Search Results (42)

This study develops a novel transverse steel damper (TSD) to enhance the seismic performance of tall-pier girder bridges, featuring superior lateral strength and energy dissipation capacity. The TSD’s design and arrangement are presented, with its hysteretic behavior simulated in ABAQUS. Key parameters (yield strength: 3000 kN; initial gap: 100 mm; post-yield stiffness ratio: 15%) are optimized through seismic analysis under near-fault ground motions, incorporating pulse characteristic investigations. The optimized TSD effectively reduces bearing displacements and results in smaller pier top displacements and internal forces compared to the bridge with fixed bearings. Due to the higher-order mode effects, there is no direct correlation between top displacements and bottom internal forces. As pier height decreases, the S-shaped shear force and bending moment envelopes gradually become linear, reflecting the reduced influence of these modes. Medium- to long-period pulse-like motions amplify seismic responses due to resonance (pulse period ≈ fundamental period) or susceptibility to large low-frequency spectral values. Higher-order mode effects on bending moments and shear forces intensify under prominent high-frequency components. However, the main velocity pulse typically masks the influence of high-order modes by the overwhelming seismic responses due to large spectral values at medium to long periods. Full article

The increasing attention to medium-voltage direct current (MVDC) distribution networks is motivated by the need to efficiently connect renewable energy sources and DC loads. However, fast and reliable protection strategies remain a key challenge due to the rapid rise and high magnitude of DC fault currents. This paper proposes a protection strategy for MVDC distribution networks considering network reconfiguration. The strategy integrates a fault-detection scheme based on the product of the rate of change in current and voltage (ROCOC × ROCOV) and a fault-identification scheme based on the ratio of the magnitudes of the positive and negative pole voltages. In a radial topology, the sign of ROCOC × ROCOV provides selectivity between internal and external faults. In multi-terminal topologies under network reconfiguration, external faults can present characteristics similar to those of internal faults. To ensure selectivity, communication is introduced between protective relays that share the same protection zone. Thresholds were set without large-scale simulations. The protection strategy was implemented in PSCAD/EMTDC and evaluated in a 37.4 kV MVDC distribution network. The strategy was validated under various fault conditions in radial and multi-terminal MVDC distribution networks, demonstrating fast, sensitive, and selective performance. The proposed strategy can contribute to the stable operation of MVDC distribution networks. Full article

Bearings are critical yet vulnerable components in mechanical equipment, with potential failures that can significantly impact system performance. As stochastic resonance methods effectively convert noise energy into fault characteristic energy within bearing vibration signals, they remain a research focus in bearing fault diagnosis. This study proposes a coupled neuron model based on biological stochastic resonance effects for processing bearing vibration signals. To enhance parameter optimization, we develop an improved deep reinforcement learning algorithm that incorporates a prioritized experience replay buffer into the network architecture. Using the SNR as the evaluation metric, the algorithm performs data screening on the replay buffer parameters before training the deep network for predicting coupled neuron model performance. In terms of experimental content, the study performed data processing on simulated signals and vibration signals of gearbox bearing faults collected in the laboratory environment. By comparing the coupled neuron model optimized with a reinforcement learning algorithm, particle swarm algorithm, and quantum particle swarm algorithm, the experimental results show that the coupled neuron model optimized with a deep reinforcement learning algorithm has the optimal signal-to-noise ratio of the output signal and recognition rate of the bearing faults, which are −13.0407 dB and 100%, respectively. The method shows significant performance advantages in realizing the energy enhancement of the bearing fault eigenfrequency and provides a more efficient and accurate solution for bearing fault diagnosis, which has important engineering application value. Full article

