Search Results (188)

Squirrel-cage induction generators often perform better without a mechanical speed sensor. Eliminating an encoder or resolver removes one of the most fragile and failure-prone components, while modern control algorithms can estimate speed with sufficient accuracy. Shaft-mounted sensors are vulnerable to heat, vibration, dust, moisture, and electrical noise; they require precise mounting and additional cabling and typically fail long before the machine itself. In many industrial and marine applications, unplanned shutdowns are more often caused by damaged sensors or cables than by the generator. Unlike sensorless speed-detection methods developed for motoring operation, the proposed approach targets the generator mode, where both phase currents and the DC-link voltage are measured. It uses two indicators: the magnitude and sign of the active current, and the instantaneous rise in DC-link voltage when the converter output frequency matches the machine’s shaft speed. Because active current remains negative over a wide frequency range during start-up, its sign change alone cannot uniquely identify the synchronization point. In generator operation, however, the DC-link capacitor voltage provides an additional criterion: the speed at which power reverses sign, indicated by a change in the sign of the DC-voltage derivative. As the inverter frequency approaches the machine rotational frequency from below, the DC voltage increases, reaches a maximum at maximum slip, and then decreases once the inverter frequency exceeds the machine speed. The article demonstrates how these signals can be used in practice to identify the rotational speed of a squirrel-cage generator. Full article

This paper presents a PSCAD-based analysis of short-circuit faults and protection characteristics in a real distribution-level microgrid that integrates a 1 MWh battery energy storage system (BESS) with a 500 kW power conversion system (PCS) and a 500 kW photovoltaic (PV) plant connected to a 22.9 kV feeder. While previous studies often rely on simplified inverter models, this paper addresses the critical gap by integrating actual manufacturer-defined control parameters and cable impedances. This allows for a precise analysis of sub-millisecond transient behaviors, which is essential for developing robust protection schemes in inverter-dominated microgrids. The PSCAD model is first verified under grid-connected steady-state operation by examining PV output, BESS power, and grid voltage at the point of common coupling. Based on the validated model, DC pole-to-pole faults at the PV and ESS DC links and a three-phase short-circuit fault at the low-voltage bus are simulated to characterize the fault current behavior of the grid, BESS and PV converters. The DC fault studies confirm that current peaks are dominated by DC-link capacitor discharge and are strongly limited by converter controls, while the AC three-phase fault is mainly supplied by the upstream grid. As an initial application of the model, an instantaneous current change rate (ICCR) algorithm is implemented as a dedicated DC-side protection function. For a pole-to-pole fault, the ICCR index exceeds the 100 A/ms threshold and issues a trip command within 0.342 ms, demonstrating the feasibility of sub-millisecond DC fault detection in converter-dominated systems. Beyond this example, the validated PSCAD model and associated data set provide a practical platform for future research on advanced DC/AC protection techniques and protection coordination schemes in real BESS–PV microgrids. Full article

To improve the accuracy of cable temperature anomaly prediction and ensure the reliability of urban distribution networks, this paper proposes a multi-scale spatiotemporal model called MSST-Net (MSST-Net) for medium-voltage power cables in underground utility tunnels. The model addresses the multi-scale temporal dynamics and spatial correlations inherent in cable thermal behavior. Based on the monthly periodicity of cable temperature data, we preprocessed monitoring data from the KN1 and KN2 sections (medium-voltage power cable segments) of Guangzhou’s underground utility tunnel from 2023 to 2024, using the Isolation Forest algorithm to remove outliers, applying Min-Max normalization to eliminate dimensional differences, and selecting five key features including current load, voltage, and ambient temperature using Spearman’s correlation coefficient. Subsequently, we designed a multi-scale dilated causal convolutional module (DC-CNN) to capture local features, combined with a spatiotemporal dual-path Transformer to model long-range dependencies, and introduced relative position encoding to enhance temporal perception. The Sparrow Search Algorithm (SSA) was employed for global optimization of hyperparameters. Compared with five other mainstream algorithms, MSST-Net demonstrated higher accuracy in cable temperature prediction for power cables in the KN1 and KN2 sections of Guangzhou’s underground utility tunnel, achieving a coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) of 0.942, 0.442 °C, and 0.596 °C, respectively. Compared to the basic Transformer model, the root mean square error of cable temperature was reduced by 0.425 °C. This model exhibits high accuracy in time series prediction and provides a reference for accurate short- and medium-term temperature forecasting of medium-voltage power cables in urban underground utility tunnels. Full article

