Search Results (69)

This study examines the behavior and control of zero-sequence parameters in IT-type electrical networks under conditions of capacitive insulation asymmetry and complex asymmetric faults on the power receiver side. Existing methods of zero-sequence analysis typically address either symmetrical network conditions or single-phase earth faults in isolation, and they often neglect the combined effects of conductor breakage, transient fault resistance, and capacitive unbalance. To overcome these limitations, this work develops an analytical model based on the general theory of electrical engineering and symmetrical components, enabling a unified description of zero-sequence voltages and currents that incorporates both insulation asymmetry and compound fault scenarios. The model establishes closed-form relationships linking zero-sequence quantities to network parameters, power receiver characteristics, and transient resistances at the fault point. The results demonstrate several previously unreported effects, including a 180° vector shift and nearly 50% reduction in zero-sequence voltage and current magnitudes during simultaneous conductor breakage and earth faults compared with conventional single-phase faults—phenomena that critically influence the correct setting of protection devices. The study further shows that capacitive insulation asymmetry alone may generate zero-sequence voltages sufficient to trigger earth-fault protection regardless of the neutral grounding mode. These findings reveal increased risks of fault escalation, misoperation of existing protection systems, and prolonged unsafe touch voltages. Overall, the derived dependencies provide a new analytical basis for improving the design and coordination of protection systems in IT-type networks. Full article

DC distribution networks exhibit inherent symmetry in their balanced power distribution and modular structure, offering high operational flexibility and making them particularly suitable for the integration of distributed generation and modern loads. This symmetric framework positions DC networks as a vital component of new power systems and a key development direction for future power supply systems in industrial and mining enterprises. However, pole-to-pole short-circuit faults disrupt this symmetry, characterized by low system damping, high fault currents, and extremely rapid current rise rates, which pose serious threats to system security and necessitate ultra-fast fault clearance. To address this issue, this paper proposes a novel pilot protection scheme inspired by symmetry principles, based on the slope of the line-mode current for pole-to-pole short-circuit faults in DC distribution networks. First, an equivalent circuit of the system before converter blocking under a pole-to-pole fault is established, and an analytical expression of the fault current is derived, incorporating symmetric analysis of modal components. Subsequently, the variation trends, amplitudes, and phase characteristics of the fault current under faults occurring in different zones of the DC line are analyzed from the perspective of modal symmetry, highlighting the symmetric and asymmetric behaviors of line-mode and zero-mode currents. Furthermore, considering the distinct symmetric properties of these currents during lightning disturbances and pole-to-pole faults, the least squares method is employed to perform linear fitting on the line-mode current, thereby capturing its symmetric variation trend. A pilot protection scheme utilizing the slope of the line-mode current is then proposed, leveraging symmetry in fault discrimination. Finally, simulation models built in MATLAB/Simulink (R2022a) are used for validation. The results demonstrate that the proposed protection method can quickly identify faults within 1.5 ms while exhibiting strong tolerance to a 20 Ω transitional resistance and 50 dB signal noise, indicating good feasibility and broad applicability, with symmetry-based analysis enhancing robustness. Full article

This paper introduces a modified soft margin optimal hyperplane (MSMOH) algorithm, which enhances the linear separating properties of support vector machines (SVMs) by placing higher penalties on large misclassification errors. This approach improves margin symmetry in both balanced and asymmetric data distributions. The research is divided into two main stages. The first stage evaluates MSMOH for synthetic data classification and its application in heart disease diagnosis. In a cross-validation setting with unknown data, MSMOH demonstrated superior average performance compared to the standard soft margin optimal hyperplane (SMOH). Performance metrics confirmed that MSMOH maximizes the margin and reduces the number of support vectors (SVs), thus improving classification performance, generalization, and computational efficiency. The second stage applies MSMOH as a novel synthesis algorithm to design a neural associative memory (NAM) based on a recurrent neural network (RNN). This NAM is used for fault diagnosis in fossil electric power plants. By promoting more symmetric decision boundaries, MSMOH increases the accurate convergence of 1024 possible input elements. The results show that MSMOH effectively designs the NAM, leading to better performance than other synthesis algorithms like perceptron, optimal hyperplane (OH), and SMOH. Specifically, MSMOH achieved the highest number of converged input elements $\left(1019\right)$ and the smallest number of elements converging to spurious memories $\left(5\right)$. Full article