Rolling bearing fault diagnosis in electric hoists faces significant challenges due to heavy noise and complex vibration interferences, which obscure fault signatures and hinder conventional demodulation methods. While existing techniques like the Teager–Kaiser energy operator (TKEO) and its variants (e.g., HO-AEO, SD-AEO) offer filterless demodulation, their susceptibility to noise and dependency on preprocessing limit diagnostic accuracy. This study proposes a Multi-resolution Higher-order Symmetric Analytic Energy Operator (MHSAEO) to address these limitations. The MHSAEO integrates three innovations: (1) dynamic non-adjacent sampling to suppress stochastic errors, (2) AM-FM dual demodulation via symmetric energy orthogonality, and (3) adaptive spectral mining for full-band feature extraction. Experimental validation on a 10-ton electric hoist bearing system demonstrates that the MHSAEO achieves signal-to-noise ratio improvements (SNRIs) of −3.83 dB (outer race faults) and −2.12 dB (inner race faults), successfully identifying the characteristic fault frequencies of both inner (145.9 Hz) and outer races in electric hoist bearings with 2nd–5th harmonics. Compared to traditional methods, the MHSAEO reduces computational time by 30.1 × (0.0328 s vs. 0.9872 s) without requiring preprocessing. The results confirm its superior anti-interference capability and real-time performance over the TKEO, HO-AEO, and hybrid denoising–TKEO approaches. Full article

The integration of variable distributed energy sources (DERs) can reduce overall system inertia, potentially impacting the transient response of both conventional and renewable generators within electrical grids. Although transient stability indicators—for instance, the Critical Clearing Time (CCT), fault-induced short-circuit current ratios, and machine parameters, including subtransient–transient reactances and associated time constants—are influenced by total system inertia, their detailed evaluation remains insufficiently explored. These parameters provide standardized benchmarks for systematically assessing the transient stability performance of conventional and photovoltaic (PV) generators as the penetration level of distributed PV systems (PVD1) increases. This study explores the relationship between conventional stability parameters and system inertia across different levels of PV penetration. CCT, a key metric for transient stability assessment, incorporates multiple influencing factors and typically increases with higher system inertia, making it a reliable comparative indicator for evaluating the effects of PV integration on system stability. To investigate this, the IEEE New England 39-bus system is adapted by replacing selected synchronous machines with PVD1 PV units and adjusting the PV penetration levels. The resulting system behavior is then compared to that of the original configuration to evaluate changes in transient stability. The findings confirm that transient and subtransient reactances, along with their respective time constants under fault conditions, are shaped not only by the characteristics of the generator on the faulted line but also by the surrounding network structure and overall system inertia. The newly introduced sensitivity parameters offer insights by capturing trends specific to conventional versus PV-based generators under different inertia scenarios. Notably, transient parameters show similar responsiveness to inertia variations to subtransient ones. This paper demonstrates that in certain scenarios, the integration of low-inertia PV generators may generate insufficient energy, which is not above critical energy during major disturbances, resulting surviving fault and subsequently an infinite CCT. While the integration of PV generators can be beneficial for their own operational performance, it may adversely impact the dynamic behavior and fault response of conventional synchronous generators within the system. This highlights the need for effective planning and control of DER integration to ensure reliable power system operation through accurate selection and application of both conventional and proposed transient stability parameters. Full article

Fault location technology is crucial for enhancing the efficiency of fault maintenance and ensuring the safety of the power supply in small current grounding systems. To address the challenge that traditional single-end positioning methods experience when identifying the reflected wave head and that the adaptability of wave head calibration methods is typically limited, a single-end location method of modulus wave velocity differences based on marine predator algorithm optimized multivariate variational mode decomposition (MVMD) and morphological gradient is proposed. Firstly, the minimum comprehensive entropy kurtosis ratio is used as the fitness function, and the marine predator algorithm is used to realize the automatic optimization of the mode number and penalty factor of the multivariate variational mode decomposition. Therefore, with the goal of decomposing the traveling wave characteristic signals with the most significant traveling wave characteristic information and the lowest noise component, the line-mode traveling wave and the zero-mode traveling wave are accurately decomposed. Secondly, the intrinsic mode function component with the smallest entropy kurtosis ratio is selected as the line-mode traveling wave characteristic signal and the zero-mode traveling wave characteristic signal, respectively, and the arrival time of the wave head is accurately calibrated by combining the morphological gradient value. Finally, the fault distance is calculated by the modulus wave velocity difference location formula and compared with the variational mode decomposition-Teager energy operator (VMD-TEO) method and the empirical mode decomposition _first-order difference method. The results show that the proposed method has the highest accuracy of positioning results, and the algorithm time is significantly reduced compared with the VMD-TEO method, and it has strong adaptability to different line types of faults, different fault initial conditions, and noise interference. Full article

