Search Results (857)

This study presents a comparative analysis of efficiency and harmonic generation in Triple Active Bridge (TAB) converters under two operating configurations: Case I, with one input source and two loads, and Case II, with two input sources and one load. Two modulation strategies, Single-Phase Shift (SPS) and Dual-Phase Shift (DPS), are evaluated through frequency-domain modeling and simulations performed in MATLAB/Simulink. The analysis is complemented by experimental validation on a laboratory prototype. The results show that DPS reduces harmonic amplitudes, decreases conduction losses, and improves output waveform quality, leading to higher efficiency compared to SPS. Harmonic current spectra and total harmonic distortion (THD) are analyzed to quantify the impact of each modulation method. The findings highlight that DPS is more suitable for applications requiring stable power transfer and improved efficiency, such as renewable energy systems, electric vehicles, and multi-source DC microgrids. Full article

This study proposes a hierarchical two-layer control framework aimed at advancing the sustainability of renewable-integrated microgrids. The framework combines droop-based primary control, PI-based voltage and current regulation, and a supervisory Model Predictive Control (MPC) layer to enhance dynamic power sharing and system stability in renewable-integrated microgrids. The proposed method addresses the limitations of conventional control techniques by coordinating real and reactive power flow through an adaptive droop formulation and refining voltage/current regulation with inner-loop PI controllers. A discrete-time MPC algorithm is introduced to optimize power setpoints under future disturbance forecasts, accounting for state-of-charge limits, DC-link voltage constraints, and renewable generation variability. The effectiveness of the proposed strategy is demonstrated on a small hybrid microgrid system that serve a small community of buildings with a solar PV, wind generation, and a battery storage system under variable load and environmental profiles. Initial uncontrolled scenarios reveal significant imbalances in resource coordination and voltage deviation. Upon applying the proposed control, active and reactive power are equitably shared among DG units, while voltage and frequency remain tightly regulated, even during abrupt load transitions. The proposed control approach enhances renewable energy integration, leading to reduced reliance on fossil-fuel-based resources. This contributes to environmental sustainability by lowering greenhouse gas emissions and supporting the transition to a cleaner energy future. Simulation results confirm the superiority of the proposed control strategy in maintaining grid stability, minimizing overcharging/overdischarging of batteries, and ensuring waveform quality. Full article

This article investigates the zonotope-based state estimation for boost converter system with Markov jump process. DC-DC boost converters are pivotal in modern power electronics, enabling renewable energy integration, electric vehicle charging, and microgrid operations by elevating low input voltages from sources like photovoltaics to stable high outputs. However, their nonlinear dynamics and sensitivity to uncertainties/disturbances degrade control precision, driving research into robust state estimation. To address these challenges, the boost converter is modeled as a Markov jump system to characterize stochastic switching, with time delays, disturbances, and noises integrated for a generalized discrete-time model. An adaptive event-triggered mechanism is adopted to administrate the data transmission to conserve communication resources. A zonotopic set-membership estimation design is proposed, which involves designing an observer for the augmented system to ensure $H_{\infty }$ performance and developing an algorithm to construct zonotopes that enclose all system states. Finally, numerical simulations are performed to verify the effectiveness of the proposed approach. Full article

The increasing deployment of stand-alone photovoltaic (PV) power supply systems is driven by their capability to convert solar irradiance into electrical energy. A typical application of such systems is solar-powered water pumping. However, since solar irradiance varies throughout the day, the maximum power output of PV panels may be lower than the load demand. A viable solution to this issue is the integration of hybrid energy storage systems (HESSs) combining batteries and supercapacitors (SCs). In this work, HESS charging and discharging control strategies were developed based on adaptive droop control, which regulates the power distribution between the SC and the battery and limits DC grid voltage deviations. In the developed method, the SC droop coefficient is adaptively adjusted in a stepwise manner depending on the SC state of charge (SoC), while the battery droop coefficient remains constant. The performance of the proposed strategies was evaluated through simulations, showing SC-battery internal loss minimization by up to 50% compared with the scenario without droop control when the SC is discharged first, and only then is the battery engaged. Step response of the converter was investigated experimentally, showing less than a 2 ms response time, and no undesired influence from the proposed control method was detected. Full article