High-temperature superconducting (HTS) DC power devices operate at cryogenic temperatures to achieve high power density for aviation applications. Ensuring reliable operation requires an optimized insulation system capable of withstanding cryogenic DC stress. In this study, finite element numerical simulations were conducted to investigate the transient behavior of electric fields in HTS cable insulation under DC stress at cryogenic temperatures. The results demonstrate that the transient field distribution is strongly temperature-dependent, leading to prolonged high-field exposure near ground terminations. Strategies to mitigate electric field enhancement are proposed to improve insulation reliability, supported by a comparative evaluation of various insulating materials. The simulation-based insights provide design guidance for developing resilient insulation systems for HTS and other cryogenic DC devices. Full article

The reliability of the Medium-Voltage Direct-Current (MVDC) power supply system is crucial for train operation, as it powers control, communication, and other critical onboard systems. Accurately locating insulation faults within this system can significantly reduce troubleshooting difficulty and prevent major operational losses. This study addresses a key challenge in applying Time-Domain Reflectometry (TDR) for fault location in single-core cables of IT systems: the incident-end impedance mismatch caused by the variable characteristic impedance of such cables, which fluctuates with installation distance from a ground plane. First, the mechanism through which this mismatch attenuates the primary fault reflection and generates secondary reflections is theoretically modeled. A resistive-capacitive (RC) coupling network is then designed to achieve bidirectional impedance matching between the test equipment and the cable under test while maintaining essential DC isolation. Simulation and experimental results demonstrate that the proposed network effectively mitigates the mismatch issue. In experiments, it increased the proportion of the primary reflected wave entering the receiver by over 30 percentage points and suppressed the secondary reflection by approximately 80%. These improvements enhance waveform clarity and signal strength, directly leading to more accurate fault location. The proposed solution, validated in a railway context, also holds significant potential for improving insulation fault diagnosis in analogous high-voltage cable applications, such as electric vehicle powertrains. Full article

Wireless charging for drones is significant for solving problems such as the frequent manual plugging and unplugging of cables. A large number of densely packed transmitting coils and fully independent on-off control can precisely track the receiver with random access location. To balance the excitation area of the transmitter, additional hardware cost, and receiving voltage fluctuation, the wireless charging system of drones based on a D3-type transmitter is proposed in this article. The circuit model considering states of multiple switches is developed for three excitation modes. The dual-coil excitation mode is selected after comparative analysis. The transmitter reconfiguration method with low hardware cost and high receiving-excitation area ratio is proposed based on one detection sensor of DC current and one relay furtherly. Finally, an experimental prototype is built to verify the theoretical analysis and proposed method. When the output voltage fluctuation is limited to ±10%, the ratios of the maximum misalignment value in the x-axis and y-axis directions to the side length of the receiver reach 66.7% and 46.7%, respectively. The receiving-excitation area ratio of 37.5% is achieved, significantly reducing the excitation area not covered by the receiver. The maximum receiving power is 289.44 W, while the DC-DC efficiency exceeds 87.05%. Full article

Despite the obvious shift in daily commuting towards electromobility, internal combustion engines (ICEs) still dominate the market, particularly in the transport sector. Their main drawback—cold-start emissions—has driven the development of active control strategies beyond passive exhaust optimizations. An electrically heated catalytic converter (EHC) helps the catalytic converter reach the light-off temperature more quickly through active control; however, it places additional demands on the already strained onboard electrical power distribution network. This paper presents a case study comparing two power supply and control configurations for managing the temperature of the EHC: (i) a smart-switch-based approach using bang-bang control, and (ii) a DC/DC converter with a proportional–integral–derivative (PID) controller. To define key target requirements for a dedicated DC/DC converter suitable for real-world conditions, measurement data such as temperature and electrical power demand were gathered through preliminary pollutant emissions tests performed in a laboratory environment using a programmable bench power supply. For the selected test procedure, engine cold-start emissions using various heater power supply scenarios were reduced by a factor of 6 for Total Hydrocarbons (THC) and by a factor of 5 for Carbon Monoxide (CO). Based on a comparative analysis of power supply parameters, a custom four-leg interleaved Buck converter was developed to meet the target power requirement and to specifically reduce voltage overstress caused by parasitic inductances in the onboard distribution network during rapid load current transients. The efficiency of the proposed DC/DC converter reached 95.8%. Unlike a bang-bang-controlled smart switch, the use of the DC/DC converter reduces both electrical and thermal stress on the vehicle’s cable harness. Full article