Fault detection is critical to the resilience and operational integrity of electrical power grids, particularly smart grids. In addition to requiring a lot of labeled data, traditional fault detection approaches have limited flexibility in handling unknown fault scenarios. In addition, since traditional machine learning models rely on historical data, they struggle to adapt to new fault patterns in dynamic grid environments. Due to these limitations, fault detection systems have limited resilience and scalability, necessitating more advanced approaches. This paper presents a hybrid technique that integrates supervised and unsupervised machine learning with Generative AI to generate artificial data to aid in fault identification. A number of machine learning algorithms were compared with regard to how they detect symmetrical and asymmetrical faults in varying conditions, with a particular focus on fault conditions that have not happened before. A key feature of this study is the application of the autoencoder, a new machine learning model, to compare different ML models. The autoencoder, an unsupervised model, performed better than other models in the detection of faults outside the learning dataset, pointing to its potential to enhance smart grid resilience and stability. Also, the study compared a generative AI-generated dataset (D2) with a conventionally prepared dataset (D1). When the two datasets were utilized to train various machine learning models, the synthetic dataset (D2) outperformed D1 in accuracy and scalability for fault detection applications. The strength of generative AI in improving the quality of data for machine learning is thus indicated by this discovery.By emphasizing the necessity of using advanced machine learning techniques and high-quality synthetic datasets, this research aims to increase the resilience of smart grid networks through improved fault detection and identification. Full article

With the increasing demands of the grid on power electronic converters, active neutral-point-clamped (ANPC) converters have been widely adopted due to their flexible modulation strategies and wide-range power regulation capabilities. To address grid-integration testing requirements for ANPC converters, this paper comparatively studies three electromagnetic transient (EMT) modeling approaches: switch-state prediction method (SPM), associated discrete circuit (ADC), and time-averaged method (TAM). Steady-state and transient simulations reveal that the SPM model achieves the highest accuracy (error ≤ 0.018%), while the TAM-based switching function model optimizes the efficiency–accuracy trade-off with 6.4× speedup versus traditional methods and acceptable error (≤2.62%). Consequently, the TAM model is implemented in a real-time hardware-in-the-loop (HIL) platform. Validation under symmetrical/asymmetrical grid faults confirms both the model’s efficacy and the controller’s robust fault ride-through capability. 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

In low-voltage (LV) distribution networks, system operating conditions are always unbalanced due to the unpredictability of the load demand in each phase, coupled with a potentially asymmetrical network structure due to different phase conductors’ sizes and lengths. The widespread diffusion of distributed generators (DGs) among network users has significantly contributed to reducing the overall load of the electrical system, but at the cost of making voltages slightly more unbalanced. In this article, an LV distribution test network equipped with several single-phase DGs has been considered, and all During-Fault Voltages (DFVs) have been studied, according to each possible type of short circuit. To provide a measure of the asymmetry of unsymmetrical voltage dips, three different indices based on the symmetrical components of the voltages have been considered; moreover, the Monte Carlo simulation (MCS) method has allowed for studying faults and asymmetries in a probabilistic manner. Through the probability density functions (pdfs) of the DFVs, it has been possible to assess the impact of single-phase DGs on the asymmetry of bus voltages due to short-circuits. Full article

