Bus Voltage Research Articles

Optimization design of dead time in the four-switch buck-boost LLC resonant converter

Purpose The purpose of this study is to solve the problem of longer dead-time in the rear bridge leg switches and lower efficiency in the Four-Switch Buck-Boost LLC Resonant Converter. Design/methodology/approach The paper adopts time-domain analysis to derive the time-domain expression for optimal dead time, analyzing the conditions for achieving soft switching of the transistors. It further explores the relationship between the dead time of the bridge arm switching transistors and the input/output of the converter under different operating conditions. Specifically, the dead time of the upper bridge arm transistors increases with the converter input voltage and decreases with the output current. In contrast, the dead time of the lower bridge arm transistors is independent of the converter output current and decreases with increasing converter input voltage. Findings By simulating and constructing a 500 W experimental prototype, experimental results indicate that designing the dead time of the switch according to the optimal dead time proposed in this paper significantly improves efficiency when the converter operates from heavy load to full load. When the transformer takes minimum input, maximum input and intermediate bus voltage inputs respectively, its peak efficiency is increased by 0.6%, 1.7% and 1.1%, respectively, compared to the traditional four-switch Buck–Boost LLC resonant converter. Originality/value Experimental validation confirms the correctness of the optimal dead time design and analyzes the impact of different operating conditions of the converter on the dead time. This is of significant importance for the rational design of switch dead times and the enhancement of converter efficiency.

Optimizing power sharing accuracy in low voltage DC microgrids considering mismatched line resistances

The main difficulties facing the operation of parallel converters in DC microgrids (DCMGs) are load sharing, circulation current, and bus voltage regulation. A droop controller is commonly used to control current sharing among parallel DC–DC converters due to its simplicity. However, the values of droop parameters impact both bus voltage regulation and the error in current sharing among converters. This study introduces an adaptive and straightforward droop control mechanism designed to estimate droop parameters. The algorithm aims to enhance both bus voltage regulation and load sharing performance within DCMGs. Additionally, to mitigate bus voltage deviation in DCMGs, the second loop shifts the droop lines to maintain voltage regulation at rated values. The proposed method has been compared with the conventional droop controller under variable input voltage and load resistance conditions. Simulation results demonstrate that the presented method outperforms the traditional control approach, achieving superior performance.

Distributed control of DC microgrids: A relaxed upper bound for constant power loads

Motivated by the increasing interest in DC microgrids, we study the distributed secondary control problem of DC microgrids which aims to simultaneously guarantee load current sharing and DC bus voltage restoration. In particular, we analyze in-depth the effects of nonlinear constant power loads (CPLs), and successfully establish an upper bound for CPLs to guarantee the stability of DC microgrids. This established bound is less conservative than existing counterparts, allowing the connected power of CPLs to be much larger than that of linear resistive loads. Moreover, this bound explicitly reveals the connections between CPLs and droop gains, i.e., the upper bound of CPLs can be increased by decreasing droop gains, and vice versa. Three case studies are provided to verify the established results.

Model Order Reduction and Stability Enhancement Control for AC/DC Converters Through State Feedback Method

In the DC distribution networks, DC bus voltage is maintained by the grid-connected converter; the controllability and reliability of the grid-connected converter significantly affect the bus voltage characteristic. To address the problem of limited stability and frequent oscillations, this paper proposes a state feedback control method for the AC/DC converter. Conventional AC/DC converter adopts the voltage-current double-closed-loop control structure with the proportional-integral (PI) controllers, which is the equivalent of the typical type II control system, but the typical type II control system cannot fully settle the stability and immunity problems. In contrast, the state feedback control strategy not only achieves the control objectives of the traditional double-closed-loop control but also reduces the AC/DC converter system model to a typical Type I system, which improves stability and thus enhances the oscillation suppression capability of the bus voltage. By selecting multiple state variables and designing the converter pole configuration range, the proposed single-loop state feedback control method manages to optimize both the dynamic and steady-state performances of the grid-connected AC/DC converter. Finally, the effectiveness of the proposed single-loop state feedback control strategy is verified through MATLAB (2018b)/Simulink software simulation and experiments on a DC distribution network platform.

A cooperative control strategy for balancing SoC and power sharing in multiple energy storage unit within DC microgrids

AbstractThis paper proposes a distributed cooperative control scheme for multiple energy storage unit (ESU) in DC microgrids to achieve the control objectives of SoC balancing, power sharing, and bus voltage recovery. In the primary control part, the proposed scheme constructs a control function between the SoC values of each ESU and the droop coefficients to dynamically adjust the droop coefficients. Through a communication network, information is exchanged with neighbouring ESUs to achieve SoC convergence. In the secondary control part, by exchanging power information with neighbouring ESUs, precise power distribution is achieved. Additionally, the proposed scheme maintains bus voltage stability. Finally, a DC microgrid simulation model and experimental platform were developed, demonstrating the feasibility and plug‐and‐play capability of the proposed control strategy through both simulation and experimental case tests.

