Search Results (77)

The totem-pole bridgeless power factor correction (PFC) circuit based on the triangular current mode (TCM) in the front-end PFC of a switching power supply has the advantage of realizing zero-voltage switching (ZVS) in the full working range. However, the TCM control based on the critical conduction mode (CRM) further increases the inductance current ripple, and the traditional input voltage AC sampling circuit increases the circuit complexity and device cost. Therefore, this paper studies the corresponding improvement technology from two dimensions. Firstly, the coordinated interleaved parallel technology is employed to design the system’s overall control-improvement strategy. This approach not only achieves full working-range ZVS but also reduces both the inductor current ripple and power device stress. Simultaneously, an optimized input voltage sampling circuit is designed to accommodate varying voltage requirements of control chip pins. This circuit demonstrates strong synchronization in both voltage and phase sampling, and the structural characteristics of the optocoupler can also suppress electrical signal interference. Finally, a 600 W totem-pole bridgeless PFC prototype is developed. The experimental results demonstrate the effectiveness of the proposed improved method. The prototype efficiency peak reaches 97.3%. Full article

Intravenous infusion is an important clinical medical intervention, and its safety is critical to patient recovery. To mitigate the elevated risk of complications (e.g., air embolism) arising from delayed response to infusion endpoints, this paper designs a flexible double pole capacitive (FPB) sensor, which includes a main pole plate, an adaptive pole plate, and a back shielding electrode. The sensor establishes a mapping between residual liquid volume in the infusion bottle and its equivalent capacitance, enabling a non-contact adaptive monitoring system. The system enables precise quantification of residual liquid levels, suppressing baseline drift induced by environmental temperature/humidity fluctuations and container variations via an adaptive algorithm, without requiring manual calibration, and overcomes the limitations of traditional rigid sensors when adapting to curved containers. Experimental results showed that the system achieved an overall sensitivity of 753.5 $\mathrm{f}\mathrm{F}/\mathrm{m}\mathrm{m}$, main pole plate linearity of 1.99%, and adaptive pole plate linearity of 0.53% across different test subjects, linearity of 0.53% across different test subjects, with liquid level resolution accuracy reaching 1 mm. These results validate the system’s ultra-high resolution (1 mm) and robust adaptability. Full article

This study presents a practical and scalable framework for the mid- to long-term distribution network planning that reflects real-world infrastructure constraints and investment requirements. While traditional methods often rely on simplified network models or reactive reinforcement strategies, the proposed approach introduces an investment-oriented planning model that explicitly incorporates physical elements such as duct capacity, pole availability, and installation feasibility. A linear programming (LP) formulation is adopted to determine the optimal routing and sizing of new facilities under technical constraints including voltage regulation, power balance, and substation capacity limits. To validate the model’s effectiveness, actual infrastructure and load data were used. The results show that the model can derive cost-efficient expansion strategies over a five-year horizon by prioritizing existing infrastructure use and flexibly adapting to spatial limitations. The proposed approach enables utility planners to make realistic, data-driven decisions and supports diverse scenario analyses through a modular structure. By embedding investment logic directly into the network model, this framework bridges the gap between high-level planning strategies and the engineering realities of distribution system expansion. Full article

Focusing on the common problems of phase-locked loop dependence, multiple current sensor requirements, a large number of controllers, and complex settings in traditional parallel active power filter (APF) control methods, this paper proposes a harmonic compensation control strategy based on an improved proportional resonant (PR) controller. The proposed method introduces an instantaneous power theory to construct a reference current model, which relies solely on grid voltage and current signals, does not require load-side current detection and phase-locked loop modules, and effectively simplifies the sensor configuration and system structure. At the same time, compared with the traditional solution that requires PR modules to be configured for each order of harmonics, this study only uses one set of PR controllers for fundamental current tracking, which has advantages in terms of compactness and computing resource occupation. To guide the controller parameter setting, this paper systematically discusses the influence of changes in K_p and K_r on pole distribution and dynamic performance based on discrete domain modeling and root locus analysis methods. The results were verified on the MATLAB/Simulink simulation platform and the 1 kVA experimental platform and compared with the traditional control method that requires the use of phase-locked loops (PLLs), load current sensors, and multiple PR controllers. The simulation and experimental results show that the proposed method has achieved a certain degree of optimization in terms of harmonic suppression effect, dynamic response performance, and system structure complexity. Full article

