Data-driven fault diagnosis in the converter system

[1]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[2]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[3]  P. N. Suganthan,et al.  A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..

[4]  Yoh-Han Pao,et al.  Stochastic choice of basis functions in adaptive function approximation and the functional-link net , 1995, IEEE Trans. Neural Networks.

[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]  Yuchen Zhang,et al.  A Multiple Randomized Learning based Ensemble Model for Power System Dynamic Security Assessment , 2018, 2018 IEEE Power & Energy Society General Meeting (PESGM).

[7]  Mihai Comanescu Design and Implementation of a Highly Robust Sensorless Sliding Mode Observer for the Flux Magnitude of the Induction Motor , 2016, IEEE Transactions on Energy Conversion.

[8]  Lei Chen,et al.  Enhanced random search based incremental extreme learning machine , 2008, Neurocomputing.

[9]  Song Li,et al.  A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection , 2016, IEEE Transactions on Power Systems.

[10]  Kit Po Wong,et al.  An Intelligent Dynamic Security Assessment Framework for Power Systems With Wind Power , 2012, IEEE Transactions on Industrial Informatics.

[11]  Yang Xia,et al.  Ensemble-based Randomized Classifier for Data-driven Fault Diagnosis of IGBT in Traction Converters , 2018, 2018 IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia).

[12]  Imed Jlassi,et al.  A Robust Observer-Based Method for IGBTs and Current Sensors Fault Diagnosis in Voltage-Source Inverters of PMSM Drives , 2017, IEEE Transactions on Industry Applications.

[13]  Rui Zhang,et al.  Real-time transient stability assessment model using extreme learning machine , 2011 .

[14]  Kit Po Wong,et al.  A Reliable Intelligent System for Real-Time Dynamic Security Assessment of Power Systems , 2012, IEEE Transactions on Power Systems.

[15]  F. E. Grubbs Sample Criteria for Testing Outlying Observations , 1950 .

[16]  Yuchen Zhang,et al.  Intelligent Early Warning of Power System Dynamic Insecurity Risk: Toward Optimal Accuracy-Earliness Tradeoff , 2017, IEEE Transactions on Industrial Informatics.

[17]  Zhao Yang Dong,et al.  Faster Detection of Microgrid Islanding Events Using an Adaptive Ensemble Classifier , 2018, IEEE Transactions on Smart Grid.

[18]  F. E. Grubbs Procedures for Detecting Outlying Observations in Samples , 1969 .

[19]  Rui Zhang,et al.  Post-disturbance transient stability assessment of power systems by a self-adaptive intelligent system , 2015 .

[20]  D. J. Hill,et al.  Feature selection for intelligent stability assessment of power systems , 2012, 2012 IEEE Power and Energy Society General Meeting.

[21]  Victor O. K. Li,et al.  Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks , 2019, IEEE Transactions on Smart Grid.