Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic

Abstract Wind power generation efficiency has been negatively affected by wind turbine (WT) faults, which makes fault detection a very important task in WT maintenance. In fault detection studies, fuzzy inference is a commonly-used method. However, it can hardly detect early faults or measure fault severities due to the singleton input and the limited linguistic terms and rules. To solve this problem, this paper proposes a WT fault detection method based on expanded linguistic terms and rules using non-singleton fuzzy logic. Firstly, a generation method of non-singleton fuzzy input is proposed. Using the generated fuzzy inputs, non-singleton fuzzy inference system (FIS) can be applied in WT fault detection. Secondly, a mechanism of expanding linguistic terms and rules is presented, so that the expanded terms and rules can provide more fault information and help to detect early faults. Thirdly, the consequent of FIS is designed by the expanded consequent terms. The defuzzified result, which is defined as the fault factor, can measure fault severities. Finally, four groups of experiments were conducted using the real WT data collected from a wind farm in northern China. Experiment results show that the proposed method is effective in detecting WT faults.

[1]  George C. Mouzouris,et al.  Nonsingleton fuzzy logic systems: theory and application , 1997, IEEE Trans. Fuzzy Syst..

[2]  Huan Long,et al.  Wind Turbine Gearbox Failure Identification With Deep Neural Networks , 2017, IEEE Transactions on Industrial Informatics.

[3]  Wei Qiao,et al.  A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods , 2015, IEEE Transactions on Industrial Electronics.

[4]  Tahar Bahi,et al.  Condition Monitoring and Fault Detection in Wind Turbine Based on DFIG by the Fuzzy Logic , 2015 .

[5]  Silvio Simani,et al.  Fault Diagnosis of a Wind Turbine Benchmark via Identified Fuzzy Models , 2015, IEEE Transactions on Industrial Electronics.

[6]  Jerry M. Mendel,et al.  Explaining the Performance Potential of Rule-Based Fuzzy Systems as a Greater Sculpting of the State Space , 2018, IEEE Transactions on Fuzzy Systems.

[7]  Meik Schlechtingen,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 2: Application examples , 2014, Appl. Soft Comput..

[8]  Yibing Liu,et al.  Pitting Fault Detection of a Wind Turbine Gearbox Using Empirical Mode Decomposition , 2014 .

[9]  Saad Mekhilef,et al.  Performance evaluation of hybrid adaptive neuro-fuzzy inference system models for predicting monthly global solar radiation , 2018 .

[10]  Chuen-Tsai Sun,et al.  Neuro-fuzzy And Soft Computing: A Computational Approach To Learning And Machine Intelligence [Books in Brief] , 1997, IEEE Transactions on Neural Networks.

[11]  Yongqian Liu,et al.  Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model , 2019, Applied Energy.

[12]  Francesc Pozo,et al.  Wind turbine fault detection and classification by means of image texture analysis , 2018, Mechanical Systems and Signal Processing.

[13]  Pedro André Carvalho Rosas,et al.  Prognostic techniques applied to maintenance of wind turbines: a concise and specific review , 2018 .

[14]  Purushottam Gangsar,et al.  Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms , 2017 .

[15]  Sofiane Achiche,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..

[16]  Ligang Wu,et al.  Reliable Filter Design for Sensor Networks Using Type-2 Fuzzy Framework , 2017, IEEE Transactions on Industrial Informatics.

[17]  Chao Liu,et al.  An unsupervised spatiotemporal graphical modeling approach for wind turbine condition monitoring , 2018, Renewable Energy.

[18]  Peter Tavner,et al.  Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition. , 2015 .

[19]  Peter Tavner,et al.  Wind turbine pitch faults prognosis using a-priori knowledge-based ANFIS , 2013, Expert Syst. Appl..

[20]  Anas Sakout,et al.  Gearbox condition monitoring in wind turbines: A review , 2018, Mechanical Systems and Signal Processing.

[21]  Ramin Moghaddass,et al.  An anomaly detection framework for dynamic systems using a Bayesian hierarchical framework , 2019, Applied Energy.

[22]  Mengshi Li,et al.  A radically data-driven method for fault detection and diagnosis in wind turbines , 2018, International Journal of Electrical Power & Energy Systems.

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

[24]  Wei Qiao,et al.  Multiscale Filtering Reconstruction for Wind Turbine Gearbox Fault Diagnosis Under Varying-Speed and Noisy Conditions , 2018, IEEE Transactions on Industrial Electronics.

[25]  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.

[26]  Xiao Lei,et al.  A generalized model for wind turbine anomaly identification based on SCADA data , 2016 .

[27]  Huaguang Zhang,et al.  A Small-Sample Wind Turbine Fault Detection Method With Synthetic Fault Data Using Generative Adversarial Nets , 2019, IEEE Transactions on Industrial Informatics.

[28]  Shuangwen Sheng,et al.  Monitoring of Wind Turbine Gearbox Condition through Oil and Wear Debris Analysis: A Full-Scale Testing Perspective , 2016 .

[29]  Wei Ren,et al.  Robustness Analysis of Asynchronous Sampled-Data Multiagent Networks With Time-Varying Delays , 2017, IEEE Transactions on Automatic Control.

[30]  Liyan Qu,et al.  Real-Time Aging Monitoring for IGBT Modules Using Case Temperature , 2016, IEEE Transactions on Industrial Electronics.

[31]  Eugeniusz Rusiński,et al.  Proactive Condition Monitoring of Low-Speed Machines , 2014 .

[32]  Zijun Zhang,et al.  Wind Turbine Modeling With Data-Driven Methods and Radially Uniform Designs , 2016, IEEE Transactions on Industrial Informatics.

[33]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[34]  M. Schlechtingen,et al.  Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study , 2013, IEEE Transactions on Sustainable Energy.

[35]  Davide Astolfi,et al.  Analyzing wind turbine directional behavior: SCADA data mining techniques for efficiency and power assessment , 2017 .

[36]  Eric Bechhoefer,et al.  Online particle-contaminated lubrication oil condition monitoring and remaining useful life prediction for wind turbines , 2015 .

[37]  Robert Ivor John,et al.  A new dynamic approach for non-singleton fuzzification in noisy time-series prediction , 2017, 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).

[38]  Fausto Pedro García Márquez,et al.  A survey of artificial neural network in wind energy systems , 2018, Applied Energy.

[39]  Davide Astolfi,et al.  Data mining techniques for performance analysis of onshore wind farms , 2015 .

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

[41]  H. L. Gray,et al.  Applied time series analysis , 2011 .

[42]  Yihui Zheng,et al.  An ensemble system to predict the spatiotemporal distribution of energy security weaknesses in transmission networks , 2020 .

[43]  Nadège Bouchonneau,et al.  A review of wind turbine bearing condition monitoring: State of the art and challenges , 2016 .

[44]  Xiyun Yang,et al.  Wind Turbine Generator Condition-Monitoring Using Temperature Trend Analysis , 2012, IEEE Transactions on Sustainable Energy.

[45]  J. Mendel Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[46]  Daniel Hissel,et al.  Online implementation of SVM based fault diagnosis strategy for PEMFC systems , 2015 .