Evaluating Adaptive Droop Control for Steady-State Power Balancing in DC Microgrids Using Controller Hardware-in-the-Loop

DC microgrids (DCMGs) have been gaining attention due to their advantages over AC microgrids. The most commonly used control technique for DCMGs is droop control. Despite its benefits, droop control has drawbacks, such as power mismatch and deviations in DC bus voltage, often caused by differences in line resistance among grid-forming power electronics converters. To address these issues, the article proposes an adaptive droop control technique to correct steady-state power imbalances between grid-forming units in the DCMG. Additionally, a hierarchical voltage level is introduced to regulate the DC bus voltage. The analyzed DCMG includes two energy storage units, electronic loads, and a renewable energy source, each with its respective power electronic converter. The proposed technique uses real-time output power measurements from the energy storage system to calculate line resistance differences, incorporating these into the adaptive droop calculation. Several operating conditions are tested using a controller hardware-in-the-loop. The results validate the proposed technique and design guidelines.

Neural network controller for hybrid energy management system applied to electric vehicles

Hybrid energy storage systems based on batteries and supercapacitors can mitigate the aging of electric vehicle batteries aging by avoiding high currents and rapid discharge cycles. This system requires energy management systems that efficiently split the power during real driving cycles. This paper outlines a design methodology for creating a Multilayer Perceptron neural controller that governs the power distribution between the storage system components. In parallel, a Proportional-Integral-Derivative controller was implemented to ensure voltage regulation in the primary DC bus, ensuring stable operation of the entire system and providing flexibility to the neural design. The controller was tuned using particle swarm optimization, hippopotamus optimization, and differential evolution algorithms, designed to minimize the battery root-mean-square current in a comprehensive vehicle simulation. The training was structured into layers to approach the physical problem and controller optimization facets. The controller’s primary goal is to minimize the battery strain, mitigating stress events and prolonging its lifespan. The energy management neural controller was designed using a simple drive cycle and later validated with a realistic cycle. The findings demonstrate the feasibility of achieving a significant reduction in current, both in peak value and on average, especially when compared to basic energy management strategies. The process uncovered a strategy that prioritizes the use of the supercapacitor in power balance, with this effect being more pronounced during critical load events. The main bus voltage remained stable, with no deviations exceeding 5%, owing to the voltage regulator’s stability. Additionally, the particle swarm optimization and the hippopotamus optimization techniques exhibited notable performance compared to the differential evolution algorithm for this problem. Subsequently, the design was evaluated in a more complex cycle, revealing a slight decrease in average battery current performance. However, it still maintained a significant reduction in peak battery current compared to traditional controllers. Nevertheless, this methodology demonstrated power management optimization efficacy and can be extended to more complex scenarios.

Modelling of Harris Hawks optimization with deep learning-assisted microgrid energy management approach

Microgrid (MG) is a potential decentralized energy distribution and generation technology that is resilient, reliable, and efficient. This small-scale power system is reliable and resilient since it connects to the grid or runs independently. Renewable energy is difficult to integrate into MG due to variable load and unreliable electricity. MG operation relies on an energy management system (EMS) to balance electricity demand and supply, reduce operational costs, and maximize renewable energy use. Intelligent control systems, optimization methods, and machine learning algorithms were used for MG EMS. The Harris Hawks optimization with deep learning-assisted microgrid energy management (HHODL-MGEM) technique is developed in this work. HHODL-MGEM comprises two main stages. In the first step, the HHODL-MGEM approach uses the Harris Hawks optimization or HHO algorithm to meet load power demands at a low cost while maintaining DC bus voltage and protecting the battery from overcharging and depletion. In the second step, long short-term memory (LSTM) networks can predict power costs. The HHODL-MGEM approach is evaluated using multiple methods. The experimental results showed that HHODL-MGEM outperforms other methods.

