, this paper proposes a physics-constrained diffusion-based scenario expansion method. It constructs a hierarchical conditional diffusion framework embedded with transient differential equations, combines a spatiotemporal decoupling analysis mechanism to capture grid topological and temporal features, and introduces a transient energy function as a stability boundary constraint to ensure the physical rationality of generated scenarios. Verification on the modified IEEE-39 bus system with a high proportion of new energy sources shows that the proposed method achieves an unstable scenario recognition rate of 98.77%, which is 3.92 and 2.65 percentage points higher than that of the Synthetic Minority Oversampling Technique (SMOTE, 94.85%) and Generative Adversarial Networks (GANs, 96.12%) respectively. The geometric mean achieves 99.33%, significantly enhancing the accuracy and reliability of TSA, and providing sufficient technical support for identifying the dynamic security boundaries of power systems. Full article

To improve the ability of line-commutated converters (LCCs) to resist commutation failure (CF) when a fault occurs on the AC side, a novel sub-module-based LCC topology actively resistant to CF is proposed in this paper. The control strategy and the parameters of the proposed sub-module are elaborately designed. The proposed LCC topology can actively resist CF by providing an auxiliary commutation voltage to the AC side, and the sub-module is conducive to the rapid recovery of the thyristor’s forward blocking ability. Additionally, the initial capacitor voltage of the sub-module is designed optimally based on the commutation mechanism. The proposed LCC system can effectively improve the ability to resist CF by increasing the commutation margin of the LCC system. Furthermore, the capacitors are charged and discharged during fault time, so the capacitor voltages do not drop too low and, thus, are better at resisting CF. Matlab/Simulink simulation results verify that the proposed LCC quickens the commutation process, promotes commutation performance, and enhances the immunity of LCCs to CF. Full article

Grid-forming (GFM) converters have been critical in DER-dominant power systems, ensuring stability, but their performance is highly sensitive to grid conditions such as system strength. Testing GFM converters under a wide range of grid strengths (from strong high-inertia systems to very weak grids) and fault scenarios is challenging, as traditional test facilities and static grid simulators have limitations. To address this problem, this paper proposes a converter-based power line emulator that provides a flexible, programmable grid environment for GFM converter testing. The emulator uses power electronic converters to mimic transmission line characteristics, allowing for the adjustment of effective grid strength (e.g., short-circuit ratio changes). The proposed approach is validated through detailed PSCAD simulations, demonstrating its ability to provide scalable weak-grid emulation and comprehensive validation of GFM converter control strategies and stability under various grid conditions. This research highlights that the converter-based emulator offers enhanced flexibility and cost-effectiveness over traditional testing setups, making it an effective tool for GFM converter performance test. Full article

Looking toward the future, governments around the world have started to change their energy mix due to climate change. The new energy mix will consist mainly of Inverter-Based Resources (IBRs), such as wind and solar power. This transition from a synchronous to a non-synchronous grid introduces new challenges in stability, resilience, and synchronization, necessitating advanced control strategies. Among these, Grid-Forming (GFM) control techniques have emerged as an effective solution for ensuring stable operations in microgrids and large-scale power systems with high IBRs integration. This paper presents a systematic review of GFM control techniques, focusing on their principles and applications. Using the PRISMA 2020 methodology, 75 studies published between 2015 and 2025 were synthesized to evaluate the characteristics of GFM control strategies. The review organizes GFM strategies, evaluates their performance under varying operational scenarios, and emphasizes persistent challenges like grid stability, inertia emulation, and fault ride-through capabilities. Furthermore, this study examines real-world implementations of GFM technology in modern power grids. Notable projects include the UK’s National Grid Pathfinder Program, which integrates GFM inverters to enhance stability, and Australia’s Hornsdale Power Reserve, where battery energy storage with GFM capabilities supports grid frequency regulation. Full article

The continuous, effective operation of grid-connected photovoltaic (GCPV) systems depends on dependable inverter failure detection. Early, precise fault diagnosis improves general system dependability, lowers maintenance costs, and saves downtime. Although computing efficiency remains a difficulty, particularly in resource-limited contexts, machine learning-based fault detection presents interesting prospects in accuracy and responsiveness. By streamlining data complexity and allowing faster and more effective fault diagnosis, dimensionality reduction methods play vital role. Using dimensionality reduction and ML techniques, this work explores inverter fault detection in GCPV systems. Photovoltaic inverter operational data was normalized and preprocessed. In the next step, dimensionality reduction using Principal Component Analysis (PCA) and autoencoder-based feature extraction were explored. For ML training four classifiers which include Random Forest (RF), logistic regression (LR), decision tree (DT), and K-Nearest Neighbors (KNN) were used. Trained on the whole standardized dataset, the RF model routinely produced the greatest accuracy of 99.87%, so efficiently capturing complicated feature interactions but requiring large processing resources and time of 36.47sec. LR model showed reduction in accuracy, but very fast training time compared to other models. Further, PCA greatly lowered computing demands, especially improving inference speed for LR and KNN. High accuracy of 99.23% across all models was maintained by autoencoder-derived features. Full article