High-voltage (HV) cable systems—particularly those insulated with cross-linked polyethylene (XLPE)—are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches—including condition-based and predictive strategies—framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment. Full article

Rapid evolution in electrified transportation and, in general, sustainability of electrical and electronic assets is turning the traditional power supply and utilization into something more complex and less known. This transition involves increasing operating voltage and specific power, as well as various types of power supply sources, from AC sinusoidal to DC and power electronics. This revolution, beneficial for asset efficiency and resilience, does come at the cost of increased risk of failure for electrical insulation systems. Intrinsic and extrinsic aging mechanisms are not completely known under DC and power electronics, and the risk of inception of partial discharges, PD, which is the most harmful extrinsic aging factor for electrical insulation, is as high, or even higher, compared with AC. To complicate the picture, electrical and electronic components can be used at different pressure levels, such as in aerospace, and it is known that partial discharge inception voltage, PDIV, drops down, and PD magnitude increases, lowering pressure. Models to predict PDIV for surface and internal discharges, as function of pressure, have been proposed recently, but they cannot be applied straightforwardly on practical asset components where type and locations of defects generating PD is unknown. This paper wants to close this application gap. Derivation and validation of an approximate, heuristic model able to predict PDIV at various pressure levels below and above the standard atmospheric pressure, SAP, are dealt with in this paper, referring to typical asset components such as cables, motors, printed circuit-boards, PCB, and under sinusoidal AC voltage. The good capability of the model to predict PDIV and any investigated pressure, from 3 to 0.05 bar, is validated by PD measurements performed using an innovative, automatic PD analytics software able to identify the typology of defect generating PD, i.e., whether surface or internal. Full article

Reliable DC cable insulation is crucial for photovoltaic (PV) systems and high-voltage DC (HVDC) networks. However, conventional materials such as cross-linked polyethylene (XLPE) and polyvinyl chloride (PVC) face challenges under prolonged DC stress—notably space charge buildup, dielectric losses, and thermal aging. Cross-linked polyolefin (XLPO) has emerged as a halogen-free, thermally stable alternative, but its comparative DC performance remains underreported. Methods: We evaluated the insulations of virgin XLPO, XLPE, and PVC PV cables under ±1 kV DC using time-domain indices (IR, DAR, PI, Loss Index), supported by MATLAB and FTIR. Multi-layer cable geometries were modeled in MATLAB to simulate radial electric field distribution, and Fourier-transform infrared (FTIR) spectroscopy was employed to reveal polymer chemistry and functional groups. Results: XLPO exhibited an IR on the order of 10⁸–10⁹ Ω, and XLPE (IR ~ 10⁸ Ω) and PVC (IR ~ 10⁷ Ω, LI ≥ 1) at 60 s, with favorable polarization indices under both polarities. Notably, they showed high insulation resistance and low-to-moderate loss indices (≈1.3–1.5) under both polarities, indicating controlled relaxation with limited conduction contribution. XLPE showed good initial insulation resistance but revealed polarity-dependent relaxation and higher loss (especially under positive bias) due to trap-forming cross-linking byproducts. PVC had the lowest resistance (GΩ-range) and near-unit DAR/PI, dominated by leakage conduction and dielectric losses. Simulations confirmed a uniform electric field in XLPO insulation with no polarity asymmetry, while FTIR spectra linked XLPO’s low polarity and PVC’s chlorine content to their electrical behavior. Conclusions: XLPO outperforms XLPE and PVC in resisting DC leakage, charge trapping, and thermal stress, underscoring its suitability for long-term PV and HVDC applications. This study provides a comprehensive structure–property understanding to guide the selection of advanced, polarity-resilient cable insulation materials. Full article

This study addresses the problem of minimizing cable costs in Standardized Photovoltaic Power Units (SPPUs) by proposing an integrated inverter placement optimization framework. A high-precision economic model is first established to quantify the cost of both direct current (DC) and low-voltage alternating-current (LV-AC) cables as a function of inverter location. To improve solution accuracy and efficiency, an enhanced particle swarm optimization algorithm, termed the Adaptive Classification Method PSO (ACM-PSO), is developed, featuring population classification strategies as well as adaptive inertia weighting and neighborhood learning strategies. The optimization process incorporates hierarchical trench planning, dynamic combiner-unit partitioning, and multi-scheme layout generation, ensuring that both spatial and economic factors are systematically considered. A case study on Unit 19 of a 350 MW flat-ground PV plant in Xinjiang, China, demonstrates that the proposed method reduces total cable investment to CNY 292,945, achieving a cost saving of 2.3–3.8% compared with conventional layouts. These results confirm not only the methodological innovation of ACM-PSO for constrained nonlinear PV layout problems, but also its practical generalizability, offering a replicable and scalable design paradigm for large-scale PV plants. Full article

