Insulation Defect Diagnostic Method for OIP Bushing Based on Multiclass LS-SVM and Cuckoo Search

Frequency-domain dielectric spectrum (FDS) is an effective testing method to reflect the changes of internal insulation status of oil-impregnated paper (OIP) bushing. In field application, the results of FDS test can be both affected by aging defects and damp defects, then the internal insulation defects of OIP bushing cannot be diagnosed in detail, which is an issue of discrimination of multiclass classification. To solve this problem, a method to diagnose the internal insulation defects of OIP bushing is proposed based on multiclass least square support vector machines (LS-SVMs) optimized by cuckoo search (CS) algorithm. First, the multiclass LS-SVM parameters are optimized by the CS algorithm. Then, the training data set is used to train the multiclass LS-SVM model, and the test data set is used for model testing. The experimental results show that the proposed method can effectively diagnose the internal insulation defects in detail, i.e., aging defects and damp defects. In addition, in the proposed method, the CS algorithm is better than the genetic algorithm and particle swarm optimization algorithm, and the convergence rate of the CS algorithm is faster than the other two algorithms.

[1]  Zhaohong Deng,et al.  Tackling Missing Data in Community Health Studies Using Additive LS-SVM Classifier , 2018, IEEE Journal of Biomedical and Health Informatics.

[2]  W. S. Zaengl,et al.  Dielectric Response Methods for Diagnostics of Power Transformers , 2003 .

[3]  Lijun Zhou,et al.  Effects of thermal aging on moisture diffusion in insulation paper immersed with mineral oil , 2018, IEEE Transactions on Dielectrics and Electrical Insulation.

[4]  Yao Zhang,et al.  Multivariable LS-SVM with moving window over time slices for the prediction of bearing performance degradation , 2018, J. Intell. Fuzzy Syst..

[5]  Youxian Sun,et al.  A Fault-Tolerant Flow Measuring Method Based on PSO-SVM With Transit-Time Multipath Ultrasonic Gas Flowmeters , 2018, IEEE Transactions on Instrumentation and Measurement.

[6]  Ping Wang,et al.  Cuckoo Search and Particle Filter-Based Inversing Approach to Estimating Defects via Magnetic Flux Leakage Signals , 2016, IEEE Transactions on Magnetics.

[7]  M. Nabi,et al.  Improved FEM model for defect-shape construction from MFL signal by using genetic algorithm , 2007 .

[8]  Ramesh C. Bansal,et al.  Non-linear LS-SVM with RBF-kernel-based approach for AGC of multi-area energy systems , 2018 .

[9]  Haiqing Li,et al.  Flow Pattern Identification Based on EMD and LS-SVM for Gas–Liquid Two-Phase Flow in a Minichannel , 2011, IEEE Transactions on Instrumentation and Measurement.

[10]  Yukio Mizuno,et al.  Distinct Fault Analysis of Induction Motor Bearing Using Frequency Spectrum Determination and Support Vector Machine , 2017, IEEE Transactions on Industry Applications.

[11]  Ayman H. El-Hag,et al.  A Novel Bias Detection Technique for Partial Discharge Localization in Oil Insulation System , 2016, IEEE Transactions on Instrumentation and Measurement.

[12]  Lilan Liu,et al.  Insulating characteristics of UHV resin impregnated paper bushing condenser's materials , 2015, 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials (ICPADM).

[13]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[14]  Giles M. Foody,et al.  Multiclass and Binary SVM Classification: Implications for Training and Classification Users , 2008, IEEE Geoscience and Remote Sensing Letters.

[15]  Jianbin Xiong,et al.  Fault Diagnosis of Rotation Machinery Based on Support Vector Machine Optimized by Quantum Genetic Algorithm , 2018, IEEE Access.

[16]  Peter A. Wallace,et al.  A dielectric frequency response model to evaluate the moisture content within an oil impregnated paper condenser bushing , 2013 .

[17]  Thomas W. Rauber,et al.  Heterogeneous Feature Models and Feature Selection Applied to Bearing Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[18]  Pinar Civicioglu,et al.  A conceptual comparison of the Cuckoo-search, particle swarm optimization, differential evolution and artificial bee colony algorithms , 2013, Artificial Intelligence Review.

[19]  C. Krishna Mohan,et al.  DiP-SVM : Distribution Preserving Kernel Support Vector Machine for Big Data , 2017, IEEE Transactions on Big Data.

[20]  Lijun Zhou,et al.  Moisture estimation for oil-immersed bushing based on FDS method: field application , 2018 .

[21]  Lijun Zhou,et al.  Moisture estimation for oil-immersed bushing based on FDS method: at a reference temperature , 2018 .

[22]  Juan Carlos García Prada,et al.  Bearing fault diagnosis based on neural network classification and wavelet transform , 2006 .

[23]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machines , 2002 .

[24]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[25]  Hong-Bin Shen,et al.  Modeling nonlinear dynamic biological systems with human-readable fuzzy rules optimized by convergent heterogeneous particle swarm , 2015, Eur. J. Oper. Res..

[26]  Wei Zhou,et al.  An Intelligent Fault Diagnosis Architecture for Electrical Fused Magnesia Furnace Using Sound Spectrum Submanifold Analysis , 2018, IEEE Transactions on Instrumentation and Measurement.

[27]  Hong-Bin Shen,et al.  OptiFel: A Convergent Heterogeneous Particle Swarm Optimization Algorithm for Takagi–Sugeno Fuzzy Modeling , 2014, IEEE Transactions on Fuzzy Systems.

[28]  Dhanraj Chitara,et al.  Cuckoo Search Optimization algorithm for designing of multimachine Power System Stabilizer , 2016, 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES).

[29]  D. Y. Wang,et al.  Frequency domain dielectric response of oil gap in time-varying temperature conditions , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[30]  Lijun Zhou,et al.  FDS analysis for multilayer insulation paper with different aging status in traction transformer of high-speed railway , 2017, IEEE Transactions on Dielectrics and Electrical Insulation.

[31]  Liu Liu,et al.  Automated Visual Inspection System for Bogie Block Key Under Complex Freight Train Environment , 2016, IEEE Transactions on Instrumentation and Measurement.

[32]  A. Srividya,et al.  Fault diagnosis of rolling element bearing using time-domain features and neural networks , 2008, 2008 IEEE Region 10 and the Third international Conference on Industrial and Information Systems.

[33]  Johan A. K. Suykens,et al.  Benchmarking Least Squares Support Vector Machine Classifiers , 2004, Machine Learning.

[34]  Tapan Kumar Saha,et al.  Particle tracing modelling on moisture dynamics of oil-impregnated transformer , 2016 .