Network-aware electric vehicle charging/discharging scheduling for grid load management in a hierarchical framework

The increasing adoption of electric vehicles (EVs) poses significant challenges for power system operations, requiring scalable coordination to mitigate their negative impacts and leverage their potential to enhance grid conditions. This paper introduces a scalable, three-layer hierarchical framework for optimal EV charge and discharge scheduling (EVCDS) that coordinates key agents: EVs, EV aggregators (EVAs), and the distribution network operator (DNO). The optimization problem is developed as an exchange problem and solved using the alternating direction method of multipliers (ADMM) in a decentralized approach. The proposed EVCDS addresses economic factors by minimizing battery degradation costs at the EV level and charging costs at the EVA level, while managing technical aspects at the DNO level by minimizing load curve variance and limiting power capacity. Moreover,voltages at network nodes are calculated using the DistFlow model to simplify the optimization and ensure compliance with standard operational limits. Compared to uncoordinated EV charging, EVCDS reduces load profile deviations by 85% and total costs by 91%, while also improving bus voltage profiles.

Power control of an autonomous wind energy conversion system based on a permanent magnet synchronous generator with integrated pumping storage

Wind energy plays a crucial role as a renewable source for electricity generation, especially in remote or isolated regions without access to the main power grid. The intermittent characteristics of wind energy make it essential to incorporate energy storage solutions to guarantee a consistent power supply. This study introduces the design, modeling, and control mechanisms of a self-sufficient wind energy conversion system (WECS) that utilizes a Permanent magnet synchronous generator (PMSG) in conjunction with a Water pumping storage station (WPS). The system employs Optimal torque control (OTC) to maximize power extraction from the wind turbine, achieving a peak power coefficient (Cp) of 0.43. A vector control strategy is applied to the PMSG, maintaining the DC bus voltage at a regulated 465 V for stable system operation. The integrated WPS operates in both motor and generator modes, depending on the excess or shortfall of generated wind energy relative to load demand. In generator mode, the WPS supplements power when wind speeds are insufficient, while in motor mode, it stores excess energy by pumping water to an upper reservoir. Simulation results, conducted in MATLAB/Simulink, show that the system efficiently tracks maximum power points and regulates key parameters. For instance, the PMSG successfully maintains the reference quadrature current, achieving optimal torque and power output. The system’s response under varying wind speeds, with an average wind speed of 8 m/s, demonstrates that the generator speed closely follows turbine speed without a gearbox, leading to efficient power conversion. The results confirm the flexibility and robustness of the control strategies, ensuring continuous power delivery to the load. This makes the system a feasible solution for isolated, off-grid applications, contributing to advancements in renewable energy technologies and autonomous power generation systems.

A novel droop coefficient to realize rapid SOC balance for distributed energy storage systems

In the realm of isolated direct-current microgrids with varying distributed energy storage unit capacities, a new energy equalization strategy is proposed. This method involves an innovative approach that integrates an improved state-of-charge equalization control integrated with a sigmoid function to adaptively adjust the droop coefficient, aiming to accelerate the state-of-charge equalization rate. Furthermore, a virtual voltage drop equalization control is designed to dynamically adjust the output current of each distributed energy storage unit through a simple proportional–integral controller. This eliminates the influence of line impedance on accurate current distribution, improving overall accuracy. Additionally, a dynamic consistency algorithm is employed to gather average information about the distributed energy storage system, reducing communication line pressure as local nodes only exchange information with neighboring nodes. Integrating the aforementioned modules, the strategy proposed in this paper achieves rapid state-of-charge equalization, precise distribution of output current, and stable maintenance of the bus voltage. Finally, rigorous analysis of experimental results under various operating conditions verifies the feasibility and effectiveness of this control strategy by using the RT-LAB.

A memristor‐sourced key for encrypted power and data synchronous transmission in switched mode power supplies

AbstractIn power and data synchronous transmission (PDST), it is crucial to modulate additional signals onto the voltage bus of the converters. This reduces the cost of monitoring and control of the power grid. There is thus the need to ensure that the data transmitted is well secured. This study, designs an electronic system that uses the common coupling point for PDST. Then Chua's memristive circuit is introduced as an entropy source for generating a random bit sequence for encrypted PDST in a switched mode power supply. The encrypted data signal is modulated using phase shift key modulation. Thereafter, the carrier signal is pulse width modulation modulated to control the switching operation of the power switch. The use of Chua's memristive circuit for generating a random number is not new; however, there is little study directly adopting its use in PDST. This allows for resource sharing since multiple signal sources can share the same bus without interference or security risk. The generated random bits are tested for randomness using National Institute of Standards and Technology test suite and autocorrelation test. MATLAB/Simulink and experimental results both confirm the effectiveness of the proposed data encryption method.