Split-shaft wind turbines decouple the turbine’s shaft from the generator’s shaft, enabling several modifications in the drivetrain. One of the significant achievements of a split-shaft drivetrain is the reduction in size of the excitation circuit. The grid-side converter is eliminated, and the rotor-side converter can safely reduce its size to a fraction of a full-size excitation. Therefore, this low-power-rated converter operates at low voltage and handles regular operations well. However, fault conditions may expose weaknesses in the converter and push it to its limits. This paper investigates the effects of the reduced-size rotor-side converter on the voltage ride-through capabilities required from all wind turbines. Four different protection circuits, including the active crowbar, active crowbar along a resistor–inductor circuit (C-RL), series dynamic resistor (SDR), and new-bridge fault current limiter (NBFCL), are employed, and their effects are investigated and compared. Wind turbine controllers are also utilized to reduce the impact of faults on the power electronic converters. One effective method is to store excess energy in the generator’s rotor. The proposed low-voltage ride-through strategies are simulated in MATLAB Simulink (2022b) to validate the results and demonstrate their effectiveness and functionality. Full article

The proliferation of power electronics in renewable-integrated grids exacerbates the challenges of nonlinearity and multivariable coupling. While the modal series method (MSM) offers theoretical foundations, it fails to provide tools to systematically quantify dynamic interactions in these complex systems. This study proposes a unified nonlinear modal analysis framework integrating second-order analytical solutions with novel nonlinear indices. Validated across diverse systems (DC microgrids and grid-connected PV), the framework yields significant findings: (1) second-order solutions outperform linearization in capturing critical oscillation/damping distortions under realistic disturbances, essential for fault analysis; (2) nonlinear effects induce modal dominance inversion and generate governing composite modes; (3) key interaction mechanisms are quantified, revealing distinct voltage regulation pathways in DC microgrids and multi-path dynamics driving DC voltage fluctuations. This approach provides a systematic foundation for dynamic characteristic assessment and directly informs control design for power electronics-dominated grids. Full article

The diagnosis of open-circuit (OC) faults in power switches is the premise for implementing fault-tolerant control, a critical aspect in ensuring the reliable operation of three-level hybrid active neutral-point-clamped (HANPC) converters in flexible distribution networks. However, existing fault diagnosis methods do not clearly reveal the relationship between the switching-state sequence state and the modulation voltage before and after the fault, which limits their applicability in grid-tied HANPC converters. In this article, a generalized voltage residuals model, taken as the primary diagnostic variable, is proposed for switch OC fault diagnosis in HANPC converters, and the physical meaning is established by introducing the metric of “the variation of the pulse equivalent area”. To distinguish between faulty switches with similar fault characteristics, the neutral current path is reconfigured with a set of rearranged gate sequences. Meanwhile, the auxiliary diagnostic variable, named the current polarity state variable, is developed by means of a sliding window counting algorithm. Additionally, as a case study, a diagnostic criterion for the single-switch fault of HANPC converters is designed by using proposed diagnostic variables. Experimental results are presented to verify the effectiveness of the proposed fault diagnosis method, which achieves accurate faulty switch identification in all tested scenarios within 25 ms. 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

With the advancement in the “dual carbon” strategy and the integration of high proportions of renewable energy sources, AC/DC ultra-high-power grids are facing new security challenges such as commutation failure and multi-infeed coupling effects. Fault diagnosis, as an important tool for assisting power grid dispatching, is essential for maintaining the grid’s long-term stable operation. Traditional fault diagnosis methods encounter challenges such as limited samples and data quality issues under complex operating conditions. To overcome these problems, this study proposes a fault sample data enhancement method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Firstly, a simulation model of the AC/DC hybrid system is constructed to obtain the original fault sample data. Then, through the adoption of the Wasserstein distance measure and the gradient penalty strategy, an improved WGAN-GP architecture suitable for feature learning of the AC/DC hybrid system is designed. Finally, by comparing the fault diagnosis performance of different data models, the proposed method achieves up to 100% accuracy on certain fault types and improves the average accuracy by 6.3% compared to SMOTE and vanilla GAN, particularly under limited-sample conditions. These results confirm that the proposed approach can effectively extract fault characteristics from complex fault data. Full article