Search Results (596)

Parallel grid-forming energy storage converters based on virtual synchronous generator (VSG) control are prone to active power oscillation and interphase circulating current under load disturbance, unit switching, and parameter mismatch conditions. To address these problems, this paper proposes a dual-layer damping control strategy that combines adaptive virtual damping in the power loop with capacitor current feedback damping in the current loop. First, the small-signal models of the LCL filter, VSG power loop, and parallel converter system are established, and the dominant oscillation modes are analyzed using eigenvalue and participation factor methods. Then, an adaptive damping coefficient is designed according to the active power deviation and frequency dynamic response to suppress low-frequency power oscillation, while a capacitor current feedback branch is introduced to reshape the LCL filter’s resonant poles and attenuate circulating current resonance. Compared with the conventional fixed-damping VSG control, the proposed method reduces active power overshoot and accelerates power redistribution under load step and unit switching conditions. In the traditional control case, the active power peaks of VSG1 and VSG2 reach approximately 30 kW and 40 kW, with an oscillation period of about 1.8 s, whereas the proposed strategy suppresses the oscillatory process and enables the output powers to rapidly reach the preset sharing ratio. In addition, the system frequency can recover to the rated value of 50 Hz without obvious steady-state deviation, and the high-frequency component of the grid-connected current and the interphase circulating current are significantly attenuated. MATLAB/Simulink simulation results verify that the proposed dual-layer damping strategy provides better power oscillation suppression, circulating current mitigation, and frequency dynamic performance than the conventional VSG control. Full article

The increasing integration of photovoltaic (PV) systems and nonlinear loads intensifies harmonic distortion and reactive power imbalance in modern power networks. Conventional shunt active power filters (SAPFs) often employ control strategies that perform poorly under uncertain and dynamic grid conditions. This paper develops a hybrid sliding mode control with disturbance observer (SMC+DOB) technique for a PV-integrated SAPF to achieve effective harmonic mitigation, reactive power compensation, and enhanced system robustness. The study models the PV-SAPF system in MATLAB/Simulink (R2025b), where the SMC ensures robust current tracking, while the DOB estimates and suppresses unknown disturbances in real-time. The controller’s performance is evaluated under varying nonlinear and reactive load conditions, as per IEEE 519-2014 standards. Simulation results show that the proposed SMC+DOB scheme reduces total harmonic distortion (THD) by 96.7%—from 31.45% to 1.05%—while maintaining DC-link voltage stability and unity power factor. The integrated control architecture enhances the dynamic performance of SAPF, providing superior harmonic suppression, fast transient recovery, and improved grid stability for PV-connected systems. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

This paper presents a case study demonstrating the power quality improvements in a 24 kV distribution system at a liquefied natural gas (LNG) industrial plant with variable speed drives (VSDs), the conventional capacitor bank, and a rooftop solar photovoltaic system. Solar photovoltaic (PV) inverters can supply harmonic currents to the grid, potentially affecting the system and causing maloperation of sensitive equipment in both the utility systems and neighboring industries connected to it. Therefore, the installation of shunt active power filters (APFs) in a 400 V system was proposed in this study. The installed locations were varied, and the corresponding power qualities were analyzed. The results were examined in terms of design and harmonic elimination. Simulations were conducted using the PSCAD/EMTDC software version 4.5. The power quality simulation and field measurement results after the APF installation were compared to demonstrate the effectiveness of the proposed solutions. The addition of APFs was found to improve the power quality. In addition to the mechanism analysis, the economic feasibility of the proposed approach was investigated. The costs of APF installation in various locations were analyzed. The results show that the proposed method can improve the power supply at a reasonable price. This work contributes to sustainable industrial energy systems by improving the reliability and power quality of photovoltaic-integrated electrical networks, thereby supporting higher penetration of renewable energy resources and stable low-carbon industrial operation. Full article

