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Least Squares Fitting Based Fault Classification in Distribution Systems
Abstract
Power systems are the largest and most complex human made systems, consisting of thousands of electrical sources, loads,
transmission and distribution lines, power transformers, circuit breakers, etc. where faults always occurred. Faults can cause personnel
and equipment safety problems, and can result in significant disruption to power supply and thus financial losses. In this paper we will
present comprehensive mathematical suite to detect and classify fault dependent models of various types of power systems. This work
will extract fault unique signatures by using polarization ellipse during the healthy condition and the polarization will be circular shape
with radius equal the rated voltage of the system, but during the fault condition the polarization will be ellipse shape and the fault
signature will be defined according the ellipse parameters major axis, minor axis, ellipticity and orientation angle, by using least
squares criterion will define the ellipse parameters this system will identify and classify. This paper will be a milestone for extended
paper based on the proposed mathematical modelling and applying it to identify, classify and localize with simulation model.
... The first parameter which must be determined is the resolution of the grid of cells. How much "where" information is required to support T&D planning is a key issue [6][7]. Usually, the higher the resolution, the better the result. ...
The aim of this paper is to provide a forecast of the peak demand for the next 15 years for electrical distribution companies in Tafila region of Jordan. The proposed methodology provides both the peak demand and its location for the next 15 years. This paper describes the Spatial Load Forecasting model used, the information provided by electrical distribution company in Jordan, the workflow followed, the parameters used and the assumptions made to run the model.
... The trajectory plane will circle with radius equal of rated voltage of the system, but for the fault condition we will examine the trajectory parameter according to the type of fault. Before we start, a number of important points must be clarified[22]. ...
A vital attribute of electrical power network is the continuity of service with a high level of reliability. This motivated many researchers to investigate power systems in an effort to improve reliability by focusing on fault detection and classification. The penetration of renewable energy resources in distribution power systems would affect the traditional fault current level and characteristics. Consequently, traditional protection arrangements developed in distribution utilities are difficult in coordination; and the reclosing scheme would be affected. With rapid developments in distribution system automation, the protection coordination and reclosing scheme based on information exchange for the distribution power system can be realized flexibly. In this paper, a new protective relaying framework to detect, classify and localize faults in an electrical power distribution system with a high level of penetration of renewable energy resources is presented. This work will extract fault unique signatures by using polarization ellipse. During the healthy condition, polarization will have a circular shape with radius equal the rated voltage of the system. However, during the fault condition, polarization will have an ellipse shape and the fault signature will be defined according to the ellipse parameters: major axis, minor axis, ellipticity and orientation angle. The least squares criterion will be used to define ellipse parameters. This system will identify, classify and localize any fault instantaneously.
... For an exponential growth, V a bY = + . In order to correctly assess the demand, the sum of the squares of the errors committed by such approximation should be minimzed [12], i.e. ...
Load forecasting is vitally important for electric industry in the deregulated economy. This paper aims to face the power crisis and to achieve energy security in Jordan. Our participation is localized in the southern parts of Jordan including, Ma’an, Karak and Aqaba. The available statistical data about the load of southern part of Jordan are supplied by electricity Distribution Company. Mathematical and statistical methods attempted to forecast future demand by determining trends of past results and use the trends to extrapolate the curve demand in the future.
This paper addresses the problems encountered by conventional distance relays when protecting double-circuit transmission lines. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions; this mutual coupling is highly nonlinear in nature. An adaptive protection scheme is proposed for such lines based on application of artificial neural network (ANN). ANN has the ability to classify the nonlinear relationship between measured signals by identifying different patterns of the associated signals. One of the key points of the present work is that only current signals measured at local end have been used to detect and classify the faults in the double circuit transmission line with double end infeed. The adaptive protection scheme is tested under a specific fault type, but varying fault location, fault resistance, fault inception angle and with remote end infeed. An improved performance is experienced once the neural network is trained adequately, which performs precisely when faced with different system parameters and conditions. The entire test results clearly show that the fault is detected and classified within a quarter cycle; thus the proposed adaptive protection technique is well suited for double circuit transmission line fault detection & classification. Results of performance studies show that the proposed neural network-based module can improve the performance of conventional fault selection algorithms.
