Showing papers on "Fault indicator published in 2009"

Fault Location on Power Networks

Abstract: Electric power systems will always be exposed to the failure of their components. When a fault occurs on a line, it is crucial for the fault location to be identified as accurately as possible, allowing the damage caused by the fault to be repaired quickly before the line is put back into service. Fault Location on Power Lines enables readers to pinpoint the location of a fault on power lines following a disturbance. If a fault location cannot be identified quickly and this causes prolonged line outage during a period of peak load, severe economic losses may occur and reliability of service may be questioned. The growth in size and complexity of power systems has increased the impact of failure to locate a fault and therefore heightened the importance of fault location research studies, attracting widespread attention among researchers in recent years. Fault location cannot be truly understood, applied, set, tested and analysed without a deep and detailed knowledge of the interiors of fault locators. Consequently, the nine chapters are organised according to the design of different locators. The authors do not simply refer the reader to manufacturers documentation, but instead have compiled detailed information to allow for in-depth comparison. Fault Location on Power Lines describes basic algorithms used in fault locators, focusing on fault location on overhead transmission lines, but also covering fault location in distribution networks. An application of artificial intelligence in this field is also presented, to help the reader to understand all aspects of fault location on overhead lines, including both the design and application standpoints. Professional engineers, researchers, and postgraduate and undergraduate students will find Fault Location on Power Lines a valuable resource, which enables them to reproduce complete algorithms of digital fault locators in their basic forms.

A particle-filtering approach for on-line fault diagnosis and failure prognosis

Abstract: This paper introduces an on-line particle-filtering (PF)-based framework for fault diagnosis and failure prognosis in non-linear, non-Gaussian systems. This framework considers the implementation of two autonomous modules. A fault detection and identification (FDI) module uses a hybrid state-space model of the plant and a PF algorithm to estimate the state probability density function (pdf) of the system and calculates the probability of a fault condition in realtime. Once the anomalous condition is detected, the available state pdf estimates are used as initial conditions in prognostic routines. The failure prognostic module, on the other hand, predicts the evolution in time of the fault indicator and computes the pdf of the remaining useful life (RUL) of the faulty subsystem, using a non-linear state-space model (with unknown time-varying parameters) and a PF algorithm that updates the current state estimate. The outcome of the prognosis module provides information about the precision and accuracy of long-term predictions, RUL expectations and 95% confidence intervals for the condition under study. Data from a seeded fault test for a UH-60 planetary gear plate are used to validate the proposed approach.

Sensor network data fault types

Abstract: This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.

Integrated Use of Time-Frequency Wavelet Decompositions for Fault Location in Distribution Networks: Theory and Experimental Validation

Abstract: The paper presents a procedure for fault location in distribution networks, based on the use of the integrated time-frequency wavelet decompositions of the voltage transients associated with the fault-originated travelling waves. The proposed analysis of time-frequency wavelet decompositions has been found to improve the identification accuracy of the frequencies associated to the characteristic patterns of a fault location with respect to a sole frequency-domain wavelet analysis. Several laboratory fault tests, carried out by means of a reduced-scale model of a distribution feeder, are used to illustrate the characteristics and assess the performances of the proposed improved procedure. The paper also illustrates the application of the proposed procedure to a transient, originated by a permanent phase-to-phase fault, measured in a real distribution network in which a post-test analysis has identified the faulted branch.

Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications

Abstract: Sensor faults continue to be a major hurdle for systems health management to reach its full potential. At the same time, few recorded instances of sensor faults exist. It is equally difficult to seed particular sensor faults. Therefore, research is underway to better understand the different fault modes seen in sensors and to model the faults. The fault models can then be used in simulated sensor fault scenarios to ensure that algorithms can distinguish between sensor faults and system faults. The paper illustrates the work with data collected from an electromechanical actuator in an aerospace setting, equipped with temperature, vibration, current, and position sensors. The most common sensor faults, such as bias, drift, scaling, and dropout were simulated and injected into the experimental data, with the goal of making these simulations as realistic as feasible. A neural network-based classifier was then created and tested on both experimental data and the more challenging randomized data sequences. Additional studies were also conducted to determine sensitivity of detection and disambiguation efficacy with respect to severity of fault conditions.

Power grid outage and fault condition management

Abstract: An outage intelligence application receives event messages indicative of occurrences associated with various devices within a power grid. The outage intelligence application determines a state of the various devices based on the event messages. Based on the event messages, the outage intelligence application can determine can determine and confirm an outage condition associate with a particular device. A fault intelligence application receives synchrophasor data for each phase in a multi-phase power grid. The synchrophasor includes phasor magnitude and phasor angle information for each phased. Based on the synchrophasor data, the fault intelligence application determines the presence of a fault involving one or more of the phases and identifies a particular fault type.