Marine Current Turbines (MCTs) play a critical role in converting the kinetic energy of water into electricity. However, due to the influence of marine organisms, marine current equipment often experiences imbalance faults. Additionally, affected by the underwater environment, the fault characteristics are submerged in disturbances such as waves and turbulence. Against the background of the above problems, this article proposes a fault detection strategy based on a Generalized Likelihood Ratio Test (GLRT) detector. Firstly, a simulation model of the MCT system is established to obtain prior knowledge. Then, combining the Matrix Pencil Method (MPM) for calculating instantaneous frequency, imbalance fault metrics are selected based on the proposed GLRT detector. At the end, the marine current turbine experimental platform is established, which can simulate imbalanced faults and environmental disturbances, helping to verify the effectiveness of the proposed strategy. The experimental results indicate that the proposed strategy can detect imbalanced faults in complex underwater environments. Imbalance faults are the main manifestation of blade attachments. Thus, it is very meaningful to accomplish fault detection in order to maintain the working order of the MCT system. Full article

With the increasing demand for safety and sustainable development in the power industry, the research on synthetic esters as a new type of insulating oil is becoming more and more important. This article takes pentaerythritol ester as the research object, simulates its fault conditions under partial discharge (low-energy discharge) and spark discharge (high-energy discharge), studies the dielectric and gas production characteristics, and explores the applicability of the existing fault diagnosis methods for synthetic esters. The results show that during partial discharge, the dielectric constant and loss of the synthetic ester increase, and the resistivity decreases. With the increase of discharge time, the relative percentage of CO continuously decreases, while that of C₂H₆ and C₂H₄ continuously increases. Since the C₂H₄ content of the synthetic ester is significantly higher than that of the mineral oil, the commonly used diagnostic methods are not suitable for the partial discharge fault diagnosis of the synthetic ester. In spark discharge, the gap distance has little impact on the starting voltage but a great impact on the breakdown time, and the dielectric performance deteriorates seriously. C₂H₂ is the characteristic gas of the synthetic ester spark discharge, and a large amount is produced, but with the increase of the gap distance, its relative percentage continuously decreases. For the spark discharge fault diagnosis of the synthetic ester, the improved three-ratio method and DUVAL triangle method have certain applicability. Comparing different discharge energies, the increase of the discharge energy aggravates the deterioration of the synthetic ester. Under high-energy discharge, the dielectric constant increases significantly, the hydrocarbons increase rapidly, and the C₂H₂ content increases sharply. Finally, the Duval triangle was modified according to the gas generation characteristics. This research can provide data support for the application and fault diagnosis of synthetic esters in power transformers. Full article

Aiming to address the difficulty in extracting the early weak fault features of bearings under complex operating conditions, a fault diagnosis method is proposed based on the adaptive fusion of time-varying filtering empirical mode decomposition (TVF-EMD) modal components and singular value decomposition (SVD) noise reduction. First, the snake optimization (SO) technique is used to optimize the TVF-EMD algorithm in order to determine the optimal parameters that match the input signal. Then, the bearing signal is divided into a number of intrinsic mode functions (IMFs) using TVF-EMD in order to reduce the nonlinearity and non-stationary characteristics of the fault signal. An index for the envelope fault information energy ratio (EFIER) is created to overcome the drawback of there being too many IMF components after TVF-EMD decomposition. The IMF components are ranked in descending order according to the EFIER, and they are fused according to the maximum principle of the energy ratio of envelope fault information until the optimal fusion component is determined. Finally, the fault feature is extracted when the optimal fusion component is denoised using SVD. Two measured bearing fault signals and simulation signals are used to validate the performance of the proposed method. The experimental findings demonstrate that the approach has good sensitive feature screening, fusion, and noise reduction capabilities. The proposed method can more precisely extract the early fault features of bearings and accurately identify fault types. Full article

