An integrated PSO for parameter determination and feature selection of ELM and its application in classification of power system disturbances

A new learning method for power system disturbances is introduced using extreme learning machine.Simultaneously optimize the feature subset and model selection for the ELM using PSO.Proposed method can improve convergence accuracy and generalization performance of ELM. This paper presents a performance enhancement scheme for the recently developed extreme learning machine (ELM) for classifying power system disturbances using particle swarm optimization (PSO). Learning time is an important factor while designing any computational intelligent algorithms for classifications. ELM is a single hidden layer neural network with good generalization capabilities and extremely fast learning capacity. In ELM, the input weights are chosen randomly and the output weights are calculated analytically. However, ELM may need higher number of hidden neurons due to the random determination of the input weights and hidden biases. One of the advantages of ELM over other methods is that the parameter that the user must properly adjust is the number of hidden nodes only. But the optimal selection of its parameter can improve its performance. In this paper, a hybrid optimization mechanism is proposed which combines the discrete-valued PSO with the continuous-valued PSO to optimize the input feature subset selection and the number of hidden nodes to enhance the performance of ELM. The experimental results showed the proposed algorithm is faster and more accurate in discriminating power system disturbances.

[1]  Whei-Min Lin,et al.  Detection and Classification of Multiple Power-Quality Disturbances With Wavelet Multiclass SVM , 2008, IEEE Transactions on Power Delivery.

[2]  M. Ringnér,et al.  Analyzing array data using supervised methods. , 2002, Pharmacogenomics.

[3]  Qian Du,et al.  Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[4]  E.F. El-Saadany,et al.  Power quality disturbance classification using the inductive inference approach , 2004, IEEE Transactions on Power Delivery.

[5]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[6]  Zwe-Lee Gaing,et al.  Wavelet-based neural network for power disturbance recognition and classification , 2004, IEEE Transactions on Power Delivery.

[7]  Sundaram Suresh,et al.  Performance enhancement of extreme learning machine for multi-category sparse data classification problems , 2010, Eng. Appl. Artif. Intell..

[8]  Sami Ekici,et al.  Classification of power system disturbances using support vector machines , 2009, Expert Syst. Appl..

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  N. Ertugrul,et al.  Investigation of Effective Automatic Recognition Systems of Power-Quality Events , 2007, IEEE Transactions on Power Delivery.

[11]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[12]  Chee Kheong Siew,et al.  Extreme learning machine: RBF network case , 2004, ICARCV 2004 8th Control, Automation, Robotics and Vision Conference, 2004..

[13]  Bijaya K. Panigrahi,et al.  Power signal classification using dynamic wavelet network , 2009, Appl. Soft Comput..

[14]  Annalisa Riccardi,et al.  Ordinal Neural Networks Without Iterative Tuning , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[15]  Pedro Antonio Gutiérrez,et al.  MELM-GRBF: A modified version of the extreme learning machine for generalized radial basis function neural networks , 2011, Neurocomputing.

[16]  Feilong Cao,et al.  Optimization approximation solution for regression problem based on extreme learning machine , 2011, Neurocomputing.

[17]  P. Saratchandran,et al.  Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis , 2007, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[18]  Habtom W. Ressom,et al.  Inversion of ocean color observations using particle swarm optimization , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[19]  H. He,et al.  A self-organizing learning array system for power quality classification based on wavelet transform , 2006, IEEE Transactions on Power Delivery.

[20]  Naif Alajlan,et al.  Differential Evolution Extreme Learning Machine for the Classification of Hyperspectral Images , 2014, IEEE Geoscience and Remote Sensing Letters.

[21]  Gerald T. Heydt,et al.  Transient power quality problems analyzed using wavelets , 1997 .

[22]  R. Venkatesh Babu,et al.  No-reference image quality assessment using modified extreme learning machine classifier , 2009, Appl. Soft Comput..

[23]  A. Y. Chikhani,et al.  Power quality detection and classification using wavelet-multiresolution signal decomposition , 1999 .

[24]  S. Premrudeepreechacharn,et al.  Power quality problem classification using wavelet transformation and artificial neural networks , 2004, IEEE PES Power Systems Conference and Exposition, 2004..

[25]  Nand Kishor,et al.  Optimal feature and decision tree based classification of power quality disturbances in distributed generation systems , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[26]  Narasimhan Sundararajan,et al.  ICGA-PSO-ELM Approach for Accurate Multiclass Cancer Classification Resulting in Reduced Gene Sets in Which Genes Encoding Secreted Proteins Are Highly Represented , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

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

[28]  Narasimhan Sundararajan,et al.  A sequential multi-category classifier using radial basis function networks , 2008, Neurocomputing.

[29]  Yakup Demir,et al.  Automatic classification of power quality events and disturbances using wavelet transform and support vector machines , 2012 .

[30]  Bijaya Ketan Panigrahi,et al.  Classification of disturbances in hybrid DG system using modular PNN and SVM , 2013 .

[31]  Arturo Garcia-Perez,et al.  Detection and Classification of Single and Combined Power Quality Disturbances Using Neural Networks , 2014, IEEE Transactions on Industrial Electronics.