This paper discusses the potential application of ANN techniques for detection of single line to ground faults and fault type classification on double circuit transmission lines with remote end infeed. Distance protection of double circuit transmission lines has been a very challenging task. The problems arise principally as a result of the mutual coupling between the two circuits under different fault conditions. An accurate algorithm for fault detection and classification of single line-to-ground faults (A1N, A2N, B1N, B2N, C1N & C2N) in double circuit transmission line considering the effects of mutual coupling, high fault resistance, varying fault location, fault inception angle and remote source infeed is presented using feed forward neural network (FFNN) algorithm. The algorithm employs the fundamental components of voltage and current signals. This technique neither requires communication link to retrieve the remote end data nor zero sequence current compensation for healthy phases are required. This is a major advantage of the proposed algorithm for protection of double circuit line fed from both the ends. Results of study on a 220 kV transmission line are presented as an illustration. Simulation results indicate that algorithm is immune to the effect of mutual coupling, fault type, fault inception angle, fault resistance, fault location and remote end infeed.
In this paper, a very successful performance fuzzy logic-based fault classification scheme has been realized. To achieve high accurate classification of all ten types of shunt faults from this approach, an improved technique has been presented in this work. The proposed technique is able to identify all ten types of shunt faults with high accurate performance in wide variety of system conditions. The faults that occurred in high fault resistances, high system loading level and high distances from relaying point are identified correctly as well as. To illustrate the performance of proposed improved technique a large numbers of test cases was generated. The simulation studies have been carried out using PSCAD/EMTDC and MATLAB fuzzy-logic toolbox.
Power quality monitors in the occasion of a disturbance can either save the actual voltage waveform that contains the event or the corresponding RMS. The latter option reduces significantly the memory that is needed for saving the event. This paper shows that even with this type of monitoring, analysis of the measurements can be in depth. The paper proposes a method for automatic classification of power system events using RMS voltage measurements. The system is tested with measurements from a distribution network and the results show that classification is possible for the considered types of events. Finally, the limitations of this type of monitoring are shown.
Analysis Function of Power System Disturbances Objective of DFR Disturbance Analysis Determination of Power System Equipment Health Through System Disturbance Analysis Description of DFR Equipment Information Required for the Analysis of System Disturbances Signals to be Monitored by a Fault Recorder DFR Trigger Settings of Monitored Voltages and Currents DFR and Numerical Relay Sampling Rate and Frequency Response Oscillography Fault Records Generated by Numerical Relaying Integration and Coordination of Data Collected from Intelligent Electronic Devices DFR Software Analysis Packages Verification of DFR Accuracy in Monitoring Substation Ground Currents Using DFR Records to Validate Power System Short-Circuit Study Models COMTRADE Standard References
More than ninety case studies shed new light on power system phenomena and power system disturbances. Based on the author's four decades of experience, this book enables readers to implement systems in order to monitor and perform comprehensive analyses of power system disturbances. Most importantly, readers will discover the latest strategies and techniques needed to detect and resolve problems that could lead to blackouts to ensure the smooth operation and reliability of any power system. Logically organized, Disturbance Analysis for Power Systems begins with an introduction to the power system disturbance analysis function and its implementation. The book then guides readers through the causes and modes of clearing of phase and ground faults occurring within power systems as well as power system phenomena and their impact on relay system performance. The next series of chapters presents more than ninety actual case studies that demonstrate how protection systems have performed in detecting and isolating power system disturbances in: Generators. Transformers. Overhead transmission lines. Cable transmission line feeders. Circuit breaker failures. Throughout these case studies, actual digital fault recording (DFR) records, oscillograms, and numerical relay fault records are presented and analyzed to demonstrate why power system disturbances happen and how the sequence of events are deduced. The final chapter of the book is dedicated to practice problems, encouraging readers to apply what they've learned to perform their own system disturbance analyses. This book makes it possible for engineers, technicians, and power system operators to perform expert power system disturbance analyses using the latest tested and proven methods. Moreover, the book's many cases studies and practice problems make it ideal for students studying power systems.