Bus voltage regulation and accurate power sharing constitute two pivotal control objectives in DC microgrids. The conventional droop control method inherently suffers from steady-state voltage deviation. Centralized control introduces vulnerability to single-point failures, with significantly degraded stability under abnormal operating conditions. Distributed control strategies mitigate this vulnerability but require careful balancing between control effectiveness and communication costs. Therefore, this paper proposes a hybrid hierarchical control architecture integrating multiple control strategies to achieve near-zero steady-state deviation voltage regulation and precise power sharing in DC microgrids. Capitalizing on the complementary advantages of different control methods, an operation-condition-adaptive hierarchical control (OCAHC) strategy is proposed. The proposed method improves reliability over centralized control under communication failures, and achieves better performance than distributed control under normal conditions. With a fault-detection logic module, the OCAHC framework enables automatic switching to maintain high control performance across different operating scenarios. For the inherent trade-off between consensus algorithm performance and communication costs, a communication topology optimization model is established with communication cost as the objective, subject to constraints including communication intensity, algorithm convergence under both normal and N-1 conditions, and control performance requirements. An accelerated optimization approach employing node-degree computation and equivalent topology reduction is proposed to enhance computational efficiency. Finally, case studies on a DC microgrid with five DGs verify the effectiveness of the proposed model and methods. 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

Microgrids have emerged as a crucial solution for addressing environmental concerns, such as reducing greenhouse gas emissions and enhancing energy sustainability. By incorporating renewable energy sources like solar and wind, microgrids improve energy efficiency and offer a cleaner alternative to conventional power grids. Among various microgrid architectures, DC microgrids are gaining significant attention due to their higher efficiency, reduced reactive power losses, and direct compatibility with renewable energy sources and energy storage systems. However, DC microgrids face stability challenges, particularly due to the presence of constant power loads (CPLs), which exhibit negative incremental impedance characteristics. These loads can destabilize the system, leading to oscillations and performance degradation. This paper explores various control strategies designed to enhance the stability and dynamic response of DC microgrids, with a particular focus on interleaved boost converters (IBCs) interfaced with CPLs. Traditional control methods, including proportional–integral (PI) and sliding mode control (SMC), have shown limitations in handling dynamic variations and disturbances. To overcome these challenges, this paper proposes a novel RST-based control strategy for IBCs, offering improved stability, adaptability, and disturbance rejection. The efficacy of the RST controller is validated through extensive simulations tests, demonstrating competitive performance in maintaining DC bus voltage regulation and current distribution. Key performance indicators demonstrate competitive performance, including settling times below 40 ms for voltage transients, overshoot limited to ±2%, minimal voltage deviation from the reference, and precise current sharing between interleaved phases. The findings contribute to advancing the stability and efficiency of DC microgrids, facilitating their broader adoption in modern energy systems. Full article

This paper presents a DC microgrid architecture with autonomous decentralized control that exhibits high resilience against increasingly common threats, such as natural disasters and cyber-physical attacks, as well as its operational characteristics under normal circumstances. The proposed system achieves autonomous decentralized cooperative control by combining a battery-integrated DC baseline, in which multiple distributed small-scale batteries are directly connected to the grid baseline, with a weakly coupled grid architecture in which each power device is loosely coupled via the grid baseline. Unlike conventional approaches that assign grid formation, inertial support, and power balancing functions to DC/DC converters, the proposed approach delegates these fundamental grid roles to the distributed batteries. This configuration simplifies the control logic of the DC/DC converters, limiting their role to power exchange only. To evaluate system performance, a four-family DC microgrid model incorporating a typical Japanese home environment, including an EV charger, was constructed in MATLAB/Simulink R2025a and subjected to one-year simulations. The results showed that with approximately 5 kW of PV panels and a 20 kWh battery capacity per household, a stable power supply could be maintained throughout the year, with more than 50% of the total power consumption covered by solar energy. Furthermore, the predicted battery life was over 20 years, confirming the practicality and economic viability of the proposed residential microgrid design. Full article