Transient Stability-Based Fast Power System Contingency Screening and Ranking

Today’s power systems are operated closer to their stability limits due to the continuously growing load demands, interface to open markets, and integration of more renewable energies. In order to provide operators with clear insight on the current system situation, near real-time power systems dynamic security assessment tools are required. One of the core elements of near real-time dynamic security assessment tools is contingency screening and ranking. Most of the commercially available tools screen and rank contingencies by using the traditional numerical integration or Transient Energy Functions (TEFs) or hybrid methods. The traditional numerical integration method is accurate but computationally intensive and has a slow assessment speed which makes it difficult to identify any insecure contingency before it happens. Despite the TEF method of transient stability analysis being relatively fast, it develops less accurate results due to models simplification and assumptions. This paper introduces transient stability based on fast and robust contingency screening and ranking using an Adaptive step-size Differential Transformation (AsDTM) method. Based on the most current snapshot from Supervisory Control and Data Accusation (SCADA) data, the proposed method triggers AsDTM-based transient stability simulation for each credible contingency and evaluates Transient Stability Indices (TSI) as the normalized weighted sum of squares of errors derived from state variables and complex bus voltages at every simulation time step. Finally, contingencies are ranked based on these TSI and the worst contingency is identified for the next detail assessment. The method is tested on IEEE 9 bus and 39 bus test systems. Test results reveal that the proposed method is faster, robust, and can be used in near real-time dynamic security assessment sessions.

Research on Fast SOC Balance Control of Modular Battery Energy Storage System

Early SOC balancing techniques primarily centered on simple hardware circuit designs. Passive balancing circuits utilize resistors to consume energy, aiming to balance the SOC among batteries; however, this approach leads to considerable energy wastage. As research progresses, active balancing circuits have garnered widespread attention. Successively, active balancing circuits utilizing capacitors, inductors, and transformers have been proposed, enhancing balancing efficiency to some extent. Nevertheless, challenges persist, including energy wastage during transfers between non-adjacent batteries and the complexity of circuit designs. In recent years, SOC balancing methods based on software algorithms have gained popularity. For instance, intelligent control algorithms are being integrated into battery management systems to optimize control strategies for SOC balancing. However, these methods may encounter issues such as high algorithmic complexity and stringent hardware requirements in practical applications. This paper proposes a fast state-of-charge (SOC) balance control strategy that incorporates a weighting factor within a modular battery energy storage system architecture. The modular distributed battery system consists of battery power modules (BPMs) connected in series, with each BPM comprising a battery cell and a bidirectional buck–boost DC-DC converter. By controlling the output voltage of each BPM, SOC balance can be achieved while ensuring stable regulation of the DC bus voltage without the need for external equalization circuits. Building on these BPMs, a sliding mode control strategy with adaptive acceleration coefficient weighting factors is designed to increase the output voltage difference of each BPM, thereby reducing the balancing time. Simulation and experimental results demonstrate that the proposed control strategy effectively increases the output voltage difference among the BPMs, facilitating SOC balance in a short time.

A novel Zero Back Power Flow (ZBPF) controlled DAB for DC bus stability and energy storage integrations in hybrid DC/AC off-grid systems

The paper presents an innovative approach for integrating energy storage devices into hybrid AC/DC grids to ensure a consistent power supply for modern loads. It introduces a Zero Back Power Flow (ZBPF) enabled bi-directional Dual Active Bridge (or, Direct AC-AC Boost) (DAB) controller strategy aimed at enhancing the power handling capacity DABs, while offering adaptable control for the microgrid system. Utilizing the proposed ZBPF control technique, the system endeavors to improve the power quality of the high-frequency AC link and regulate power transfer across DAB converter. Furthermore, a power management control strategy is described to facilitate efficient power flow among the diverse sources and loads within the hybrid off-grid system via battery system. By incorporating multiple utility features, the system aims to maintain stable bus voltages and mitigate potential power supply reliability issues. The paper concludes with simulation results validating the efficacy of the proposed ZBPF DAB control algorithms in grid forming operational modes, demonstrating the feasibility and effectiveness of the proposed solution. Additionally, the proposed ZBPF controller is tested on a hardware platform using batteries. An experimental setup has been conducted to verify the operation of the proposed ZBPF modulation scheme for a DAB converter in a practical setting. It involves implementing the ZBPF modulation scheme using a Hardware-in-the-Loop (HIL) setup and testing the DAB converter under both forward and reverse power flow modes.

