Search Results (204)

The rapid integration of solar power into distribution grids has intensified challenges related to frequency instability caused by fluctuating renewable generation. These unexpected frequency variations are difficult to capture using traditional or supervised methods because they emerge from nonlinear, rapidly changing inverter grid interactions and often lack labelled examples. To address this, the present work introduces a unique, frequency-centric framework for unsupervised detection and root cause analysis of grid anomalies using high-resolution micro-Phasor Measurement Unit ($\mu$PMU) data. Unlike previous studies that focus primarily on voltage phasors or rely on predefined event labels, this work employs SpectralNet, a deep spectral clustering approach, integrated with autoencoder-based feature learning to model the nonlinear interactions between frequency, ROCOF, voltage, and current. These methods are particularly effective for unexpected frequency variations because they learn intrinsic, hidden structures directly from the data and can group abnormal frequency behavior without prior knowledge of event types. The proposed model autonomously identifies distinct root causes such as unbalanced loads, phase-specific faults, and phase imbalances behind hazardous frequency deviations. Experimental validation on a real solar-integrated distribution feeder in the UK demonstrates that the framework achieves superior cluster compactness and interpretability compared to traditional methods like K-Means, GMM, and Fuzzy C-Means. The findings highlight SpectralNet’s capability to uncover subtle, nonlinear patterns in $\mu$PMU data, offering an adaptive, data-driven tool for enhancing grid stability and situational awareness in renewable-rich power systems. Full article

This paper presents a novel multi-loop control strategy for Vienna rectifiers that eliminates coordinate transformations while achieving superior performance under adverse grid conditions. Unlike conventional $dq$-frame controllers that suffer from computational complexity and degraded performance during unbalanced conditions, the proposed $abc$-frame scheme achieves a power factor of $98\%$ with total harmonic distortion (THD) below $5\%$ across all operating conditions. The system exhibits a settling time under 120 μs for $90\%$ load transients and ensures robust operation during Type A voltage sags while maintaining a $94\%$ power factor. Furthermore, it guarantees zero steady-state neutral point deviation. The controller employs a dual-loop architecture with high-gain current tracking and PI-based voltage regulation, validated through extensive PSIM/C++ co-simulations at 120 kw. Comparative analysis demonstrates a $35\%$ reduction in computational burden relative to $dq$-frame alternatives, while fully complying with IEEE-519:2022 standards. These results highlight the proposed method as a practical and robust solution for industrial rectification applications requiring grid-fault tolerance. Full article

The ongoing decarbonisation of power systems is displacing synchronous generators (SGs) with converter-based plants, requiring a consistent assessment of grid-following inverters (GFLIs) and grid-forming inverters (GFMIs). Using an openly available four-bus root-mean-square (RMS) benchmark modelled in DIgSILENT PowerFactory, this work compares three generation configurations: (i) a single local SG connected at the point of common coupling; (ii) the same generator combined with a GFLI; and (iii) the generator combined with a GFMI. These configurations are evaluated under three disturbance scenarios: (1) a balanced load step, (2) an unbalanced double line-to-ground fault at low short-circuit ratio (SCR) with temporary islanding and single-shot auto-reclose, and (3) full islanding with under-frequency load shedding (UFLS), partial resynchronisation, and staged restoration. For the tested tuning ranges and within this RMS benchmark, the grid-forming configuration behaves as a low-impedance source at the point of common coupling in the phasor sense, yielding higher frequency nadirs during active-power disturbances and faster positive-sequence voltage recovery under weak and unbalanced conditions than the SG-only and SG+GFLI cases. During islanding, it supports selective UFLS, secure resynchronisation, and orderly load restoration. Rather than introducing new control theory, this work contributes a reproducible RMS benchmarking framework that integrates low-SCR operation, unbalance, and restoration sequences with a documented cross-technology tuning procedure. The findings indicate system-level improvements in frequency resilience and voltage recovery for the tested benchmark relative to the alternative configurations, while recognising that instantaneous device-level effects and broader generality will require electromagnetic-transient (EMT) or hybrid EMT/RMS validation in future work. Full article

Bearing health monitoring is essential for ensuring the reliability and operational safety of induction machines, as bearing faults remain among the most frequent failure modes in rotating electrical equipment. This work contributes to condition monitoring by enhancing the robustness of health indicators and developing a supply-frequency-independent health indicator (HI) for bearing fault diagnosis using Motor Current Signature Analysis (MCSA). The objective is to design an HI capable of reliably representing the bearing degradation state under varying operating conditions, particularly when the supply frequency changes. To achieve this, the study briefly examines the key physical mechanisms governing the detectability of bearing-related spectral signatures—including rotational frequency, unbalanced magnetic pull, eddy currents, skin effect, and hydrodynamic forces. The theoretical analysis establishes the overall trend expected under varying supply frequencies and clarifies how these phenomena collectively influence the spectral characteristics of the fault components and the frequency-dependent evolution of their amplitudes. These insights are experimentally validated using induction machines fitted with bearings of two fault severities. Leveraging this physical understanding, a modified regression-based compensation model is introduced to reduce the frequency-dependent variation in the HI. The resulting compensating factor effectively stabilizes the frequency response, producing a more consistent and monotonic degradation trend across the tested conditions. The proposed method is computationally lightweight, does not require run-to-failure data or detailed physical modeling, and is suitable for real-time implementation. By integrating physical insight with data-driven modeling, this work presents a practical and frequency-independent HI framework that can be readily deployed within digital-twin-based condition monitoring architectures for induction machines. Full article

