Fault Waveforms Research Articles

Study on a Kind of Simulation Training System of Photoelectric Theodolite

A kind of system of target simulation training of photoelectric theodolite is presented. On the basis of the experiment duties, almost 40 operation courses are set according to five modules, which are equipment operation, parameter testing, maintaining, troubleshooting and task flow. Modeling strategy of several kinds of aircraft track is studied and set. The modeling and controlling of the key parts of photoelectric theodolite are achieved with the software of UG and Unity3D. Then the fault waveform of the equipment failure is completed by the soft LabVIEW. With the software Eduis, the multimedia of the operation courses are done. Then scientific and reasonable assessment model is studied and set to evaluate the operation of the trainees. Finally, with the examples of the trainees exams and troops using, it shows the system of target simulation training of photoelectric theodolite meets the requirements.

Arc flash fault detection in wind farm collection feeders based on current waveform analysis

High impedance faults (HIFs) are easy to occur in collective feeders in wind farms and may cause the cascading of wind generators tripping. This kind of faults is difficult to be detected by traditional relay or fuse due to the limited fault current values and the situation is worse in wind farms. The mostly adopted HIF detection algorithms are based on the 3rd harmonic characteristic of the fault zero-sequence currents, whereas these 3rd harmonics are very easy to be polluted by wind power back-to-back converters. In response to this problem, the typical harmonic characteristic of HIF arc flash based on Mayr’s arc model is first analyzed, and the typical fault waveforms of HIF in wind farm are presented. Then the performance of the harmonic based HIF detection algorithm is discussed, and a novel detection algorithm is proposed from the viewpoint of time domain, focusing on the convex and concave characteristic of zero-sequence current at zero-crossing points. A HIFs detection (HIFD) prototype implementing the proposed algorithm has been developed. The sensitivity and security of the algorithm are proved by field data and RTDS experiments.

A novel and comprehensive single terminal ANN based decision support for relaying of VSC based HVDC links

HVDC technology is increasingly important for long distance bulk power transmission, but existing protection relaying techniques for such a system are subject to limitations. This paper presents a novel Artificial Neural Network (ANN) based on an algorithm for fault detection, location and classification in VSC-HVDC systems. Taking advantage of the ability of ANNs to identify and classify patterns, the proposed algorithm is able to detect and correctly classify a fault occurring at either the rectifier substation on the DC line or at the inverter substation. Therefore, such a scheme can be used as a decision support tool or as a backup protection. Only local signals are used at the rectifier substation and no communication link is necessary, thus improving the system’s protection reliability and reducing the overall cost of the hardware implementation. A detailed VSC-HVDC system is described and used to simulate a number of fault scenarios in the system. Using the resulting fault waveforms, a comprehensive decision support scheme is developed and described, paying particular attention to the signal processing chain and design of the specific ANNs for each relaying task. Finally, a detailed analysis of the influence of key fault parameters on the limits of the algorithm’s performance is carried out.

Automating Transmission-LineFault Root Cause Analysis

This research demonstrates that transmission-line faults can be classified automatically according to their underlying cause, and lays a foundation for operational classification of transmission-line faults in system control centers. The transmission-line fault waveforms are characterized by instantaneous symmetrical component analysis to describe the transient and steady-state fault conditions. Using a large fault record and waveform database, classification features based on the waveform and external environmental characteristics have been identified to develop single-nearest-neighbor classifiers that identify the underlying cause of transmission-line faults, and good classification accuracy has been achieved.

Fault diagnosis of star-connected auto-transformer based 24-pulse rectifier

Abstract This paper proposes a fault diagnosis method for star-connected auto-transformer based 24-pulse rectifier unit (ATRU) by integrating artificial neural networks (ANN) with wavelet packet decomposition (WPD) and principal component analysis (PCA). The WPD is employed to extract the features of different fault waveforms of the output voltage of the rectifier. PCA is adopted to reduce the dimensionality of the extracted feature vectors, which leads to fast computation of the algorithm. Back Propagation (BP) neural network is adopted to classify the fault types and determine the fault location according to the extracted features. These faults are simulated in real-time simulation platform and the obtained data are then analyzed with MATLAB toolbox, and finally verified with digital signal processor. Compared with other diagnosis methods, the proposed method shows better performance and faster computing speed.

