Methodologies in power systems fault detection and diagnosis

Power systems frequently experience variations in their operation, which are mostly manifested as transmission line faults. Over the past decade, various techniques of fault diagnosis have been developed to ensure reliable and stable operation of power systems. This paper reviews the current literature on advanced application of fault diagnosis in power systems. Application of different fault diagnosis schemes is presented, with emphasis on reliable fault detection and classification of power system faults. The motivation behind applications of emerging process history, or pattern recognition, techniques in power system fault diagnosis has been reviewed. An extensive review of advanced mathematical techniques, in pattern recognition methods, involving wavelet transform, artificial neural networks and support vector machines has been presented. The paper also introduces a novel unsupervised technique of quarter-sphere support vector machine for power system fault detection and classification and reviews its application as future research in the developing area of fault diagnosis.

[1]  A. Ngaopitakkul,et al.  Discrete Wavelet Transform and Support Vector Machines Algorithm for Classification of Fault Types on Transmission Line , 2012 .

[2]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[3]  Raj Aggarwal,et al.  Artificial neural networks in power systems. III. Examples of applications in power systems , 1998 .

[4]  J. L. Aravena,et al.  Power system fault detection and state estimation using Kalman filter with hypothesis testing , 1991 .

[5]  Farid Melgani,et al.  Classification of Electrocardiogram Signals With Support Vector Machines and Particle Swarm Optimization , 2008, IEEE Transactions on Information Technology in Biomedicine.

[6]  Chih-Jen Lin,et al.  A comparison of methods for multiclass support vector machines , 2002, IEEE Trans. Neural Networks.

[7]  Ethem Alpaydin,et al.  Support Vector Machines for Multi-class Classification , 1999, IWANN.

[8]  Marimuthu Palaniswami,et al.  Centered Hyperspherical and Hyperellipsoidal One-Class Support Vector Machines for Anomaly Detection in Sensor Networks , 2010, IEEE Transactions on Information Forensics and Security.

[9]  Vojislav Kecman,et al.  Support Vector Machines – An Introduction , 2005 .

[10]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[11]  Shigeo Abe.  Support Vector Machines for Pattern Classification , 2010, Advances in Pattern Recognition.

[12]  Gerald T. Heydt,et al.  Applications of the windowed FFT to electric power quality assessment , 1999 .

[13]  H. S. Chandrasekharaiah,et al.  Fault detection and diagnosis of power systems using artificial neural networks , 1991, Proceedings of the First International Forum on Applications of Neural Networks to Power Systems.

[14]  Subhojit Ghosh,et al.  Detection and classification of faults on six phase transmission line using ANN , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[15]  Xu Yao,et al.  Faults Detection for Power Systems , 2010 .

[16]  Peter Schlattmann,et al.  Theory and Algorithms , 2009 .

[17]  D. Thukaram,et al.  An Intelligent Approach Using Support Vector Machines for Monitoring and Identification of Faults on Transmission Systems , 2006, 2006 IEEE Power India Conference.

[18]  Nasser Tleis,et al.  Power Systems Modelling and Fault Analysis: Theory and Practice , 2007 .

[19]  Ganapati Panda,et al.  Application of minimal radial basis function neural network to distance protection , 2001 .

[20]  Daniel S. Yeung,et al.  Structured large margin machines: sensitive to data distributions , 2007, Machine Learning.

[21]  D. O. Koval.  Power system disturbance patterns , 1989 .

[22]  Pituk Bunnoon,et al.  Fault Detection Approaches to Power System: State-of-the-Art Article Reviews for Searching a New Approach in the Future , 2013 .

[23]  F. Mahmood,et al.  Application of wavelet multi-resolution analysis & perceptron neural networks for classification of transients on transmission line , 2008, 2008 Australasian Universities Power Engineering Conference.

[24]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part II: Qualitative models and search strategies , 2003, Comput. Chem. Eng..

