Search

Today high speed protection of power system elements is necessary and realizable. This paper introduces time-domain protection techniques for transmission lines using voltage and current signals in Cartesian coordinates. Three techniques are proposed using this approach for line protection. Direction of fault is obtained using change in angle between Cartesian planes of voltage and current. In the second application on unit protection scheme, internal and external faults in transmission lines are discriminated using dot product of the normals to both end current planes. Fault type identification is obtained using the angle between incremental changes in currents in Cartesian coordinates. The output of each protection function using the new concept is derived in a quarter-cycle. The performance of each of the proposed techniques is tested for load variation, high resistance fault, different fault types, power swing, fault inception angles and signal with noise. Further, the proposed techniques are evaluated using field data from a 400 kV double circuit line. [ABSTRACT FROM AUTHOR]

Several schemes are available in the literature to protect transmission lines (TLs). These schemes are either transmission network-specific or require modifications in case of any changes in the TL structure. FACTS devices are often incorporated in TLs to increase power transfer and improve controllability and stability. There have been many developments in the field of protection of FACTS-TL. However, the search for a scheme that can successfully adapt to the system changes and can be applied to almost any transmission network to identify the line faults is still ongoing. With advancements in communication technology and global positioning system-enabled time-tagged data, the voltage and current data can be synchronized and, therefore, the pilot relaying may become more viable. Utilizing these technological advances, a digital pilot relaying scheme, for the protection of TLs with or without series compensation and any type of short-circuit fault under any loading condition, is proposed in this paper. [ABSTRACT FROM AUTHOR]

This paper presents a decision tree based high-speed intelligent scheme for series-compensated transmission line protection using differential phase angle of superimposed current (DPASC). The conventional distance relaying scheme undergoes many shortcomings in transmission line protection in the presence of series compensation devices. The proposed scheme calculates the differential phase angle of current signals using full cycle discrete Fourier transform. The DPASC for each phase is used as an input to the data mining model known as decision tree (DT). The DT is used to generate thresholds to detect and classify the faults since determination of robust thresholds is an important challenge in protective relay engineering. The proposed method has been tested for different operating modes of transmission line in SIMIULINK/MATLAB software. Simulation results indicate that the proposed method is able to detect and classify the faults with acceptable accuracy, in less than half cycle. [ABSTRACT FROM AUTHOR]

Accurate pin-pointing of faults on transmission lines minimizes outage time, labor, and costs. This letter proposes a simple yet effective ensemble Kalman filter (EnKF) approach to accurately locating the faults on transmission lines within half-cycle time. The approach is easy to implement and does not require foreknowledge of either the fault type or an approximate guess of the fault location. Extensive results validate the effectiveness and accuracy of the EnKF-based fault location approach. [ABSTRACT FROM AUTHOR]

This paper presents a fault detection and classification scheme for a shunt [static var compensator (SVC)] compensated line. The proposed relaying scheme is based on the concept of superimposed sequence components-based integrated impedance (SSCII). For an internal fault, the magnitude of SSCII is small and for an external fault, it is very large. In the SVC compensated line, a fault forces SVC to vary its impedance. Superimposed components are injected due to this impedance variation, along with the fault components. These SVC-injected components are treated as fault-injected components and, therefore, even the sound phases are detected as faulty phases. To avoid such failure, fault-injected superimposed components have been extracted by using the modified prefault data, which is estimated according to SVC impedance variations. The superimposed components measured using the modified prefault data consist of fault-injected superimposed components only. The proposed scheme has been tested for all types of faults, different values of fault resistances, and several fault locations and SVC locations. The results demonstrate that the proposed scheme successfully detects and classifies the faults. Also, the proposed scheme is robust against variations in fault resistance, source impedance, and SVC location. [ABSTRACT FROM AUTHOR]

This paper presents a traveling-wave-based method for fault classification and localization for three-terminal power transmission systems. In the proposed method, the discrete wavelet transform is utilized to extract transient information from the recorded voltages. Support-vector-machine classifiers are then used to classify the fault type and faulty line/half in the transmission networks. Bewley diagrams are observed for the traveling-wave patterns and the wavelet coefficients of the aerial mode voltage are used to locate the fault. Alternate Transients Program software is used for transients simulations. The performance of the method is tested for different fault inception angles, different fault resistances, nonlinear high impedance faults, and nontypical faults with satisfactory results. [ABSTRACT FROM AUTHOR]

This paper presents a fault classification method with faulted phase selection action for single-circuit transmission lines which is based on the symmetrical components of reactive power. The proposed method does not need any threshold to operate and it is thus a setting-free method which is an exceptional attribute for a protective function. The evaluation of different fault cases reveals the capability of the proposed method. [ABSTRACT FROM AUTHOR]

Explicit fault analysis is the basis of protection for the bipolar HVDC Line. The characteristics of the initial values of traveling waves under various internal fault conditions are investigated on the basis of the symmetrical component analysis. The criteria of fault classification and faulty-pole selection are put forward based on the zero- and positive-sequence backward traveling waves, and an integrated traveling wave-based protection scheme is proposed. The simulations based on real-time digital simulation show that the proposed scheme can detect faults rapidly, determine the fault type effectively, and select the faulty pole correctly. [ABSTRACT FROM AUTHOR]

This paper is the second part of a series of two papers addressing a hybrid framework for achieving fault detection, classification, and location, simultaneously. The proposed framework is formed by a variety of analysis techniques, including symmetrical component analysis, wavelet transforms, principal component analysis, support vector machines, and adaptive structure neural networks. In our previous paper, the mathematical foundation of this framework with numerical results obtained by computer-based simulations has been presented. This paper is devoted to discuss the field-programmable gate-array implementation and experimental results acquired by using real-world scenarios. The hardware implementation of the runtime training technique in the proposed framework is an evolvable hardware tested by the power signals used in a power company transmission network for performance evaluation. The runtime training technique allows the FPGA to have learning and re-training capabilities. The main purpose of this paper is to show the applicability of the proposed framework on a hardware platform and test the framework's robustness and evolvability against noises from the system and measurements. [ABSTRACT FROM AUTHOR]

Re-energizing an overhead distribution feeder safely is a major consideration for a utility's safe work practice. One way to improve the safety is to determine whether the feeder still experiences short circuits before it is energized. In this paper, a novel fault detection technique is proposed to detect if a de-energized distribution system still experiences short-circuit faults. The proposed method involves injecting a thyristor-generated-controllable signal into the de-energized feeder. The feeder voltage and current responses are analyzed to determine if a fault still exists. A thyristor gating control strategy and fault detection algorithm are also developed in this paper to detect all possible types of faults that can occur in a system. The effectiveness of the proposed method has been verified through theoretical analysis, computer simulations, and lab tests. [ABSTRACT FROM AUTHOR]

To ensure safe re-energizing of an overhead distribution feeder after it is de-energized for an extended period, a novel fault detection technique by controlling a thyristor-based device is proposed in a companion paper. The device connected in parallel with a breaker or recloser can inject electrical pulses with adjustable strength for the downstream fault detection in a de-energized system. The proposed method can effectively detect different kinds of asymmetrical faults based on the unbalanced fault currents. However, the unbalanced current-based fault detection scheme is not effective for three-phase symmetrical faults detection. Furthermore, a stalled motor or a shunt-connected capacitor bank in the downstream may also behaves like a short-circuit. Therefore, a fault detection algorithm based on the analysis of the harmonic impedance of the de-energized system is developed in this paper. This method is very effective for the symmetrical fault detection and for distinguishing a stalled motor and capacitor bank from a fault. Extensive lab test results are provided in the paper to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Most works in power systems event classification concern classifying an event according to the morphology of the corresponding waveform. An important and even more difficult problem is the classification of the event underlying cause. However, the lack of labeled data is more problematic in this second scenario. This paper proposes a framework based on frame-based sequence classification (FBSC), the Alternative Transient Program (ATP), and a public dataset to advance research in this area. As a proof of concept, a thorough evaluation of automatic classification of short circuits in transmission lines is discussed. Simulations with different preprocessing (e.g., wavelets) and learning algorithms (e.g., support vector machines) are presented. The results can be reproduced at other sites and elucidate several tradeoffs when designing the front end and pattern recognition stages of a sequence classifier. For example, when considering the whole event in an offline scenario, the combination of the raw front end and a decision tree is competitive with wavelets and a neural network. [ABSTRACT FROM PUBLISHER]

In this paper, a novel method for fault classification in single-circuit transmission lines is presented. The proposed method needs voltages and currents of only one side of the protected line. After detecting the exact time of fault inception, the fault type is recognized by means of a decision-tree algorithm (DT) which is formerly trained by applying the odd harmonics of the measured signals, up to the 19th. Simulation results have shown that the proposed method can classify the faults in less than a quarter of a cycle with highest possible accuracy. [ABSTRACT FROM PUBLISHER]

In this paper, a novel method for fault classification of double-circuit transmission lines is presented. The proposed method needs voltages and currents of only one side of the protected line. After detecting the exact time of fault inception and calculating the odd harmonics of the measured signals, up to the nineteenth, a decision tree algorithm has been employed for recognition of the intercircuit fault type. Also, the proposed method is extended for classification of crossover faults in these transmission lines. Simulation results have shown that the proposed method can classify the faults in less than a quarter of a cycle with the highest possible accuracy. [ABSTRACT FROM PUBLISHER]

