Search Results (206)

Under dynamic loading conditions, the output voltage of proton exchange membrane fuel cells (PEMFCs) exhibits nonlinear degradation characterized by non-Gaussian fluctuations, abrupt changes, and long-range temporal dependence, which are difficult to model using conventional short-correlation or remaining useful life (RUL) prediction approaches. To capture both historical dependency and stochastic jump behavior, this study proposes a SHAP-driven mechanism–data fusion fractional stochastic degradation model based on fractional Brownian motion (fBm) and fractional Poisson process (fPp) for degradation trend forecasting. A terminal voltage mechanism model considering activation, ohmic, and concentration polarization losses is first established, and SHapley Additive exPlanations (SHAP) analysis is employed to quantify the contributions of multi-source operational variables and enhance interpretability. The Hurst exponent is then used to verify long-range dependence and jump characteristics in the voltage sequence. Subsequently, fBm is integrated with a fPp to construct a unified stochastic degradation framework capable of jointly describing continuous decay and discrete abrupt variations, enabling multi-step probabilistic prediction with confidence intervals. Validation on the publicly available FCLAB FC1 and FC2 datasets shows that the proposed model achieves superior overall performance under both steady and dynamic conditions, with MAPE/RMSE/R² of 0.027%/0.00178/0.9895 and 0.056%/0.00259/0.9896, respectively, outperforming fBm, Wiener, WTD-RS-LSTM, and CNN-LSTM methods. The proposed approach provides accurate and interpretable degradation forecasting for PEMFC health management and maintenance decision support. Full article

Security threats in the integrated circuit (IC) supply chain are intensifying as demand drives fabrication to off-shore, potentially untrusted foundries. To mitigate theft and reverse engineering, recent work has focused on logic locking, encryption, and camouflaging. This paper introduces a probabilistic logic-driven algorithm that selects optimal locations for polymorphic gate replacement to strengthen circuit protection. Our approach leverages emerging polymorphic devices—namely the Giant Spin-Hall Effect (GSHE) switch, the 5-terminal magnetic domain wall motion (DWM) device, and the threshold-voltage-defined (TVD) switch—to diversify functional behavior and obscure true circuit intent. Evaluated on ISCAS-85 and ISCAS-89 benchmarks under state-of-the-art SAT and AppSAT Attacks, the proposed method substantially increases decryption time while achieving a marked improvement in Output Corruption Rate (OCR) relative to prior techniques. In particular, by deploying the GSHE Switch at the highest-probability nodes, we achieve more than 40% OCR along with strong resilience against SAT and AppSAT Attacks, further demonstrating the effectiveness of the proposed approach as a practical and scalable hardware obfuscation strategy. Full article

With the expansion of urban rail transit, increased train operation density, and the large-scale grid integration of renewable energy such as offshore photovoltaic power, traction power supply systems face stricter requirements for operational safety, power supply reliability and energy utilization efficiency. Offshore photovoltaic power, integrated into the traction power supply network via flexible DC transmission technology, promotes renewable energy consumption, but its random and volatile output overlaps with time-varying traction loads, increasing the complexity of DC-side fault characteristics and protection control. Flexible DC technology is a core direction for next-generation traction substations, and three-level converters (key energy conversion units) have advantages over traditional two-level topologies. However, their P-O-N three-terminal DC-side topology introduces new faults (e.g., PO/ON bipolar short circuits, O-point-to-ground faults), making traditional protection strategies ineffective. In addition, wide system current fluctuation (0.5–3 kA) and offshore photovoltaic power fluctuation easily cause fixed-threshold protection maloperation, and the coupling mechanism among modulation strategies, DC bus capacitor voltage dynamics and fault current paths is unclear. To solve these bottlenecks, this paper establishes a simulation model of the system based on the PSCAD/EMTDC(A professional simulation software for electromagnetic transient analysis in power systems V4.5.3) platform, analyzes the transient electrical characteristics of three-level converters under traction and braking conditions for typical faults, clarifies the coupling mechanism, proposes a condition-adaptive fault identification strategy, and designs a reconfigurable fault energy handling system with bypass thyristors and adaptive crowbar circuits. Simulation and hardware-in-the-loop (HIL) experiments show that the proposed scheme completes fault identification and protection within 2–3 ms, suppresses fault peak current by more than 70%, limits DC bus overvoltage within ±10% of the rated voltage, and has good post-fault recovery performance. It provides a reliable and engineering-feasible protection solution for related systems and technical references for similar flexible DC system protection design. Full article