Incipient Bearing Fault Detection via Motor Stator Current Noise Cancellation Using Wiener Filter

Abstract: Current-based monitoring can offer significant economic savings and implementation advantages over traditional vibration monitoring for bearing fault detection. The key issue in current-based bearing fault detection is to extract bearing fault signatures from the motor stator current. Since the bearing fault signature in the stator current is typically very subtle, particularly when the fault is at an incipient stage, it is difficult to detect the fault signature directly. Therefore, in this paper, the bearing fault signature is detected alternatively by estimating and removing nonbearing fault components via a noise cancellation method. In this method, all the components of the stator current that are not related to bearing faults are regarded as noise and are estimated by a Wiener filter. Then, all these noise components are cancelled out by their estimates in a real-time fashion, and a fault indicator is established based on the remaining components which are mainly caused by bearing faults. Machine parameters, bearing dimensions, nameplate values, and the stator current spectrum distribution are not required in the method. The results of online experiments with a 20-hp induction motor under multiple load levels have confirmed the effectiveness of this method.

Detection Method for Open-Circuit Fault in Neutral-Point-Clamped Inverter Systems

Abstract: This paper presents a fault detection method for open-circuit faults of a switching device in neutral-point-clamped inverter systems, which is based on the inherent characteristic of continuous pulsewidth modulation The proposed method is achieved by measuring the pole voltage and its duration time The pole voltage includes information of switching states in the inverter system but not affected by the load Therefore, a fault condition of the inverter system itself can be diagnosed through analysis of pole voltage Compared to conventional fault detection methods, the proposed fault detection method has faster detection capability that is within two sampling times and is much simpler to implement Therefore, the use of the proposed method could minimize harmful effects such as imbalance of dc-link voltage and overstress on other switching devices The validity of the proposed fault detection method is verified through experimental results

Decision tree-based fault zone identification and fault classification in flexible AC transmissions-based transmission line

Abstract: Transmission line distance relaying for flexible AC transmission lines (FACTS) including thyristor controlled series compensator (TCSC), STATCOM, SVC and unified power flow controller (UPFC) has been a very challenging task. A new approach for fault zone identification and fault classification for TCSC and UPFC line using decision tree (DT) is presented. One cycle post fault current and voltage samples from the fault inception are used as input vectors against target output ‘1’ for fault after TCSC/UPFC and ‘0’ for fault before TCSC/UPFC for fault zone identification. Similarly, the DT-based classification algorithm takes one cycle data from fault inception of three phase currents along with zero-sequence current and voltage, and constructs the optimal DT for classifying all ten types of shunt faults in the transmission line fault process. The algorithm is tested on simulated fault data with wide variations in operating parameters of the power system network including noisy environment. The results indicate that the proposed method can reliably identify the fault zone and classify faults in the FACTs-based transmission line in large power network.

Instantaneous Frequency of the Left Sideband Harmonic During the Start-Up Transient: A New Method for Diagnosis of Broken Bars

Abstract: In this paper, a new method for detecting the presence of broken rotor bars is presented. The proposed approach is valid for induction machines started at constant frequency and consists of extracting the instantaneous frequency (IF) of the left sideband harmonic (LSH) from the start-up current (LSHst), via the Hilbert transform. It is shown that, in the case of machines with one or several broken bars, the IF of the LSHst exhibits a very characteristic and easy to identify pattern, which is physically justified. This paper also shows that, if the IF of the LSHst is represented against the slip, a universal fault indicator (nondependent neither on the machine characteristics nor on the starting conditions) can be defined. This fault indicator consists of the correlation between the experimental IF of the LSHst and its theoretical evolution. This approach is theoretically introduced and experimentally validated by testing a commercial motor in faulty and healthy conditions, under different operating conditions.

Elimination of Multiple Estimation for Fault Location in Radial Power Systems by Using Fundamental Single-End Measurements

Abstract: This paper presents a conceptual approach for eliminating the multiple estimation problem of impedance-based fault location methods applied to power distribution systems, using the available measurements of current and voltage fundamentals at the power substation. Three test systems are used to identify the faulted lateral obtaining high performance, even in the case of similar feeder configurations. This approach shows that it is possible to obtain a unique fault location, eliminating the problem of multiple estimation in tree-shaped radial systems using the single-end measurements at the distribution substation. Finally, this approach also contributes to improve the power continuity indexes in distribution systems by the opportune zone fault location.