A novel technique for fault detection and classification in the extremely high-voltage transmission line using the fault transients is proposed in this paper. The novel technique, called wavelet singular entropy (WSE), incorporates the advantages of the wavelet transform, singular value decomposition, and Shannon entropy. WSE is capable of being immune to the noise in the fault transient and not being affected by the transient magnitude so it can be used to extract features automatically from fault transients and express the fault features intuitively and quantitatively even in the case of high-noise and low-magnitude fault transients. The WSE-based fault detection is performed in this paper, which proves the availability and superiority of WSE technique in fault detection. A novel algorithm based on WSE is put forward for fault classification and it is verified to be effective and reliable under various fault conditions, such as fault type, fault inception time, fault resistance, and fault location. Therefore, the proposed WSE-based fault detection and classification is feasible and has great potential in practical applications. [ABSTRACT FROM PUBLISHER]

Accurate fault classification and swift faulted-phase selection are the bases of protection for transmission systems. This paper presents the algorithm of fault classification and faulted. phase selection based on the initial current traveling wave. The characteristics of various faults are investigated on the basis of the Karenbauer transform. The criteria of fault classification and faulted-phase selection are put forward. In order to extract the traveling wave from postfault signals and to construct the algorithm, wavelet transform technology is adopted. The simulations based on the electromagnetic transients program have been done to verify the performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Two of the most expected objectives of transmission line protection are: 1) differentiating precisely the internal faults from external and 2) indicating exactly the fault type using one end data only. This paper proposes an improved solution based on wavelet transform and self-organized neural network. The measured voltage and current signals are preprocessed first and then decomposed using wavelet multiresolution analysis to obtain the high frequency details and low frequency approximations. The patterns formed based on high frequency signal components are arranged as inputs of neural network #1, whose task is to indicate whether the fault is internal or external. The patterns formed using low frequency approximations are arranged as inputs of neural network #2, whose task is to indicate the exact fault type. The new method uses both low and high frequency information of the fault signal 10 achieve an advanced line protection scheme. The proposed approach is verified using frequency-dependent transmission line model and the test results prove its enhanced performance. A discussion of the application issues for the proposed approach is provided at the end where the generality of the proposed approach and guidance for future study are pointed out. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method for transmission-line fault detection and classification using oscillographic data. The fault detection and its clearing time are determined based on a set of rules obtained from the current waveform analysis in time and wavelet domains. The method is able to single out faults from other power-quality disturbances, such as voltage sags and oscillatory transients, which are common in power systems operation. An artificial neural network classifies the fault from the voltage and current waveforms pattern recognition in the time domain. The method has been used for fault detection and classification from real oscillographic data of a Brazilian utility company with excellent results. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fault diagnosis method for a group of distribution transformers using the graphical information of frequency responses. For a group of transformers with similar capacities and ratings, the patterns of transformer's magnitude plots appear to be almost identical with minor variations of amplitudes and locations of existing peaks and valleys in the plots. Therefore, it is reasonable to establish prototypes composed of several peaks and valleys for magnitude plots and to investigate their changes for the purpose of diagnosis. We select six most significant regions for check regions, and their movements are encoded into a ternary string which is then compared with strings already constructed for various types of faults. Each string-by-string comparison is quantified into correlation values by computing Hamming distances and weighting selected bits. The fault type whose correlation values is largest is determined as a most probable fault in a test transformer. The diagnosis performance of the proposed method is proven to be reliable via experiment data on several types of faults. [ABSTRACT FROM AUTHOR]

The great development in computing power has allowed the implementation of artificial neural networks (ANNs) in the most diverse fields of technology. This paper shows how diverse ANN structures can be applied to the processes of fault classification and fault location in overhead two-terminal transmission lines, with single and double circuit. The existence of a large group of valid ANN structures guarantees the applicability of ANNs in the fault classification and location processes. The selection of the best ANN structures for each process has been carried out by means of a software tool called SARENEUR. [ABSTRACT FROM AUTHOR]

In this paper, a fuzzy-logic-based algorithm to identify the type of faults for digital distance protection system has been developed. The proposed technique is able to accurately identify the phase(s) involved in all ten types of shunt faults that may occur in a transmission line under different fault resistances, inception angle, and loading levels. The proposed method needs only three line-current measurements available at the relay location and can perform the fault classification task in about a half-cycle period. Thus, the proposed technique is well suited for implementation in a digital distance protection scheme. [ABSTRACT FROM PUBLISHER]

Series capacitor protected by metal-oxide varistor and air-gap arrangement imposes problems to line protection and other online decisions. Discrete wavelet transform integrated with a fuzzy logic system is designed for fault classification of a transmission line possessing a series capacitor at the midpoint. The approach uses information obtained from the wavelet decomposition of current signals for faulty phase selection and section identification. Two different FLSs are designed for the two classification objectives in this paper. [ABSTRACT FROM AUTHOR]