Underactuated systems, such as the Cart–Pole and Acrobot, pose significant control challenges due to their inherent nonlinearity and limited actuation. Traditional control methods often struggle to achieve stable and optimal performance in these complex scenarios. This paper presents a novel stable reinforcement learning (RL) approach for underactuated systems, integrating advanced exploration–exploitation mechanisms and a refined policy optimization framework to address instability issues in RL-based control. The proposed method is validated through extensive experiments on two benchmark underactuated systems: the Cart–Pole and Acrobot. In the Cart–Pole task, the method achieves long-term balance with high stability, outperforming traditional RL algorithms such as the Proximal Policy Optimization (PPO) in average episode length and robustness to environmental disturbances. For the Acrobot, the approach enables reliable swing-up and near-vertical stabilization but cannot achieve sustained balance control beyond short time intervals due to residual dynamics and control limitations. A key contribution is the development of a hybrid PPO–sliding mode control strategy that enhances learning efficiency and stabilities for underactuated systems. Full article

To address the challenges of prolonged current isolation times and high dependency on varistors in traditional flexible short-circuit fault isolation schemes for DC systems, this paper proposes a rapid fault isolation circuit design based on an adaptive solid-state circuit breaker (SSCB). By introducing an adaptive current-limiting branch topology, the proposed solution reduces the risk of system oscillations induced by current-limiting inductors during normal operation and minimizes steady-state losses in the breaker. Upon fault occurrence, the current-limiting inductor is automatically activated to effectively suppress the transient current rise rate. An energy dissipation circuit (EDC) featuring a resistor as the primary energy absorber and an auxiliary varistor (MOV) for voltage clamping, alongside a snubber circuit, provides an independent path for inductor energy release after faults. This design significantly alleviates the impact of MOV capacity constraints on the fault isolation process compared to traditional schemes where the MOV is the primary energy sink. The proposed topology employs a symmetrical bridge structure compatible with both pole-to-pole and pole-to-ground fault scenarios. Parameter optimization ensures the IGBT voltage withstand capability and energy dissipation efficiency. Simulation and experimental results demonstrate that this scheme achieves fault isolation within 0.1 ms, reduces the maximum fault current-to-rated current ratio to 5.8, and exhibits significantly shorter isolation times compared to conventional approaches. This provides an effective solution for segment switches and tie switches in millisecond-level self-healing systems for both low-voltage (LVDC, e.g., 750 V/1500 V DC) and medium-voltage (MVDC, e.g., 10–35 kV DC) smart DC distribution grids, particularly in applications demanding ultra-fast fault isolation such as data centers, electric vehicle (EV) fast-charging parks, and shipboard power systems. Full article

This study examined the design and optimization of low-speed external frame motors featuring Halbach-type and olive-shaped magnet structures to improve performance in spacecraft control moment gyroscopes (CMGs). Our research was driven by the urgent need for precise, high-torque, low-speed motors in CMGs, where conventional designs, including Halbach-type and traditional radial magnet configurations, are hindered by manufacturing complexity and excessive torque pulsation. This study focused on optimizing rotor pole configurations to enhance efficiency and torque stability. An olive-shaped magnet structure provides a more uniform magnetic field distribution in the air gap, substantially reducing magnetic field harmonics and minimizing cogging torque and torque pulsation—critical performance factors for low-speed applications. Comparative analysis reveals that the olive-shaped motor achieves a peak torque of 0.312 N·m with a torque pulsation of 0.9 mN·m, maintaining an amplitude below 0.3%. This demonstrates a 20% improvement compared to the Halbach-type motor’s torque pulsation of 1.15 mN·m. Moreover, the olive-shaped motor exhibits superior stability in air-gap magnetization under different loads, ensuring high efficiency and robust operation. By streamlining magnet assembly while enhancing electromagnetic performance, this study offers a cost-effective, high-precision solution for CMG systems. These findings underscore the olive-shaped magnet motor’s potential to advance motor technology for aerospace applications. Full article

The intelligent extraction of highway assets is pivotal for advancing transportation infrastructure and autonomous systems, yet traditional methods relying on manual inspection or 2D imaging struggle with sparse, occluded environments, and class imbalance. This study proposes an enhanced MinkUNet-based framework to address data scarcity, occlusion, and imbalance in highway point cloud segmentation. A large-scale dataset (PEA-PC Dataset) was constructed, covering six key asset categories, addressing the lack of specialized highway datasets. A hybrid conical masking augmentation strategy was designed to simulate natural occlusions and enhance local feature retention, while semi-supervised learning prioritized foreground differentiation. The experimental results showed that the overall mIoU reached 73.8%, with the IoU of bridge railings and emergency obstacles exceeding 95%. The IoU of columnar assets increased from 2.6% to 29.4% through occlusion perception enhancement, demonstrating the effectiveness of this method in improving object recognition accuracy. The framework balances computational efficiency and robustness, offering a scalable solution for sparse highway scenes. However, challenges remain in segmenting vegetation-occluded pole-like assets due to partial data loss. This work highlights the efficacy of tailored augmentation and semi-supervised strategies in refining 3D segmentation, advancing applications in intelligent transportation and digital infrastructure. Full article