Fault diagnosis is critical for ensuring the reliability of reciprocating pumps in industrial settings. However, challenges such as strong noise interference and unbalanced conditions of existing methods persist. To address these issues, this paper proposes a novel fusion framework integrating a multiple-branch residual module and a hybrid attention module for reciprocating pump fault diagnosis. The framework introduces a multiple-branch residual module with parallel depth-wise separable convolution, dilated convolution, and direct mapping paths to capture complementary features across different scales. A hybrid attention module is designed to achieve adaptive fusion of channel and spatial attention information while reducing computational overhead through learnable gate mechanisms. Experimental validation on the reciprocating pump dataset demonstrates that the proposed framework outperforms existing methods, achieving an average diagnostic accuracy exceeding 98% even in low signal-to-noise ratio (SNR = −3 dB) environments. This research provides a new perspective for mechanical fault diagnosis, offering significant advantages in diagnostic accuracy, robustness, and industrial applicability. Full article

As an important part of the ship’s power system, the bearing operation status of the propulsion motor is directly related to the reliability and safety of the whole system. However, in the field of marine propulsion motor bearing fault diagnosis, the data imbalance problem seriously affects the performance of the fault detection model. Due to the scarcity of fault data relative to normal operation data, traditional diagnostic methods are ineffective in dealing with unbalanced data. To solve this problem, a dynamic class weighting solution is proposed. The dynamic class weighting method introduces the weight coefficient λ on the basis of the traditional class weighting, which can adjust the class weight value in real time according to the training situation, and comprehensively considers the data distribution and the training situation to ensure that the model can learn better even in the case of insufficient data. Testing on the imbalanced distribution of bearing natural-failure data shows that the proposed method achieves a 5.25% improvement in diagnostic accuracy compared to direct training. Compared with traditional class-weighted approaches, diagnostic accuracy is enhanced by 3.56%, effectively mitigating the impact of scarce and unevenly distributed failure data on model training. Full article

Single-phase ground faults (SPGFs) in isolated neutral medium-voltage networks are difficult to detect, especially under high transient resistance. This paper proposes a centralized ground fault protection unit (CGFPU) that combines zero-sequence current (ZSC) magnitude and phase-angle analysis to enhance selectivity. Simulation results show that as transient resistance increases from 1 Ohm to 10 kOhm, fault currents decrease significantly, yet the CGFPU reliably identifies the faulty feeder by exploiting the characteristic 180° phase shift of ZSC phasors. The method remains selective with angular deviations up to ±20° and distinguishes between feeder and busbar faults. Compared with conventional amplitude- or model-based techniques, the proposed approach achieves faster detection, lower computational complexity, and robustness against unbalanced and charging currents. Furthermore, the CGFPU operates adaptively in alarm or trip mode depending on fault severity, thus preserving continuity for high-resistance faults and ensuring rapid isolation of bolted faults. These contributions establish a practical, scalable, and future-ready solution for SPGF protection in medium-voltage isolated neutral networks. 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

A dual-loop fault-tolerant control scheme is investigated for UAV attitude control systems subject to actuator faults, input saturation, and external disturbances in this paper. In the outer loop of attitude angles, a nonlinear dynamic inversion controller is developed as baseline controller for fast response and is augmented by a neural network disturbance observer to enhance the adaptability and robustness. Considering input saturation, actuator faults, and external disturbances in the inner loop of attitude angle velocities, the unbalanced input saturation is first converted into a time-varying system with unknown parameters and disturbances using a nonlinear function approximation method. An L1 adaptive fault-tolerant controller is then introduced to compensate for the effects of lumped uncertainties including system uncertainties, actuator faults, external disturbances, and approximation errors, and the stability and performance boundaries are verified by Lyapunov theorem and L1 reference system. Some simulation examples are carried out to demonstrate its effectiveness. Full article

This study investigates the electromagnetic and NVH characteristics of an outer-rotor surface-mounted permanent magnet synchronous generator (SPMSG) for wave energy applications, focusing on the effect of rotor eccentricity. To reflect potential fault due to manufacturing or assembly defects, a 0.5 mm rotor eccentricity was introduced in finite element method (FEM) simulations. The torque ripple waveform was analyzed using fast Fourier transform (FFT) to identify dominant harmonic components that generate unbalanced electromagnetic forces and induce structural vibration. These harmonic components were further examined under variable marine operating conditions to evaluate their impact on acoustic radiation and vibration responses. Based on the simulation and analysis results, a design-stage methodology is proposed for predicting vibration and noise by targeting critical harmonic excitations, providing practical insights for marine generator design and improving long-term operational reliability in wave energy systems. Full article