Research of Small Current Grounding Fault Classification Based on Phase Plane Method

In order to solve the problem of single-phase ground fault classification, phase plane method is used. We use phase plane method to analysis single-phase ground fault waveform data first, so a series of discrete points is getting. The discrete points are divided by region on the phase plane to statistics the numbers in each region, so these numbers constitute the feature vector what we required. Use nearest neighbor classification method to achieve single-phase ground fault data classification. Empirical results show that this method can divided the faults into five to find fault reason, thereby improving the diagnostic capability of fault status and fault reason in distribution network.

Fault Line Selection Research on Single Phase Ground Fault of Small Current Grounding System Based on Wavelet Theory

For the problem of single-phase ground fault line selection of small current grounding system, in this paper, according to the wavelet theory advantage processing capabilities of transient mutation signal ,line selection criterion based on wavelet transform modulus maximum principle of singularity detection is proposed . By analyzing the single-phase ground fault waveform of small current grounding system in the neutral point not effective grounding system, steady state and transient characteristics of fault occurs is obtained, building bus with three outlets of 10KV small current grounding system simulation model by using MATLAB software, and verifying the validity though a large number of simulation experiments on the method.

METHOD FOR ENGINE WAVEFORM ANALYSIS AND FAULT DIAGNOSIS BASED ON SFB AND HHT

Traditional engine waveform analysis in time-domain fails to perform an accurate fault diagnosis when the fault waveform is very close to the normal waveform in time domain. A novel engine waveform analysis method is presented. In this paper, the aim is to perform fault diagnosis efficiently under such circumstances. This method is proposed by combining a new technique, called sensitive frequency band (SFB) selection, with the developed Hilbert–Huang transform (HHT). This can alleviate "mode mixing" by removing noise from the engine waveforms and reveal the time–frequency characteristics for a signal by deriving its time–frequency spectrum (TFS) distribution. The method is then applied to analyze the engine injector-pulse-width waveforms, and it works well for signal noise reduction and fault diagnosis.

Diagnosis Method for Analog Circuit Hard fault and Soft Fault

Because the traditional BP neural network slow convergence speed, easily falling in local minimum and the learning process will appear oscillation phenomena. This paper introduces a tolerance analog circuit hard fault and soft fault diagnosis method based on adaptive learning rate and the additional momentum algorithm BP neural network. Firstly, tolerance analog circuit is simulated by OrCAD / Pspice circuit simulation software, accurately extracts fault waveform data by matlab program automatically. Secondly, using the adaptive learning rate and momentum BP algorithm to train neural network, and then applies it to analog circuit hard fault and soft fault diagnosis. With shorter training time, high precision and global convergence effectively reduces the misjudgment, missing, it can improve the accuracy of fault diagnosis and fast. DOI: http://dx.doi.org/10.11591/telkomnika.v11i9.3301

Analysis in the Mercedes-Benz Automotive Fault Diagnosis

The analysis is a method to use the intrinsic link between things to reasonably infer the results of the unknown things, we can use Benz internal structure and the intrinsic link to analyze failure step by step to get an accurate diagnosis purpose, and the purpose of the paper is connecting the Mercedes-Benz automotive fault diagnostic to interpret the fault tree analysis and waveform analysis.

Fault classification method for inverter based on hybrid support vector machines and wavelet analysis

A new classification method for fault waveform is proposed based on discrete orthogonal wavelet transform (DOWT) and hybrid support vector machine (hybrid SVM) for fault type of a three-phase voltage inverter. The waveforms of output voltage obtained from the faulty inverter are decomposed by DOWT into wavelet coefficient matrices, through which we can obtain singular value vectors acted as features of time-series periodic waveforms. And then a multi-classes classification method based on a new Huffman Tree structure is presented to realize 1-v-r SVM strategy. The extracted features are applied to hybrid SVM for determining fault type. Compared to employing the structure based on ordinary binary tree, the superiority of the proposed SVM method is shown in the success of fault diagnosis because the average Loo-correctness of the SVM based on Huffman tree structure exceed the general SVM 3.65%, and the correctness reaches 99.6%.