Delays are known to complicate closed-loop stability analysis and controller design. In the present work, the problem of the stabilization and control of a particular class of linear systems with several real/complex conjugate stable poles and one unstable pole with delay in the direct path is considered. In this work, a methodology is proposed to design a hybrid predictor that consists of continuous and discrete signals to address systems with delay. The proposed hybrid predictor provides a continuous estimation of variables of interest, which is an important consideration, in contrast to the control strategies based on a discrete domain approach. The key point of this proposal is to guarantee the existence of the hybrid predictor, without any restriction on the delay size, when the delay is not divisible by an integer for an appropriate implementation of the sampling period, as in the traditional discrete approach. Furthermore, the proposed methodology is not restricted by the order of the plant, its instability or the size of the delay. The effectiveness of the results is illustrated by numerical simulations performed on academic examples. Full article

In order to improve the current dynamic performance of induction motor (IM) drive systems, an internal model current decoupling control strategy is proposed to suppress the stator’s internal coupling effect. First, the IM mathematical model in the complex frequency domain is established, and the expressions of the coupling terms are derived. Then, according to the zero-pole distribution diagram and Bode plots, the design details for the structure and parameters of the internal model controller are presented in a continuous domain, and the impact of stator inductance mismatch on decoupling performance is analyzed. In addition to considering sampling and control delays, the IM mathematical model is established in the discrete domain, and the principle of the controller parameter is presented. Finally, the experimental results prove that the proposed internal model current decoupling control strategy can effectively improve the dynamic performance of IMs. Compared with the traditional feedforward current decoupling control strategy, the proposed method has superior decoupling performance under the operating conditions of low switching frequency. At the same time, a better steady-state performance is obtained by the proposed internal model control strategy. Full article

The AC/DC transmission system is an important component of the power system, and the cross-circuitry Fault diagnosis of the AC/DC transmission system plays an important role in ensuring the normal operation of power equipment and personal safety. The traditional AC/DC transmission detection methods have the characteristics of complex detection processes and low fault line identification rates. Aiming at such problems, this paper proposes a new method of cross-circuitry Fault diagnosis based on the AC/DC transmission system based on a blind signal separation algorithm. Firstly, the method takes the typical cross-circuitry Fault scenario as an example to construct the topology diagram of the AC/DC power transmission system. Then, the electrical signals of the AC system and the DC system of the AC/DC power transmission system are collected, and the collected signals are extracted by the blind signal separation algorithm. Then, aiming at the cross-circuitry Fault problem of the DC system, the electrical quantities of the positive and negative poles on the rectifier side and the inverter side are collected, and the characteristics of the electrical quantities are analyzed by wavelet to determine the fault. At the same time, aiming at the problem of the cross-circuitry Fault of the AC system, three fault types of cross-circuitry Fault, ground fault, and intact fault are set up, and the electrical quantities of A, B, and C are collected on the same side, and the characteristics of three-phase electrical quantities are analyzed by wavelet. Finally, the cross-circuitry Fault judgment interval of the AC/DC system is set as the basis of fault judgment. After experimental verification, the relative error of the model is 1.4683%. The crossline fault identification method of the AC/DC transmission system based on the blind source separation algorithm proposed in this paper can accurately identify the crossline fault location and identify the fault type. It also provides theoretical and experimental support for power system maintenance personnel to maintain equipment. Full article

In Daoist cosmology, the southern celestial star deities are represented by terms such as the Southern Dipper, the Southern Palace, the Southern Chang, and the South Pole, each with specific cosmological significance. These concepts are closely intertwined with the themes of longevity and fire-refining, yet they often blend together to such an extent that clear distinctions become difficult. Through an exploration of this series of concepts, this paper reveals that during the Six Dynasties, the ancient Lingbao scriptures inherited the mythological tradition of earlier religions, in which “fire” symbolized the alchemical refining process. These scriptures extol “fire” as a powerful force for purification and regeneration, and it is said that the Primordial Heavenly Lord once employed flames to refine the “true scriptures”, which represented the cosmic primordial essence. Such fiery transformation finds vivid expression in the legend of A-Qiuzeng. By bathing in sacred flames, this female ascetic underwent bodily transformation into a male form, exemplifying fire’s role as a catalyst for the transformation of existence. Crucially, the Lingbao scriptures utilized the Five Elements doctrine (with fire corresponding to the south) to synthesize the disparate cults of southern celestial star deities into a coherent system. This synthesis highlights the sacred religious function of the southern celestial star deities in “refining death and transcending life” through fire. Moreover, it distills their philosophical significance in mediating the transitions between life and death. Since their compilation during the Six Dynasties, this paradigm has continuously influenced the development of Daoist doctrines of refining and transcendence, as well as their corresponding ritual frameworks. Full article