To address the persistent challenges in diagnosing bearing faults, this study proposes an intelligent diagnostic framework based on the principle that mechanical faults manifest as symmetry-breaking phenomena in a system’s vibration signals. In a healthy motor, vibration signals exhibit a high degree of symmetry, whereas faults introduce identifiable and distinct asymmetries. This study constructs a high-fidelity digital twin model based on the five-dimensional model theory to simulate both the symmetrical (healthy) state and various asymmetrical faulty states of motor bearings—specifically, inner race, outer race, and rolling element faults—thereby effectively addressing the critical issue of data scarcity. Building upon this framework, fault features characterizing these asymmetries are accurately extracted using an optimized variational mode decomposition (VMD) algorithm and subsequently classified with a convolutional neural network–bidirectional long short-term memory (CNN-BiLSTM) model. The results validate the model’s ability to accurately replicate bearing-fault data. The proposed diagnostic method achieves a stable and high average accuracy of 98.44 ± 0.41% over multiple runs on the simulation data. Furthermore, its effectiveness was validated on a public real-world bearing dataset, where it achieved an accuracy of over 95%, demonstrating its robustness and potential for industrial applications by effectively identifying fault-induced asymmetries. Full article

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

Enhancing the resilience of renewable energy systems in ultra-weak grids is crucial for promoting sustainable energy adoption and ensuring a reliable power supply during disturbances. Ultra-weak grids characterized by a very low Short-Circuit Ratio, less than 2, and high grid impedance significantly impair voltage and frequency stability, imposing challenging conditions for Inverter-Based Resources. To address these challenges, this paper considers a 110 KVA, three-phase, two-level Voltage Source Converter, interfacing a 700 V DC link to a 415 V AC ultra-weak grid. X/R = 1 is controlled using Sinusoidal Pulse Width Modulation, where the Grid-Connected Converter operates in Grid-Forming Mode to maintain voltage and frequency stability under a steady state. During symmetrical and asymmetrical faults, the converter transitions to Grid-Following mode with current control to safely limit fault currents and protect the system integrity. After fault clearance, the system seamlessly reverts to Grid-Forming Mode to resume voltage regulation. This paper proposes an improved control strategy that integrates voltage feedforward reactive power support and virtual capacitor-based virtual inertia using Active Disturbance Rejection Control, a robust, model-independent controller, which rapidly rejects disturbances by regulating d and q-axes currents. To test the practicality of the proposed system, real-time implementation is carried out using the OPAL-RT OP4610 platform, and the results are experimentally validated. The results demonstrate improved fault current limitation and enhanced DC link voltage stability compared to a conventional PI controller, validating the system’s robust Fault Ride-Through performance under ultra-weak grid conditions. Full article

This study addresses the long-standing difficulty of predicting the remaining useful life (RUL) of rolling bearings from highly non-stationary vibration signals by proposing WaveAtten, a symmetry-aware deep learning framework. First, mirror-symmetric and bi-orthogonal Daubechies wavelet filters are applied to decompose each raw signal into multi-scale approximation/detail pairs, explicitly preserving the left–right symmetry that characterizes periodic mechanical responses while isolating asymmetric transient faults. Next, a bidirectional sparse-attention module reinforces this structural symmetry by selecting query–key pairs in a forward/backward balanced fashion, allowing the network to weight homologous spectral patterns and suppress non-symmetric noise. Finally, the symmetry-enhanced features—augmented with temperature and other auxiliary sensor data—are fed into a long short-term memory (LSTM) network that models the symmetric progression of degradation over time. Experiments on the IEEE PHM2012 bearing dataset showed that WaveAtten achieved superior mean squared error, mean absolute error, and R² scores compared with both classical signal-processing pipelines and state-of-the-art deep models, while ablation revealed a 6–8% performance drop when the symmetry-oriented components were removed. By systematically exploiting the intrinsic symmetry of vibration phenomena, WaveAtten offers a robust and efficient route to RUL prediction, paving the way for intelligent, condition-based maintenance of industrial machinery. Full article

