Voltage Fluctuations Research Articles

Integration of Wind and Solar Hybrid Power Generation Systems into The Grid and Control of Power Quality with Fuzzy Logic

In this study, a grid-connected hybrid system composed of a solar and wind power generation system is modelled and analyzed in Matlab/Simulink. In addition, the quality of power generated by the hybrid power generation system is improved by using Fuzzy Logic (FL) and Proportional-Integral (PI) controllers. Measurements of the frequency, voltage, power and harmonics of the system and the grid are evaluated. In addition, the voltage and frequency fluctuation times prior to synchronization between the hybrid generation system and the grid are analyzed. The voltage rises that occur on the grid while the system is idling have been checked. After synchronization, the voltage, power, frequency and phase angle values measured at the load and at the grid in the hybrid system were observed individually. According to the measured values obtained as a result of the simulation study, the performance of the hybrid power generation system is analyzed.

Comprehensive Analysis of Impedance Source Converters and Quasi-Z-Source Converters for Renewable Power Generation

As global attention on renewable energy continues to grow, impedance source converters (ISCs), including Z-source converters (ZSCs) and quasi-Z-source converters (QZSCs), have become focal points in research on renewable energy generation systems. These converters are valued for their adaptability to a broad range of voltage fluctuations, as well as their ability to enhance system efficiency and stability. Building upon the foundations of ZSCs and QZSCs, this paper explores and summarizes their applications in several pivotal areas of renewable energy research. These include addressing challenges such as low and fluctuating input voltages in hybrid renewable energy supply systems, their application in batteries of electric vehicles, and the implementation of high-performance buck-boost DC-DC converters. Additionally, the paper analyzes the strengths and weaknesses associated with various applications. Ultimately, the paper concludes with recommendations and future outlooks for improving the stability and efficiency of ZSCs and QZSCs in practical applications.

A Coil With All‐Plane High Misalignment Tolerance for Wireless Power Transfer

ABSTRACTThe output voltage of a magnetically resonance‐coupled wireless power transfer (WPT) system is very sensitive to the coil alignment. In this paper, a novel quadrupole coil structure as a receiving coil for static charging of automated guided vehicles (AGVs) is proposed to improve the mutual inductance profile under XOY plane misalignment conditions during AGV charging. The enhanced coil structure effectively reduces the output power and voltage fluctuations. An experimental prototype is established to validate the feasibility of the proposed WPT system. The experimental results show that the output voltage fluctuation is only 10.85% over a 77.78% coil area offset area covering the whole charging plane.

Vacuum DC breakdown characteristics between grid electrodes with ion sputtering

Abstract In ion thrusters, grid electrodes are biased at high voltage to extract and accelerate ions. However, they are susceptible to vacuum breakdown which considerably undermines the ion thruster performance. To optimize the grid electrode design and improve the ion thruster longevity, the impact of ion sputtering on the vacuum DC breakdown characteristics of grid electrodes are systemically examined. The adopted seven-aperture grid electrodes are made of three materials including molybdenum, graphite, and stainless steel, which are exposed to Ar⁺ and Xe⁺ sputtering at energies of 400 eV and 1000 eV for durations ranging from 0.5 to 8 hours. It is found that the initial breakdown voltage, which means the first breakdown voltage after sputtering, generally decreased regardless of the electrode material as the sputtering time increased. However, the breakdown voltage rapidly increases with more repeated discharges. Each kind of electrode material demonstrates unique responses to ion sputtering and breakdowns. In comparison with metal electrodes, the graphite surface relatively easy to appear defects but exhibites the least insulation degradation. The metal surface remains relatively smooth, but breakdown voltage is significantly affected, with electrode materials such as stainless steel failing to recover to their initial insulation levels. Xe⁺ sputtering leads to greater breakdown voltage fluctuations during repeated discharges due to higher sputtering yield. Dark current is measured, and field enhancement factor is calculated to reveal correlations between local electric field and the breakdown voltage. Furthermore, micro particles are identified as major contributor to the first few times breakdown. Based on above observations, two kinds of distinct discharge mechanisms, including the cascade discharge induced by micro particles and the discharge induced by field emission are proposed to interpret experimental data.