Taking a dual-mode grid-connected/off-grid storage inverter as the research subject, control models for both grid-connected and off-grid operation modes were established. For the grid-to-off-grid transition, an improved adaptive active frequency drift islanding detection algorithm was proposed, which employs a cubic power-based detection method when frequency deviation is small to reduce positive feedback speed, and a parabola-based detection method when frequency deviation is large to enhance positive feedback speed. Compared with the traditional active frequency drift islanding detection algorithm, the proposed method can ensure islanding detection speed while effectively reducing the current total harmonic distortion during grid-connected operation. Experiments conducted on a storage inverter prototype demonstrated stable operation in both grid-connected and off-grid modes. The results indicate that the proposed control strategy enables rapid identification of operating conditions and mode switching, significantly improving the stability and reliability of the inverter during transition, thus laying a foundation for the autonomous operation of dynamic microgrids. Full article

Photovoltaic (PV)-storage systems operating in weak grids are affected by high grid impedance, transient voltage disturbances, and measurement noise, which can degrade frequency regulation, increase converter current stress, and impose high-frequency current fluctuations on the battery. To address these issues, this paper proposes a multi-timescale transient-state sensing and signal-processing framework for grid-forming PV-hybrid storage systems. The proposed framework combines three coordinated functions. First, a frequency-domain HESS power-decoupling mechanism separates high-frequency transient power components and assigns them to the supercapacitor, while the battery mainly handles low-frequency energy variations. Second, a voltage-deviation-driven adaptive virtual inductance is introduced to increase the equivalent output impedance during voltage-sag events and reduce transient inrush current. Third, a noise-resilient frequency sensing strategy based on a filtered frequency derivative and a dead-band for false-trigger suppression is developed to reduce noise-induced false triggering in adaptive inertia and damping control. Comparative simulations indicate that under the tested weak-grid conditions, the proposed method reduces the transient inrush-current peak by 53.2%, decreases the maximum dynamic frequency deviation by approximately 75%, and improves the active-power regulation speed by more than 50%. These results indicate that the proposed sensing-oriented framework can improve transient response while reducing converter and battery current stress in PV-storage systems connected to high-impedance grids. Full article

Virtual synchronous generators (VSGs) are prone to transient power angle instability and short-circuit current overshoot under symmetrical short-circuit grid faults. To address the limitation that existing transient control strategies fail to simultaneously guarantee power angle stability and fault current limiting, a coordinated control strategy combining dynamic active power reference regulation and adaptive virtual impedance is designed. Specifically, the active power reference is dynamically adjusted in accordance with the voltage sag magnitude at the point of common coupling (PCC), which effectively narrows the acceleration area of the virtual rotor and maintains the transient power angle near its rated value to prevent the risk of system loss of synchronism. On this basis, an adaptive virtual impedance control scheme is designed to accurately calculate and implement the optimal current-limiting impedance on demand, confining the steady-state fault current within the allowable threshold. Finally, the effectiveness of the designed strategy is verified on the Matlab/Simulink simulation platform. Simulation results demonstrate that the designed strategy achieves the coordination between transient power angle stability and fault current limiting, thus improving the operational stability of the VSG grid-connected system under symmetrical short-circuit grid faults. Full article

The direct-quadrature (dq) axis control method is a widely employed approach for off-grid and grid-connected inverters in solar photovoltaic (PV) systems that can regulate active and reactive power control. Conventional fixed-gain dq-axis PI controllers may exhibit degraded transient performance and reduced harmonic suppression capability under highly dynamic operating conditions. This article proposes an innovative control scheme of an inverter-based islanded microgrid consisting of PV generation and battery energy storage systems (BESS) that can deliver stable power sharing and robust voltage regulation even under highly dynamic operating conditions. An improved inverter control method based on an artificial neural network-based proportional integral (ANN-PI) controller is investigated to accurately control the dq-axis approach for the DC-link and voltage control loops. The suggested system was validated under MATLAB/Simulink to prove the effectiveness of the proposed controller. The achieved results indicate that the ANN-PI controller presents a high convergence speed and low overshoot with a low total harmonic distortion (THD) index of 3.9% under resistive and inductive loads, thus meeting the IEEE power quality standards. Full article