Fault Diagnosis Method Research of Aircraft Ignition System based on Waveform Image Matching

This paper focuses on aircraft engine ignition system. Fault characteristic database containing fault waveforms of different fault types are obtained by many experiments. The outputting ignition waveform is made waveform image matching with all the typical fault waveforms of fault characteristic database by waveform image matching algorithm, then fault type of ignition system can be determined.

Characterization of fault zones by analysis of aftershock waveform data

Large property contrasts between materials in a fault zone and the surrounding rock are often produced by repeating earthquakes. Fault zones are usually characterized by fluid concentration, clay-rich fault gouge, increased porosity, and dilatant cracks. Thus, fault zones are thought to have re- duced seismic velocities than the surrounding rocks. In this article, we first investigated the synthetic waveforms at a linear array across a vertical fault zone by using 3D finite difference simulation. Syn- thetic waveforms show that when sources are close to, inside, or below the fault zone, both arrival times and waveforms of P- and S-waves vary systematically across the fault zone due to reflections and transmissions from boundaries of the low-velocity fault zone. The arrival-time patterns and waveform characteristics can be used to determine the fault zone structure. Then, we applied this method to the aftershock waveform data of the 1992 Landers M7.4 and the 2008 Wenchuan (汶川) M8.0 earthquakes. Landers waveform data reveal a low-velocity zone with a width of approximately 270-370 m, and P- and S-wave velocity reductions relative to the host rock of approximately 35%-60%; Wenchuan wave- form data suggest a low-velocity zone with a width of approximately 220-300 m, and P- and S-wave ve- locities drop relative to the host rock of approximately 55%.

Investigating the Option of Removing the Antialiasing Filter From Digital Relays

Digital relays traditionally employ sampling rates of less than 100 samples/cycle. In order to avoid aliasing due to fault transients, these relays employ an analog antialiasing filter before critical-sampling (Nyquist rate) the input waveforms coming from instrument transformers. In many applications of electrical engineering, oversampling (greater than the Nyquist rate) has long been used to simplify the requirements of an antialiasing filter with a sharp cutoff; in some cases, the filter can even be eliminated. This paper investigates this option for a digital relay. The performance of a traditional digital relay is compared with a method that uses oversampling without using an antialiasing filter. By processing a comprehensive array of fault waveforms from Electromagnetic Transients Program simulations, a suitable oversampling rate is suggested. A comparison of phasor estimates using the traditional relay and the proposed method is made for different operating and fault conditions. The results suggest that oversampling can eliminate the antialiasing filter traditionally employed in digital relays.

HVDC 시스템의 고장 시 소음 및 진동 분석

초록·키워드 목차 오류제보하기 본 논문은 HVDC 시스템의 고장 시 고장 원인과 소음의 원인 및 기계적인 균열에 관하여 분석한 논문이다. 본 논문에는 전기설비의 소음과 진동의 원인을 분석하였고, 시스템의 고장을 정량적으로 분석하였다. 최종적으로 고장전류와 소음과 진동 방정식을 이용하여 Smoothing 리액터의 지지물의 균열의 원인이 분석되었다. #HVDC #Audible Noise 요약Abstract1. 서론2. HVDC시스템에서 소음3. 사고 분석4. 결론References저자소개

A Digital Simulator for Determining the Performance Limits of Computer Relays

Unlike most other digital simulators that generate test waveforms for specific system configurations, playback previously recorded fault waveforms, or randomly generated artificial waveforms, the digital simulator presented in this paper generates the worst-case waveforms for specified bounds on noise components present in the relay input signals. As a result, the performance limits of a computer relay can be determined, for specified bounds on its noise components, by performing a single test only. The parameters that define the worst-case waveforms are obtained by utilizing a modified univariate search algorithm. The PC-based simulator was implemented using a general-purpose multifunction card and a graphical programming package called Visual Designer. It did not require any text-based programming for tasks such as interfacing, digital-to-analog conversion, and outputting the discrete analog values of the generated waveform under hardware control. For demonstrating the operation of the digital simulator, a computer relay was also developed in the laboratory and its performance limits were determined.