[25]  Whei-Min Lin,et al.  A Fault Classification Method by RBF Neural Network with OLS Learning Procedure , 2001 .

[26]  M. Geethanjali,et al.  Combined wavelet transfoms and neural network (WNN) based fault detection and classification in transmission lines , 2009, 2009 International Conference on Control, Automation, Communication and Energy Conservation.

[27]  Liu Chun-Lin,et al.  A Tutorial of the Wavelet Transform , 2010 .

[28]  Tu Xu,et al.  A New Sphere-Structure Multi-Class Classifier , 2009, 2009 Pacific-Asia Conference on Circuits, Communications and Systems.

[29]  Heikki Mannila,et al.  Principles of Data Mining , 2001, Undergraduate Topics in Computer Science.

[30]  Avinash Kumar Sinha,et al.  Application of wavelet multiresolution analysis for classification of faults on transmission lines , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[31]  Ijaz Haider Naqvi,et al.  Support Vector Machine based fault detection & classification in smart grids , 2012, 2012 IEEE Globecom Workshops.

[32]  Raj Aggarwal,et al.  Artificial neural networks in power systems. II. Types of artificial neural networks , 1998 .

[33]  Raj Aggarwal,et al.  Artificial neural networks in power systems. I. General introduction to neural computing , 1997 .

[34]  Ibrahim Farhat.  Fault detection, classification and location in transmission line systems using neural networks , 2003 .

[35]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[36]  H. K. Srinivas,et al.  Application of Artificial Neural Network and Wavelet Transform for Vibration Analysis of Combined Faults of Unbalances and Shaft Bow , 2010 .

[37]  A.K. Pradhan,et al.  Neural fault classifier for transmission line protection - a modular approach , 2006, 2006 IEEE Power Engineering Society General Meeting.

[38]  Biswarup Das,et al.  Combined Wavelet-SVM Technique for Fault Zone Detection in a Series Compensated Transmission Line , 2008, IEEE Transactions on Power Delivery.

[39]  K. S. Srinivasan,et al.  Role of an Artificial Neural Network and a Wavelet Transform for Condition Monitoring of the Combined Faults of Unbalance and Cracked Rotors , 2010 .

[40]  Miguel Lázaro-Gredilla,et al.  Adaptive One-Class Support Vector Machine , 2011, IEEE Transactions on Signal Processing.

[41]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Trans. Inf. Technol. Biomed..

[42]  Min Wang,et al.  Classification of power quality disturbances using time-frequency ambiguity plane and neural networks , 2001, 2001 Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.01CH37262).

[43]  Sami Ekici,et al.  Energy and entropy-based feature extraction for locating fault on transmission lines by using neural network and wavelet packet decomposition , 2008, Expert Syst. Appl..

[44]  Oscar L. Chacón,et al.  Neural network approach to fault detection in electric power systems , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[45]  Xiaoguang Hu,et al.  An intelligent fault diagnosis method of high voltage circuit breaker based on improved EMD energy entropy and multi-class support vector machine , 2011 .

[46]  Ling Huang,et al.  Support Vector Machines, Data Reduction, and Approximate Kernel Matrices , 2008, ECML/PKDD.

[47]  Josiah L. Munda,et al.  Determinant-based feature extraction for fault detection and classification for power transmission lines , 2011 .

[48]  Abdelsalam Elhaffar,et al.  Power transmission line fault location based on current traveling waves , 2008 .

[49]  Ali Falifla,et al.  On line fault detection for transmission line using power system stabilizer signals , 2007 .

[50]  M. Karthikeyan,et al.  Wavelet - Support Vector Machine Approach for classification of Power Quality Disturbances , 2009 .

[51]  H. Khorashadi-Zadeh,et al.  Transmission Line Fault Detection & Phase Selection using ANN , 2003 .

[52]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. I. Theoretical foundation , 2000 .

[53]  Bernhard Schölkopf,et al.  Learning with kernels , 2001 .

[54]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[55]  Z. Gaing.  Wavelet-based neural network for power disturbance recognition and classification , 2004 .

