Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdep...Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdependencies between system elements.This study proposes a new hybrid method that combines the domain knowledge via knowledge graphs(KGs)and the traditional feature-based data.Incorporation of contextual relationships through construction of graph embedding methods,such as Node2Vec,can capture meaningful information,such as the relationships among key parameters(e.g.wind speed,rotor Revolutions Per Minute(RPM),and temperature)in the enriched feature representations.These node embeddings,when augmented with the original data,can be used to allow the model to learn and generalize better.As shown in results achieved on experimental data,the augmented ML model(with KG)is much better at predicting with the help of accuracy and error measure compared to traditional ML methods.Paired t-test analysis proves the statistical validity of this improvement.Moreover,graph-based feature importance increases the interpretability of the model and helps to uncover the structurally significant variables that are otherwise ignored by the common methods.The approach provides an excellent,knowledge-guided manner through which intelligent fault detection can be executed on WT systems.展开更多
Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculat...Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.展开更多
Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and ...Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.展开更多
Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or...Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.展开更多
As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Pr...As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.展开更多
Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach ...Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.展开更多
The installation of wind energy has increased rapidly around the world. The grid codes about the wind energy require wind turbine (WT) has the ability of fault (or low voltage) ride-through (FRT). To study the FRT ope...The installation of wind energy has increased rapidly around the world. The grid codes about the wind energy require wind turbine (WT) has the ability of fault (or low voltage) ride-through (FRT). To study the FRT operation of the wind farms, three methods were discussed. First, the rotor short current of doubly-fed induction generator (DFIG) was limited by introducing a rotor side protection circuit. Second, the voltage of DC bus was limited by a DC energy absorb circuit. Third, STATCOM was used to increase the low level voltages of the wind farm. Simulation under MATLAB was studied and the corresponding results were given and discussed. The methods proposed in this paper can limit the rotor short current and the DC voltage of the DFIG WT to some degree, but the voltage support to the power system during the fault largely depend on the installation place of STATCOM.展开更多
With the commissioning of the 750-kV Hexi power transmission and transformation project, the first stage of the 10-GW class Jiuquan Wind Power Base project was completed and put into operation this year. However, disc...With the commissioning of the 750-kV Hexi power transmission and transformation project, the first stage of the 10-GW class Jiuquan Wind Power Base project was completed and put into operation this year. However, disconnections involving some wind turbines took place quite a few times in Jiuquan recently, which have caused significant impacts on the power grid and drawn extensive attentions both domestically and abroad. Take the typical faults in Jiuquan for examples, the basic situations are presented and the causes of the fault on February 24 th are analyzed. Then the corresponding solutions are put forward afterwards.展开更多
This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine （WT） system. This system is composed of a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory. The ...This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine （WT） system. This system is composed of a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory. The demonstrated mechanism lies under the taxonomy of active fault-tolerant control （FTC） systems, namely online redesign based approach. In the proposed approach, we do not use any a priori information about the model of the turbine in real-time. In fact, we use online measurements generated by the WT. Based on the given control specifications, and the observed measurement an occurred fault is accommodated by reconfiguring the online controller such that the WT generates rated power even under faulty conditions. Second, no explicit fault diagnosis （FD） module is used in this approach. As a result, issues of model uncertainty, false alarms, etc. associated with an integrated FD and controller reconfiguration approach to FTC systems are not experienced here.展开更多
The diode rectifier unit(DRU)-based high-voltage DC(DRU-HVDC) system is a promising solution for offshore wind energy transmission thanks to its compact design, high efficiency, and strong reliability. Herein we inves...The diode rectifier unit(DRU)-based high-voltage DC(DRU-HVDC) system is a promising solution for offshore wind energy transmission thanks to its compact design, high efficiency, and strong reliability. Herein we investigate the feasibility of the DRU-HVDC system considering onshore and offshore AC grid faults, DC cable faults, and internal DRU faults. To ensure safe operation during the faults, the wind turbine(WT) converters are designed to operate in either current-limiting or voltage-limiting mode to limit potential excessive overcurrent or overvoltage. Strategies for providing fault currents using WT converters during offshore AC faults to enable offshore overcurrent and differential fault protection are investigated. The DRU-HVDC system is robust against various faults, and it can automatically restore power transmission after fault isolation. Simulation results confirm the system performance under various fault conditions.展开更多
Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the...Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.展开更多
Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators an...Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.展开更多
文摘Fault sensing in wind turbine(WT)generator bearings is essential for ensuring reliability and holding down maintenance costs.Feeding raw sensor data to machine learning(ML)model often overlooks the enveloping interdependencies between system elements.This study proposes a new hybrid method that combines the domain knowledge via knowledge graphs(KGs)and the traditional feature-based data.Incorporation of contextual relationships through construction of graph embedding methods,such as Node2Vec,can capture meaningful information,such as the relationships among key parameters(e.g.wind speed,rotor Revolutions Per Minute(RPM),and temperature)in the enriched feature representations.These node embeddings,when augmented with the original data,can be used to allow the model to learn and generalize better.As shown in results achieved on experimental data,the augmented ML model(with KG)is much better at predicting with the help of accuracy and error measure compared to traditional ML methods.Paired t-test analysis proves the statistical validity of this improvement.Moreover,graph-based feature importance increases the interpretability of the model and helps to uncover the structurally significant variables that are otherwise ignored by the common methods.The approach provides an excellent,knowledge-guided manner through which intelligent fault detection can be executed on WT systems.