Ideally, the vibration signal of a rolling bearing should be symmetrical. However, in practical operation, the vibration signals in both time and frequency domains often exhibit asymmetry due to factors such as load, speed, and wear. The relatively weak composite fault characteristics are easily masked. Although the Feature Modal Decomposition (FMD) method is outstanding in diagnosing composite faults in bearings, its effectiveness is easily constrained by parameter selection. To address this, this paper proposes a stepwise parameter adaptive FMD method combined with a clustering algorithm, specifically designed for diagnosing composite faults in rolling bearings. Firstly, this study employs the Density Peak Clustering algorithm to determine the number of modes n in the composite fault vibration signal. Subsequently, considering the signal spectral energy and modal characteristics, a new composite fault index is formulated, namely, the adaptive weighted frequency domain kurtosis-to-information entropy ratio, as the fitness function. The Whale Optimization Algorithm determines the filter length L and the number of segments K, thereby achieving step-wise signal decomposition. Through in-depth analysis of signal symmetry and asymmetry, simulation and experimental verification confirm the effectiveness of this method. Compared with four other index-optimized FMD methods and traditional techniques, this method significantly reduces the influence of parameters on FMD, is capable of separating the characteristic frequencies related to composite faults, and performs excellently in the diagnosis of composite faults in rolling bearings. Full article

The modular multilevel converter (MMC) high-voltage direct current (HVDC) transmission technology is essential for overcoming the challenges of large-scale renewable energy integration. Line protection is critical for ensuring system safety. However, existing protection methods for MMC-HVDC transmission lines face difficulties in withstanding both high resistance and noise interference, frequently leading to failures in detecting internal high-resistance faults or triggering false operations due to noise. This paper first derives the theoretical expression of the line-mode voltage through analytical methods. By analyzing the second derivative of the line-mode voltage under different fault conditions, this paper constructs a criterion based on the ratio of the integrals of the positive and negative components of the second derivative of the line-mode voltage. This criterion enables effective fault discrimination by utilizing the characteristic differences in the second-derivative waveform. The proposed criterion allows for precise fault identification, requiring only a 0.5 ms time window to detect faults. Additionally, this criterion is highly resistant to transition resistance, remaining unaffected by resistances up to 500 Ω. Moreover, an entropy-based auxiliary criterion is introduced to prevent false operations caused by noise interference. Simulation results using PSCAD/EMTDC demonstrate that the proposed protection scheme can swiftly and reliably detect faults, with a detection time of 0.5 ms and robust performance against both high transition resistance and noise interference. Full article