Faster joint control of battery and supercapacitor-based HESS in DC microgrid using MPC with hybrid PI

DC microgrids (MGs) with hybrid energy storage systems (HESSs) are getting popular due to their simple structures. The other notable benefit is extending the expensive battery’s lifespan by co-sharing the load with supercapacitor. This paper proposes a novel control technique for the HESS in DC microgrid, which combines model predictive control (MPC) with faster joint control. This innovative approach aims to reduce the overshoot and improve dc-link voltage stabilization. Firstly, faster joint control dispatches the uncompensated power from battery to supercapacitor (SC), smoothing stress in the battery. Then, MPC method helps to manage the current in both the battery and the SC to align with dynamic current targets. Moreover, the incorporation of Laguerre functions within the MPC framework is introduced to improve system performance and enable faster reactions to changes. Simulation and experimental validations reveal that the newly proposed control strategy effectively reduces voltage fluctuations and enhances system stability, outperforming advanced previous methods such as deadbeat control, model predictive control with iterative learning control, model predictive current control and traditional double-loop proportional integral (PI) control.

Self-Locking Domino Logic Pipelines: Application in RISC-V Architectures in FPGA

This paper presents the design and implementation of a self-locking domino logic pipeline controller for a RISC-V processor implemented on an FPGA. The emphasis is on asynchronous circuit design, which offers advantages such as enhanced resilience to supply voltage fluctuations, optimized power efficiency, and the elimination of clock-related issues such as skew and single-point failures. By leveraging the asynchronous Globally Asynchronous Locally Synchronous (GALS) systems and domino logic, the controller ensures hazard-free operation while maintaining race-free processing. The asynchronous approach, integrated into a 32- bit RISC-V processor, allows for flexible and energy-efficient operation, thereby demonstrating its potential for performance-critical applications. This paper high- lights the contrasts between the asynchronous design and the traditional synchronous multicycle processor, demonstrating the benefits of asynchronous systems in terms of power consumption and performance. A significant contribution of this design is the pipeline’s completion detection mechanism, which ensures that each processing stage locks until valid results are obtained, thereby markedly enhancing system stability. Furthermore, the paper investigates the parallelization of domino gates and introduces an asynchronous Arithmetic Logic Unit (ALU), which further optimizes performance through self-locking mechanisms. The power, performance, and area (PPA) analysis of the design demonstrates considerable improvements in throughput (up to 10%) and reduced latency per instruction in comparison to its synchronous counterpart, while maintaining moderate resource utilization on an FPGA. The results indicate that asynchronous domino logic pipelines may offer a promising approach for achieving energy-efficient and high-performance processors in future computing architectures.

Study on Grid Stability of Electric Vehicle Bidirectional Charging Device Based on Bidirectional Power Electronic Converter

With the increasing number of electric vehicles (EVs), V2G (Vehicle-to-Grid) and G2V (Grid-to-Vehicle) technologies have garnered growing attention as essential methods for energy flow between EVs and the power grid. This study examines the impact of EV charging devices, based on bidirectional power electronic converters, on grid stability. As EVs become more widely adopted, V2G and G2V technologies are extensively applied to enable bidirectional energy flow between EVs and the grid. This study analyzes different topologies of power electronic converters and explores how optimized control strategies can enhance grid voltage and frequency stability. The research shows that bidirectional charging devices provide significant advantages in addressing voltage fluctuations and frequency instability, especially in peak shaving and valley filling, effectively easing grid load. Furthermore, future developments in bidirectional charging devices will focus on intelligent control and synergistic optimization with renewable energy to improve grid stability and power quality. The research methods include analysis and comparison of different converter topologies. Results indicate that bidirectional charging devices have a significant effect in resolving grid instability issues, supporting the further advancement of smart grids).