A novel application of neural network approach to protection of transmission line is demonstrated in this paper. Different system faults on a protected transmission line should be detected and classified rapidly and correctly. This paper presents the use of neural networks as a protective relaying pattern classifier algorithm. The proposed method uses current signals to learn the hidden relationship in the input patterns. Using the proposed approach, fault detection, classification and faulted phase selection could be achieved within a quarter of cycle. An improved performance is experienced once the neural network is trained sufficiently and suitably, thus performing correctly when faced with different system parameters and conditions. Results of performance studies show that the proposed neural network- based module can improve the performance of conventional fault selection algorithms. In this paper, a new scheme is proposed for fast and reliable fault detection and phase selection. The proposed method uses an artificial neural network-based scheme. Various transient system faults are modeled and an ANN- based algorithm is used for recognition of these patterns. Performance of the proposed scheme is evaluated using various fault types and encouraging results are obtained. It is shown that the algorithm is able to perform fast and correctly for different combinations of fault conditions, e.g. fault type, fault resistance, fault inception angle, fault location, prefault power flow direction and system short circuit level.
Detection of faulty transient is a important problem in transient protection. In order to detect out the faults timely and reliably, a set of effective method for fault detection would be necessary. This paper built a model of 500 kV HV transmission line and simulated the fault detection of single- phase-to-ground uder various conditions.Based on the definition of WEE,WTE, WSE,WTFE,WAE and WDE, the paper put them into application of faulty transient detection for the first time and analyzed the detecting ability of each wavelet entropy. Finally, the simulations which compared wavelet entropy with wavelet modulo maximum prove that the fault detection based on wavelet entropy is superior to other methods.
The ability to detect and classify the type of fault plays a great role in the protection of power system. This procedure is required to be precise with no time consumption. In this paper detection of fault type has been implemented using wavelet analysis together with wavelet entropy principle. The simulation of power system is carried out using PSCAD/EMTDC. Different types of faults were studied obtaining various current waveforms. These current waveforms were decomposed using wavelet analysis into different approximation and details. The wavelet entropies of such decompositions are analyzed reaching a successful methodology for fault classification. The suggested approach is tested using different fault types and proven successful identification for the type of fault.
We report here on the design and implementation of analog circuitry which allows the processing of the components of vector waveforms to obtain polarization information. The waveforms are the voltages derived from orthogonal sensors such as magnetometers or seismometers which provide information on the time variation of vector processes. The circuits take the voltages from the individual sensors and process them to produce estimates of the spectral matrix at a particular frequency. From the components of the spectral matrix one may determine the wave polarization parameters including the signal power, ellipticity, handedness, and orientation of the polarization ellipse. In cases where the information of interest to the investigator depends upon the relatively slow modulations of a carrier and not upon the frequency of the carrier itself, the preprocessing of the spectral information as described here allows the investigator to sample the data at a much reduced rate, or with a narrower bandwidth, without loss of information. We have implemented the circuits described in our investigation of electromagnetic waves in the ULF (3 mHz–3 Hz) frequency band and have achieved a reduced demand for storage and processing capacities in the project.
This paper presents the development of an algorithm based on discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying the power system faults. The proposed technique consists of a preprocessing unit based on discrete wavelet transform in combination with PNN. The DWT acts as extractor of distinctive features in the input current signal, which are collected at source end. The information is then fed into PNN for classifying the faults. It can be used for off-line process using the data stored in the digital recording apparatus. Extensive simulation studies carried out using MATLAB show that the proposed algorithm not only provides an accepted degree of accuracy in fault classification under different fault conditions but it is also reliable, fast and computationally efficient tool.
This paper presents an approach for the fault classification in transmission line using multi-class support vector machine (SVM). This approach uses information obtained from the wavelet decomposition of post fault current signals as input to SVM for classification of various faults that may occur in transmission line. Using MATLAB Simulink, dataset has been generated with different types of fault and system variables, which include fault resistance, fault distance and fault inception angle. The proposed method has been extensively tested on a 240-kV, 200-km transmission line under variety of fault conditions. The results indicate that the proposed technique is accurate and robust for a variation in system parameter and fault conditions.
