Search Results (75)

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

Focused on the contradiction between the steady-state error of active power and the dynamic oscillation caused by the virtual damping characteristics of the virtual synchronous generator (VSG) under disturbances during grid-connected operation, this article proposes an adaptive virtual inertia regulation and compensation method (PFFCVSG_AJ) based on an active power differential feedforward compensation strategy (PFFCVSG). Firstly, this article presents the working and control principles of VSG, analyzing its control mechanisms through a small-signal model. Models for VSG’s active power, reactive power, and virtual impedance components are established, with particular focus on the impact of the damping coefficient on active power regulation. Based on the PFFCVSG, an adaptive virtual inertia adjustment method is introduced to resolve the inherent inertia deficiency in PFFCVSG control, the influence of the moment of inertia on PFFCVSG is theoretically analyzed, and a dynamic adjustment mechanism for moment of inertia is developed based on the rate of change in frequency (RoCoF). Finally, simulation validation using MATLAB/Simulink (MathWorks, R2022b, Natick, MA, USA) demonstrates that the proposed PFFCVSG_AJ strategy effectively eliminates active power steady-state deviation, suppresses active power dynamic oscillation, and mitigates the frequency overshoot issue prevalent in traditional PFFCVSG. Experimental verification is conducted via a TMS320F28378DPTPS-based control platform, confirming the algorithm’s effectiveness under sudden load variations, and that the power quality of the power grid is not affected under the premise of efficient grid connection. Full article

Hydraulically driven flexible robotic arms (HDFRAs) play an indispensable role in industrial precision operations such as aerospace assembly and nuclear waste handling, owing to their high power density and adaptability to complex environments. However, inherent mechanical flexibility-induced vibrations, hydraulic nonlinear dynamics, and electromechanical coupling effects lead to multi-timescale control challenges, severely limiting high-precision trajectory tracking performance. The present study introduces a novel hierarchical control framework employing dual-timescale perturbation analysis, which effectively addresses the constraints inherent in conventional single-timescale control approaches. First, the system is decoupled into three subsystems via dual perturbation parameters: a second-order rigid-body motion subsystem (SRS), a second-order flexible vibration subsystem (SFS), and a first-order hydraulic dynamic subsystem (FHS). For SRS/SFS, an adaptive fast terminal sliding mode active disturbance rejection controller (AFTSM-ADRC) is designed, featuring a dual-bandwidth extended state observer (BESO) to estimate parameter perturbations and unmodeled dynamics in real time. A novel reaching law with power-rate hybrid characteristics is developed to suppress sliding mode chattering while ensuring rapid convergence. For FHS, a sliding mode observer-integrated sliding mode coordinated controller (SMO-ISMCC) is proposed, achieving high-precision suppression of hydraulic pressure fluctuations through feedforward compensation of disturbance estimation and feedback integration of tracking errors. The globally asymptotically stable property of the composite system has been formally verified through systematic Lyapunov-based analysis. Through comprehensive simulations, the developed methodology demonstrates significant improvements over conventional ADRC and PID controllers, including (1) joint tracking precision reaching ${10}^{-4}$ rad level under nominal conditions and (2) over 40% attenuation of current oscillations when subjected to stochastic disturbances. These results validate its superiority in dynamic decoupling and strong disturbance rejection. Full article

This paper presents a fully integrated bandpass filter (BPF) with high tunability based on a novel differential active inductor (DAI), designed for sensor interface circuits operating in the ultra-high frequency (UHF) band. The design of the proposed DAI is based on a symmetrical configuration, utilizing a differential amplifier for the feedforward transconductance and a common-source (CS) transistor for the feedback transconductance. By integrating a cascode scheme with a feedback resistor, the quality factor of the active inductor is significantly improved, leading to enhanced mid-band gain for the bandpass filter. To facilitate independent tuning of the BPF‘s center frequency and mid-band gain, an active resistor adjustment and bias voltage control are employed, providing precise control over the filter’s operational parameters. Post-layout simulations and process corner results are conducted with 0.13 µm CMOS technology at 1.2 V supply voltage. The proposed second order BPF achieves a broad tuning range of 280 MHz to 2.426 GHz, with a passband gain between 8.9 dB and 16.54 dB. The design demonstrates a maximum noise figure of 16.54 dB at 280 MHz, an input-referred 1 dB compression point of −3.78 dBm, and a third-order input intercept point (IIP3) of −0.897 dBm. Additionally, the BPF occupies an active area of only 68.2×30 µm², including impedance-matching part, and consumes a DC power of 14–20 mW. The compact size and low power consumption of the design make it highly suitable for integration into modern wireless sensor interfaces where performance and area efficiency are critical. Full article