A CNN‐LSTM Method Based on Voltage Deviation for Predicting the State of Health of Lithium‐Ion Batteries

ABSTRACTEnsuring the accurate estimation of the state of health (SOH) of lithium‐ion batteries (LIBs) is essential for the reliability and safe operation of battery management systems. The prediction of SOH has witnessed significant advancements recently, largely propelled by the powerful nonlinear modeling capabilities of deep learning. Despite these advancements, the intricate nature of the battery degradation process poses a challenge in accurately simulating it using measurement data. In this paper, we introduce a novel approach by focusing on the charging voltage deviation, which is defined as the discrepancy between the charging voltage and its average value over each charge/discharge cycle. This deviation is rooted in the electrochemical reactions that lead to capacity decay and voltage fluctuations. We propose a convolutional neural network‐long short‐term memory (CNN‐LSTM) hybrid framework aimed at estimating the SOH of the battery. For each charge/discharge cycle, a conventional CNN is employed to extract key capacity features from sequential charging data, encompassing voltage deviation, current, and charging duration. Following this, an LSTM network is leveraged to build the long‐term dependencies of battery capacities, facilitating the SOH prediction process. The experimental results indicate that our model not only simplifies the computational complexity but also significantly enhances the precision of SOH predictions. This innovative approach holds promise for the advancement of battery management systems, ensuring their continued reliability and safety.

Enhancing the power quality in radial electrical systems using optimal sizing and selective allocation of distributed generations.

Optimizing energy resources is a major priority these days. Increasing household energy demand often leads to the deterioration of poorly sized distribution networks. This paper presents a method for energy compensation and optimization in radial distribution systems (ORDS). By integrating distributed generations (DG), an approach is used to evaluate voltage and power profiles, as well as power losses on radial distributed systems (PLRDSs). After integrating distributed generations, improved voltage and power profiles are established. A potential solution to power compensation and blackouts (PCB) can also be the use of hybrid distributed generation systems (HDGSs) that reinforce radial distribution networks (RDNs) by improving power quality. Accordingly, a proposed configuration system is shown in this work to inject multiple renewable energy sources (MRES) from selected regulated nodes. The feasibility of the proposed system is evaluated using particle swarm optimization (PSO), which was used to locate stable nodes and locations, sensitive to voltage fluctuations. The proposed approach is based on the evaluation of the power losses and voltage profiles of the IEEE 33 bus and IEEE 69 bus standards This MATLAB-based method establishes an objective function that converges more quickly to the optimal results.

Advanced control strategy for AC microgrids: a hybrid ANN-based adaptive PI controller with droop control and virtual impedance technique

In this paper, an improved voltage control strategy for microgrids (MG) is proposed, using an artificial neural network (ANN)-based adaptive proportional-integral (PI) controller combined with droop control and virtual impedance techniques (VIT). The control strategy is developed to improve voltage control, power sharing and total harmonic distortion (THD) reduction in the MG systems with renewable and distributed generation (DG) sources. The VIT is used to decouple active and reactive power, reduce negative power interactions between DG’s and improve the robustness of the system under varying load and generation conditions. Simulation findings under different tests have shown significant improvements in performance and computational simulation. The rise time is reduced by 60%, the overshoot is reduced by 80%, the THD of the voltage is reduced by 75% (from 0.99 to 0.20%), and the THD of the current is reduced by 69% (from 10.73 to 3.36%) compared to the conventional PI controller technique. Furthermore, voltage and current THD values were maintained below the IEEE-519 standard limits of 5% and 8%, respectively, for the power quality enhancement. Fluctuations in voltage and frequency were also maintained at 2% tolerance and 1% tolerance, respectively, across all voltage limits, which is consistent with international norms. Power-sharing errors were reduced by 50% after conducting the robustness tests against the DC supply and load disturbances. In addition, the proposed strategy outperforms the previous control techniques presented at the state of the art in terms of adaptability, stability and, especially, the ability to reduce the THD, which validates its effectiveness for MG systems control and optimization under uncertain conditions.