Reliable state-of-charge (SoC) estimation is crucial for safe and efficient battery management. However, it is challenging in practice. Terminal-voltage sensitivity becomes weak in open-circuit-voltage (OCV) plateau regions. Model uncertainty also persists at practical sampling periods. To tackle this issue, this paper proposes a discrete-time, model-based SoC estimation framework. This framework combines a dual-polarization equivalent-circuit model with a tuning-light sliding-mode observer. It is specifically designed for digitally sampled battery management systems. The modeling stage includes: (i) a discrete-time DP representation suitable for embedded use, (ii) a shape-preserving PCHIP reconstruction of the OCV–SoC curve and its derivative, and (iii) an effective-slope regularization mechanism that maintains non-vanishing output sensitivity even in flat OCV regions. On top of this structure, a boundary-layer SMO is developed with output-error shaping, model-driven gain scaling, and simple bias-compensation terms based on integral correction and leaky Coulomb counting. A discrete-time Lyapunov analysis is conducted directly on the surface dynamics. This analysis shows finite-time reaching to the boundary layer and a practical limit on the steady-state error that depends on the sampling period, disturbance level, and boundary-layer width. Numerical tests on a DP model identified from experimental data indicate that the proposed method achieves SoC accuracy similar to a switching-gain adaptive SMO. The results confirm the benefits of a model-centric design. The discrete-time formulation and convergence proof, which do not depend on high sampling rates, provide robustness advantages over traditional sliding-mode methods. The proposed method also performs better than a tuned EKF in plateau regions, requiring much less tuning effort. Full article

Conventional multi-channel ultrasonic motor (USM) drive systems commonly adopt a one-motor–one-driver architecture, in which each drive channel requires an independent isolated power supply and inverter stage. As the number of motors increases, the system volume and structural complexity grow significantly. To address this issue, this paper proposes a shared DC-bus multi-channel drive architecture for traveling-wave USM. In the proposed scheme, multiple half-bridge power stages are connected in parallel to a common high-voltage DC-bus to achieve centralized energy supply and distributed driving. A DC-side midpoint reference network is introduced to establish an AC voltage reference under a unipolar DC supply, while an independent series matching inductor is employed in each channel to shape the half-bridge output into a quasi-sinusoidal motor-terminal voltage through resonant filtering. Based on the equivalent electrical model of the USM, a unified analytical model is established to analyze the voltage formation mechanism under shared DC-bus conditions. Time-domain simulations and experimental tests are carried out on a two-channel prototype operating at a 150 V DC-bus and a 40 kHz switching frequency. The results demonstrate stable quasi-sinusoidal output voltages, preserved phase consistency, and limited inter-channel coupling during parallel operation. Compared with conventional independent-supply solutions, the proposed architecture achieves an approximately 27% reduction in overall system volume for a three-motor configuration, demonstrating good scalability for compact multi-channel USM drive systems. Full article

In large wind farms, uneven voltage distribution caused by feeder impedance and turbine spacing may pose safety hazards and reduce operational efficiency. This paper proposes a two-layer voltage coordination optimal control method for wind farms that balances both grid-connection point voltage levels and turbine-end voltage safety. The outer layer tracks voltage commands issued by the AVC master station at the point of common coupling (PCC), while the inner layer establishes a global optimization model considering generator terminal voltage safety. The second-order cone relaxation method converts nonlinear constraints into solvable convex forms. Through a two-layer iterative solution, it achieves optimal allocation of active and reactive power between wind turbines and static var compensators (SVGs) within the field, thereby enhancing the active power output at the wind farm port and increasing the system’s reactive power margin. Simulation results demonstrate that compared to conventional unified power factor control, the proposed method effectively enhances terminal voltage security, increases wind farm power generation, and boosts system reactive power reserve capacity while stably tracking PCC voltage commands. Full article