Classification of ground faults in distribution lines according to phase-plane trajectories of waveforms

Recently, a greater demand for stable supply of electric power has resulted from a higher standard of living. It becomes important that the causes and locations of ground faults in distribution lines be found early and defects be repaired as soon as possible. Therefore, a unit for recording the waveform I0 of ground fault's current and the waveform V0 of its voltage is installed in distribution substations. The establishment of a technology to distinguish fault causes automatically is being hastened. This paper presents a new classification method for ground fault waveforms, based on phase-plane trajectories for the current or voltage and their differential values. Examinations of ground fault waveforms based on real data show that the waveform of current I0 is more suitable than that of voltage V0 for the classification of waveforms at ground faults and that the trajectory of each of the three types of waveforms, such as sine waves, trapezoidal waves, and spikes, has all of the characteristics in its figure. It is also found from the distribution of 167 sampling points on the phase plane that any waveforms at a real ground fault may be classified into three characteristic point distributions, which gives the possibility of easy display for the classification of ground fault causes. © 1998 Scripta Technica. Electr Eng Jpn, 122(1): 8–16, 1998

Development of an expert system for analyzing faults in power systems based on waveform recognition by artificial neural networks

In restoration of a power system, appropriate steps need to be taken in early stages to avoid damage to the public caused by insufficient power supply. The first step in the restoration is the analysis of the fault that has produced the power failure. Today, a large amount of data will be available at the occurrence of a fault due to the use of advanced communication systems using digital relays and optical fibers. Such systems are intended to obtain data from the relays and circuit breakers (CBs) under operation and the voltage and current during operation and the prefault and postfault periods. This paper presents a hybrid fault analyzing expert system using data based on protective devices and phasor measurement. This system is a combination of an artificial neural network (ANN) and an expert system (ES). The ANN is used to simplify the analysis of current and voltage waveforms, which is considered difficult due to the presence of a fault resistance and the line impedance. Since a fault waveforms contains various ambiguous factors depending on the nature of the fault resistance, load current and other system parameters, the use of ANN is effective making such determinations.

A Travelling Wave-Based Fault Locator for Two- and Three-Terminal Networks

The high frequency components in fault waveforms present undesirable effects to most fault location algorithms and as a consequence filtering of postfault signals, to remove the high frequency transients, is essential for accurate fault location. A fault location algorithm that derives from travelling wave principles can cope with high frequency transients since these basically depend on travelling wave phenomena. The development of such an algorithm is the objective of this paper. Using the telegraph equations as a line model, voltage and current samples taken at one end of a line within the first 5 ms of fault inception are used to generate instantaneous voltage and current profiles for the rest of the transmission line. The voltage and current estimation is based on the solution of the equations of the line model by the method of characteristics. Criteria functions involving any of the square of the voltage, the square of the current, or the product of the two are applied for determination of the fault position. Fault position is given by the peak variation in tangent to the above basic functions. The algorithm finds application in fault location on two and three-terminal networks at both transmission and distribution levels.

An Accurate Method for Fault Location on Electric Power Transmission Lines

This paper proposes a new method for fault location on electric power transmission lines. The problem is here approached by identifying from pre-fault records an equivalent circuit for the far end of a transmission line. Based on the assumption that this equivalent is valid for the first few post fault cycles the position of the fault is calculated by solving the system of nonlinear equations, which can be formulated for each type of fault, using recorded fault waveforms. These equations are solved by applying Newton-Raphson technique. The method is finally examined by analysing two computer simulated faults on a 130 kV transmission line.