Fault Transients Research Articles

A fault detection and location algorithm for the LVDC interconnection network in rural area

AbstractLow voltage DC (LVDC) microgrids (MGs) can be linked together through an interconnection network to enhance the utilization of their energy resources in remote locations, particularly in rural low‐income areas. However, the identification of the fault is challenging due to the fast fault transients and equipment limitations, where there are no sensors and DC circuit breakers (DCCBs) in the lines. To solve this problem, this article proposes a fault detection and location algorithm without requiring extra sensors and DCCBs in lines. The proposed algorithm uses the sensors of the interface converters to detect the fault. Following this, a coordinated current injection method is used to identify the faulty element by coordinating converters with disconnectors. This process employs two strategies “weight check” and “scope check” to minimize the time and the number of actions. The algorithm is robust to various fault impedance, fault types and network topology modifications. The effectiveness of the algorithm is validated through a series of simulation case studies.

Fault signature extraction of rolling bearings under variable speed via time–frequency overlap group sparse representation

The high-fidelity extraction of the nonperiodic fault transients of rolling bearings under variable speed is of significant importance, while still a challenge for early-stage fault diagnosis of major equipment such as aero engines and wind turbines. A method termed continuous symmetric Laplace wavelet transform (CSLWT) enhanced time–frequency overlap group sparse (OGS) representation is proposed in this paper. A symmetric Laplace mother wavelet function is proposed which could satisfy the admissibility condition of wavelet decomposition and reconstruction, upon which the CSLWT is fabricated and adopted as the sparse representation dictionary to match the nonperiodic fault transients. Since the decomposition coefficients of nonperiodic fault impulses via the CSLWT exhibit group sparsity on the time–frequency plane with unknown intervals, the time–frequency OGS model is adopted and then solved for extraction of the nonperiodic fault transients. Moreover, a data driven, multi-objective optimization strategy is presented for determining the hyper-parameters of the sparse model. The correlated kurtosis of angular envelope signal (CK-AE) and harmonic-to-noise energy ratio of the order spectrum (HNR-OS) are introduced as the optimization objectives, and the Pareto front is solved based on the NSGA-II algorithm, upon which the acceptable solutions set is chosen from the Pareto front, and the proper hyper-parameters are further determined via proposed normalized solutions distance density sorting of the acceptable solutions set. The performance of the proposed method is validated via processing both simulated and experimental vibration signals, as well as compared with some tradition fault diagnosis methods.

Research on Data-Driven Fault Scenario Simulation Model of Distributed Network

Distribution lines are critical components of today's power system, and can directly impact on power supply security and stability. An effective power system protection system should detect any faults as soon as they occur. There are two steps in fault diagnosis. One is defect classification, which has already achieved excellent accuracy rates. As a result, this study concentrates on the opposite objective, which is fault location. This research introduces a War Strategy Water Wave Optimization (WSWWO) based Deep Q Network for radial distribution networks using synchronous generators for Distributed Generation (DG). The algorithm estimates the fault site by analyzing voltage and current samples at the main feeder head, and scheduled active and reactive power injections by network synchronous generators. A full-order synchronous machine model was utilized to analyze the dynamic behavior of DG plants during fault transients. Here, the Deep Q Network is trained by WSWWO, and hybridized by two optimization algorithms, War Strategy Optimization (WSO) and Water Wave Optimization (WWO). Furthermore, the experimental results revealed that the WSWWO-Deep Q Network outperformed state-of-the-art models in terms of Accuracy and Error, with values of 0.997 and 0.141, respectively.

Advanced Fault Detection in Power Systems Using Wavelet Transform: SIMULINK-Based Implementation and Analysis

Traditional methods struggle to find faults in power transmission lines. This paper presents an approach for short transmission lines, leveraging the power of wavelet transforms. Traditional methods analyze time-domain signals, limiting their ability to differentiate fault transients. Wavelet transforms, offering a combined time-frequency analysis, provide a deeper understanding of these transients. A detailed short transmission line model is built in SIMULINK. Diverse fault scenarios are meticulously simulated, and current signals undergo wavelet transform analysis. Key features extracted from the wavelet coefficients act as fingerprints of potential faults. These features are then utilized to develop a robust fault detection algorithm specifically designed for short transmission lines. The proposed method promises enhanced fault detection capabilities compared to existing techniques in this domain. The results, presented in subsequent sections, will shed light on the effectiveness of wavelet transforms in empowering smarter and more reliable transmission line operations.

Application of the Polynomial Chaos Expansion to the Uncertainty Propagation in Fault Transients in Nuclear Fusion Reactors: DTT TF Fast Current Discharge

Nuclear fusion reactors are composed of several complex components whose behavior may be not certain a priori. This uncertainty may have a significant impact on the evolution of fault transients in the machine, causing unexpected damage to its components. For this reason, a suitable method for the uncertainty propagation during those transients is required. The Monte Carlo method would be the reference option, but it is, in most of the cases, not applicable due to the large number of required, repeated simulations. In this context, the Polynomial Chaos Expansion has been considered as a valuable alternative. It allows us to create a surrogate model of the original one in terms of orthogonal polynomials. Then, the uncertainty quantification is performed repeatedly, relying on this much simpler and faster model. Using the fast current discharge in the Divertor Tokamak Test Toroidal Field (DTT TF) coils as a reference scenario, the following method has been applied: the uncertainty on the parameters of the Fast Discharge Unit (FDU) varistor disks is propagated to the simulated electrical and electromagnetic relevant effects. Eventually, two worst-case scenarios are analyzed from a thermal–hydraulic point of view with the 4C code, simulating a fast current discharge as a consequence of a coil quench. It has been demonstrated that the uncertainty on the inputs (varistor parameters) strongly propagates, leading to a wide range of possible scenarios in the case of accidental transients. This result underlines the necessity of taking into account and propagating all possible uncertainties in the design of a fusion reactor according to the Best Estimate Plus Uncertainty approach. The uncertainty propagation from input data to electrical, electromagnetic, and thermal hydraulic results, using surrogate models, is the first of its kind in the field of the modeling of superconducting magnets for nuclear fusion applications.

Electro dynamic model of eddy currents in EU DEMO TF coil casing during major plasma disruption

The conceptual design of the EU DEMO reactor is currently ongoing within the EUROfusion consortium. Many different fault transients must be considered and carefully analyzed in the design phase; one of the most severe is the major plasma disruption (MPD), which causes several drawbacks on the magnet system. During a disruption, the plasma current decreases extremely fast, causing a fast variation of the magnetic field, which in turn induces an electric field. In presence of conductive materials, e.g., coil casing and vacuum vessel (VV), the electric field induces large eddy currents which deposit power by Joule effect. The conductive regions are tightly coupled on different timescales through the magnetic field induced by the eddy currents: the eddy currents in the VV influence the magnetic field evolution in the TF coil casing, thus affecting the power deposition in the latter. The aim of this work is the evaluation of the power deposited within the TF coil casing during a major plasma disruption due to eddy currents. The power deposition has been evaluated by means of the 3D-FOX, a finite element (FE) tool developed at Politecnico di Torino. The computed power deposition is used as input to the thermal-hydraulic (TH) simulation, performed with the 4C code, with the aim of assessing the erosion of the temperature margin given by MPD.

Faulty-Feeder Detection for Single Phase-to-Ground Faults in Distribution Networks Based on Waveform Encoding and Waveform Segmentation

Faulty feeder detection helps ensure the stability and safety of power grids after single-phase-to-ground (SPG) faults occur in distribution networks. The existing detection techniques identify the faulty feeder by extracting representative fault features, while they fail to show reliable detection performance due to variable fault conditions and complex fault transients. To address these drawbacks, this paper proposes a method based on waveform encoding and waveform segmentation. Since the waveforms have complete fault features in fault signals, it is suitable to recognize the signals on the waveform scale, rather than extracting and fusing several fault features. Firstly, raw sampled zero-sequence voltage (ZSV) and zero-sequence current (ZSC) are processed by using the proposed encoding method, and the ZSV-ZSC image can be generated quickly. Secondly, to learn and understand the waveforms of ZSV and ZSC, a two-path fully convolutional network (FCN) is established to make pixel-wise prediction on the ZSV-ZSC image. Finally, the fault degree of each feeder can be estimated based on the segmented waveform in the ZSV-ZSC image. The performance evaluation is implemented in the NVIDIA Jetson Xavier embedded platform, and the experimental results demonstrate that the proposed method can identify the faulty feeder with high accuracy within 28 ms.

Induction Machine Emulation For Open Circuit and Short Circuit Grid Faults

The focus of the present research work is to develop a robust induction machine emulator. The emulator can be used to test different types of open circuit and short circuit grid faults along with starting and loading transients. Power hardware in loop (PHIL) technology is used for the machine emulation. It requires a highly accurate and adaptable model for the chosen test cases and opted emulator configuration. For open circuit fault test case, the induction machine mathematical model is developed systematically to emulate the machine backemf at the open circuit terminals. The proposed model adapts to the chosen emulator configuration by incorporating the emulator parameters in it. Finally, the novel induction machine emulator proves its robustness and performance not only for open circuit faults but also for short circuit, starting and loading fault transients. Open circuit auto reclosing analysis and considerations in developing reclosing algorithms are detailed. Identification of different fault signatures, which aid fault diagnosis are also briefly presented.

Sparse Low-Rank Matrix Estimation With Nonconvex Enhancement for Fault Diagnosis of Rolling Bearings

The diagnosis of bearing early fault is significant and fundamental in the machine condition monitoring. The accurate and effective diagnosis is of great importance to avoid further serious accidents. However, existing sparse low-rank (SLR) methods for bearing fault diagnosis suffer from underestimation of amplitude and inaccurate approximation of singular values. Therefore, in this paper, a novel sparse low-rank matrix estimation method with nonconvex enhancement (SLRNE) is proposed, extracting the fault transients from observed noisy signal. Specifically, fault transients have both sparse and low-rank properties in time-frequency domain. Based on this, a SLR optimization model is proposed to simultaneously promote the above two properties via truncated nuclear norm (TNN) and generalized minimax concave (GMC) penalty function. The two nonconvex functions aim to promote low-rank property and sparsity respectively. Then, based on derived convexity conditions of the optimization problem, convex optimization algorithm, alternating direction method of multipliers (ADMM) and forward-and-backward splitting (FBS) algorithm, are applied to obtain global optimal solution. In the iterative algorithm, a weighting strategy is designed for the singular value threshold operator to enhance the effect of fault feature extraction. Simulated and experimental signals verify the effectiveness of SLRNE and contrast experiments verify its superiority.

Research on DC Line Protection Method of Large PV Plant Based on Transient Direction

Due to the complex fault transients associated with DC convergence in photovoltaic (PV) plants and the large number of power electronics used in systems, the protection device is unable to determine the location of faults, and thus the protection is inadequate. This paper proposes a protection scheme for the DC system of a large PV plant. The accurate model of the PV plant is built in consideration of the DC system, including the current limiter. The differential voltage and current are utilized to realize the location of the fault section. The voltage transients are used to locate the fault within the section accurately. Study cases in PSCAD/EMTDC show the proposed protection scheme is effective and practically useful for PV plants.

Faulty feeder detection based on image recognition of current waveform superposition in distribution networks

Faulty feeder detection is essential for maintaining the security and stability of energy supply in distribution networks. However, it is rather difficult to identify a specific faulty feeder owing to small fault currents and complex fault transients. To improve the detection accuracy, this study proposes a faulty-feeder detection method based on image recognition of superimposed zero-sequence currents. A convolutional neural network (CNN) is utilized to recognize the superimposed currents in the same plot, rather than a raw single current, which can realize correlation comparisons between the currents. In addition, the zero-sequence currents of different feeders are superimposed according to a specific sequence, and the CNN can adapt to the changing topologies of distribution networks while conducting correlation comparisons. Because zero-sequence currents decay rapidly over time, an attention learning block is embedded into the CNN to enhance the discriminative capability. A total of 14,718 sets of experimental data obtained from simulations and practical distribution networks were collected to verify the effectiveness of the proposed method. Comparisons with other traditional methods and learning-based methods adopted in previous studies justify the superiority of the proposed method in terms of detection accuracy and detection efficiency. Therefore, the proposed method can be implemented in real distribution networks for faulty feeder detection.

Heterogeneous learning method of ensemble classifiers for identification and classification of power quality events and fault transients in wind power integrated microgrid

This paper proposes heterogeneous based ensemble Classifiers (voting and stacking method) to identify and classify different power system disturbances (power quality (PQ), faults, transients, and wind power variation) in wind integrated microgrid network. In the pre-processing stage of classification, a Discrete wavelet transform (DWT) technique is applied to extract the features from power system disturbance signals. The classification process for the proposed ensemble models involves two levels of classification. At the first level, the extracted features from the simulated power system events are used to learn the different individual base classifiers (logistic regression (LR), K-Nearest Neighbor (KNN), and J48 Decision tree (JDT)]. In second stage, a Meta-level classification is carried out based on predictions of base classifiers to get final predictions of class labels. First, the proposed ensemble models are utilized to discriminate the power system disturbances under random varying wind power condition and the predictive results (classification accuracy and performance indices) of ensemble models are compared with individual base classifiers (LR, KNN., and JDT). In addition, a sensitivity analysis is carried out under real time varying wind power condition and noisy environment of event signals to verify the effectiveness of ensemble models in further level. Furthermore, the robustness of proposed stacking ensemble model is verified with classification of single and combined PQ events of synthetic data, generated from the mathematical based PQ model software Predictions results under the different conditions show that stacking ensemble model offers substantial performance and discriminates the power disturbances with higher accuracy of classification than base classifiers and voting ensemble model.