Most electric vehicles use 400 V batteries, while some companies are moving to 800 V to reduce current in electric drives. More cars are expected to adopt 800 V at the DC terminals of the batteries, but 400 V will remain common for the duration of this transition, so future off-board chargers must support a wide voltage output range. Silicon carbide switches are used to keep the power–electronics interface compact and scalable. The AC/DC stage of a modular silicon carbide-based interface is designed using a T-type active front end and a dual active bridge. The T-type front end is optimized with a genetic algorithm. The resulting model is used to tune the inner current and outer voltage controllers. Bode analysis shows an inner current loop bandwidth of 4.25 kHz with a phase margin of ${53}^{°}$ and a gain margin of 30 dB. The outer voltage loop reaches 50 Hz with a phase margin of ${108}^{°}$ and a gain margin of 33 dB. The controller is implemented on a dSPACE MicroLabBox. Tests show peak efficiency of 98.5% in G2V mode and 98.3% V2G mode. THD stays under 5% above 4 kW and reaches 3% at peak power. Full article

Ionic polymer–metal composite (IPMC) sensors generate voltages or currents when subjected to deformation. The magnitude and time constant of the electrical response vary significantly with ambient humidity and water content. However, most conventional physical models focus solely on cation dynamics and do not consider water dynamics. In addition to cation dynamics, Zhu’s model explicitly incorporates the dynamics of water. Consequently, Zhu’s model is considered one of the most promising approaches for physical modeling of IPMC sensors. This paper presents exact analytical solutions to Zhu’s model of IPMC sensors for the first time. The derivation method transforms Zhu’s model into the frequency domain using Laplace transform-based analysis together with linear approximation, and subsequently solves it as a boundary value problem of a set of linear ordinary differential equations. The resulting solution is expressed as a transfer function. The input variable is the applied bending deformation, and the output variables include the open-circuit voltage or short-circuit current at the sensor terminals, as well as the distributions of cations, water molecules, and electric potential within the polymer. The obtained transfer functions are represented by irrational functions, which typically arise as solutions to a system of partial differential equations. Furthermore, this paper presents analytical approximations of the step response of the sensor voltage or current by approximating the obtained transfer functions. The steady-state and maximum values of the time response are derived from these analytical approximations. Additionally, the relaxation behavior of the sensor voltage is characterized by a key parameter newly derived from the analytical approximation presented in this paper. Full article

To address the lack of inertia in full-power converter wind turbines and the inability of existing mechanical speed regulation technologies to achieve power smoothing without converters, this paper proposes a novel hybrid wind energy storage system integrating a Continuously Variable Transmission (CVT) and an electromechanical flywheel. This system establishes a cascaded topology featuring “CVT-based source-side speed regulation and electromechanical flywheel-based terminal power stabilization.” By utilizing the CVT for speed decoupling and introducing the flywheel via a planetary differential branch, the system retains physical inertia by eliminating large-capacity converters and overcomes the bottleneck of traditional mechanical transmissions, which struggle to balance constant frequency with stable power output. Simulation results demonstrate that the proposed system reduces the active power fluctuation range by 47.60% compared to the raw wind power capture. Moreover, the required capacity of the auxiliary motor is only about 15% of the rated power, reducing the reliance on power electronic converters by approximately 85% compared to full-power converter systems. Furthermore, during a grid voltage dip of 0.6 p.u., the system restricts rotor speed fluctuations to within 0.5%, significantly enhancing Low Voltage Ride-Through (LVRT) capability. Full article