Single-phase short-circuit faults are severe asymmetrical fault modes in high renewable energy power systems. They can easily cause large-scale renewable energy to enter the low-voltage ride-through (LVRT) state. When such symmetrical or asymmetrical faults occur in the transmission channels of high-proportion wind power clusters, they may trigger the tripping of thermal power units and a transient voltage drop in most wind turbines in the high-proportion wind power area. This causes an instantaneous active power deficiency and poses a low-frequency oscillation risk. To address the deficiencies of wind turbine units in fault ride-through (FRT) and active frequency regulation capabilities, a power emergency support scheme for wind power clusters based on doubly fed variable-speed pumped storage dynamic excitation is proposed. A dual-channel energy control model for variable-speed pumped storage units is established via AC excitation control. This model provides inertia support and FRT energy simultaneously through AC excitation control of variable-speed pumped storage units. Considering the transient stability of the power network in the wind power cluster transmission system, this scheme prioritizes offering dynamic reactive power to support voltage recovery and suppresses power oscillations caused by power deficiency during LVRT. The electromagnetic torque completed the power regulation within 0.4 s. Finally, the effectiveness of the proposed strategy is verified through modeling and analysis based on the actual power network of a certain region in Northeast China. Full article

A spectrogram is one of the most effective tools for visualizing dynamic signal changes in industrial processes. In many cases, these signals exhibit certain forms of symmetry—whether in time, frequency, or statistical properties. This paper proposes a novel visualization methodology based on an adaptive nonlinear quantization framework, which intentionally introduces asymmetry to enhance diagnostic-critical features of the power spectrum. Unlike conventional linear quantizers that preserve uniform sensitivity across the range, the nonlinear approach enables selective emphasis of transient or low-energy components, improving visibility under varying signal-to-noise conditions. The design of both symmetric (linear) and asymmetric (nonlinear) quantizers is presented, including their mathematical foundations and visual effects on deterministic, stochastic, and pulsed signals. Entropy-based metrics are used to evaluate information content in the visualized spectrograms. The results demonstrate the proposed technique’s potential for enhancing fault detection, monitoring, and industrial diagnostics. Full article

With the rapid development of renewable energy technologies, large numbers of photovoltaic (PV) and battery energy storage systems (BESS) have been connected to distribution networks. However, both PV and the BESS are inverter interfaced power sources, which may cause the traditional protection relays to mis-operate or mal-operate. Moreover, according to the latest grid connection specifications, PV and BESS are required to absorb negative sequence current during asymmetric faults of distribution networks, indicating that they both must adopt new control strategies during the fault ride through period. In response to the above challenges, this work first studies the fault ride through control strategies of PV and BESS when different phase-to-phase faults occur according to the latest grid connection requirements. Second, it analyzes the negative sequence impedance characteristics of PV and BESS under asymmetric faults and quantitatively calculates its variation range. Third, during symmetric faults, the differences in fault current provided by PV and BESS and those provided by the large power grid are compared. Then, this work proposes a fault direction detection principle for the distribution network with PV and BESS. For asymmetric phase-to-phase faults, this principle detects the fault direction by using the negative sequence power angle; for symmetric faults, it detects the fault direction by using the reactive current and active current. Finally, simulation tests are carried out to verify the operation performance of the proposed principle. Full article

The present study investigates the control strategy of a novel doubled-fed induction generator (DFIG) with a series grid-side converter (SGSC) during grid faults. The rotor-side inverter is subject to a control strategy derived from the Model Predictive Current Control (MPCC) theory, which is implemented during periods of fault occurrence; for the series grid-side converter, the positive and negative sequence component control is implemented during both steady state and fault periods to enhance system stability and performance. The proposed coordinated control strategy is implemented on a doubly fed turbine with SGSC, while taking into account different degrees of symmetric and asymmetric faults to further evaluate the efficacy of the proposed method. The results of the simulations demonstrate the efficacy of the model-predictive current control scheme applied to the rotor-side converter under conditions of asymmetric faults. This enables the suppression of a range of phenomena, including rotor overcurrent, stator overcurrent, and overvoltage, electromagnetic torque ripple, and DC bus voltage during low-voltage ride-through (LVRT), among others. The present study confirms the viability of implementing positive and negative sequences of voltage separation control in the SGSC during both grid faults and steady state. This approach is expected to minimize the switching of SGSC control strategies and thereby reduce output power fluctuations. The Rotor Side Converter (RSC) and SGSC can perform coordinated control during faults, and the proposed method is able to improve low-voltage ride-through performance compared to existing methods, thereby preventing damage to the converter under multiple fault conditions. Full article