With the high penetration of distributed photovoltaic and storage systems, active distribution grids are prone to experiencing “active power surplus and reactive power shortage” during the evening peak, leading to voltage sags at the network end. Although electric vehicle (EV) grid-connected inverters possess four-quadrant reactive power regulation capabilities without causing the additional chemical cyclic aging of the battery cells, existing dispatch systems often treat them as unconditional response resources, overlooking users’ actual willingness to cede control and the associated strategic interactions. To address this, this paper proposes a “grid-load” coordinated reactive power optimization strategy that accounts for EV users’ willingness to respond: a Logit model incorporating price incentives, initial energy consumption, and parking duration is constructed based on discrete choice theory. By combining a truncated normal distribution with the Monte Carlo method to eliminate micro-sampling errors, a model of the expected reactive power capacity of charging stations under dynamic incentives is established; considering the physical constraints of SVCs and EVs, a scalarized single-objective optimization model is constructed with grid loss-equivalent costs, ancillary service costs, and voltage deviation as objectives, and solved using an improved particle swarm optimization algorithm with linearly decreasing weights. Simulations on a modified 33-node IEEE system incorporating storage indicate that this strategy can assign optimal compensation prices to each node based on the spatial value of reactive power. Compared to traditional single-voltage regulation and fixed subsidies, it not only stabilizes the grid-wide voltage within a safe range but also avoids overcompensation, achieving global optimization of both power quality and economic efficiency. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

The increasingly prominent “double-high” characteristics (high penetration of renewable energy and high proportion of power electronic devices) in modern power systems pose severe challenges to secure and stable operation, especially due to wideband oscillations induced by grid-connected inverters. In view of the fact that existing impedance modeling for grid-forming control often neglects the decoupling effect of the LC filter capacitor and the dynamics of inner voltage/current loops, leading to inaccurate characterization of mid-to-high frequency impedance, this paper aims to establish more accurate impedance models for grid-connected inverters and to develop effective oscillation mitigation methods accordingly. First, the harmonic linearization method is adopted to derive refined positive- and negative-sequence impedance analytical models for NPC inverters under both grid-following and grid-forming control. Second, simulation-based frequency scanning is conducted to validate the accuracy of the proposed models, and the differences in system resonance characteristics under the two control modes are comparatively analyzed. Finally, oscillation suppression strategies based on active damping and virtual impedance are, respectively, designed. The results show that the proposed models can accurately characterize mid-to-high frequency impedance, reveal the distinct resonance mechanisms of different control modes, and the proposed suppression strategies can effectively attenuate wideband oscillations. These findings provide theoretical foundations and practical technical pathways for stability analysis and optimization design of inverter-grid systems in high-renewable-penetration scenarios. Full article

When a fault occurs in the power grid to which the Virtual Synchronous Generator (VSG) is connected, it leads to overcurrent phenomena, which threatens the safety of the inverter and easily results in device damage. Although existing direct current limiting unit (CLU) control strategies can restrict the fault current, the input active power command far exceeds the power output, causing the virtual rotor to continuously accelerate. This leads to power angle divergence and a subsequent loss of synchronization. To address the conflict between direct current-limiting control and system transient stability, this paper proposes a control strategy based on the Particle Swarm Optimization (PSO) algorithm to modify the active power command, building upon existing direct current-limiting VSG control. During grid faults, the output current is constrained to its maximum value, leading to a reduction in the system’s output power. By leveraging the PSO algorithm, the proposed strategy decreases the active power command to minimize the power mismatch between the command and the output. This maximizes the system’s transient stability by minimizing the rotor acceleration torque and effectively suppressing excessive power angle deviation. Meanwhile, the active power command reduction is introduced as a penalty term to maximize the active power output capability during the fault period. Simulation results demonstrate that, compared to VSG with only direct current-limiting control, the proposed strategy significantly enhances the transient stability and transmission efficiency of the VSG under long-term fault conditions across various grid voltage sag scenarios. Furthermore, it ensures a seamless transition from the fault state to normal operation during short-term faults. Full article

