Search Results (269)

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

This paper proposes a GNSS-compatible method for characterizing the motion of UAV-based navigation enhancement platforms, designed to provide reliable navigation and positioning services in emergency scenarios where GNSS signals are unavailable or severely degraded. The method maps UAV trajectories into standard GNSS navigation messages by establishing a correspondence between ephemeris parameters and platform positions through coordinate transformation and Taylor series expansion. To address modeling inaccuracies, the approach incorporates truncation error analysis and motion-assumption compensation via parameter optimization. This design enables UAV-mounted pseudolite systems to broadcast GNSS-compatible signals without modifying existing receivers, significantly enhancing rapid deployment capabilities in complex or degraded environments. Simulation results confirm precise positional representation in static scenarios and robust error control under dynamic motion through higher-order modeling and optimized broadcast strategies. UAV flight tests demonstrated a theoretical maximum error of 0.4262 m and an actual maximum error of 3.1878 m under real-world disturbances, which is within operational limits. Additional experiments confirmed successful message parsing with standard GNSS receivers. The proposed method offers a lightweight, interoperable solution for integrating UAV platforms into GNSS-enhanced positioning systems, supporting timely and accurate navigation services in emergency and disaster relief operations. Full article

In this paper, a robust neural adaptive controller is proposed for the trajectory tracking control problem of unmanned surface vessels (USVs), considering model uncertainty, time-varying environmental disturbance, and actuator saturation. First, measurement errors in acceleration signals are eliminated through filtering techniques and a series of auxiliary variables, and after linearly parameterizing the USV dynamic model, a parameter adaptive update law is developed based on Lyapunov’s second method to estimate unknown dynamic parameters in the USV dynamics model. This parameter adaptive update law enables online identification of all USV dynamic parameters during trajectory tracking while ensuring convergence of the estimation errors. Second, a radial basis function neural network (RBF-NN) is employed to approximate unmodeled dynamics in the USV system, and on this basis, a robust damping term is designed based on neural damping technology to compensate for environmental disturbances and unmodeled dynamics. Subsequently, a trajectory tracking controller with parameter adaptation law and robust damping term is proposed using Lyapunov theory and adaptive control techniques. In addition, finite-time auxiliary variables are also added to the controller to handle the actuator saturation problem. Signal delay compensators are designed to compensate for input signal delays in the control system, thereby enhancing controller reliability. The proposed controller ensures robustness in trajectory tracking under model uncertainties and time-varying environmental disturbances. Finally, the convergence of each signal of the closed-loop system is proved based on Lyapunov theory. And the effectiveness of the control system is verified by numerical simulation experiments. Full article

Smart tools are limited by actuation–sensing integration and structural redundancy, making it difficult to achieve compactness, ultra-precision feed, and immediate feedback. This paper proposes a self-sensing giant magnetostrictive actuator-based turning tool (SSGMT), which enables simultaneous actuation and output sensing without external sensors. A multi-objective optimization model is first established to determine the key design parameters of the SSGMT to improve magnetic transfer efficiency, system compactness, and sensing signal quality. Then, a dynamic hysteresis model with a Hammerstein structure is developed to capture its nonlinear characteristics. To ensure accurate positioning and a robust response, a hybrid control strategy combining feedforward compensation and adaptive feedback is implemented. The SSGMT is experimentally validated through a series of tests including self-sensing displacement accuracy and trajectory tracking under various frequencies and temperatures. The prototype achieves nanometer-level resolution, stable output, and precise tracking across different operating conditions. These results confirm the feasibility and effectiveness of integrating actuation and sensing in one structure, providing a promising solution for the application of smart turning tools. Full article

Wide-speed regulation control strategies for Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely applied in industrial fields. However, traditional algorithms are prone to being affected by motor parameter mismatches, sensor sampling errors, and other disturbances under complex operating conditions, leading to insufficient robustness. In order to enhance dynamic performance while simultaneously ensuring robustness, we analyzed the limitations of traditional control strategies and, based on this, proposed an improved control framework. A Multi-Axis Coordinated Extended State Observer(MCESO)-based robust control framework was developed for full-speed domain operation, which enhances disturbance rejection capability against parameter uncertainties and abrupt load changes through hierarchical disturbance estimation. Subsequently, the effectiveness and stability of the proposed method were verified through theoretical analysis and simulation studies. Compared with traditional control strategies, this method can effectively observe and compensate for a series of complex issues such as nonlinear disturbances during operation without requiring additional hardware support. Finally, extensive experimental tests were carried out on a 500 W IPMSM dual-motor drive platform. The experimental results demonstrated that, even under harsh operating conditions, the proposed scheme can effectively suppress torque ripple and significantly reduce current harmonics. Full article

