A novel fault diagnostic method in power converters for wind power generation system

Abstract In order to decrease the downtime, enhance maintenance efficiency, and reduce the management cost of the wind power system, fault diagnosis technologies are considered as powerful tools for its good operation and maintenance. For example, high fault diagnosis accuracy can ensure effective fault-tolerant control and improve the durability of wind turbines under fault conditions. However, fault diagnosis accuracy can be easily affected by nonlinear and noise of measured signals under different working conditions. In this paper, a novel fault diagnostic method of power converters is proposed for the wind power generation system. In the proposed method, the measured output voltage is firstly processed by ensemble empirical mode decomposition (EEMD), and a series of intrinsic mode functions (IMF) can be obtained. Norm entropy (NE) is then calculated based on the statistical characteristics of the IMFs, and the extracted IMF-NE information is used to describe the diagnostic features. The effectiveness and reliability of the proposed method are then validated in a simulated 1.5 MW doubly-fed wind power system. The results show that the final diagnostic accuracy of 22 fault modes is 99.57% for wind speed variation, and the diagnosis accuracy can be maintained above around 70% for different signal to noise ratio. Compared with the other advanced fault diagnosis methods, the proposed method shows outstanding performance in terms of robustness, high accuracy, and simple implementation without complex parameter tuning.

[1]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[2]  Hanlin Liu,et al.  A Data-Driven Fault Diagnosis Methodology in Three-Phase Inverters for PMSM Drive Systems , 2017, IEEE Transactions on Power Electronics.

[3]  S. Selvaperumal,et al.  Fault Detection and Classification with Optimization Techniques for a Three-Phase Single-Inverter Circuit , 2016 .

[4]  Athanasios N. Safacas,et al.  Open-Circuit Fault Diagnosis for Matrix Converter Drives and Remedial Operation Using Carrier-Based Modulation Methods , 2014, IEEE Transactions on Industrial Electronics.

[5]  Yide Wang,et al.  Fault diagnosis method based on FFT-RPCA-SVM for Cascaded-Multilevel Inverter. , 2016, ISA transactions.

[6]  J. Ribrant,et al.  Survey of Failures in Wind Power Systems With Focus on Swedish Wind Power Plants During 1997–2005 , 2007, IEEE Transactions on Energy Conversion.

[7]  Weiqiang Chen,et al.  Logic-Based Methods for Intelligent Fault Diagnosis and Recovery in Power Electronics , 2017, IEEE Transactions on Power Electronics.

[8]  António J. Marques Cardoso,et al.  A New Algorithm for Real-Time Multiple Open-Circuit Fault Diagnosis in Voltage-Fed PWM Motor Drives by the Reference Current Errors , 2013, IEEE Transactions on Industrial Electronics.

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  Alice M. Agogino,et al.  Design of machine learning models with domain experts for automated sensor selection for energy fault detection , 2019, Applied Energy.

[11]  Zhaohui Gao,et al.  Fault diagnosis of star-connected auto-transformer based 24-pulse rectifier , 2016 .

[12]  Mo-Yuen Chow,et al.  Condition Monitoring, Diagnosis, Prognosis, and Health Management for Wind Energy Conversion Systems , 2015, IEEE Trans. Ind. Electron..

[13]  S. D. Lokhande,et al.  Neural Network Fault Diagnosis of Voltage Source Inverter under variable load conditions at different frequencies , 2016 .

[14]  Sergio Martín-Martínez,et al.  Wind turbine reliability: A comprehensive review towards effective condition monitoring development , 2018, Applied Energy.

[15]  Alan J. Watson,et al.  Fault Detection for Modular Multilevel Converters Based on Sliding Mode Observer , 2013, IEEE Transactions on Power Electronics.

[16]  Mounira Berkani,et al.  Ageing and Failure Modes of IGBT Modules in High-Temperature Power Cycling , 2011, IEEE Transactions on Industrial Electronics.

[17]  Demba Diallo,et al.  Three-Level NPC Inverter Incipient Fault Detection and Classification using Output Current Statistical Analysis , 2019, Energies.

[18]  Yide Wang,et al.  A Principal Components Rearrangement Method for Feature Representation and Its Application to the Fault Diagnosis of CHMI , 2017 .

[19]  Fouad Slaoui-Hasnaoui,et al.  Wind Turbine Condition Monitoring: State-of-the-Art Review, New Trends, and Future Challenges , 2014 .

[20]  Mohamed Benbouzid,et al.  A Brief Status on Condition Monitoring and Fault Diagnosis in Wind Energy Conversion Systems , 2009 .

[21]  Dawei Xiang,et al.  An Industry-Based Survey of Reliability in Power Electronic Converters , 2011, IEEE Transactions on Industry Applications.

[22]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[23]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[24]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[25]  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.

[26]  Liangliang Cheng,et al.  Open-Switch Fault-Diagnostic Method for Back-to-Back Converters of a Doubly Fed Wind Power Generation System , 2018, IEEE Transactions on Power Electronics.

[27]  Elhoussin Elbouchikhi,et al.  Cascaded H-Bridge Multilevel Inverter System Fault Diagnosis Using a PCA and Multiclass Relevance Vector Machine Approach , 2015, IEEE Transactions on Power Electronics.

[28]  Wei Qiao,et al.  A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part I: Components and Subsystems , 2015, IEEE Transactions on Industrial Electronics.

[29]  Hongwen He,et al.  A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm , 2018, Applied Energy.

[30]  Cui Bowen,et al.  A fault diagnosis method for three-phase rectifiers , 2013 .

[31]  Andrew Kusiak,et al.  The prediction and diagnosis of wind turbine faults , 2011 .

[32]  Myung-Joong Youn,et al.  An MRAS-Based Diagnosis of Open-Circuit Fault in PWM Voltage-Source Inverters for PM Synchronous Motor Drive Systems , 2013, IEEE Transactions on Power Electronics.

[33]  Jin Zhao,et al.  A Real-Time Multiple Open-Circuit Fault Diagnosis Method in Voltage-Source-Inverter Fed Vector Controlled Drives , 2016, IEEE Transactions on Power Electronics.

[34]  Yan Dong,et al.  Fault Diagnosis of Wind Turbine Power Converter Considering Wavelet Transform, Feature Analysis, Judgment and BP Neural Network , 2019, IEEE Access.

[35]  Daniel U. Campos-Delgado,et al.  An Observer-Based Diagnosis Scheme for Single and Simultaneous Open-Switch Faults in Induction Motor Drives , 2011, IEEE Transactions on Industrial Electronics.

[36]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[37]  Jin Zhao,et al.  Current similarity based open-circuit fault diagnosis for induction motor drives with discrete wavelet transform , 2017, Microelectron. Reliab..

[38]  Yi Chai,et al.  A survey of fault diagnosis for onshore grid-connected converter in wind energy conversion systems , 2016 .