Dynamic reconfiguration of multiobjective distribution networks considering the variation of load and DG using a novel LDEDBO algorithm

To address the challenges related to active power dissipation and node voltage fluctuation in the practical transformation of power grids in the field of new energy such as wind and photovoltaic power generation, an improved Dung Beetle Optimization Algorithm Based on a Hybrid Strategy of Levy Flight and Differential Evolution (LDEDBO) is proposed. This paper systematically addresses this issue from three aspects: firstly, optimizing the DBO algorithm using Chebyshev chaotic mapping, Levy flight strategy, and differential evolution algorithm; secondly, validating the algorithm’s feasibility through real-time network reconfiguration at random time points within a 24-h period; and finally, applying the LDEDBO to address the dynamic reconfiguration problems of the IEEE-33 and IEEE-69 node bus. The simulation indicates that the power dissipation of the IEEE-33 node bus is decreased by 28.94% and the minimum node voltage is elevated from 0.9273 p.u to 0.9447 p.u after the reconstruction with LDEDBO. The power dissipation of the IEEE-69 node bus is reduced by 36.45%, and the minimum node voltage is increased from 0.9224 p.u to 0.9481 p.u. The LDEDBO enhances both the pace of convergence and the precision of the optimization model, leading to a superior solution for the switching combination.

Circulating Current Suppression Combined with APF Current Control for the Suppression of MMC Voltage Fluctuations

Modular Multilevel Converters (MMCs) are widely used in HV and MVDC transmission. However, their application causes the voltage level to increase, and the number of sub-modules also increases. Problems such as circulating currents and sub-module voltage fluctuations should not be neglected. Considering the coupling relationship between the circulating current and the sub-module capacitor voltage fluctuation, the circulating current suppressing controller (CCSC) and the active power filter (APF) techniques are used to solve the two problems mentioned above simultaneously. Firstly, in order to reduce the influence of clutter on the tracking of the target components of the CCSC, a Second-Order Generalized Integrator (SOGI) is added to accurately lock the main 2nd and 4th harmonic components in the circulating current. Secondly, an APF is added on top of the circulating current suppression, and the two methods can be mutually reinforcing in their roles. The APF applies the strategy of current inner-loop dominant and voltage outer-loop bias control. It is regarded as a whole for absorbing the voltage fluctuations of the sub-module and also eliminates the error caused by the inductive voltage. Finally, the effectiveness of the above method is verified in MATLAB/Simulink, which demonstrates that the proposed method provides better suppression of both circulating current and sub-module voltage fluctuations compared to the conventional MMC that only incorporates APF.

Multi-Fault-Tolerant Operation of Grid-Interfaced Photovoltaic Inverters Using Twin Delayed Deep Deterministic Policy Gradient Agent

