Sensitivity analysis of a single phase fault locator based on support vector machines

This paper presents a sensitivity analysis of a fault locator, for power distribution systems, aimed to determine those parameters that have the greatest influence on the location performance. The sampling process for sensitivity analysis is performed with an optimal Tabu based Latin hypercube method, and it was combined with the regression analysis to determine the influence of each parameter on the fault locator. The proposed methodology is tested in a standard IEEE 34-bus power distribution system, which is subdivided into 12 zones, considering a database of 66420 different fault conditions. Sensitivity analysis shows that standarization method is the one that has the greatest influence on the fault locator performance.

[1]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[2]  A. Felipe.,et al.  Implementación y comparación de técnicas de localización de fallas en sistemas de distribución basadas en minería de datos , 2013 .

[3]  Sandra Milena Pérez Londoño,et al.  Classification methodology and feature selection to assist fault location in power distribution systems , 2008 .

[4]  Joaquim Melendez,et al.  Comparison of impedance based fault location methods for power distribution systems , 2008 .

[5]  F. Glover,et al.  Handbook of Metaheuristics , 2019, International Series in Operations Research & Management Science.

[6]  Narayanan Kumarappan,et al.  Accurate fault location on EHV lines using both RBF based support vector machine and SCALCG based neural network , 2010, Expert Syst. Appl..

[7]  Javier M. Moguerza,et al.  Support Vector Machines with Applications , 2006, math/0612817.

[8]  Juan José Mora Flórez,et al.  Strategy based on genetic algorithms for an optimal adjust of a support vector machine used for locating faults in power distribution systems , 2010 .

[9]  Runze Li,et al.  Design and Modeling for Computer Experiments , 2005 .

[10]  Kenny Q. Ye,et al.  Algorithmic construction of optimal symmetric Latin hypercube designs , 2000 .

[11]  Dimitris Kanellopoulos,et al.  Data Preprocessing for Supervised Leaning , 2007 .

[12]  G. Venter,et al.  An algorithm for fast optimal Latin hypercube design of experiments , 2010 .

[13]  Mireia Farrús,et al.  Fusión de sistemas de reconocimiento basados en características de alto y bajo nivel , 2007 .

[14]  Anil K. Jain,et al.  Large-scale evaluation of multimodal biometric authentication using state-of-the-art systems , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Arun Ross,et al.  Score normalization in multimodal biometric systems , 2005, Pattern Recognit..

[16]  A. Herrera-Orozco,et al.  Load modeling for fault location in distribution systems with distributed generation , 2012, 2012 Sixth IEEE/PES Transmission and Distribution: Latin America Conference and Exposition (T&D-LA).

[17]  J. Dagenhart,et al.  The 40-/spl Omega/ ground-fault phenomenon , 2000 .

[18]  Boonserm Kijsirikul,et al.  Adaptive Directed Acyclic Graphs for Multiclass Classification , 2002, PRICAI.

[19]  J. José.,et al.  Localización de faltas en sistemas de distribución de energía eléctrica usando métodos basados en el modelo y métodos basados en el conocimiento , 2006 .

[20]  J. Mora-Florez,et al.  Fault location method based on the determination of the minimum fault reactance for uncertainty loaded and unbalanced power distribution systems , 2010, 2010 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America (T&D-LA).

[21]  Ye Zhang,et al.  Improved hyperspectral land-cover analysis using relevance vector machine , 2010, 2010 IEEE International Conference on Image Processing.

[22]  Ingeniero Electricista,et al.  ESTUDIO E IMPLEMENTACION DE UNA HERRAMIENTA BASADA EN MAQUINAS DE SOPORTE VECTORIAL APLICADA A LA LOCALIZACION DE FALLAS EN SISTEMAS DE DISTRIBUCION , 2012 .

[23]  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.

[24]  Sandra Pérez-Londoño,et al.  DISEÑO DE UNA HERRAMIENTA EFICIENTE DE SIMULACIÓN AUTOMÁTICA DE FALLAS EN SISTEMAS ELÉCTRICOS DE POTENCIA , 2010 .

[25]  Zyad Shaaban,et al.  Normalization as a Preprocessing Engine for Data Mining and the Approach of Preference Matrix , 2006, 2006 International Conference on Dependability of Computer Systems.