The integration of grid-forming energy storage systems (GFM-ESSs) provides essential support for the stable operation of grid-connected converters in renewable energy systems. However, GFM-ESSs may exhibit low-frequency oscillations in response to grid state variations, posing a threat to power system stability. To address this challenge, this paper proposes a fast continuous optimization method for the active power–frequency control loop of multi-VSG-based GFM-ESSs. First, a parameter coupling model for multiple VSGs is established, and an internal parameter decoupling control strategy is proposed. Subsequently, an iterative optimization model based on a gradient-based master–slave game is developed, in which the minimization of converter frequency deviation serves as the leader’s objective, while the minimization of system frequency deviation acts as the follower’s objective. Frequency fluctuations are further mitigated through tracking differentiator-based active power compensation. The effectiveness of the proposed method is validated through simulation with six GFM-ESS units integrated into a modified IEEE 33-node system featuring six renewable energy stations. Simulation results demonstrate that the proposed approach significantly suppresses frequency fluctuations while also reducing the response time and the rate of frequency change under grid disturbance conditions. Full article

Grid-connected photovoltaic (PV) systems can serve not only as sources of active power but also as active power conditioners for improving power quality. This paper proposes an integrated control strategy for a single-phase grid-connected reduced-switch-count T-type inverter that simultaneously performs maximum power point tracking (MPPT) without a DC-DC conversion stage, compensates for nonlinear load harmonics, and minimises switching losses through a tailored multi-carrier pulse-width modulation (PWM) algorithm. A novel reference current derivation method based on a single-phase dq transformation framework unifies MPPT and active power filtering within a single control loop. The proposed system was validated through MATLAB/Simulink 2025b simulations for a 3500 W PV array supplying a nonlinear RL load with a full-bridge diode rectifier exhibiting a load current total harmonic distortion (THD) of approximately 46%. Simulation results demonstrate an MPPT efficiency of 99.8% at full irradiance (1000 W/m²), an overall system efficiency above 97%, and a grid current THD below 4% across the full irradiance operating range (0–1000 W/m²). Dynamic performance under step irradiance changes was also evaluated: the DC bus voltage deviation remains within 5 V for P&O step sizes between 0.00005 V and 0.0002 V, and the grid current THD recovers to below 5% within 2–6 grid cycles following each irradiance transition. Full article

The increased penetration of renewable energy sources (RES) in the electrical grid has necessitated the concept of a Virtual Synchronous Generator (VSG) control which is used to make grid-connected power electronic converters behave as synchronous generators. While VSG controls are suitable for supporting the inertia of a microgrid, their use leads to grid instability in the event of a disturbance. This research addresses this limitation by integrating a fully connected Feedforward Neural Network (FCNN) into a VSG control to dynamically adjust the damping coefficient and inertia constant in real time. This approach could enhance system stability by reducing frequency and active power oscillations during grid disturbances, particularly during partial load rejection. To evaluate the effectiveness of the proposed method, a supervised learning-based FCNN was trained on VSG damping behavior under various grid disturbances. The trained model was then implemented in a simulation environment to regulate the VSG parameters dynamically. Simulation results show the neural network-based approach reduces high overshoots at the point of disturbance in active power and frequency oscillations; however, the VSG signal settles faster after the grid disturbance. These findings highlight the potential of machine learning in enhancing the stability of VSG-based microgrids, offering a computationally efficient solution for improving transient response and power-sharing performance. Full article