Barrier certificate is a powerful tool for verifying they safety property of dynamical systems. In this paper, we introduce an innovative learner–refiner framework for synthesizing polynomial barrier certificates. The framework comprises a learner and a refiner, which work inductively to generate barrier certificates. More specifically, the learner trains barrier certificate candidates represented by feedforward neural networks with polynomial activations, while the refiner utilizes sums of squares (SOS) programming to either validate the candidates or recover valid barrier certificates. Our framework achieves great efficiency via supervised learning, and it ensures formal soundness using SOS-based verification. We implement the LR4BC tool, and we perform a comprehensive experimental evaluation using several benchmarks. The results demonstrate that our tool not only successfully synthesizes polynomial barrier certificates undetected via the SOS-based tool PRoTECT but also achieves a significant speedup in efficiency compared to the neural network-based tool FOSSIL 2.0. Full article

The integration of hybrid alternating current (AC) and direct current (DC) networks has gained relevance due to the growing demand for more flexible, efficient, and reliable electrical systems. A key aspect of this integration is the parallelization of power converters, which presents several technical challenges, such as current sharing imbalances, circulating currents, and control complexity. This paper proposes a distributed control architecture for parallel resonant CLLC dual active bridge (DAB) converters to address these issues in hybrid AC–DC networks and microgrids. The approach includes a master voltage controller to regulate the output voltage and distributed local current controllers to ensure load balance. The approach minimizes the difference between the output and input voltages, allowing for independent control of power flow. Simulation and experimental results show significant improvements. The system stability has been demonstrated experimentally. Transient response has been improved with response time 80% lower using the feed-forward term. The system maintained stability with current sharing deviations below 3% under full and low load conditions. Finally, scalability is ensured by the proposed distributed controller because the central power controller is not affected by the number of units in parallel used in the application. This solution is suitable for advanced hybrid networks and microgrid applications. Full article

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs. Full article

With the large-scale integration of renewable energy into off-grid DC systems, the stability issues caused by their fluctuations have become increasingly prominent. The dual active bridge (DAB) converter, as a DC-DC converter suitable for high power and high voltage level off-grid DC systems, plays a crucial role in maintaining and regulating grid stability through its control methods. However, the existing control methods for DAB are inadequate: linear control fails to meet dynamic response requirements, while nonlinear control relies on detailed model structures and parameters, making the control design complex and less accurate. To address this issue, this paper proposes a feedforward control strategy for a DC-DC converter in an off-grid hydrogen production system based on a linear extended state observer (LESO) and super-twisting sliding mode control (STSMC). Firstly, a reduced-order simplified model of the DAB was constructed through the structure of DAB. Then, based on the reduced-order simplified model, a feedforward control based on LESO and STSMC was designed, and its stability was analyzed. Finally, a simulation comparison of PI, LESO + terminal sliding mode control (TSMC), and LESO + STSMC control methods was conducted in a DC off-grid hydrogen production system. The results verified the proposed control method’s enhancement of the DAB’s rapid dynamic response capability and the system’s transient stability. Full article

