Search Results (189)

Solar energy solutions have become increasingly popular worldwide due to the growing need for renewable energy. This article presents a photovoltaic (PV) system connected to a three-phase power grid, modeled under varying climatic conditions. It consists of two conversion stages, a DC-DC Boost converter and a DC-AC inverter. The former uses a variable-step P&O based on fuzzy logic control to maximize the power of the photovoltaic panels, allowing for greater tracking accuracy than traditional P&O techniques. Inverters with phase-locked loop technology improve the performance of grid-connected PV systems by using a conventional PI controller that has a faster response. Using Matlab/Simulink environments, the entire system and control techniques are evaluated and verified. The simulation results confirm the effectiveness and robustness of the proposed system. Full article

This scoping review analyzes the role of inertia in converter-dominated microgrids, with an emphasis on hybrid AC/DC architectures. Following the PRISMA-ScR methodology, 54 studies published between 2015 and 2025 were identified, screened, and synthesized. The review addresses two key aspects, inertia estimation methods and control strategies for emulating inertia via power converters, emphasizing the role of the interlinking converter (ILC) as a bidirectional interface for inertia support between the AC and DC subsystems. This work addresses several limitations of prior reviews: their narrow scope, often overlooking advanced data-driven approaches such as machine learning; the lack of systematic classifications, hindering a comprehensive overview of existing methods; and the absence of practical guidance on selecting appropriate techniques for specific conditions. The findings show that conventional estimation methods are insufficient for low-inertia grids, necessitating adaptive and data-driven approaches. Virtual inertia emulation strategies—such as Virtual Synchronous Machines, Virtual Synchronous Generators, Synchronverters, and ILC-based controls—offer strong potential to enhance frequency stability but remain challenged by scalability, adaptability, and robustness. The review highlights critical research gaps and future directions to guide the development of resilient hybrid microgrid control strategies. Full article

This paper presents a robust and computationally efficient fault classification framework for wind energy conversion systems (WECS), built upon a Robust Random Vector Functional Link Network (Robust-RVFLN) and validated through real-time simulations on a Real-Time Digital Simulator (RTDS). Unlike existing studies that depend on high-dimensional feature extraction or purely data-driven deep learning models, our approach leverages a compact set of five statistically significant and physically interpretable features derived from rotor torque, phase current, DC-link voltage, and dq-axis current components. This reduced feature set ensures both high discriminative power and low computational overhead, enabling effective deployment in resource-constrained edge devices and large-scale wind farms. A synthesized dataset representing seven representative fault scenarios—including converter, generator, gearbox, and grid faults—was employed to evaluate the model. Comparative analysis shows that the Robust-RVFLN consistently outperforms conventional classifiers (SVM, ELM) and deep models (CNN, LSTM), delivering accuracy rates of up to 99.85% for grid-side line-to-ground faults and 99.81% for generator faults. Beyond accuracy, evaluation metrics such as precision, recall, and F1-score further validate its robustness under transient operating conditions. By uniting interpretability, scalability, and real-time performance, the proposed framework addresses critical challenges in condition monitoring and predictive maintenance, offering a practical and transferable solution for next-generation renewable energy infrastructures. Full article

In this study, a novel comparison between single phase 7-Level Packed U—Cell (PUC) inverter and single phase 9-Level Cross Switches Cell (CSC) inverter with Model Predictive Controller (MPC) for solar grid-tied applications is presented. Our innovation introduces a unique approach by integrating PV solar panels in PUC and CSC inverters in their two DC links rather than just one which increases power density of the system. Another key benefit for the proposed models lies in their simplified design, offering improved power quality and reduced complexity relative to traditional configurations. Moreover, both models feature streamlined control architectures that eliminate the need for additional controllers such as PI controllers for grid reference current extraction. Furthermore, the implementation of Maximum Power Point Tracking (MPPT) technology directly optimizes power output from the PV panels, negating the necessity for a DC-DC booster converter during integration. To validate the proposed concept’s performance for both inverters, extensive simulations were conducted using MATLAB/Simulink, assessing both inverters under steady-state conditions as well as various disturbances to evaluate its robustness and dynamic response. Both inverters exhibit robustness against variations in grid voltage, phase shift, and irradiation. By comparing both inverters, results demonstrate that the CSC inverter exhibits superior performance due to its booster feature which relies on generating voltage level greater than the DC input source. This primary advantage makes CSC a booster inverter. Full article