This paper presents a cost-efficient estimation method, the filtering observer (FOBS), which provides a smooth estimation through prior estimation, enhancing the field-oriented control (FOC) performance of motion control systems by estimating the angular rotor position, angular rotor velocity, and disturbance torque of permanent magnet synchronous motors (PMSMs). The cost-effective FOBS demonstrates characteristics akin to optimal estimating methods and employs arbitrary pole placement, facilitating more straightforward adjustment of the FOBS gain. The non-linear characteristics of low-resolution and low-cost encoders, the computation of angular rotor velocity using traditional techniques, and disturbances over broad frequency ranges in the servo drive system impair the efficacy of the motion control system. As a cost-effective solution, the FOBS minimizes the deficiencies of the low-cost encoder, reduces oscillations and measurement delays in the speed feedback signal, and provides smooth estimation of disturbance torque. Based on the results from experiments, the FOBS was compared against traditional approaches and the performance of the motion control system was examined. Also, the performance of the motion control system was investigated. The results indicate that these enhancements were achieved with low processing power and an easily implementable estimate technique suitable for low-cost industrial systems. Full article

With the growing penetration of renewable energy sources, ensuring the stability and reliability of Medium-Voltage Direct Current (MVDC) systems has become more critical than ever. A single fault in MVDC systems can cause significant disturbances, necessitating rapid and precise diagnostics to prevent equipment damage and maintain continuous power supply. In this work, we present a Bidirectional Gated Recurrent Unit (Bi-GRU) model that both classifies and locates MVDC faults. By capturing the temporal behavior of voltage signals, the Bi-GRU framework surpasses traditional algorithms such as Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM) networks. Furthermore, the proposed approach addresses multiple fault scenarios including PTP (Pole-to-Pole), PPTG (Positive Pole-to-Ground), and NPTG (Negative Pole-to-Ground) while preserving real-time diagnostic capabilities. In extensive tests, our model achieves an overall accuracy of 95.54% and an average fault detection time below 1.3 ms, meeting real-world operational requirements. To assess robustness, sensor noise was artificially introduced to emulate realistic conditions. Despite these challenging inputs, our method consistently maintained high diagnostic accuracy, confirming its practicality and reliability. Consequently, the proposed scheme demonstrates a significant contribution toward improving the safety and dependability of MVDC systems, even under noisy conditions. Full article

This paper proposes an innovative algorithm for forecasting the motion response of floating offshore wind turbines by employing force-to-motion transfer functions and state-space models. Traditional numerical integration techniques, such as the Newmark-β method, frequently struggle with inefficiencies due to the heavy computational demands of convolution integrals in the Cummins equation. Our new method tackles these challenges by converting the problem into a system output calculation, thereby eliminating convolutions and potentially enhancing computational efficiency. The procedure begins with the estimation of force-to-motion transfer functions derived from the hydrostatic and hydrodynamic characteristics of the wind turbine. These transfer functions are then utilized to construct state-space models, which compactly represent the system dynamics. Motion responses resulting from initial conditions and wave forces are calculated using these state-space models, leveraging their poles and residues. We validated the proposed method by comparing its calculated responses to those obtained via the Newmark-β method. Initial tests on a single-degree-of-freedom (SDOF) system demonstrated that our algorithm accurately predicts motion responses. Further validation involved a numerical model of a spar-type floating offshore wind turbine, showing high accuracy in predicting responses to both regular and irregular wave conditions, closely aligning with results from conventional methods. Additionally, we assessed the efficiency of our algorithm over various simulation durations, confirming its superior performance compared to traditional time-domain methods. This efficiency is particularly advantageous for long-duration simulations. The proposed approach provides a robust and efficient alternative for predicting motion responses in floating offshore wind turbines, combining high accuracy with improved computational performance. It represents a promising tool for enhancing the development and evaluation of offshore wind energy systems. Full article