[56]  S. Seyedtabaii,et al.  Comparison of fourier & wavelet transform methods for transmission line fault classification , 2010, 2010 4th International Power Engineering and Optimization Conference (PEOCO).

[57]  M. Uyar,et al.  An effective wavelet-based feature extraction method for classification of power quality disturbance signals , 2008 .

[58]  M.A.P. Rodrigues,et al.  Fault diagnosis in electrical power systems using artificial neural networks , 1999, PowerTech Budapest 99. Abstract Records. (Cat. No.99EX376).

[59]  P. Purkait,et al.  A novel wavelet assisted neural network for transmission line fault analysis , 2008, 2008 Annual IEEE India Conference.

[60]  N. Kumarappan,et al.  Comparative Study of Fault Identification and Classification on EHV Lines Using Discrete Wavelet Transform and Fourier Transform Based ANN , 2008 .

[61]  D. Thukaram,et al.  Application of support vector machines for fault diagnosis in power transmission system , 2008 .

[62]  R. K. Aggarwal,et al.  A Novel Approach to the Classification of the Transient Phenomena in Power Transformers Using Combined Wavelet Transform and Neural Network , 2001, IEEE Power Engineering Review.

[63]  M.A. El-Sharkawi,et al.  Support vector machines for transient stability analysis of large-scale power systems , 2004, IEEE Transactions on Power Systems.

[64]  Irene Y. H. Gu,et al.  Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods , 2007, EURASIP J. Adv. Signal Process..

[65]  Irene Yu-Hua Gu,et al.  Bridge the gap: signal processing for power quality applications , 2003 .

[66]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[67]  A. Y. Chikhani,et al.  Genetic Algorithms Based Economic Dispatch for Cogeneration Units Considering Multiplant , 2022 .

[68]  D. Thukaram,et al.  Artificial neural network and support vector Machine approach for locating faults in radial distribution systems , 2005, IEEE Transactions on Power Delivery.

[69]  Ying-Hong Lin,et al.  An adaptive PMU based fault detection/location technique for transmission lines. I. Theory and algorithms , 2000 .

[70]  Dake He,et al.  A New Orientation for Multi-Class SVM , 2007 .

[71]  S. Osowski,et al.  Accurate fault location in the power transmission line using support vector machine approach , 2004, IEEE Transactions on Power Systems.

[72]  Marimuthu Palaniswami,et al.  CESVM: Centered Hyperellipsoidal Support Vector Machine Based Anomaly Detection , 2008, 2008 IEEE International Conference on Communications.

[73]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.

[74]  Chuan Tang,et al.  An Improved Method for Multi-class Support Vector Machines , 2010, 2010 International Conference on Measuring Technology and Mechatronics Automation.

[75]  Bo Wang,et al.  An Improved Hyper-sphere Support Vector Machine , 2007, Third International Conference on Natural Computation (ICNC 2007).

[76]  G. Panda,et al.  Power Quality Analysis Using S-Transform , 2002, IEEE Power Engineering Review.

[77]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[78]  Shyh-Jier Huang,et al.  Feasibility of fractal-based methods for visualization of power system disturbances , 2001 .

[79]  A. K. David,et al.  Application of artificial neural network in fault location technique , 2000, DRPT2000. International Conference on Electric Utility Deregulation and Restructuring and Power Technologies. Proceedings (Cat. No.00EX382).

[80]  Saad B. Qaisar,et al.  Quarter-Sphere SVM: Attribute and Spatio-Temporal correlations based Outlier & Event Detection in wireless sensor networks , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[81]  Edward J. Powers,et al.  Power quality disturbance waveform recognition using wavelet-based neural classifier. II. Application , 2000 .

[82]  Philip S. Yu,et al.  Outlier detection for high dimensional data , 2001, SIGMOD '01.

[83]  P.B.D. Gupta,et al.  Application of RBF neural network to fault classification and location in transmission lines , 2004 .