To address the challenges posed by harmonic distortion and DC offset in the power grid, this paper proposes a novel Phase-Locked Loop (PLL) architecture tailored for single-phase grid-connected systems. The design integrates an Enhanced Adaptive Second-Order Generalized Integrator (EA-SOGI) with a Quasi-Proportional Resonant (QPR) controller. The proposed EA-SOGI extends the conventional SOGI by incorporating an all-pass filter and an additional integrator, which enhance the symmetry of the orthogonal signals and effectively suppress the estimation errors caused by DC offset. In addition, the conventional PI controller is replaced by a QPR controller, whose parameters are tuned using a hybrid Levy-AsyLnCPSO optimization algorithm to improve frequency locking performance and enhance system robustness under steady-state conditions. Simulation and experimental results demonstrate that the proposed PLL achieves a Total Harmonic Distortion (THD) as low as 2.8653% based on Fast Fourier Transform (FFT) analysis, indicating superior adaptability compared to conventional PLL structures and validating its effectiveness in DC offset suppression and harmonic mitigation. Full article

Microgrid systems play a vital role in managing distributed energy resources like solar, wind, batteries, and supercapacitors. However, maintaining stable AC/DC bus voltages and minimizing grid reliance under dynamic conditions is challenging. Traditional control methods such as Sliding Mode Controllers (SMCs) suffer from issues like chattering and slow convergence, reducing practical effectiveness. This paper proposes a hybrid AC/DC microgrid that operates in both grid-connected and islanded modes while ensuring voltage stability and efficient energy use. A Conditional-Based Super-Twisting Sliding Mode Controller (CBSTSMC) is employed to address the limitations of conventional SMCs. The CBSTSMC enhances system performance by reducing chattering, improving convergence speed, and offering better tracking and disturbance rejection. To further refine controller performance, an Improved Grey Wolf Optimization (IGWO) algorithm is used for gain tuning, resulting in enhanced system robustness and precision. An Energy Management System (EMS) is integrated to intelligently regulate power flow based on renewable generation and storage availability. The proposed system is tested in real time using a Texas Instruments Delfino C2000 microcontroller through a Controller-in-the-Loop (CIL) setup. The simulation and hardware results confirm the system’s ability to maintain stability and reliability under diverse operating scenarios, proving its suitability for future smart grid applications. Full article

The rapid evolution of modern power systems, driven by the large-scale integration of renewable energy sources and the emergence of smart grids, presents new challenges in maintaining grid stability, power quality, and control reliability. As critical interfacing elements, three-phase pulse width modulation (PWM) converters must now ensure resilient and efficient operation under increasingly adverse and dynamic grid conditions. This paper proposes an adaptive neural network-based virtual flux (VF) estimator for sensorless predictive direct power control (PDPC) of PWM converters under nonideal grid voltage conditions. The proposed estimator is realized using an adaptive linear neuron (ADALINE) configured as a quadrature signal generator, offering robustness against grid voltage disturbances such as voltage unbalance, DC offset and harmonic distortion. In parallel, a PDPC scheme based on the extended pq theory is developed to reject active-power oscillations and to maintain near-sinusoidal grid currents under unbalanced conditions. The resulting VF-based PDPC (VF-PDPC) strategy is validated via real-time simulations on the OPAL-RT platform. Comparative analysis confirms that the ADALINE-based estimator surpasses conventional VF estimation techniques. Moreover, the VF-PDPC achieves superior performance over conventional PDPC and extended pq theory-based PDPC strategies, both of which rely on physical voltage sensors, confirming its robustness and effectiveness under non-ideal grid conditions. Full article