An architecture for fault detection and isolation based on fuzzy methods

Abstract: Model-based fault detection and isolation (FDI) is an approach with increasing attention in the academic and industrial fields, due to economical and safety related matters. In FDI, the discrepancies between system outputs and model outputs are called residuals, and are used to detect and isolate faults. This paper proposes a model-based architecture for fault detection and isolation based on fuzzy methods. Fuzzy modeling is used to derive nonlinear models for the process running in normal operation and for each fault. When a fault occurs, fault detection is performed using the residuals. Then, the faulty fuzzy models are used to isolate a fault. The FDI architecture proposed in this paper uses a fuzzy decision making approach to isolate faults, which is based on the analysis of the residuals. Fuzzy decision factors are derived to isolate faults. An industrial valve simulator is used to obtain several abrupt and incipient faults, which are some of the possible faults in the real system. The proposed fuzzy FDI architecture was able to detect and isolate the simulated abrupt and incipient faults.

Determining fault's type and accurate location in distribution systems with DG using MLP Neural networks

Abstract: Finding and designing new methods for determining type and exact location of faults in power system has been a major subject for power system protection engineers in recent years. Fault locating in transmission networks is not very hard and complicated due to low impedance of faults. This job is usually done by distance relays. But, in distribution networks, because of high impedance of fault and its vast variety and also simplicity of protective devices, determining the exact location of faults is very complicated. On the other hand, penetration of distribution generation into distribution networks reinforces the necessity of designing new protection systems for these networks. One of the main capabilities that can improve the efficiency of new protection relays in distribution systems is exact fault locating. In this paper, a new approach for determining the exact fault type and location in distribution systems including distributed generation using MLP neural networks is presented. In the suggested method, after determining the fault type, by normalizing the fault current of the main source, the corresponding trained neural network has been activated and the exact location of occurred fault has been derived. The presented method has been implemented on a sample distribution network, simulated by DIgSILENT Power Factory 13.2, and its performance has been tested. The simulation results show high performance and accuracy of the method and substantiate that it can be used in modern heuristic protection schemes in distribution systems.

A Fault Location Algorithm Based on Circuit Analysis for Untransposed Parallel Transmission Lines

Abstract: This paper proposes a fault-location algorithm for ultra-high-voltage untransposed parallel transmission lines that only use the voltages and currents at the local end. The proposed algorithm uses the voltage equation for the faulted phase of the faulted line. The equation contains the fault distance, fault resistance, and fault current. To obtain the fault current, Kirchhoff's voltage law is applied on the loops of three phases consisting of the faulted line and the adjacent parallel line. The fault current can be represented in terms of the fault distance. Inserting the fault current into the voltage equation results in an equation that contains only two parameters (i.e., the fault distance and fault resistance). The fault distance is estimated by solving the equation. Test results indicated that the algorithm accurately estimates the fault distance regardless of the fault resistance and mutual coupling effects.

Estimation of faults in DC electrical power system

Abstract: This paper demonstrates a novel optimization-based approach to estimating fault states in a DC power system. The model includes faults changing the circuit topology along with sensor faults. Our approach can be considered as a relaxation of the mixed estimation problem. We develop a linear model of the circuit and pose a convex problem for estimating the faults and other hidden states. A sparse fault vector solution is computed by using l 1 regularization. The solution is computed reliably and efficiently, and gives accurate diagnostics on the faults. We demonstrate a real-time implementation of the approach for an instrumented electrical power system testbed at NASA. Accurate estimates of multiple faults are computed in milliseconds on a PC. The approach performs well despite unmodeled transients and other modeling uncertainties present in the system.

Fault current limiter allocation and sizing in distribution system in presence of distributed generation

Abstract: Expose of distributed generation (DG) to the distribution network increases the fault current level. This will give rise to fault current which is normally greater than interrupt capability of breakers and fuses. The introduction of Solid State Fault Current Limiters (SSFCLs) becomes an effective way for suppressing such a high short-circuit current fault in distribution systems. In this paper, the effect of proposed SSFCL on reduction of fault current is investigated. Then genetic algorithm is employed to search for the optimal number, locations and size of proposed SSFCL. The Numerical and simulation results show the efficiency of proposed GA based FCL allocation and sizing method in terms of minimization of distribution protection system cost.

Learning-based strategy for reducing the multiple estimation problem of fault zone location in radial power systems

Abstract: A learning-based strategy that uses support vector machines and k nearest neighbours is proposed for locating the faulted zone in radial power systems, specifically in distribution networks. The main goal is to reduce the multiple estimation of the fault location, inherent in those methods that use single end measurements. A selection of features obtained from the fundamentals of voltages and currents, measured at the power substation, are analysed and used as inputs of the proposed zone locator. Performance of several combinations of these features considering all fault types, different short-circuit levels and variation of the fault resistance, and the system load is evaluated. An application example illustrates the high precision to locate the faulted zone, obtained with the proposed methodology. The proposal provides appropriate information for the prevention and opportune attention of faults, requires minimum investment and overcomes the multiple estimation problem of the classic impedance based methods.