Induction motors (IMs) are widely used in variable-speed electric drive systems, where the motor is supplied by a voltage source inverter (VSI). Thus, PWM inverter–IM combination presents several issues that can degrade system performance, particularly overvoltage phenomena when long cables are used. In inverter-fed drive systems, the physical separation between the converter and the motor often requires long motor cables, which can significantly affect voltage stress. As the inverter’s output pulses propagate through the cable, voltage reflections and high-frequency oscillations occur at the motor terminals. We theoretically and experimentally investigate the effect of three PWM methods, namely Space Vector (SVPWM), Selective Harmonic Elimination PWM (SHEPWM), and Random PWM (RPWM) strategies, on overvoltage at the terminals of an induction motor fed by a PWM inverter through a long cable. The simulation results exhibit the validity and efficiency of SVPWM control to reduce overvoltage for different cable lengths. In addition, in order to reduce and eliminate all overvoltage peaks, three filters are proposed and evaluated: an RC filter, an RLC filter, and a compensator. The proposed PWM strategies are assessed using equivalent experimental results obtained on an induction motor fed by a two-level VSI. The experimental tests demonstrate also the efficiency of the SVPWM compared to other strategies. Full article

The safe and stable operation of distribution networks relies on the real-time monitoring, analysis, and feedback of power quality data. However, with the continuous advancement of distribution network construction, the number of distributed power electronic devices has increased significantly, leading to frequent power quality issues such as voltage fluctuations, harmonic pollution, and three-phase unbalance in distribution terminals. Therefore, the augmentation and processing of power quality data have become crucial for ensuring the stable operation of distribution networks. Traditional methods for augmenting and processing power quality data fail to consider the differentiated characteristics of burrs in signal sequences and neglect the comprehensive consideration of both time-domain and frequency-domain features in disturbance identification. This results in the distortion of high-frequency fault information, and insufficient robustness and accuracy in identifying Power Quality Disturbance (PQD) against the complex noise background of distribution networks. In response to these issues, we propose a power quality data augmentation and processing method for distribution terminals considering high-frequency sampling. Firstly, a burr removal method of the sampling waveform based on a high-frequency filter operator is proposed. By comprehensively considering the characteristics of concavity and convexity in both burr and normal waveforms, a high-frequency filtering operator is introduced. Additional constraints and parameters are applied to suppress sequences with burr characteristics, thereby accurately eliminating burrs while preserving the key features of valid information. This approach avoids distortion of high-frequency fault information after filtering, which supports subsequent PQD identification. Secondly, a PQD identification method based on a dual-channel time–frequency feature fusion network is proposed. The PQD signals undergo an S-transform and period reconfiguration to construct matrix image features in the time–frequency domain. Finally, these features are input into a Convolutional Neural Network (CNN) and a Transformer encoder to extract highly coupled global features, which are then fused through a cross-attention mechanism. The identification results of PQD are output through a classification layer, thereby enhancing the robustness and accuracy of disturbance identification against the complex noise background of distribution networks. Simulation results demonstrate that the proposed algorithm achieves optimal burr removal and disturbance identification accuracy. Full article

With the rapid growth in coal demands driven by economic development, the applications scenarios of long-distance, high-power mining operations in underground coal mines has gradually been expanded. Taking voltage regulation technology for long-distance power supply in underground coal mines as the research object, this paper analyzed the mechanisms behind voltage fluctuation hazards and the status quo of existing voltage regulation technologies in coal mines and put forward a voltage regulation technology for long-distance power supply in tunneling faces of coal mines based on on-load voltage regulation. On this basis, a voltage regulation device for long-distance power supply in underground coal mines was designed and applied to the long-distance power supply system of Wanli Coal Mine’s tunneling faces. All indicators met the design requirements, validating the effectiveness of the device and the applicability of the research outcomes, and providing an effective solution for addressing voltage fluctuations in the long-distance power supply of underground coal mines. Full article