In order to meet the needs of grid integration of various renewable energy sources and promote long-distance power transmission, a hybrid multi-infeed DC system architecture consisting of a line-commutated converter (LCC) and a modular multilevel converter (MMC) is constructed. Focusing on the issue of traditional differential protection refusing to operate under high-resistance grounding faults and failing under symmetrical faults, a dual-criteria protection mechanism is proposed in this paper. By integrating current differential and voltage criterion, the accurate identification of various types of AC line faults can be realized. A hybrid DC system simulation model was built on MATLAB, the sampled data was decoupled, and the differential quantity was calculated to test the dual-criteria protection mechanism. The simulation results show that the proposed protection mechanism can effectively identify various faults within the hybrid DC multi-feed system area and faults outside the area and has robustness to complex working conditions such as high-resistance grounding and three-phase short circuits, which improves the sensitivity, selectivity, and adaptability of the protection. This method is designed for AC line protection under the disturbance of multi-infeed DC systems. It is not directly applicable to pure DC microgrids. The concept can be extended to AC/DC hybrid microgrids by adding DC-side protection criteria and re-calibrating thresholds. Full article

The increasing complexity of Distributed Computing (DC) systems requires advanced failure-prediction models to enhance reliability and efficiency. This study proposes a comprehensive methodology for developing generic machine learning (ML) models capable of cross-layer and cross-platform failure-prediction without requiring platform-specific retraining. Using the Grid5000 failure dataset from the Failure Trace Archive (FTA), we explored Linear and Logistic Regression, Random Forest, and XGBoost to predict three critical metrics: Time Between Failures (TBF), Time to Return/Repair (TTR), and Failing Node Identification (FNI). Our approach involved extensive exploratory data analysis (EDA), statistical examination of failure patterns, and model evaluation across the cluster, site, and system levels. The results demonstrate that XGBoost consistently outperforms the other models, achieving near-perfect 100% accuracy for TBF and FNI, with robust generalisability across diverse DC environments. In addition, we introduce a hierarchical DC architecture that integrates these failure-prediction models. In the form of a use case, we also demonstrate how service providers can use these prediction models to balance service reliability and cost. Full article

Hybrid AC/DC microgrids (HADCMGs), which integrate renewable energy sources and battery storage systems, often face significant stability challenges due to their inherently low inertia and highly variable power inputs. To address these issues, this paper proposes a novel, robust composite controller based on backstepping fast terminal sliding mode control (BFTSMC). This controller is further enhanced with a virtual capacitor to emulate synthetic inertia and with a fractional power-based reaching law, which ensures smooth and finite-time convergence. Moreover, the proposed control strategy ensures the effective coordination of power sharing between AC and DC sub-grids through bidirectional converters, thereby maintaining system stability during rapid fluctuations in load or generation. To achieve optimal control performance under diverse and dynamic operating conditions, the controller gains are adaptively tuned using the marine predators algorithm (MPA), a nature-inspired metaheuristic optimization technique. Furthermore, the stability of the closed-loop system is rigorously established through control Lyapunov function analysis. Extensive simulation results conducted in the MATLAB/Simulink environment demonstrate that the proposed controller significantly outperforms conventional methods by eliminating steady-state error, reducing the settling time by up to 93.9%, and minimizing overshoot and undershoot. In addition, real-time performance is validated via processor-in-the-loop (PIL) testing, thereby confirming the controller’s practical feasibility and effectiveness in enhancing the resilience and efficiency of HADCMG operations. Full article

To meet the demands of deep grid integration of renewable energy and long-distance power transmission, this paper presents a hybrid multi-infeed DC system architecture that includes an AC power source (AC), a voltage source converter (VSC), and a modular multilevel converter (MMC). Addressing the limitations of traditional differential protection—such as insufficient sensitivity under high-resistance grounding and susceptibility to false operations under out-of-zone disturbances—this paper introduces an enhanced current differential criterion based on dynamic phasor analysis. By effectively decoupling DC bias and load current components and optimizing the calculation of action and braking quantities, the proposed method enables the rapid and accurate identification of typical faults, including high-resistance grounding, three-phase short circuits, and out-of-zone faults. A multi-scenario simulation platform is built using MATLAB to thoroughly validate the improved criterion. Simulation results demonstrate that the proposed method offers excellent sensitivity, selectivity, and resistance to false operations in multi-infeed complex systems. It achieves fast fault detection (~2.0 ms), strong sensitivity to high-resistance internal faults, and low false tripping under a variety of test scenarios, providing robust support for next-generation DC protection systems. Full article