A novel technique for online estimation of the fundamental frequency of unbalanced three-phase power systems is proposed. Based on Clarke's transformation and widely linear complex domain modeling, the proposed method makes use of the full second-order information within three-phase signals, thus promising enhanced and robust frequency estimation. The structure, mathematical formulation, and theoretical stability and statistical performance analysis of the proposed technique illustrate that, in contrast to conventional linear adaptive estimators, the proposed method is well matched to unbalanced system conditions and also provides unbiased frequency estimation. The proposed method is also less sensitive to the variations of the three-phase voltage amplitudes over time and in the presence of higher order harmonics. Simulations on both synthetic and real-world unbalanced power systems support the analysis.
A new approach for fault detection in power system network using time-frequency analysis is presented in this paper. The S-transform with complex window is used for generating frequency contours(S-contours), which distinguishes the faulted condition from no-fault. Here the fault current data for one cycle back and one cycle from the fault inception is processed through S-transform to generate time-frequency patterns with varying window. The generated time-frequency patterns clearly distinguishes the faulted condition from un-faulted.
We present a novel way of extending rotary-component and polarization analysis to nonstationary random signals. If a complex signal is resolved into counterclockwise and clockwise rotating phasors at one particular frequency only, it traces out an ellipse in the complex plane. Rotary-component analysis characterizes this ellipse in terms of its shape and orientation. Polarization analysis looks at the coherence between counterclockwise and clockwise rotating phasors and whether there is a preferred rotation direction of the ellipse (counterclockwise or clockwise). In the nonstationary case, we replace this ellipse with a time-dependent local ellipse that, at a given time instant, gives the best local approximation of the signal from a given frequency component. This local ellipse is then analyzed in terms of its shape, orientation, and degree of polarization. A time-frequency coherence measures how well the local ellipse approximates the signal. The ellipse parameters and the time-frequency coherence can be expressed in terms of the Rihaczek time-frequency distribution. Under coordinate rotation, the ellipse shape, the degree of polarization, and the time-frequency coherence are invariant, and the ellipse orientation is covariant. The methods presented in this paper provide an alternative to ellipse decompositions based on wavelet ridge analysis.
The analysis of transmission line faults is essential to the
proper performance of a power system. It is required if protective
relays are to take appropriate action and in monitoring the performance
of relays, circuit breakers and other protective and control elements.
The detection and classification of transmission line faults is a
fundamental component of such fault analysis. Here, the authors describe
how a neural network, trained to recognize patterns of transmission line
faults, has been incorporated in a PC-based system that analyzes data
files from substation digital fault recorders
This paper introduces advanced pattern recognition algorithm for classifying the transmission line faults, based on combined use of neural network and fuzzy logic. The approach utilizes self-organized, supervised Adaptive Resonance Theory (ART) neural network with fuzzy decision rule applied on neural network outputs to improve algorithm selectivity for a variety of real events not necessarily anticipated during training. Tuning of input signal preprocessing steps and enhanced supervised learning are implemented, and their influence on the algorithm classification capability is investigated. Simulation results show improved algorithm recognition capabilities when compared to a previous version of ART algorithm for each of the implemented scenarios.
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.
For optimal polarization reception in the presence of interference and noise, a new signal-to-interference-and-noise-ratio (SINR) equation is derived from two triangles in polarization sphere based on the polarization ellipse parameters. The local optimal solutions of SINR on the great circle tracks and the small circle tracks are obtained. Some analytic solutions in special polarization schemes are presented. Several optimal strategies are proposed. An optimal polarization scheme called Three Steps' Searching and Comparing (TSSC) is suggested. The comparison among these optimal schemes is given. Simulation results show that TSSC scheme closely approaches the global optimum quickly and efficiently.
Automated fault analysis
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Fault diagnosis of parallel transmission lines using wavelet based ANFIS
- K S Swarup
- N Kamaraj
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Wavelet entropy measure definition and its application for transmission line fault detection and identification
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Z.X. He, X.Q. Chen, L. Fan, Wavelet entropy measure
definition and its application for transmission line fault
detection and identification, in: International Conference
on Power System Technology, Chong Qing, China, Oct.
22-26, 2006.