To address the insulation degradation caused by moisture intrusion due to damage to the outer sheath of power cables, this study systematically analyzed the charge transport characteristics of XLPE cables at different water immersion stages using polarization/depolarization current (PDC) measurements. An evaluation method for assessing water immersion levels was proposed based on conductivity, charge density, and charge mobility. Experiments were conducted on commercial 10 kV XLPE cable samples subjected to accelerated water immersion for durations ranging from 0 to 30 days. PDC data were collected via a custom-built three-electrode measurement platform. The results indicated that with increasing immersion time, the decay rate of polarization/depolarization currents slowed, the steady-state current amplitude rose significantly, and the DC conductivity increased from 1.86 × 10⁻¹⁷ S/m to 2.70 × 10⁻¹⁵ S/m—a nearly two-order-of-magnitude increase. The Pearson correlation coefficient between charge mobility and immersion time reached 0.96, indicating a strong positive correlation. Additional tests on XLPE insulation slices showed a rapid rise in conductivity during early immersion, a decrease in breakdown voltage from 93.64 kV to 66.70 kV, and enhanced space charge accumulation under prolonged immersion and higher electric fields. The proposed dual-parameter criterion (conductivity and charge mobility) effectively distinguishes between early and advanced stages of cable water immersion, offering a practical approach for non-destructive assessment of insulation conditions and early detection of moisture intrusion, with significant potential for application in predictive maintenance and insulation diagnostics. Full article

As a critical component in high-voltage cable accessories, ethylene-propylene-diene monomer (EPDM) reinforced insulation faces severe issues of surface discharge and bulk breakdown at the insulation interface. To enhance the electrical resistance of EPDM bulk and insulation interfaces, the 4-allyloxy-2-hydroxybenzophenone was employed as a voltage stabilizer to modify EPDM. A systematic study was conducted on the influence of the voltage stabilizer on the DC breakdown strength of EPDM, the anti-migration properties of the voltage stabilizer, and its effect on the surface breakdown voltage of EPDM. Additionally, pressure and surface breakdown test setups were designed. The results indicate that the DC breakdown strength of EPDM decreases with increasing external pressure, and this decline is more pronounced in EPDM modified with the voltage stabilizer. Surface breakdown experiments demonstrate that the voltage stabilizer has a positive effect on improving the surface breakdown voltage of EPDM, with a more significant enhancement observed at the EPDM/XLPE bilayer dielectric interface. Surface potential tests reveal that the grafted voltage stabilizer introduces numerous shallow traps, inhibiting surface charge accumulation and thereby increasing the surface breakdown voltage. Full article

This paper aims to unveil the symmetry–asymmetry transition mechanisms in transient fault waveforms of offshore wind power AC/DC transmission systems, addressing the critical limitation of traditional simulation methods of the fact that they cannot characterize the dynamic evolution of system symmetry, such as static impedance adjustment failing to capture transient asymmetry caused by parameter imbalance or converter control. It proposes a fault waveform simulation approach integrating mechanism analysis, scenario extraction, and model optimization. Key contributions include clarifying the quantitative links between key system parameters like submarine cable capacitance and inductance and symmetry–asymmetry characteristics, defining the transient decay rate oscillation frequency and voltage peak as core indicators to quantify symmetry breaking intensity; classifying typical fault scenarios into a symmetry-breaking type with synchronous three-phase imbalance and a persistent asymmetry type with zero-sequence and negative-sequence distortion based on symmetry evolution dynamics and revising grid-connection test indices such as lowering the low-voltage ride-through threshold and specifying the voltage type for different test objectives; and constructing a simplified embedded RLC second-order model with symmetry–asymmetry constraints to reproduce the whole process of symmetric steady state–fault symmetry breaking–recovery symmetry reconstruction. Simulation results verify the method’s effectiveness, with symmetry indicator reproduction errors ≤ 5% and asymmetric feature fitting goodness R² ≥ 0.92, which confirms that the method can effectively reveal the symmetry–asymmetry mechanisms of offshore wind power fault transients and provides reliable technical support for improving offshore wind power fault simulation accuracy and grid-connection test reliability, laying a theoretical basis for the grid-connection testing of offshore wind turbines and promoting the stable operation of offshore wind power systems. Full article

Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data. Full article