Shear-dominated hazards, such as induced earthquakes, pose an escalating threat to the sustainability and safety of the geothermal exploitation. Variations in fault orientations and compression–shear stress ratios exert a profound influence on the failure processes underlying these disasters. To better understand these effects on the shear failure mechanisms of hot dry rocks, mode-II fracturing tests on granites were conducted at varying loading angles (specifically, 55°, 60°, 65°, and 70°). These tests were accompanied by a comprehensive analysis of the mechanical properties, energy dissipation behavior, acoustic emission (AE) responses, and digital image correlation (DIC)-extracted displacement fields. The tensile–shear properties of stress-induced microcracks were discerned via AE characteristic parameter analysis and DIC displacement decomposition, and the mode-II fracture energy release rate was quantitatively characterized. The results reveal that with increasing compression–shear loading angles, the mechanical properties of granites are weakened, and the elastic strain energy at peak stress gradually decreases, while the slip-related dissipated energy increases. Throughout the fracturing process, the AE count progressively climbs and reaches a peak near catastrophic failure, with an upsurge in low-frequency and high-amplitude AE events. Microcrack distribution concentrates aggregation along the shear plane, reflecting the emergent displacement discontinuities evident in DIC contours. Both the AE characteristic parameter analysis and DIC displacement decomposition demonstrate that shear-sliding constitutes the paramount mechanism, and the fraction of shear-oriented microcracks and the ratio of tangential versus normal displacement escalate with increases in shear stress. This analysis is supported by the heightened propensity for transgranular microcracking events observed through scanning electron microscopy. As the shear-to-compression stress increases, the energy concentration along the shear band intensifies, with the gradient of the fitting line between cumulative AE energy and slip displacement steepening, indicative of a heightened mode-II energy release rate. These results contribute to a deeper understanding of the mode-II fracture mechanism of rocks, thereby providing a foundational basis for early warnings of shear-dominant geomechanical disasters, and improving the safety and sustainability of subsurface rock engineering. Full article

Grid-forming (GFM) converters are regarded as the most promising solution for grid-connected converters of renewable energy due to their robustness against weak grids. However, attributable to their voltage source characteristics, GFM converters may experience overcurrent issues during large disturbances. The virtual impedance (VI) method is an effective method to solve this problem. Nevertheless, there exists a contradiction between the demands for VI posed by current limitations and transient stability. Firstly, the analytical equations of virtual impedance for limiting the fault steady-state current of a GFM converter with different depths of grid voltage sag are solved. On that basis, an adaptive method based on virtual impedance is proposed to limit the fault current. Moreover, the analytical equation of the output active power of the converter when using adaptive virtual impedance to limit the fault current is solved. On this basis, the effect of the virtual impedance ratio on the transient stability is investigated. Finally, an adaptive virtual-impedance-based current-limiting method with the functionality of transient stability enhancement for a grid-forming converter is innovatively proposed. The enhancement effect of the method is verified by the equal area criterion method and phase portrait method. Finally, the efficacy of the proposed method in limiting fault currents and enhancing transient stability is validated through hardware-in-the-loop (HIL) experiments. Full article

In order to improve the reliability and life of the wind turbine, this paper takes the rolling bearing in the experimental platform of the wind turbine as the research object. In order to obtain the intrinsic mode function (IMF) of each fault type, the original signals of different fault states of the rolling bearing on the experimental platform are decomposed by using the overall average empirical mode decomposition method (EEMD) and the wavelet packet decomposition method (WPD), respectively. Then the energy ratio of the IMF component of the different types of faults to the total energy value is calculated and the eigenvectors of different types of faults are constructed. The extreme learning machine (ELM) and probabilistic neural network (PNN) are used to learn fault types and eigenvector samples to identify the faults of the rolling bearing. It is found that the bearing fault characteristics obtained by the WPD method are more obvious, and the results obtained by the same recognition method are ideal; and the PNN method is obviously superior to the extreme learning machine method in bearing fault recognition rate. Full article

The carrier eccentricity error and gear compound faults are most likely to occur simultaneously in an actual planetary gear train (PGT). Various faults and errors are coupled with each other to generate a complex dynamic response, which makes the diagnosis of PGT faults difficult in practice. In order to analyze the joint effect of the error and the compound faults in a PGT, a carrier eccentricity error model is proposed and incorporated into the TVMS model by considering the time-varying center distance, line of action (LOA), meshing angle, and contact ratio. Then, the TVMS of the cracked gear is derived based on the potential energy method. On this basis, the dynamic model of a PGT with both the carrier eccentricity error and compound gear cracks as internal excitations are established. Furthermore, the meshing characteristics and dynamic responses of the PGT are simulated to investigate the compound fault features. A series of experiments are conducted to further analyze the influence of the compound fault on the vibration response. The relevant conclusions can provide a reference for the compound fault diagnosis of a PGT in practice. Full article