The elevated penetration of renewable energy has seen a significant increase in the integration of inverter-based resources (IBRs) into the electricity network. According to various industrial standards on interconnection and interoperability, IBRs should be able to withstand variability in grid conditions. Positive sequence voltage-oriented control (PSVOC) with a feed-forward decoupling approach is often adopted to ensure closed-loop control of inverters. However, the dynamic response of this control scheme deteriorates during fluctuations in the grid voltage due to the sensitivity of proportional–integral controllers, the presence of the direct- and quadrature-axis voltage terms in the cross-coupling, and predefined saturation limits. As such, a twin delayed deep deterministic policy gradient-based voltage-oriented control (TD3VOC) is formulated and trained to provide effective current control of inverter-based resources under various dynamic conditions of the grid through transfer learning. The actor–critic-based reinforcement learning agent is designed and trained using the model-free Markov decision process through interaction with a grid-connected photovoltaic inverter environment developed in MATLAB/Simulink® 2023b. Using the standard PSVOC method results in inverter input voltage overshoots of up to 2.50 p.u., with post-fault current restoration times of as high as 0.55 s during asymmetrical faults. The designed TD3VOC technique confines the DC link voltage overshoot to 1.05 p.u. and achieves a low current recovery duration of 0.01 s after fault clearance. In the event of a severe symmetric fault, the conventional control method is unable to restore the inverter operation, leading to integral-time absolute errors of 0.60 and 0.32 for the currents of the d and q axes, respectively. The newly proposed agent-based control strategy restricts cumulative errors to 0.03 and 0.09 for the d and q axes, respectively, thus improving inverter regulation. The results indicate the superior performance of the proposed control scheme in maintaining the stability of the inverter DC link bus voltage, reducing post-fault system recovery time, and limiting negative sequence currents during severe asymmetrical and symmetrical grid faults compared with the conventional PSVOC approach.

ESTIMASI TEGANGAN OPEN CIRCUIT DAN RESISTANSI INTERNAL BATERAI MENGGUNAKAN ALGORITMA RECURSIVE LEAST SQUARES

This study employs the Recursive Least Squares (RLS) algorithm to estimate the Open Circuit Voltage (OCV) and internal resistance (Ro) of a battery in real-time, based on measured current and voltage data. The results demonstrate that RLS can produce accurate estimates of OCV and Ro, with a Mean Squared Error (MSE) of 0.031, indicating a very small difference between the predicted and measured terminal voltage. The estimated OCV is stable, while Ro remains consistently within the range of 0.075 to 0.175 ohms, despite fluctuations in voltage due to changes in current. These findings show that the RLS algorithm can be effectively applied in battery management systems to dynamically and in real-time estimate parameters, which is crucial for applications such as electric vehicle battery monitoring and energy storage systems.

Morphing control strategy of wide input voltage DR‐LLC converter for vehicle power supply

AbstractElectric vehicles have experienced substantial global growth. However, significant fluctuations in the output voltage of power batteries and abrupt changes in the input voltage of DC–DC converters pose major challenges to their safety and reliability, thereby impeding sustainable development. This paper proposes a frequency‐prediction‐based topology morphing control strategy (FPTMC) for a dual resonant cavity LLC (DR‐LLC) converter. The strategy aims to accommodate a wide range of input voltages while enhancing the reliability of the vehicle's power supply. Firstly, the topology of the DR‐LLC converter is introduced, utilizing a fundamental wave analysis method for modelling purposes. This analysis results in three distinct voltage gain models corresponding to various topology modes, thereby enabling the converter to manage a wide spectrum of input voltages. Then, a geometrically simplified state plane analysis is employed to derive switching frequency models for the three topology modes, facilitating real‐time prediction of the necessary switching frequency during topology transitions. The proposed control strategy, which relies on frequency prediction, effectively mitigates the problem of voltage spikes during these transitions. Finally, both simulation and experimental results validate the accuracy and feasibility of the proposed wide input voltage control strategy for DR‐LLC converters in vehicle power supply systems.

Research on Structural Optimization and Excitation Control Method Using a Two-Dimensional OWPT System for Capsule Robots Based on Non-Equivalent Coils.

The rapid development of wireless power transfer (WPT) technology has provided new avenues for supplying continuous and stable power to capsule robots. In this article, we propose a two-dimensional omnidirectional wireless power transfer (OWPT) system, which enables power to be transmitted effectively in multiple spatial directions. This system features a three-dimensional transmitting structure with a Helmholtz coil and saddle coil pairs, combined with a one-dimensional receiving structure. This design provides sufficient internal space, accommodating patients of various body types. Based on the magnetic field calculation and finite element analysis, the saddle coil structure is optimized to enhance magnetic field uniformity; to achieve a two-dimensional rotating magnetic field, a phase difference control method for the excitation signal is developed through the analysis of circuit topology and quantitative synthesis of non-equivalent magnetic field vectors. Finally, an experimental prototype is built, and the experimental results show that the one-dimensional transmitting coil achieves a minimum received voltage stability of 94.5% across different positions. When the three-dimensional transmitting coils operate together, a two-dimensional rotating magnetic field in the plane is achieved at the origin, providing a minimum received power of 550 mW with a voltage fluctuation rate of 7.68%.