Apologies can offer solace and compensate for intangible and emotional harm in social and legal contexts. In some areas of law, an apology offered by a defendant will be factored into the assessment of damages awarded to vindicate the plaintiff’s rights and compensate for loss. This is the case in Australia, the jurisdiction primarily considered in this article, and in many other jurisdictions. There is a danger, however, of assuming that because apologies are compensatory in some sense, they can be used as a basis to reduce damages in tort law more generally. Even though general damages for non-pecuniary loss in fault-based torts are incommensurate to a monetary amount, they are still intended to compensate for actual loss. Empowering defendants to reduce their damages exposure by apologizing might incentivize meaningful apologies which are valued by plaintiffs. It might also create perverse incentives for plaintiffs and defendants alike, further unbalancing a system in which plaintiffs are already at risk of under-compensation. And it raises uncomfortable questions of evidence, reciprocity, agency and expertise which are yet to be fully explored. We argue for these reasons that it is not currently defensible to reduce an award of general damages for negligence, especially for personal injuries, on the basis of an apology by the defendant. Full article

This paper presents WaveCORAL-DCCA, an unsupervised domain adaptation (UDA) framework specifically developed to address data distribution shifts and operational variabilities (OVs) in rotor fault diagnosis. The framework introduces the novel integration of discrete wavelet transformation for robust time–frequency feature extraction and an enhanced deep canonical correlation analysis (DCCA) network with correlation alignment (CORAL) loss for superior domain-invariant representation learning. This combination enables more effective alignment of source and target feature distributions without requiring any labelled data from the target domain. Comprehensive validation on both experimental and numerically simulated rotor datasets across three health conditions—i.e., normal, unbalanced, and misaligned—demonstrates that WaveCORAL-DCCA achieves an average diagnostic accuracy of 95%. Notably, it outperforms established UDA benchmarks by at least 5–17% in cross-domain scenarios. These results confirm that WaveCORAL-DCCA provides robust generalisation across machines, fault severities, and operational conditions, even with scarce target domain samples, offering a scalable and practical solution for industrial rotor fault diagnosis. Full article

This work proposes an effective control technique for enhancing the stability of Doubly Fed Induction Generator-Based Wind Turbines (DFIG-WTs) connected to the grid during voltage sag and swell events, ensuring the reliable and efficient operation of wind energy systems integrated with the grid. The proposed approach integrates a Dynamic Voltage Restorer (DVR) in series with a Wind Turbine Generator (WTG) output terminal to enhance the Fault Ride-Through (FRT) capability during grid disturbances. To develop a flexible control strategy for both unbalanced and balanced fault conditions, a combination of feedforward and feedback control based on a sliding mode control (SMC) for DVR converters is used. This hybrid strategy allows for precise voltage regulation, enabling the series compensator to inject the required voltage into the grid, thereby ensuring constant generator terminal voltages even during faults. The SMC enhances the system’s robustness by providing fast, reliable regulation of the injected voltage, effectively mitigating the impact of grid disturbances. To further enhance system performance, Model Predictive Control (MPC) is implemented for the Rotor-Side Converter (RSC) within the back-to-back converter (BTBC) configuration. The main advantages of the predictive control method include eliminating the need for linear controllers, coordinate transformations, or modulators for the converter. Additionally, it ensures the stable operation of the generator even under severe operating conditions, enhancing system robustness and dynamic response. To validate the proposed control strategy, a comprehensive simulation is conducted using a 2 MW DFIG-WT connected to a 120 kV grid. The simulation results demonstrate that the proposed control approach successfully limits overcurrent in the RSC, maintains electromagnetic torque and DC-link voltage within their rated values, and dynamically regulates reactive power to mitigate voltage sags and swells. This allows the WTG to continue operating at its nominal capacity, fully complying with the strict requirements of modern grid codes and ensuring reliable grid integration. Full article

The hydropower unit is the core of the hydropower station, and maintaining the safety and stability of the hydropower unit is the first essential priority of the operation of the hydropower station. However, the complex environment increases the probability of the failure of hydropower units. Therefore, aiming at the complex diversity of hydropower unit faults and the imbalance of fault data, this paper proposes a fault identification method based on modified fractional-order hierarchical fluctuation dispersion entropy (MFHFDE) and AdaBoost-stochastic configuration networks (AdaBoost-SCN). First, the modified hierarchical entropy and fractional-order theory are incorporated into the multiscale fluctuation dispersion entropy (MFDE) to enhance the responsiveness of MFDE to various fault signals and address its limitation of overlooking the high-frequency components of signals. Subsequently, the Euclidean distance is used to select the fractional order. Then, a novel method for evaluating the complexity of time-series signals, called MFHFDE, is presented. In addition, the AdaBoost algorithm is used to integrate stochastic configuration networks (SCN) to establish the AdaBoost-SCN strong classifier, which overcomes the problem of the weak generalization ability of SCN under the condition of an unbalanced number of signal samples. Finally, the features extracted via MFHFDE are fed into the classifier to accomplish pattern recognition. The results show that this method is more robust and effective compared with other methods in the anti-noise experiment and the feature extraction experiment. In the six kinds of imbalanced experimental data, the recognition rate reaches more than 98%. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article