Rotary Blood Pump (RBP) is a commonly used ventricular assist device. However, the constant speed operation of the blood pump leads to a reduction of blood flow pulsatility, which triggers a series of adverse reactions. In this paper, a pulsatile physiological control with feed-forward compensation (FFC) is designed to regulate the rotational speed in real time to accurately output pulsatile blood flow to address this problem. The coupled model of the Rotary Blood Pump and cardiovascular system (CVS) is established in the SIMULINK software as the research object. The designed pulsatile physiological control algorithm contains the feed-forward compensation-based pulsatile control and anti-reflux algorithm, switching the applicable algorithm based on the pump flow. When the flow rate is higher than the threshold, feed-forward compensation is introduced and combined with PI feedback control to improve the performance of pulsation tracking; when the flow rate is lower than the threshold, it is switched to the anti-reflux algorithm to gradually increase the pump speed. Simulation shows that the designed feed-forward compensation link reduces the tracking error of the pulsatile physiological control by 80%. In the case of a 50% sudden change of physiological parameters, it can track quickly and stably and avoid reflux. The pulsatile performance and ventricular unloading performance are better compared with no feed-forward compensation pulsation control as well as constant-speed control. An increase of 30 mmHg in aortic beat-to-beat differential pressure was achieved in the extracorporeal circulation experiments, which is important for the realization of pulsatile flow control of the Rotary Blood Pump. Full article

This paper proposes a supercapacitor-battery hybrid energy storage scheme based on a series-parallel hybrid compensation structure and model predictive control to address the increasingly severe power quality issues in oilfield microgrids. By adopting the series-parallel hybrid structure, the voltage compensation depth can be properly improved. The model predictive control with a current inner loop is employed for current tracking, which enhances the response speed and control performance. Applying the proposed hybrid energy storage system in an oilfield DC microgrid, the fault-ride-through ability of renewable energy generators and the reliable power supply ability for oil pumping unit loads can be improved, the dynamic response characteristics of the system can be enhanced, and the service life of energy storage devices can be extended. This paper elaborates on the series-parallel compensation topology, operational principles, and control methodology of the supercapacitor-battery hybrid energy storage. A MATLAB/Simulink model of the oilfield DC microgrid employing the proposed scheme was established for verification. The results demonstrate that the proposed scheme can effectively isolate voltage sags/swells caused by upstream grid faults, maintaining DC bus voltage fluctuations within ±5%. It achieves peak shaving of oil pumping unit load demand, recovery of reverse power generation, stabilization of photovoltaic output, and reduction of power backflow. This study presents an advanced technical solution for enhancing power supply quality in high-penetration renewable energy microgrids with numerous sensitive and critical loads. Full article

The sub-synchronous oscillation (SSO) characteristics and suppression strategies of a hybrid system comprising doubly fed induction generator (DFIG)-based wind turbines and synchronous pumped storage units connected to the power grid via series-compensated transmission lines are analyzed. A modular modeling approach is used to construct a detailed system model, including the wind turbine shaft system, DFIG, converter control system, synchronous machine, excitation system, power system stabilizer (PSS), and series-compensated transmission lines. Eigenvalue calculation-based small-signal stability analysis is conducted to identify the dominant oscillation modes. Suppression measures are also developed using relative participation analysis, and simulations are carried out to validate the accuracy of the model and analysis method. The analysis results indicate that the SSO phenomenon is primarily influenced by the electrical state variables of the DFIG system, while the impact of the state variables of the synchronous machine is relatively minor. When the level of series compensation in the system increases, SSO is significantly exacerbated. To address this issue, a sub-synchronous damping controller (SSDC) is incorporated on the rotor side of the DFIG. The results demonstrate that this method effectively mitigates the SSO and significantly enhances the system’s robustness against disturbances. Furthermore, a simplified modeling approach is proposed based on relative participation analysis. This method neglects the dynamic characteristics of the synchronous machine while considering its impact on the steady-state impedance and initial conditions of the model. These findings provide theoretical guidance and practical insights for addressing and mitigating SSO issues in hybrid renewable energy systems composed of DFIGs and synchronous machines. Full article