Stability Analysis of DC Microgrids: Insights for Enhancing Renewable Energy Integration, Efficiency and Power Quality

In the current context of smart grids, microgrids have proven to be an effective solution to meet the energy needs of neighborhoods and collective buildings. This study investigates the voltage behavior and other critical parameters within a direct current (DC) microgrid to enhance system efficiency, stability, and reliability. The dynamic performance of a DC microgrid is analyzed under varying load and generation conditions, with particular emphasis on the voltage response and load-sharing mechanisms required to ensure stable operation. The findings indicate that specific control strategies, particularly droop methods, are effective in mitigating voltage fluctuations, enhancing power quality, and ensuring proper load distribution across multiple sources. This study also addresses significant challenges, including voltage regulation and fault resilience, to provide guidelines for designing robust and efficient DC microgrids. These insights are essential to inspire further advancements in control strategies and facilitate the practical deployment of DC microgrids as a sustainable solution for distributed energy systems, especially in scenarios prioritizing high DC load penetration and renewable energy integration.

Magnetization fluctuations and magnetic after-effect probed via the anomalous Hall effect

Taking advantage of the anomalous Hall effect, we electrically probe low-frequency magnetization fluctuations at room temperature in a thin ferromagnetic Pt/Co/AlOx layer stack with perpendicular magnetic anisotropy. We observe a strong enhancement of the Hall voltage fluctuations within the hysteretic region of the magnetization loop. Analyzing both the temporal evolution of the anomalous Hall voltage and its frequency-dependent noise power density, we identify two types of magnetic noise: abrupt changes in the magnetic domain configuration, evident as Barkhausen-like steps in the Hall voltage time trace, yield a noise power density spectrum scaling with frequency as 1/fβ with β≈2.0. In contrast, quasistationary magnetization configurations are connected with a magnetic noise power density with an exponent β≈1.0. The observation of Barkhausen steps and relaxation effects shows that the magnetic system is in a nonstationary state in the hysteresis region, such that the fluctuation-dissipation theorem cannot be expected to hold. However, the time-dependent change in the Hall voltage for constant magnetic field strength resembles the integrated noise power. Published by the American Physical Society 2024

Grid-Forming Fuel Cell System for an Islanded AC Grid

This paper proposes a two-stage converter that can black start an isolated AC Microgrid with a Fuel Cell (FC) as the primary energy source. The first stage is connected to the FC and employs a Three-Leg Interleaved Boost DC/DC Converter (IBC), while the second is a Three-Phase Voltage Source Converter (VSC). The DC/DC stage utilizes a Cascade Voltage Control (CVC) to mitigate voltage fluctuations in the DC-link caused by the variability of the FC voltage. For the DC/AC stage, three distinct grid-forming (GFM) strategies are implemented with two of them with multi-loop cascaded structure and one with a single-loop structure. The power circuit of the system is simulated using the Real-Time Simulator (RTS) HIL 602+ from Typhoon-HIL, with the control strategies embedded on the Digital Signal Processor (DSP) TMS320F28379D - F28379D LaunchPad from Texas Instruments (TI). The performance of the cases are verified through CHIL simulations for a balanced and unbalanced inductive load steps. The results demonstrate that for both tests the GFM single loop structure presents smoother transients and shorter recovery times. Additionally, for the unbalanced loads, all the cases present similar results for the DC variables with more pronounced differences at the AC side.

Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator Theory

The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults.