Fault Transients Research Articles

Emulation of an Induction Machine for Unbalanced Grid Faults

A laboratory power level 5.0 hp induction machine emulation with grid interface for various asymmetric fault transients is implemented in this article. It is done in real time with power-hardware-in-loop (PHIL) technology. The PHIL emulation procedure permits the testing of complex control strategies of an ac microgrid while determining the machine behavior during grid faults. Since asymmetric grid faults generate opposite sequence components in the individual harmonic currents, a novel emulator control strategy with combination of proportional-integral (PI) and proportional resonant (PR) controllers is proposed to mimic the behavior of the real machine under grid faults. A step-by-step procedure for designing the PI and PR controllers is given in detail. An improved induction machine mathematical model with trapezoidal integration is also suggested for precise tracking of the emulator. An emulator laboratory setup is developed with Opal-Rt as the real-time controller and linear amplifier as the emulating device. Simulation and experimental results are provided to validate the tracking capability of the proposed emulator. The results confirm that with the proposed control, emulator currents track the real machine currents.

Optimal Shunt Busbar Capacitor Placement for Selective Protection of Large-Scale VSC-MTDC Grids

This paper studies optimal shunt busbar capacitor placement for large-scale VSC-MTDC grids to accurately discriminate between faults inside each protection zone and external faults. The installed capacitors work as filters, which attenuate higher frequencies for faults outside the zone and keep internal fault transients unchanged. The objective function is a trade-off between total installation and maintenance cost and the discrimination sufficiency, while constraints related to the grid operation must be satisfied. Discrete wavelet transform (DWT) is used to calculate wavelet energies and to define relay detection criteria. The proposed method is applied to CIGRE large-scale VSC-MTDC grid in PSCAD/EMTDC. MATLAB is used for DWT-based signal processing with Python used as an interface between PSCAD and MATLAB for optimization based on the Watercycle optimization algorithm (WCA). Multiple faults with different characteristics are simulated to test the optimal results in different situations. The optimal results show that the proposed principle accurately discriminates between internal and external faults. Simulation results prove accurate detection of the faulty section using both two-sided measurement and one-sided measurement with half of measuring and protection equipment. The sensitivity of the method to faults close to the measuring points and the impact of different DWT levels is analysed.

Resonance based fault location for DC photovoltaic integration system

Photovoltaic (PV) power DC integration systems can be applied in remote areas in which conventional AC cable integration is inhibited by PV collection and transmission distance. However, unpredictable DC fault transients create challenges for traditional fault location methods. To address this issue, a high-frequency resonance estimation based fault location technique is proposed in this paper. Series resonance is generated by discharging the DC capacitor through a current-limiting reactor, which weakens the attenuation of high-frequency signals at the line boundary. The relationship between resonance current and fault distance can then be obtained by analyzing the fault current loop, which is immune to the states of the converter. Compared with the traditional fault location method, the proposed method does not require additional high-frequency injection sources and very high sampling frequency. A flexible DC photovoltaic integration system is also built in PSCAD/EMTDC and the simulation results verify the accuracy of the proposed location method.

Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis

Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index.

A Cosine Similarity-Based Centralized Protection Scheme for dc Microgrids

Unlike the phasor measurement-based protection in ac systems, the protection of dc systems deals with complex fault transients which mandate the isolation of the faulted segment within few milliseconds as continued fault current leads to overheating issue in power electronic converters. To this end, several works have been suggested-based on the unit and nonunit protections for dc microgrids. Threshold selection and protection coordination are the challenges associated with nonunit protection. Similarly, communication delay and link failure limit the application of unit protection. To address these issues, this article presents a robust centralized protection scheme for dc microgrids, which is resilient to communication delay and link failure. It uses the current of each line segment to compute the similarity of current change at both ends of the line segment to derive the protection decision. To overcome the communication failure from one end of the line segment or even from multiple segments, the proposed method uses data from adjacent segments to derive the protection decision correctly. Using power systems computer aided design (PSCAD)/electromagnetic transients and direct current (EMTDC) environment, the performance of the proposed method is evaluated for various cases and compared with available techniques. Finally, the accuracy of the protection algorithm is validated under experimental conditions.

A supervised principal component analysis-based approach of fault localization in transmission lines for single line to ground faults

Fault localization in power transmission line has been a key area of research, especially with the advent of modern soft computation-based digital relaying schemes. In this article, a principal component analysis (PCA)-based method of fault detection and localization in an overhead transmission line is proposed. The analysis is carried out for single line to ground faults only as these faults encompass about 70–90% among all the faults in transmission lines; although this generalized method could also be extended to cater other fault prototypes too. A fault in a line causes disturbances in the form of high-frequency transient oscillations which are developed immediately after the fault. The peaks and crests of these fault transients are investigated as key features from the receiving end three-phase voltage signals. These feature points are fed to a PCA-based fault localizer algorithm as supervised inputs. The proposed model is tested with a large set of fault signals, varying all the three major influencing parameters like fault locations, fault resistances as well as fault inception angle. Power system noise is also incorporated additionally in the fault signals order to incorporate more robustness to the localizer model. An average localization percentage error of 0.871% ranging from 0.011 to 2.413% is obtained from this work. The proposed work requires only a maximum of nearly 10.1% of post-cycle fault signal for analysis, which is remarkably less compared to several other schemes.

The influence of power system parameters on breaks of fault currents in the generator circuit breaker

The purpose of this paper is to study the factors that influence on the delayed current zeros phenomena in the generator network. For this purpose the equivalent circuit of the generator network was developed. The fault transients simulations have been performed by means of the Matlab/Simulink software package. Consequently, the dependence characteristics of the delayed current zeros phenomenon on the external power system parameters (inductance, length of power lines, reactive power balance) were obtained. The amount of reactive power generated by power lines depends on its length. This power, in turn, is consumed by the generation network and external power system. The greater the reactance of the external power system, the greater of this reactive power the generator consumes, which, in turn, increases direct-axis armature reaction and direct component of the short-circuit currents. The long arcing time by the delayed current zeros phenomena will cause damage to the transformer, circuit breaker and can negatively affect the stability of the power system. These results are also demonstrated numerically.

Sparse feature extraction based on periodical convolutional sparse representation for fault detection of rotating machinery

Sparse fault transient extraction is the primary step in rotating machine fault detection. In the present paper, periodical convolutional sparse representation (PCSR) is proposed for reliable separation of fault transients imbedded in raw vibration signals. Specifically, a sparse optimization problem of PCSR is constructed, in which periodical fault transients and harmonic components are sparsely represented by a learned dictionary and Fourier dictionary, and the periodicity and group sparsity of sparse coefficients related to sparse fault transients are also incorporated. Meanwhile, to further promote the sparsity of the sparse coefficients, a non-convex function is also introduced into the optimization problem. In addition, an iterative algorithm is developed to resolve the constructed sparse optimization problem, and the parameter selection method is also investigated to ensure the fault transient extraction ability of PCSR. The performance of the proposed PCSR is assessed via a synthetic and actual vibration signal. The results illustrate that the proposed PCSR has an excellent ability in fault transient extraction and machine fault detection.

Improving the wide-area PMU-based fault location method using ordinary least squares estimation

This paper presents an approach which allows system-wide fault location using only a sparse set of synchronized phasor measurements. The proposed algorithm combines post-fault voltage prediction and sparse estimation. The resulting method enables practical application of the sparse estimation formulation by using Prony analysis to predict post-fault steady-state voltages at those buses equipped with phasor measurement units. The prediction requires only a short transient recording which is typically available before the actuation of protective relays. Following the prediction step, the least angle regression based sparse estimation algorithm is employed to identify the fault location. However, for particular conditions usually associated with small fault currents, the sparse estimation algorithm may not be able to correctly identify the fault location. To mitigate this problem, ordinary least squares is used to increase the robustness of the proposed method. A 3-Phase model of the IEEE 118 bus system is developed on the alternative transients program to simulate realistic fault transients and test the performance and accuracy of the proposed fault location method.

A Hardware-in-the-Loop Platform for DC Protection

Real-time (RT) simulation of power and energy conversion systems allows engineers to interface both simulation- and hardware-based controls using controller hardware-in-the-loop (CHiL) simulation of networks of power electronic converters (PECs) to de-risk highly developmental systems, such as next generation electrified transportation systems and dc microgrids. CHiL exploration and performance verification moves a design from technology readiness level (TRL) 3 to TRL 4 without incurring significant cost investments in developmental hardware platforms, which otherwise discourages such endeavors. An RT CHiL simulation platform suitable for explorations of protective equipment, protection schemes, and networked PEC dc and mixed dc-ac power distribution architectures must be capable of simulating common-mode behavior, various grounding schemes, and fault transients at sufficiently high resolution. This article demonstrates this capability using a latency-based linear multistep compound (LB-LMC) simulation method implemented in a commercially sustainable, adaptable, and expandable FPGA-based test and instrumentation platform. The proposed CHiL platform achieves RT power system simulations, including detailed switching commutations of networked PECs, with 50-ns resolution, and faithfully produces resonant and transient behaviors associated with line-to-ground (LG) and line-to-line (LL) faults and fault recovery in ungrounded PEC-based dc systems. This resolution in RT cannot be achieved with today's commercial off-the-shelf CHiL platforms. This article demonstrates the need for high-resolution RT simulation of LG and LL faults within dc systems, and demonstrates a CHiL approach that enables dc protection design explorations and protective control hardware testing while taking into account the realistic aspects that affect fault characteristics in PEC-based dc systems, such as cable current rating and length, cable and PEC parasitic LG capacitance, and PEC internal response to fault scenarios.

Surge Compression for Improved Fault Location Accuracy in Full Transient-Based Methods

This paper proves that the accuracy of single-ended fault location techniques based on the correlation of fault transients in power lines can be significantly improved by inverse-filtering transients first. The reason for this improved accuracy is found in the reduction of the duration of the fault surge signal, thus increasing the sensitivity of fault-location metrics to the fault position. Reported results show that by using the proposed filtering strategy, faster sampling rates systematically and significantly improve the location accuracy, as opposed to the case when no filter is applied, for which virtually no improvement is observed. Because of its higher spatial selectivity, the proposed fault-location technique required the development of a new synchronization method, which ensures a precise transient synchronization without the need for high-speed sampling. It is concluded that by jointly using the proposed transient filtering and synchronization solutions, correlation-based fault location can be significantly improved even when using sampling rates as low as 100 kHz. Extensive numerical simulations for a three-phase transmission line confirm an improvement exceeding one order of magnitude in the fault location accuracy, for both low- and high-impedance faults.

Sparse Low-Rank Based Signal Analysis Method for Bearing Fault Feature Extraction

The condition monitoring of rolling element bearings (REBs) is essential to maintain the reliable operation of rotating machinery, and the difficulty lies in how to estimate fault information from the raw signal that is always overwhelmed by severe background noise and other interferences. The method based on a sparse model has attracted increasing attention because it can capture deep-level fault features. However, when processing a signal with complex components and weak fault features, the performance of sparse model-based methods is often not ideal. In this work, the fault information-based sparse low-rank algorithm (FISLRA) is proposed to abstract the fault information from a noisy signal interfered with by background noise and external interference. Concretely, a sparse and low-rank model is formulated in the time-frequency domain. Then, a fast-converging algorithm is derived based on the alternating direction method of multipliers (ADMM) to solve the formulated model. Moreover, to further highlight the periodical transients, a correlated kurtosis-based thresholding (CKT) scheme proposed in this paper is also incorporated to solve the proposed low-rank spares model. The superiority of the proposed FISLRA over the traditional sparse low-rank model (TSLRM) and spectral kurtosis (SK) is proved by simulation analysis. In addition, two experimental signals collected from a bearing test rig are utilized to demonstrate the efficiency of the proposed FISLRA in fault detection. The results illustrate that compared to the TSLRM method, FISLRA can effectively extract periodical fault transients even when harmonic components (HCs) are present in the noisy signal.

Hierarchical Frequency-Domain Sparsity-Based Algorithm for Fault Feature Extraction of Rolling Bearings

Rolling bearings are the crucial parts in rotating machines and its fault detection is indispensable for ensuring operational reliability of entire mechanical system. To accurately extract fault-induced fault features from vibration signal, this article proposes a hierarchical frequency-domain sparsity-based algorithm (HFDSA). Concretely, the algorithm is established based on the frequency-domain sparsity property of bearing fault features, in which a customized penalty function incorporating weighting strategy is introduced for hierarchically penalizing sparse coefficients. In addition, an iterative convergence algorithm is deduced to solve optimization problem in the proposed method HFDSA and parameter selection method is discussed in detail. Finally, the effectiveness of the HFDSA is verified through the simulation analysis and case studies. The results demonstrate that the HFDSA can efficiently extract fault transients in noisy vibration signal and achieve fault detection of rolling bearings.

Periodical sparse low-rank matrix estimation algorithm for fault detection of rolling bearings

Early bearing fault detection is crucial to avoid catastrophic accidents. However, the repetitive defect impulses indicating bearing fault are buried in heavy background noise. In the paper, a novel periodical sparse low-rank (PSLR) matrix estimation algorithm is proposed for extracting repetitive transients from noisy signal. Concretely, periodical group sparsity and low-lank property of fault transients in time–frequency domain are first revealed, and then an optimization problem is proposed for simultaneously promoting these two properties. Meanwhile, to further highlight the sparsity of fault features, the non-convex penalty functions are incorporated into the optimization problem. Then, for solving the proposed optimization problem, an iterative algorithm is derived based on alternating direction method of multipliers (ADMM) and majorization–minimization (MM), in which the traditional soft-thresholding operation is replaced by the proposed Gini-guided fault information thresholding (FIT) scheme to enhance fault transient extraction. Finally, simulated and real signals confirm the performance of proposed PSLR in extracting defect impulses from noisy vibration signal.

From Polynomial Fitting to Kernel Ridge Regression: A Generalized Difference Filter for Encoder Signal Analysis

Encoder signal analysis (ESA) provides a novel tool for condition monitoring of machinery. Nevertheless, how to enhance the incipient fault signatures using ESA, especially in noisy measurements, remains a challenging issue. In view of this limitation, a new method termed generalized difference filter (GDF) is proposed for weak feature enhancement of encoder signals. To better capture the fault transients, kernel ridge regression is first introduced for signal approximation in high-dimensional feature space. A stochastic error minimization scheme is then proposed to improve approximation accuracy in a data-driven manner. Finally, a fast algorithm for GDF is developed based on the Gaussian kernel. With this method, the denoising and differencing of encoder signals can be treated in a unified framework. The effectiveness of the proposed method is validated by both simulated studies and experimental data.

Voltage and Current Measuring Technologies for High Voltage Direct Current Supergrids: A Technology Review Identifying the Options for Protection, Fault Location and Automation Applications

After the occurrence of a DC-side feeder faults on HVDC transmission systems, protection and fault detection systems are anticipated to minimize their onerous effects, by initiating fault-clearing actions such as selective tripping of circuit breakers. Following the successful fault clearance, a subsequent action of significant importance, is the meticulous estimation of its location as a means to accelerate the line restoration, reduce down-time, limit recovery and repair costs, and hence elevate the overall availability and reliability of the transmission system. In order to capture DC-side fault transients for protection and fault location applications, measuring equipment is required to be placed on HVDC installations. This paper focuses primarily on reviewing the available technologies from the perspective of enabling protection, fault location and automation applications in HVDC systems. The review constitutes a mapping of protection and fault location functions, against the available voltage and current measuring technologies, ultimately unlocking insights for selecting measuring equipment based on the desirable characteristics of protection and fault location systems. The review also revealed that the frequency characteristics of each sensing scheme, primarily refers to the bandwidth of the primary sensor, whereas the overall bandwidth of the complete measuring scheme may be further restricted by the secondary converter and corresponding data acquisition system and signal processing electronics. It was also identified that the use of RC voltage dividers has prevailed for voltage measurements for HVDC applications, due to their superior advantages. The choice of a suitable device for current measurement, depends mainly on the fault detection method used and the frequency range it operates. In particular, the review revealed that fault detection and protection methods are mainly concentrated in a frequency spectrum ranging from a few kHz to 100 kHz, while fault location methods require measurements with a frequency range from 100 kHz up to 2 MHz.

Transient Current Waveform Similarity Based Protection for Flexible DC Distribution System

Unlike the traditional system frequency measurement-based protection in ac distribution systems, the protection in dc systems must deal with complex fault transients and identify the faulty section within milliseconds, as power electronic devices can only endure a fault current for several milliseconds. To address this problem, this paper presents a novel protection principle using transient current waveform similarity for a flexible dc distribution system. Fault current waveform characteristics are presented in detail, and cosine transient waveform similarity is adopted to fully distinguish internal and external faults. Hardware tests and simulation results show that the proposed protection can identify internal faults in less than 0.5 ms based on different fault impedances and measurement noise.

Transient extraction based on minimax concave regularized sparse representation for gear fault diagnosis

Extraction of fault-induced transients is one of the difficulties in gear fault diagnosis as the vibration signal is composed of multiple components. Sparse representation is one of the effective methods for extracting transients from such noisy and multicomponent vibration signals. However, conventional sparse representation approaches have the problems of amplitude underestimation, computational inefficiency and mono-component orientation. To address such problems, this paper first proposes to use minimax concave penalty function to construct the objective function as the minimax concave penalty function outperforms other penalty functions in preserving high-amplitude components based on the comparisons among different non-convex penalty functions. Then, the multiple over-complete dictionaries are established based on Fourier bases, where every two elements of each dictionary form a tight frame, enabling to simplify calculation and avoid computing high-dimensional inverse matrix when performing iterative optimization. Since multiple dictionaries are constructed, the multi-components of gear vibration signal can be extracted, respectively. With the constructed dictionaries, the sparse representation coefficients can be calculated using split augmented Lagrangian shrinkage algorithm and the repetitive transients can then be extracted. The simulation study and experimental analysis show that the fault transients and harmonic components can be accurately as well as computational-effectively extracted without underestimation of high amplitude components. Comparison results between minimax concave, arctan and L1 norm also prove the superior high-amplitude preserving ability of the proposed method.

Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Non-stationary vibration signals of a gearbox under varying speed display complicated modulations, which lead to intense sidebands thereby resulting in difficulty to identify the presence of a fault. Variational mode decomposition (VMD) being highly adaptive, effective in attenuating mode-mixing problem, low computational time requirement and therefore it is suitable to decompose a modulated multi-component non-stationary gearbox vibration signal. This research work thus utilizes the merits of VMD for demodulation and to diagnose localized gear tooth faults under real-time speed variation. In the present study, the vibration signals of a gearbox for different faults were acquired under complete speed variation (i.e., run-up, random fluctuation, and coast-down). To identify the symptoms confirming the presence of faults, the vibration signals were decomposed by VMD thereby demodulating the raw vibration signal. The decomposed VMFs exhibited the presence of transients due to faults and were analyzed statistically. To state the effectiveness of VMD in exhibiting fault by extracting fault transients, the performance of VMD was compared with the performance of recently developed wavelet based empirical wavelet transform (EWT) and flexible analytic wavelet transform (FAWT). The fault detection performance of VMD outperforms EWT as demonstrated by simulated signal and validated by the experimental investigations. FAWT was found ineffective in decomposing the vibration signal under varying speed.

On-Line Cable Condition Monitoring Using Natural Power Disturbances

On-line cable insulation condition monitoring can significantly reduce the outages caused by insulation failure. This paper proposes a new method to achieve this by utilizing the natural power disturbances originating from outside of the cable, such as capacitor switching and fault transients. In this method, the relative permittivity of the cable insulation is used as the indicator to be monitored, which is closely associated with cable tanδ. The relative permittivity is extracted from the power disturbances and their responses, which are of sufficient strength and a wide range of frequencies. This method is beneficial to the on-line monitoring for it does not need the accurate remote synchronized phase angle information. Case studies and sensitivity studies demonstrate the good performance of the proposed method.