This study presents the development of a buck–boost converter for application in unmanned guided vehicles (UGVs). The converter was designed with its input connected to a lithium iron phosphate battery pack and its output connected to an inverter. This configuration enabled the inverter, which powered the drive motor, to receive a stable DC voltage, thereby mitigating the effects of battery voltage fluctuations and enhancing the overall system stability. A pulse-width modulation (PWM) controller was employed to regulate the developed buck–boost converter. During the transition from buck mode to buck–boost mode, both power MOSFETs were simultaneously turned on; however, the datasheet of the PWM controller did not provide operational details or characteristic curve analysis for this mode. Therefore, this study derived the relationship between voltage gain and duty cycle ratio for the transition mode. To analyze the input voltage versus duty cycle characteristics, the linear equation was employed. This analytical model was adjusted to meet different converter specifications developed for experimental validation. Furthermore, the external-connect test capacitor method was used to extract the equivalent parasitic inductance and capacitance present in the practical circuit of the buck–boost converter. Based on these parameters, a snubber circuit was designed and connected across the drain–source terminals of the power MOSFETs to suppress voltage spikes occurring at the junctions. Finally, the developed buck–boost converter prototype was installed on an unmanned guided vehicle to convert the power from the lithium battery pack into the input power required by two inverters. A computer host was used to control the motor speed. By measuring the output voltage and current of the buck–boost converter, its electrical functionality and performance specifications were verified. The dimensions of the developed UGV chassis prototype were 40 cm in length, 45 cm in width, and 18.3 cm in height. Full article

This paper presents an adaptive fuzzy filter applied to processing a signal from a voltage sensor fed to the input of an object in an automatic temperature control system with a PI controller. (1) The research goal was to develop an algorithm for processing the signal from an RMS voltage sensor, measured at the terminals of a heating element in a temperature control system with a PI controller, in a way that ensures good dynamic properties while maintaining an appropriate level of accuracy. (2) The paper presents a method for designing an adaptive fuzzy filter by synthesizing a first-order low-pass infinite impulse response (IIR) filter and a fuzzy model of the dependence of this filter parameter value on the modulus of the derivative of the measured quantity. The application of a model with a symmetric input and output structure and a modified fuzzy model with asymmetry resulting from the uneven distribution of modal values of singleton fuzzy sets at the output was shown. The innovation in the proposed solution is the use of a signal from a PI controller to determine the derivative module of the measured quantity and, using a fuzzy model, linking its instantaneous value with a digital filter parameter in the measurement chain with a sensor monitoring the signal at the input of the controlled object. It is demonstrated that the signal generated by the PI controller can be used in a control system to continuously determine the modulus of the time derivative of the signal measured at the input of the controlled object, also indicating the limitations of this method. The signal from the PI controller can also be used to select filter parameters. In such a situation, it can be treated as a reference signal representing the useful signal. The mean square error (MSE) was adopted as the criterion for matching the signal at the filter output to the reference signal. (3) Based on a comparative analysis of the results of using an adaptive fuzzy filter with a classic first-order IIR filter with an optimal parameter in the MSE sense, it was found that using a fuzzy filter yields better results, regardless of the structure of the fuzzy model used (symmetric or asymmetric). (4) The paper demonstrates that in the tested temperature control system, introducing a simple fuzzy model with one input characterized by three fuzzy sets, relating the modulus of the derivative of the signal developed by the PI controller to the value of the first-order IIR filter parameter, into the voltage sensor signal-processing algorithm gave significantly better results than using a first-order IIR filter with a constant optimal parameter in terms of MSE. The best results were obtained using a fuzzy model in which an intentional asymmetry in the modal values of the output fuzzy sets was introduced. Full article

Wiegand sensors are essential components in self-powered Internet of Things (IoT) nodes, as they can output pulse voltages without an external power supply. Previous research has established that the attachment of ferrite beads to Wiegand wire terminals substantially enhances the sensor’s pulse voltage output. However, the fundamental mechanism responsible for this enhancement remains unclear at the microscopic magnetic level. This investigation systematically examines how ferrite bead attachments alter magnetization reversal processes, Barkhausen jump characteristics, and the energy output in Wiegand wires. Experimental results reveal that ferrite beads enhance irreversible magnetization, modify interaction distributions, and transform the magnetic structure of Wiegand wires. These modifications collectively result in a 1.5–2.0 times higher pulse voltage amplitude and 30–40% greater output energy, establishing a theoretical framework for Wiegand sensor optimization. The research methodology combines vibrating sample magnetometer (VSM) measurements with first-order reversal curve (FORC) analysis to elucidate the underlying micromagnetic mechanisms. Full article