The voltage source converters convert the DC to AC in order to interface distributed generation units with the utility grid, typically using an LCL filter to smooth the modulated wave. However, the LCL filter can introduce resonance, potentially cause instability, and necessitate the use of damping techniques, such as active damping, which utilizes feedback from the current control loop to suppress resonance. This paper presents a comprehensive performance assessment of four current-feedback-based active damping (AD) techniques—converter current feedback (CCF), CCF with capacitor current feedback (CCFAD), grid current feedback (GCF), and GCF with capacitor current feedback (GCFAD)—under a broad range of realistic grid disturbances and low switching frequency conditions. Unlike prior works that often analyze individual feedback strategies in isolation, this study highlights and compares their dynamic behavior, robustness, and total harmonic distortion (THD) in eight operational scenarios. The results reveal the severe instability of GCF in the absence of damping and the superior inherent damping property of CCF while demonstrating the comparable effectiveness of GCFAD. Moreover, a simplified yet robust design methodology for the LCL filter is proposed, enabling the filter to maintain stability and performance even under significant variations in grid impedance. Additionally, a sensitivity analysis of switching frequency variation is included. The findings offer valuable insights into selecting and implementing robust active damping methods for grid-connected converters operating at constrained switching frequencies. The effectiveness of the proposed methods has been validated through both MATLAB/Simulink simulations and hardware-in-the-loop (HIL) testing. Full article

As the scale of shipboard power systems expands, their vulnerability becomes increasingly prominent. Identifying vulnerable points in ship power grids is essential for enhancing system stability, optimizing overall performance, and ensuring safe navigation. To address this issue, this paper proposes an algorithm based on fuzzy local dimension and topology (FLDT). The algorithm distinguishes contributions from nodes at different radii and within the same radius to a central node using fuzzy sets, and then derives the final importance value of each node by combining the local dimension and topology. Experimental results on nine datasets demonstrate that the FLDT algorithm outperforms degree centrality (DC), closeness centrality (CC), local dimension (LD), fuzzy local dimension (FLD), local link similarity (LLS), and mixed degree decomposition (MDD) algorithms in three metrics: network efficiency (NE), largest connected component (LCC), and monotonicity. Furthermore, in a ship power grid experiment, when 40% of the most important nodes were removed, FLDT caused a network efficiency drop of 99.78% and reduced the LCC to 2.17%, significantly outperforming traditional methods. Additional experiments under topological perturbations—including edge addition, removal, and rewiring—also show that FLDT maintains superior performance, highlighting its robustness to structural changes. This indicates that the FLDT algorithm is more effective in identifying and evaluating vulnerable points and distinguishing nodes with varying levels of importance. Full article

This paper proposes a novel control strategy for a dual-stage grid-connected solar photovoltaic (PV) system designed to ensure reliable and efficient operation under unstable grid conditions. The strategy incorporates a Phase-Locked Loop (PLL)-based positive sequence estimator for accurate detection of grid voltage disturbances, including sags, swells, and fluctuations in solar irradiance. A dynamic DC-link voltage regulation mechanism is employed to minimize converter power losses and enhance the performance of the Voltage Source Converter (VSC) under weak grid scenarios. The control scheme maintains continuous maximum power point tracking (MPPT) and unity power factor (UPF) operation, thereby improving overall grid power quality. The proposed method is validated through comprehensive simulations and real-time hardware implementation using the OPAL-RT OP4510 platform. The results demonstrate compliance with IEEE Standard 519, confirming the effectiveness and robustness of the proposed strategy. Full article

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