A Fault Location Method Using Direct Convolution: Electromagnetic Time Reversal or Not Reversal

Electromagnetic time reversal (EMTR) is drawing increasing interest in short-circuit fault location. In this letter, we investigate the classic EMTR fault location methods and find that it is not necessary to reverse the obtained signal in time which is a standard operation in these methods before injecting it into the network. The effectiveness of EMTR fault location method results from the specific similarity of the transfer functions in the forward and reverse processes. Therefore, we can inject an arbitrary type and length of source in the reverse process to locate the fault. Based on this observation, we propose a new EMTR fault location method using direct convolution. This method is different from the traditional methods, and it only needs to pre-calculate the assumed fault transients for a given network, which can be stored in embedded hardware. The faults can be located efficiently via direct convolution of the signal collected from a fault and the pre-stored calculated transients, even using a fraction of the fault signal.

Deep-Learning Based Fault Events Analysis in Power Systems

The identification of fault types and their locations is crucial for power system protection/operation when a fault occurs in the lines. In general, this involves a human-in-the-loop analysis to capture the transient voltage and current signals using a common format for transient data exchange for power systems (COMTRADE) file. Then, protection engineers can identify the fault types and the line locations after the incident. This paper proposes intelligent and novel methods of faulty line and location detection based on convolutional neural networks in the power system. The three-phase fault information contained in the COMTRADE file is converted to an image file and extracted adaptively by the proposed CNN, which is trained by a large number of images under various kinds of fault conditions and factors. A 500 kV power system is simulated to generate different types of electromagnetic fault transients. The test results show that the proposed CNN-based analyzer can classify the fault types and locations under various conditions and reduce the fault analysis efforts.

A Voting Approach of Ensemble Classifier for Detection of Power Quality in Islanded PV Microgrid

In this research, an ensemble classifier using a voting method is proposed to categorize different Power Quality events (PQEs), switching, and fault transients in solar photovoltaic (PV) connected Microgrid (MG) network. For this analysis, an islanded MG network model is developed in the MATLAB-Simulink software framework to study the various power system disturbances (PQEs, switching transients, and Line to Ground (LG) fault). The voltage/current signals of various power disturbances are used for the extraction of features by applying a discrete wavelet transform technique. Then, the individual base classifiers (Bayesian Net, Naive Bayesian, and Logistic Regression) are trained by using the extracted features in the first stage. Subsequently, the voting method of Meta classification is applied to obtain the final predictions of class events in the second level, based on the predictions of base classifiers. The classification and statistical analysis are carried out under standard test conditions (STC) and real-time-varying solar irradiance of PV. The evaluation results of classification and statistical analysis clearly show that the proposed ensemble classifier offers improved classification accuracy with superior performance than individual base classifiers under STC and uncertain conditions of solar PV.

Smart Technology Based Empirical Mode Decomposition (EMD) Approach for Autonomous Transmission Line Fault Detection Protection

Many novel technologies of property energy and cell, solar power, batteries, and high-efficient combustion are widely investigated to conserve energy and reduce emissions. Transmission lines (TLs) play a serious role in transmitting generated electricity to different distribution units in facility engineering. The transmission lines function as a link between shoppers and a Power Station. Faults usually occur within the transmission when positioned in an open field. Quick identification and sick line faults square measures required for the conventional operation of the plant. A way like distinct moving ridge rework (DWT) and (EMD) is used to locate and identify faults to resolve this disruption. DWT is used to break down fault transients, as a result of which the info can be collected at the same time in each time and frequency domain. EMD decomposes the TLs voltage into Intrinsic Mode operation (IMFs). Four varieties of fault signals are square measurements produced by the grid-connected facility. Line faults square measure induced MATLAB/Simulink mistreatment.