The dual three-phase permanent magnet synchronous motor (DTP-PMSM) under a V/f control framework is widely applied in belts, fans, pumps, etc. However, the oscillation in power and rotor speed is difficult to quantify and suppress, due to the higher-order model of the DTP-PMSM. Thus, a power-revolutions per minute (RPM) reduced-order model and power control strategy of the DTP–PMSM are proposed for oscillation description and suppression. Firstly, according to the structure and V/f control framework, the reduced-order model is proposed under a power-RPM scale with coupled performances between sub-PMSMs, and then the decoupled method is employed. Moreover, the oscillated performances of power and rotor speed are detailed in small signals. Secondly, a power control strategy is proposed, including active power feedforward for active damping and reactive power droop control for high power quality and approaching optimal torque per ampere. Compared with the traditional strategies, the proposed method can achieve a stable and efficient operation, with a higher power factor of the DTP–PMSM, less stator current, and lower electromechanical power loss. Finally, an experimental platform of the DTP–PMSM is set up for the correctness and superiority of the proposed method. Full article

This paper describes the use of an electric drive as an acoustic actuator for active noise cancellation (ANC). In the presented application, the idea is to improve the noise, vibration, harshness (NVH) characteristics of passenger cars without using additional active or passive damper systems. Many of the already existing electric drives in cars are equipped with the required hardware components to generate noise and vibration, which can be used as compensation signals in an ANC application. To demonstrate the applicability of the idea, the electrical power steering (EPS) motor is stimulated with a control signal, generated by an adaptive feedforward controller, to reduce harmonic disturbances at the driver’s ears. As it turns out, the EPS system generates higher harmonics of the harmonic compensation signal due to nonlinearities in the acoustic transfer path using a harmonic excitation signal. The higher harmonics impair an improvement in the subjective hearing experience, although the airborne noise level of the harmonic disturbance signal can be clearly reduced at the driver’s ears. Therefore, two methods are presented to reduce the amplitude of the higher harmonics. The first method is to limit the filter weights of the algorithm to reduce the amplitude of the harmonic compensation signal. The filter amplitude limitation also leads to a lower amplitude of the higher harmonics, generated by the permanent magnet synchronous machine (PMSM). The second method uses a parallel structure of adaptive filters to actively reduce the amplitude of the higher harmonics. Finally, the effectiveness of the proposed ANC system is demonstrated in two real driving situations, where in one case a synthetic noise/vibration induced by a shaker on the front axle carrier is considered to be the disturbance, and in the other case, the disturbance is a harmonic vibration generated by the combustion engine. In both cases, the subjective hearing experience of the driver could be clearly improved using the EPS motor as ANC actuator. Full article

Parvalbumin expressing (PV+) GABAergic interneurons are fast spiking neurons that provide powerful but relatively short-lived inhibition to principal excitatory cells in the brain. They play a vital role in feedforward and feedback synaptic inhibition, preventing run away excitation in neural networks. Hence, their dysfunction can lead to hyperexcitability and increased susceptibility to seizures. PV+ interneurons are also key players in generating gamma oscillations, which are synchronized neural oscillations associated with various cognitive functions. PV+ interneuron are particularly vulnerable to aging and their degeneration has been associated with cognitive decline and memory impairment in dementia and Alzheimer’s disease (AD). Overall, dysfunction of PV+ interneurons disrupts the normal excitatory/inhibitory balance within specific neurocircuits in the brain and thus has been linked to a wide range of neurodevelopmental and neuropsychiatric disorders. This review focuses on the role of dysfunctional PV+ inhibitory interneurons in the generation of epileptic seizures and cognitive impairment and their potential as targets in the design of future therapeutic strategies to treat these disorders. Recent research using cutting-edge optogenetic and chemogenetic technologies has demonstrated that they can be selectively manipulated to control seizures and restore the balance of neural activity in the brains of animal models. This suggests that PV+ interneurons could be important targets in developing future treatments for patients with epilepsy and comorbid disorders, such as AD, where seizures and cognitive decline are directly linked to specific PV+ interneuron deficits. Full article