[84]  A.K. Sinha,et al.  A comparison of Fourier transform and wavelet transform methods for detection and classification of faults on transmission lines , 2006, 2006 IEEE Power India Conference.

[85]  Marimuthu Palaniswami,et al.  Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks , 2007, 2007 IEEE International Conference on Communications.

[86]  Ralf Herbrich,et al.  Learning Kernel Classifiers: Theory and Algorithms , 2001 .

[87]  Suhaas Bhargava Ayyagari.  ARTIFICIAL NEURAL NETWORK BASED FAULT LOCATION FOR TRANSMISSION LINES , 2011 .

[88]  Michael R. Lyu,et al.  Learning large margin classifiers locally and globally , 2004, ICML.

[89]  S. Santoso,et al.  Power quality assessment via wavelet transform analysis , 1996 .

[90]  Timo Sorsa,et al.  Neural networks in process fault diagnosis , 1991, IEEE Trans. Syst. Man Cybern..

[91]  T. Lobos,et al.  Automated classification of power-quality disturbances using SVM and RBF networks , 2006, IEEE Transactions on Power Delivery.

[92]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[93]  Kazimierz Wilkosz,et al.  Utilization of support vector machine classifiers to power system topology verification , 2010, 2010 Modern Electric Power Systems.

[94]  N. Shahid,et al.  Energy efficient outlier detection in WSNs based on temporal and attribute correlations , 2011, 2011 7th International Conference on Emerging Technologies.

[95]  J Stones.  43 – Power Quality , 2003 .

[96]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[97]  Yuan Ye,et al.  Power quality disturbances classification Based on multi-class classification SVM , 2009, 2009 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS).

[98]  Andrzej Cichocki,et al.  Neural networks for optimization and signal processing , 1993 .

[99]  K. Shanti Swarup,et al.  Binary SVM Approach for Security Assessment and Classification in Power Systems , 2009, 2009 Annual IEEE India Conference.

[100]  Xueqin Zhang,et al.  Support Vector Machines for Anomaly Detection , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[101]  M. Kezunovic,et al.  Transmission Line Boundary Protection Using Wavelet Transform and Neural Network , 2007, IEEE Transactions on Power Delivery.

[102]  A. G. Pipe,et al.  Intelligent control architectures for fault diagnosis in electrical power distribution networks , 2003, Proceedings of the 2003 IEEE International Symposium on Intelligent Control.

[103]  Karthikeyan Kasinathan,et al.  POWER SYSTEM FAULT DETECTION AND CLASSIFICATION BY WAVELET TRANSFORMS AND ADAPTIVE RESONANCE THEORY NEURAL NETWORKS , 2007 .

[104]  Vijay S. Kale,et al.  Detection and Classification of Faults on Parallel Transmission Lines Using Wavelet Transform and Neural Network , 2008 .

[105]  A.I. Megahed,et al.  Usage of wavelet transform in the protection of series-compensated transmission lines , 2006, IEEE Transactions on Power Delivery.

[106]  J. Montaño,et al.  Wavelet and neural structure: a new tool for diagnostic of power system disturbances , 2001 .

[107]  Azah Mohamed,et al.  Transient Stability Assessment of a Power System Using PNN and LS-SVM Methods , 2007 .

[108]  James S. Thorp,et al.  Computer Relaying for Power Systems , 2009 .

[109]  Robert P. W. Duin,et al.  Data domain description using support vectors , 1999, ESANN.

[110]  Paul M. Frank,et al.  New developments using AI in fault diagnosis , 1996 .

[111]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[112]  Bernhard Schölkopf,et al.  Estimating the Support of a High-Dimensional Distribution , 2001, Neural Computation.

[113]  R. P. Maheshwari,et al.  Transmission line fault detection and classification , 2011, 2011 International Conference on Emerging Trends in Electrical and Computer Technology.

[114]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part III: Process history based methods , 2003, Comput. Chem. Eng..