Decentralized Suppression Strategy of Common Ground Circulating Current Caused by Grounding Fault in PV Modules in Single-Phase PV Grid-Connected Systems

The complicated series-parallel wiring and aged insulation of photovoltaic (PV) power plants leave the system prone to PV grounding faults. The resultant common ground circulating current will increase the operation stress of the switching devices and jeopardize system stability. To address these problems, a comprehensive decentralized control strategy is proposed in this paper to suppress the circulating currents. The influence of the PV grounding fault on the system stability is firstly analyzed. Based on the equivalent form of the circulating currents, a control strategy is designed to suppress the common ground circulating current. Furthermore, to ensure the stable currents and DC-link voltages during grounding fault transients, another control strategy is designed to operate the three-level PV interfacing converter for suppressing the overshoots of the currents and DC-link voltages. With the two controllers and the three-level converter, the generated circulating currents can be effectively eliminated in both steady and transient states. It is worthy of mentioning that the proposed methods are a decentralized control scheme, which can improve the stability and scalability of the system. The effectiveness of the proposed solution is validated by performing the real-time hardware-in-loop tests.

DC Fault Study of a Point-to-Point HVDC System Integrating Offshore Wind Farm Using High-Temperature Superconductor DC Cables

This paper presents a feasibility study of an offshore wind farm (OWF) HVDC integration system using high-temperature superconductor (HTS) DC cables. A representative ±100 kV/2 GW point-to-point OWF HVDC system is proposed including HTS DC cables and two converter stations using modular multilevel converters (MMCs). To be compatible with the high current rating of the HTS cables, each of the offshore and onshore converter stations consists of three MMCs in parallel. To study the interaction between HTS DC cables and MMCs, a multiple lumped π-section model of a HTS DC cable considering electrical and thermal functionality is developed. This paper provides a critical assessment of the proposed HVDC-HTS system, with emphasis on the performance under fast DC fault transients. Detailed simulations presented in this paper reveal that the HVDC-HTS system provides effective current limiting against DC cable short circuit faults.

Interpretable online updated weights: Optimized square envelope spectrum for machine condition monitoring and fault diagnosis

Bearings are key components of rotating machines, and their condition monitoring and fault diagnosis have received much attention from academia and industry in recent years. Existing fault diagnosis methods can be generally classified into signal processing-based fault characteristic frequency (FCF) identification methods and machine learning-based model classification methods. Though the former owns clear physical meaning, its integration with monitoring data for automatically optimizing square envelope spectrum for identification of FCF is not fully explored. As for the latter, lacking explicit interpretability of learned model weights limits its wide applications. In this paper, to address these critical issues, the integration of online monitoring data with cyclostationarity of fault transients is sufficiently studied and utilized to construct a fault cyclostationarity-based convex optimization model. The maximum logarithmic likelihood estimation is then used to solve the convex optimization model. Moreover, an online weight updating algorithm is developed to relieve the requirement of historical data and to make the weight updating of the proposed optimization model adaptive to online monitoring data. Subsequently, interpretable online updated weights as an optimized square envelope spectrum (OSES) are proposed to enhance the identification of FCF and its harmonics in the OSES. And a three-dimensional (3D) OSES and a detector with an alarming threshold are designed to eliminate the need for label information and further to simultaneously achieve incipient fault time detection, fault type diagnosis, and online fault evolution monitoring. A practical-project bearing dataset and two experimental bearing run-to-failure datasets are used to validate the effectiveness and superiority of interpretable online updated weights as the OSES and 3D OSES. Results demonstrate that the integration of fault characteristics with machine learning theory (e.g., convex optimization) is a new promising physics-informed perspective to monitor and diagnose machine faults.

Transient Fault Current Analysis of IIRESs Considering Controller Saturation

Fault characteristics analysis normally provides a theoretical foundation for the protection design. Although conventional fault analysis of inverter-interfaced renewable energy sources (IIRESs) can characterize post-fault steady-state at the power frequency, it might not effectively present the fault transient. This can negatively affect the protection based on fault transient data within 20–30 ms after a grid fault. Thus, the fault transient of IIRESs needs to be fully investigated. Given this, the nonlinear effect of controller saturation is considered in this study. The domain of attraction (DOA) is first established to estimate the practical working state of the controller within the current loop. The transient fault current (TFC) analytical model is then proposed in accordance with the nonlinear control response of the controller under different working stages, which reveals a detailed transient transition of the fault current. Meanwhile, the proposed model is low-order and presents high applicability in different fault scenarios. Simulation results indicate that the TFC analytical accuracy obtained using the proposed method is improved by at most 50% relative to the accuracy reported in published studies. Field short-circuit test data are ultimately used to verify the performance of the proposed TFC analytical model.