The Fault Detection Problem

Abstract: One of the most important challenges in distributed computing is ensuring that services are correct and available despite faults. Recently it has been argued that fault detection can be factored out from computation, and that a generic fault detection service can be a useful abstraction for building distributed systems. However, while fault detection has been extensively studied for crash faults, little is known about detecting more general kinds of faults. This paper explores the power and the inherent costs of generic fault detection in a distributed system. We propose a formal framework that allows us to partition the set of all faults that can possibly occur in a distributed computation into several fault classes . Then we formulate the fault detection problem for a given fault class, and we show that this problem can be solved for only two specific fault classes, namely omission faults and commission faults . Finally, we derive tight lower bounds on the cost of solving the problem for these two classes in asynchronous message-passing systems.

Neural network-based approach for early detection of cascading events in electric power systems

Abstract: This study proposes neural modelling and fault diagnosis methods for the early detection of cascading events in electric power systems. A neural-fuzzy network is used to model the dynamics of the power transmission system in fault-free conditions. The output of the neural-fuzzy network is compared to measurements from the power system and the obtained residuals undergo statistical processing according to a fault detection and isolation algorithm. If a fault threshold, defined by the fault detection and isolation (FDI) algorithm, is exceeded then deviation from normal operation can be detected at its early stages and an alarm can be launched. In several cases fault isolation can be also performed, that is the sources of fault in the power transmission system can be also identified. The performance of the proposed methodology is tested through simulation experiments.

Application of Wigner-Ville distribution and probability neural network for scooter engine fault diagnosis

Abstract: An expert system for internal combustion engine fault diagnosis using Wigner-Ville distribution for feature extraction and probability neural network for fault classification is described in this paper. Most of the conventional techniques for fault signal analysis in a mechanical system are based chiefly on the difference of signal amplitude in the time and frequency domains. Unfortunately, in some conditions the performance is limited, such as when analysis signals are non-stationary. In the present study, the Wigner-Ville distribution is proposed for sound emission signal features classification, because it provides high resolution of instantaneous energy density both in time and frequency domains. Meanwhile, the instantaneous power spectrum is presented to obtain high-energy density when the engine fault condition occurs. These features of signals are classified using the probability neural network. To examine the efficiency of the probability neural network, both back-propagation and radial basis function neural networks are used in comparison with fault classification. The experimental results showed all three networks can achieve high recognition rate with feature extraction using Wigner-Ville distribution method. It also suggested the probability neural network can complete training in an extremely short time.

A Novel Single-Phase Adaptive Reclosure Scheme for Transmission Lines With Shunt Reactors

Abstract: Single-phase adaptive reclosure (SPAR) schemes applied to transmission lines have been an effective method to improve the stability of power system. Attractive techniques have been proposed for SPAR schemes based on the tripped fault-phase voltage. But for transmission lines with shunt reactors, the voltage is so minor that the error of voltage transformer limits the applications of voltage-based SPAR schemes. To overcome this disadvantage, a new scheme using differential current between the calculated current based on transient fault model and the actual measured current from the fault phase of shunt reactor is proposed in this paper. The calculated current is obtained by using the transient fault model whether there is a transient or permanent fault. In the case of transient fault, the calculated current is close to the measured current due to the actual fault model being close to the calculated model. As for the permanent fault, the calculated current is quite different from the measured current. Therefore, the differential current of the fault phase can be employed to distinguish permanent fault from transient fault. At last, the Alternative Transients Program simulation results show that the developed algorithm can identify the permanent fault correctly and reliably, and is promised to be applied to SPAR for transmission lines with shunt reactors.

A new method for fault detection during power swing in distance protection

Abstract: During a power swing, currents and voltages behave such as a fault. Therefore, Power swing blocking function in distance relays is necessary to discriminate between a power swing and a fault. Otherwise power swings can be considered as a fault and causes relay trip. The main problem happens when during power sings a fault occurs. In this case, distance relays should be unblocked. In this paper, a new method based on the DC component of fault currents will be proposed to detect a fault during power swing blocking. The proposed method can detect single-phase to ground, two-phase to ground and three-phase fault. Applying the new method on a sample network reveals the features of the method.

Call-stack pattern matching for problem resolution within software

Abstract: A method of diagnosing a fault condition within software can include, responsive to a fault condition within a computing system belonging to an organization, automatically sending call-stack information for the fault condition to a first server within the organization. Within the first server, the call-stack information for the fault condition can be compared with call-stack information from prior fault conditions that occurred within the organization to determine whether the call-stack information for the fault condition matches call-stack information from one of the prior fault conditions. The method further can include sending the call-stack information to a second server for comparison with call-stack information from prior fault conditions that occurred within at least one different organization if the call-stack information for the fault condition does not match.