From the perspectives of theoretical design and practical application, the existing fault diagnosis methods with the complex identification process owing to manual feature extraction and the insufficient feature extraction for time series data and weak fault signal is not suitable for AC/DC microgrids. Thus, this paper proposes a fault diagnosis method that integrates a convolutional neural network (CNN) with a long short-term memory (LSTM) network and attention mechanisms. The method employs a multi-scale convolution-based weight layer (Weight Layer 1) to extract features of faults from different dimensions, performing feature fusion to enrich the fault characteristics of the AC/DC microgrid. Additionally, a hybrid attention block-based weight layer (Weight Layer 2) is designed to enable the model to adaptively focus on the most significant features, thereby improving the extraction and utilization of critical information, which enhances both classification accuracy and model generalization. By cascading LSTM layers, the model effectively captures temporal dependencies within the features, allowing the model to extract critical information from the temporal evolution of electrical signals, thus enhancing both classification accuracy and robustness. Simulation results indicate that the proposed method achieves a classification accuracy of up to 99.5%, with fault identification accuracy for noisy signals under 10 dB noise interference reaching 92.5%, demonstrating strong noise immunity. Full article

Bipolar DC microgrids gain significant attention for their flexible structure, high power supply reliability, and strong compatibility with distributed power sources. However, inter-pole voltage imbalance undermines system operational stability. An isolated bipolar bidirectional three-port converter with voltage self-balancing capability is proposed in this paper, which can serve as the interface between the energy storage system and bipolar bus while achieving automatic voltage balance between poles. Unlike traditional bidirectional grid-connected voltage balancers (VBs), the proposed converter requires no additional voltage monitoring or complex control systems. The operating modes, soft-switching boundary conditions, and inter-pole voltage self-balancing mechanism are elaborated. A 1 kW experimental prototype has been built to validate the theoretical analysis of the proposed converter. Full article

This paper presents a novel partial-power DC-DC converter architecture specifically designed for Photovoltaic (PV) energy systems. Unlike traditional full-power converters, the proposed topology processes only a fraction of the total power, resulting in improved overall efficiency, reduced component stress, and lower system cost. The converter is integrated into a PV-based energy system and regulated by a dual-loop control strategy consisting of a Proportional-Integral (PI) voltage controller and an inner Sliding-Mode Controller (SMC) for current regulation. This control scheme ensures robust tracking performance under dynamic variations in irradiance, load, and reference voltage. The paper provides a comprehensive mathematical model and control formulation, emphasizing the robustness and fast transient response offered by SMC. Simulation results obtained in MATLAB-Simulink, along with real-time implementation on the OPAL-RT hardware-in-the-loop (HIL) platform, confirm the effectiveness of the proposed design. The system achieves stable voltage regulation with low ripple and accurate current tracking. Compared to conventional boost configurations, the proposed converter demonstrates superior performance, particularly under moderate voltage conversion conditions. The system achieves high efficiency levels, validated through both analytical estimation and real-time hardware-in-the-loop (HIL) implementation. Its high efficiency, scalability, and real-time control feasibility make it a promising solution for next-generation PV systems, battery interfacing, and DC-microgrid applications. Full article

The Honeycomb Distribution Network is a new distribution network architecture that utilizes the Smart Power-Exchange Station (SPES) to enable power interconnection and mutual assistance among multiple microgrids/distribution units, thereby supporting high-proportion integration of distributed renewable energy and promoting a sustainable energy transition. To promote the continuous and reliable operation of the Honeycomb Distribution Network, this paper proposes a Hierarchical Switching Control Strategy to address the issues of DC bus voltage (U_dc) fluctuation in the SPES of the Honeycomb Distribution Network, as well as the state of charge (SOC) and charging/discharging power limitation of the energy storage module (ESM). The strategy consists of the system decision-making layer and the converter control layer. The system decision-making layer selects the main converter through the importance degree of each distribution unit and determines the control strategy of each converter through the operation state of the ESM’s SOC. The converter control layer restricts the ESM’s input/output active power—this ensures the ESM’s SOC and input/output active power stay within the power boundary. Additionally, it combines the Flexible Virtual Inertia Adaptive (FVIA) control method to suppress U_dc fluctuations and improve the response speed of the ESM converter’s input/output active power. A simulation model built in MATLAB/Simulink is used to verify the proposed control strategy, and the results demonstrate that the strategy can not only effectively reduce U_dc deviation and make the ESM’s input/output power reach the stable value faster, but also effectively avoid the ESM entering the unstable operation area. Full article