The rapid progress in artificial intelligence technologies has significantly impacted the global economy, driving transformative changes in manufacturing and giving rise to intelligent manufacturing. In this context, multivariable time-series data have become an essential resource for modern industries. This paper introduces the Point Energy Technology, an advanced system for energy monitoring and data acquisition developed by our team. The system has been successfully deployed with several industrial partners, including a combined heat and power system in a local industrial park. Despite its capabilities, data loss remains a persistent issue, which is often caused by measurement or transmission errors during the data collection and transfer stages. These errors result in the loss of vital data samples for effective process monitoring and control. To tackle this issue, we present a convolutional transformer imputation model that is based on self-attention to generate missing data samples. This model effectively captures both historical and future sequence information through an enhanced masking mechanism while also incorporating local dependency information through the symmetrically balanced use of convolution and self-attention. To evaluate the performance of the proposed model against classical models, the energy-related data from a local industrial park were used in this experiment. Considering the real-world conditions, the missing data were categorized into two types: continuous missing and random missing. The experimental results demonstrate that our model produced high-quality data samples, effectively compensating for gaps in the multivariable time-series data. Full article

Conventional Distributed Power Flow Controllers (DPFCs) rely on third-harmonic currents to facilitate active power exchange between the series side and the system, requiring specific Δ/YN and YN/Δ transformer configurations at branch terminals. This limitation restricts their application in distribution networks. To overcome these constraints, this paper proposes a Novel Distributed Power Flow Controller (NDPFC) topology specifically designed for distribution networks. This design eliminates the need for third-harmonic currents and specific transformer configurations, enhancing deployment flexibility. The paper first explains the NDPFC operating principles and verifies its power flow regulation capabilities through a typical distribution network system. Furthermore, we develop electromagnetic transient mathematical models for both series and shunt components of the NDPFC, proposing a triple-loop control strategy for Series-I and Series-II control methods to enhance system robustness and control precision. A systematic stability analysis confirms the proposed controller’s robustness under various operating conditions. Simulation results demonstrate that in various distribution network scenarios, the NDPFC effectively achieves comprehensive power flow regulation, compensates three-phase imbalances, and facilitates renewable energy integration, significantly improving distribution network power quality. A comparative analysis shows that the NDPFC achieves 15% faster response times and 12% lower losses compared to conventional power flow controllers. Full article

Hybrid simulation integrates numerical and experimental techniques to analyze structural responses under static and dynamic loads. It physically tests components that are not fully characterized while modeling the rest of the structure numerically. Over the past two decades, hybrid testing platforms have become increasingly modular and versatile. This paper presents the development of a robust hybrid testing software framework at the National Laboratory for Civil Engineering (LNEC), Portugal, and evaluates the efficiency of its algorithms. The framework features a LabVIEW-based control and interface application that exchanges data with OpenSees via the OpenFresco middleware using a TCP/IP protocol. Designed for slow to real-time hybrid testing, it employs a predictor–corrector algorithm for motion control, enhanced by an adaptive time series (ATS)-based error tracking and delay compensation algorithm. Its modular design facilitates the integration of new simulation tools. The framework was first assessed through simulated hybrid tests, followed by validation via a hybrid test on a two-bay, one-story steel moment-resisting frame, where one exterior column was physically tested. The results emphasized the importance of the accurate system identification of the physical substructure and the precise calibration of the actuator control and delay compensation algorithms. Full article