This article proposes a proportional-integral-resonant (PIR) current control strategy for a wind-driven brushless doubly fed generator (WDBDFG) during network unbalance. Firstly, four control objectives of WDBDFG, including eliminating unbalanced currents of power winding (PW), pulsations of control winding (CW) currents, torque, and PW power, are discussed and different from current controls in which the references to PW currents were computed; the CW current references are derived here. Then, an improved CW current controller using a PIR controller is proposed to achieve different control objectives. In contrast with current controls, CW currents are not involved with sequence extraction in the proposed control and can be totally regulated only in a positive synchronous reference frame. Hence, the system control structure is greatly simplified, and dynamic characteristics are improved. Furthermore, in order to obtain completely decoupled control of current and average power, feedforward control, considering all the couplings and perturbances, is also applied in CW current loops. Simulation results for a 2 MW grid-connected WDBDFG show that the proposed control is capable of achieving four control objectives, including canceling CW current distortion, PW current unbalance, pulsations of PW active power or pulsations of reactive power, and machine torque. Its dynamic process is much more smoothly and quickly than that of current controls, and therefore the proposed control has better dynamic control characteristics during network unbalance. Full article

This article presents a proportional–derivative (PD) type output voltage regulator without the current feedback, taking into account system parameter and load variations. The main advantages are given as follows: First, the first-order output voltage derivative observer is developed without the requirement of system parameter information, which makes it possible to stabilize the system without current sensing. Second, a simple self-tuner implements the feedback-loop adaptation by updating the desired dynamics accordingly. Third, the observer-based active damping injection for the PD-type controller results in the closed-loop system order reduction to 1 by the pole-zero cancellation, including the disturbance observer as a feed-forward term. The prototype uninterruptible power supply system comprised of a 3 kW three-phase inverter, inductors, and capacitors verifies the practical merits of the proposed technique for linear and nonlinear loads. Full article

Compared with isolated converters, transformerless converters are a preferred choice in low-voltage grids due to their efficiency and lower cost. However, leakage current and common mode (CM) voltage appear through the converter and ground in hybrid grids, which consist of AC and DC subgrids. The leakage current and CM voltage seriously influence operation and power quality in low-voltage distribution systems. This paper proposes a common-ground-type (CGT) converter equipped with a CM decoupling control strategy to eliminate the leakage current and CM voltage. A CM model is derived, and the leakage current and CM voltage are analyzed in detail. A CGT four-leg converter is constructed to eliminate the high frequency CM voltage. A dual DQ current control loop is developed to suppress the DC double-frequency ripple. Additionally, an active damping method is proposed, based on the neutral current feed-forward plus inductor current feedback, to attenuate the low frequency CM voltage. The proposed converter and control strategy guarantees excellent performance in suppressing leakage current and CM voltage. The DC voltage of the converter connected to the DC grid maintains stability and symmetry. The leakage current is significantly reduced, and the leakage current suppression performance is improved by 83%. The high frequency CM voltage is attenuated from 50%u_dc to 2%u_dc, and the low frequency CM voltage is suppressed from approximately 32%u_dc to 3%u_dc, which is a significant improvement compared with the traditional method. In addition, the proposed control strategy has good transient performance when the load changes abruptly. Finally, an experimental platform is established to validate the feasibility and performance. The experiment results showed that the proposed control strategy improves the system performance and power quality. Full article

The higher penetration of renewable energy sources in current and future power grids requires effective optimization models to solve economic dispatch (ED) and optimal power flow (OPF) problems. Data-driven optimization models have shown promising results compared to classical algorithms because they can address complex and computationally demanding problems and obtain the most cost-effective solution for dispatching generators. This study compares the forecast performance of selected data-driven models using the modified IEEE 39 benchmark system with high penetration of wind power generation. The active and reactive power load data of each bus are generated using Monte Carlo simulations, and synthetic wind power data are generated by utilizing a physical wind turbine model and wind speed samples withdrawn from a Weibull distribution. The objective is to design and evaluate an enhanced deep learning approach for the nonlinear, nonconvex alternating current optimal power flow (ACOPF) problem. The study attempts to establish relationships between loads, generators, and bus outcomes, utilizing a multiple-input, multiple-output (MIMO) workflow. Specifically, the study compares the forecast error reduction of convolutional neural networks (CNNs), deep feed-forward neural networks (DFFNNs), combined/hybrid CNN-DFFNN models, and the transfer learning (TL) approach. The results indicate that the proposed combined model outperforms the CNN, hybrid CNN-DFFNN, and TL models by a small margin and the DFFNN by a large margin. Full article