Vector Reconfiguration on a Bidirectional Multilevel LCL-T Resonant Converter

With the development of distributed energy technology, the establishment of the energy internet has become a general trend, and relevant research about the core component, energy router, has also become a hotspot. Therefore, the bidirectional isolated DC–DC converter (BIDC) is widely used in AC–DC–AC energy router systems, because it can flexibly support the DC bus voltage ratio and achieve bidirectional power flow. This paper proposes a novel vector reconfiguration on a bidirectional multilevel LCL-T resonant converter in which an NPC (neutral-point clamped) multilevel structure with a flying capacitor is introduced to form a novel active bridge, and a coupling transformer is specially added into the active bridge to achieve multilevel voltage output under hybrid modulation. In addition, an LCL-T two-port vector analysis is adopted to elaborate bidirectional power flow which can generate some reactive power to realize zero-voltage switching (ZVS) on active bridges to improve the efficiency of the converter. Meanwhile, due to the symmetry of the LCL-T structure, the difficulty of the bidirectional operation analysis of the power flow is reduced. Finally, a simulation study is designed with a rated voltage of 200 V on front and rear input sources which has a rated power of 450 W with an operational efficiency of 93.8%. Then, the feasibility of the proposed converter is verified.

Integrated control strategy for bus voltage stability and power sharing in hybrid microgrid with renewable energy integration

Integrated control strategy for bus voltage stability and power sharing in hybrid microgrid with renewable energy integration

A Modified Control Strategy for Three-Phase Four-Switch Active Power Filters Based on Fundamental Positive Sequence Extraction

Three-phase four-switch active power filters (APFs) have attracted attention due to their low amount of semiconductors and low cost. The traditional control strategy of three-phase four-switch APFs usually includes phase-locked loops (PLLs) and rotating coordinate transformation for harmonic detection, resulting in complicated calculations and increased computation. In this paper, a modified control strategy for three-phase four-switch APFs based on fundamental positive sequence extraction is proposed, eliminating PLLs and rotating coordinate transformation with trigonometric calculations. Harmonic extraction is based on the fundamental positive sequence extraction method, while non-locked phase loop coordinate transformation is proposed to eliminate trigonometric calculations. Quasi-PR control is adopted for current tracking, and DC voltage control is designed to suppress voltage imbalance between the two split capacitors on the DC side. The space vector pulse width modulation (SVPWM) method is modified for a reduced-switch APF topology. The proposed control strategy guarantees excellent harmonic compensation: harmonic currents are significantly suppressed when the APFs are working, the THD of the source current decreases to 3.86%, the bus voltage fluctuation on the DC side is small, the voltage remains stable, and the computational complexity is reduced. Finally, a simulation and an experimental hardware platform are established to validate the feasibility and performance of the proposed control strategy. The experimental results show that it has good performance in suppressing harmonics and improving power quality.

Ground Fault Detection of Photovoltaic and Energy Storage DC Converter Load on User Side

With the rapid development of DC power supply technology, the operation, maintenance, and fault detection of DC power supply equipment and devices on the user side have become important tasks in power load management. DC/DC converters, as core components of photovoltaic and energy storage DC systems, have issues with detecting ground faults on the positive and negative input/output buses, leading to difficulties in troubleshooting device malfunctions and potentially endangering user safety. To address these issues, a method for detecting ground faults on the positive and negative buses of a synchronous buck photovoltaic and energy storage DC/DC converter is proposed, which involves the comprehensive measurement of multi-point common-mode voltages. This method collects the input positive bus voltage, output positive bus voltage, switch voltage, and the common-mode voltage at the midpoint of the bridge arm, then sums these after removing the switching harmonics. By analyzing the characteristic differences of the summed voltage under the ground fault modes of the positive and negative input/output buses, characteristic parameters are extracted to establish a ground fault identification method, thereby achieving effective detection of ground faults in the photovoltaic and energy storage DC/DC converter. Finally, the effectiveness of the method proposed in this paper was validated through simulations and experiments.

A Hybrid Technique for Bidirectional Smart Charging of EVs Using BLDC Motor and Bidirectional Converter

This paper introduces a novel hybrid approach for bidirectional smart charging of electric vehicles (EVs) powered by brushless DC (BLDC) motors. The proposed technique, named Atomic Orbital Search-Rat Swarm Optimizer (AOS-RSO), leverages the Atomic Orbital Search (AOS) for optimizing energy storage management and the Rat Swarm Optimizer (RSO) for enhancing motor control. The modular hybrid energy storage system (HESS) is designed with five modules, each consisting of a bidirectional DC-DC converter connecting a battery and super capacitor module. A multi-level cascaded converter (MCC) connects the HESS to the BLDC motor inverter of the EV drive, controlling the DC bus voltage. The AOS-RSO approach efficiently manages the state-of-charge (SOC) across the modules, achieving a balanced SOC of 98%. Implemented in MATLAB, the method is evaluated in both open and closed-loop systems and compared with existing techniques. Results show the proposed method achieves 96% efficiency in simulation and 94% in experiments, with a CPU time of 2.84 s. This approach significantly advances smart charging solutions for EVs, offering improved energy management and operational efficiency.