This paper proposes a frequency-modulated continuous-wave synthetic aperture radar (FMCW-SAR) imaging system for fast target detection. The system’s antenna array improves azimuthal resolution while maintaining low complexity using a 44-element equivalent virtual array and improves the data acquisition efficiency by employing the trigger and MCU control board. A series of improved algorithms are adopted to increase the speed of radar imaging and achieve fast detection. To solve the problem of large data volumes in traditional array antenna switching control methods, an array switching control algorithm is proposed based on the enhanced ordered statistical constant false alarm rate (EOS-CFAR). The data volume is reduced by dividing the array into several subarrays in advance. The echo signals acquired by the array switching control method are not continuous in the azimuthal direction, and data anomalies are handled by interpolating and compensating the received radar data to form compensated periodic data. The coherent background is subtracted from the padded signal using recursive averaging, resulting in high-resolution imaging while improving the data-processing speed. The TensorFlow-based Omega-K algorithm is employed for synthetic aperture radar (SAR) imaging, which customizes the optimization of TensorFlow for array radar signals. For the radar signal phase optimization, an improved Adam Optimizer optimizes the phase of the radar signal to maintain phase smoothing, thereby improving the clarity of the radar image. The Omega-K algorithm is optimized by TensorFlow and accelerated on the GPU to improve the efficiency of the large-scale fast Fourier transform (FFT) and Stolt interpolation operations, which improves the speed of radar imaging and enables fast detection. Full article

This paper proposes a combined maximum efficiency tracking and improved active disturbance rejection control (ADRC) strategy for an inductive power transfer (IPT) system, addressing issues of reduced efficiency and voltage fluctuations under load variations. The transmission characteristics of the inductor–capacitor–capacitor and series (LCC-S) IPT system are analyzed, and the relationship between transmission efficiency and the secondary DC-DC converter’s duty cycle is derived. Maximum efficiency tracking is achieved by adjusting the secondary converter’s duty cycle via the primary side Buck converter. An improved ADRC controller enhances dynamic voltage regulation by reducing the extended state observer’s order and incorporating model information for better disturbance compensation. Experimental results show that the proposed approach improves average transmission efficiency by 12% and maintains constant output voltage under varying loads. The controller requires fewer parameters than linear active disturbance rejection control (LADRC), with faster responses and smaller voltage fluctuations than PI and LADRC controllers. Full article

Series–series (SS) wireless power transfer (WPT) systems are used in many applications because of their simple circuit structure. Compared with higher-order complex compensation topology, they are suitable for more demanding applications, such as rail trams with high power requirements but limited space for the coupling mechanism. However, the characteristics of their voltage source also put forward higher requirements for the control strategy. Improving the dynamic response performance of an SS compensation WPT system without any communication between the primary and secondary sides is the key issue. This paper proposes an improved variable step-size maximum power point tracking control strategy with the mutual inductance identification. Compared with the conventional P&O control, it can achieve a faster response and more accurate tracking, which are very important to the WPT for rail transit. A method of the mutual inductance identification based on the weight of parameter sensitivity is proposed. Based on the results of the identified mutual inductance, to make the system transfer the maximum power, the duty ratio of the receiver is adjusted to approach the corresponding equivalent load. To deal with the change of the mutual inductance, a condition of terminating the searching process of the maximum power point and re-identifying the mutual inductance is proposed. A simulation and experimental platform is built for verification. The results show that the proposed control strategy can quickly respond to the variation of the mutual inductance and load and achieve accurate maximum power point location, which improves the performance of the SS compensation WPT system. Full article

Focusing on an energy-saving winch-type heave compensation system applicable to real working conditions, with the objective of enhancing compensation accuracy, a wavelet neural network was employed for platform velocity prediction, and the prediction results were applied to velocity disturbance compensation control. Initially, the ITTC two-parameter spectrum was utilized to generate wave spectral diagrams under different sea conditions, along with displacement and velocity data of the floating platform’s heave motion. Subsequently, a time-series-based wavelet neural network velocity prediction model was developed, trained, and tested. Comparative analyses were performed on prediction performance differences across varying prediction steps and sea condition levels. Then, the effectiveness of the time-series-based wavelet neural network prediction model was validated through a valve-controlled hydraulic cylinder heave motion simulation system. Experimental results indicated that the wavelet neural network-based velocity prediction method effectively improved the compensation accuracy of the winch-type heave compensation system. Finally, after verifying the effectiveness of the wavelet neural network prediction model based on time series, the compensation performance of the system after adding the velocity prediction module was tested and verified using the winch-type heave compensation simulation test bench built by the research team. After experimental verification, after adding velocity prediction, the compensation accuracy of the system was improved by 19% compared with that without velocity prediction. Full article