In microgrids, battery chargers/dischargers are used to manage power flow between the battery and the DC bus and to regulate the DC bus voltage, ensuring safe operating conditions for sources and loads. These actions contribute to enhancing the sustainability of the microgrid by improving energy efficiency, extending battery life, and ensuring reliable operation. The classical converter adopted to implement the battery chargers/dischargers is the boost converter, which avoids high current harmonic injection into the battery because of its continuous input current. But due to the discontinuous output current, it introduces high current harmonics into the DC bus. This also occurs in Sepic, Zeta, or other DC/DC converters with discontinuous input or output currents. One exception is the Cuk converter, which has both continuous input and output currents. However, in the Cuk converter, the intermediate capacitor voltage is higher than the input and output voltages, thus imposing high stress on the semiconductors and requiring a costly capacitor with high energy storage. Therefore, this paper proposes the design of a battery charger/discharger based on a non-electrolytic capacitor boost converter. This topology provides continuous input and output currents, which reduces harmonic component injection, extends battery life, and increases operation efficiency. Moreover, it requires a lower intermediate capacitor voltage, thereby enhancing reliability. The design of this battery charger/discharger requires an adaptive sliding-mode controller to ensure global stability and accurate bus voltage regulation. A formal stability analysis and design equations are provided. The proposed solution is validated through detailed simulations, while the adaptive sliding-mode controller is specifically tested using a detailed software-in-the-loop approach. Full article

With the wide application of battery energy storage systems (BESSs) in DC microgrids, BESSs are facing increasingly severe cyber threats, among which, false data injection attacks (FDIAs) seriously undermine the accuracy of battery state estimation by tampering with sensor measurement data. To address this problem, this paper proposes an improved generative adversarial network (WGAN-GP)-based detection and defence method for FDIAs in battery energy storage systems. Firstly, a more perfect FDIA model is constructed based on the comprehensive consideration of the dual objectives of circumventing the bad data detection (BDD) system of microgrid and triggering the effective deviation of the system operating state quantity; subsequently, the WGAN-GP network architecture introducing the gradient penalty term is designed to achieve the efficient detection of the attack based on the anomalous scores output from the discriminator, and the generator reconstructs the tampered measurement data. Finally, the state prediction after repair is completed based on Gaussian process regression. The experimental results show that the proposed method achieves more than 92.9% detection accuracy in multiple attack modes, and the maximum reconstruction error is only 0.13547 V. The overall performance is significantly better than that of the traditional detection and restoration methods, and it provides an effective technical guarantee for the safe and stable operation of the battery energy storage system. Full article

This paper proposes a nonlinear control strategy for a microgrid, comprising a PV generator, wind turbine, battery, solid oxide fuel cell (SOFC), electrolyzer, and a three-phase four-leg voltage source inverter (VSI) with an LC filter. The microgrid is designed to supply unbalanced AC loads while maintaining high power quality. To address chattering and enhance control precision, a super-twisting algorithm (STA) is integrated, outperforming traditional PI, IP, and classical SMC methods. The four-leg VSI enables independent control of each phase using a dual-loop strategy (inner voltage, outer current loop). Stability is ensured through Lyapunov-based analysis. Scalar PWM is used for inverter switching. The battery, SOFC, and electrolyzer are controlled using integral backstepping, while the SOFC and electrolyzer also use Lyapunov-based voltage control. A hybrid integral backstepping–STA strategy enhances PV performance; the wind turbine is managed via integral backstepping for power tracking. The system achieves voltage and current THD below 0.40%. An energy management algorithm maintains power balance under variable generation and load conditions. Simulation results confirm the control scheme’s robustness, stability, and dynamic performance. Full article