文摘Based on the multi-loop method, the rotating torque and speed of theinduction machine are analyzed. The fluctuating components of the torque and speed caused by rotorwinding faults are studied. The models for calculating the fluctuating components are put forward.Simulation and computation results show that the rotor winding faults will cause electromagnetictorque and rotating speed to fluctuate; and fluctuating frequencies are the same and their magnitudewill increase with the rise of the severity of the faults. The load inertia affects the torque andspeed fluctuation, with the increase of inertia, the fluctuation of the torque will rise, while thecorresponding speed fluctuation will obviously decline.
基金Supported by National Key R&D Projects(Grant No.2018YFB0905500)National Natural Science Foundation of China(Grant No.51875498)+1 种基金Hebei Provincial Natural Science Foundation of China(Grant Nos.E2018203439,E2018203339,F2016203496)Key Scientific Research Projects Plan of Henan Higher Education Institutions(Grant No.19B460001)
文摘Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in highfrequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.
基金This project is supported by Provincial Science Foundation of Education Office of Hebei(No.Z2004455)Youth Research Fundation of State Power of China(No.SPQKJ02-10).
文摘Rotor vibration characteristics are first analyzed, which are that the rotor vibration of fundamental frequency will increase due to rotor winding inter-turn short circuit fault, air-gap dynamic eccentricity fault, or imbalance fault, and the vibration of the second frequency will increase when the air-gap static eccentricity fault occurs. Next, the characteristics of the stator winding parallel branches circulating current are analyzed, which are that the second harmonics circulating current will increase when the rotor winding inter-turn short circuit fault occurs, and the fundamental circulating current will increase when the air-gap eccentricity fault occurs, neither being strongly affected by the imbalance fault. Considering the differences of the rotor vibration and circulating current characteristics caused by different rotor faults, a method of generator vibration fault diagnosis, based on rotor vibration and circulating current characteristics, is developed. Finally, the rotor vibration and circulating current of a type SDF-9 generator is measured in the laboratory to verify the theoretical analysis presented above.
文摘As the demand for wind energy continues to grow at exponential rate, reducing operation and maintenance (O & M) costs and improving reliability have become top priorities in wind turbine maintenance strategies. Prediction of wind turbine failures before they reach a catastrophic stage is critical to reduce the O & M cost due to unnecessary scheduled maintenance. A SCADA-data based condition monitoring system, which takes advantage of data already collected at the wind turbine controller, is a cost-effective way to monitor wind turbines for early warning of failures. This article proposes a methodology of fault prediction and automatically generating warning and alarm for wind turbine main bearings based on stored SCADA data using Artificial Neural Network (ANN). The ANN model of turbine main bearing normal behavior is established and then the deviation between estimated and actual values of the parameter is calculated. Furthermore, a method has been developed to generate early warning and alarm and avoid false warnings and alarms based on the deviation. In this way, wind farm operators are able to have enough time to plan maintenance, and thus, unanticipated downtime can be avoided and O & M costs can be reduced.
文摘Support vector machines and a Kalman-like observer are used for fault detection and isolation in a variable speed horizontalaxis wind turbine composed of three blades and a full converter. The support vector approach is data-based and is therefore robust to process knowledge. It is based on structural risk minimization which enhances generalization even with small training data set and it allows for process nonlinearity by using flexible kernels. In this work, a radial basis function is used as the kernel. Different parts of the process are investigated including actuators and sensors faults. With duplicated sensors, sensor faults in blade pitch positions,generator and rotor speeds can be detected. Faults of type stuck measurements can be detected in 2 sampling periods. The detection time of offset/scaled measurements depends on the severity of the fault and on the process dynamics when the fault occurs. The converter torque actuator fault can be detected within 2 sampling periods. Faults in the actuators of the pitch systems represents a higher difficulty for fault detection which is due to the fact that such faults only affect the transitory state(which is very fast) but not the final stationary state. Therefore, two methods are considered and compared for fault detection and isolation of this fault: support vector machines and a Kalman-like observer. Advantages and disadvantages of each method are discussed. On one hand, support vector machines training of transitory states would require a big amount of data in different situations, but the fault detection and isolation results are robust to variations in the input/operating point. On the other hand, the observer is model-based, and therefore does not require training, and it allows identification of the fault level, which is interesting for fault reconfiguration. But the observability of the system is ensured under specific conditions, related to the dynamics of the inputs and outputs. The whole fault detection and isolation scheme is evaluated using a wind turbine benchmark with a real sequence of wind speed.
文摘The installation of wind energy has increased rapidly around the world. The grid codes about the wind energy require wind turbine (WT) has the ability of fault (or low voltage) ride-through (FRT). To study the FRT operation of the wind farms, three methods were discussed. First, the rotor short current of doubly-fed induction generator (DFIG) was limited by introducing a rotor side protection circuit. Second, the voltage of DC bus was limited by a DC energy absorb circuit. Third, STATCOM was used to increase the low level voltages of the wind farm. Simulation under MATLAB was studied and the corresponding results were given and discussed. The methods proposed in this paper can limit the rotor short current and the DC voltage of the DFIG WT to some degree, but the voltage support to the power system during the fault largely depend on the installation place of STATCOM.
文摘With the commissioning of the 750-kV Hexi power transmission and transformation project, the first stage of the 10-GW class Jiuquan Wind Power Base project was completed and put into operation this year. However, disconnections involving some wind turbines took place quite a few times in Jiuquan recently, which have caused significant impacts on the power grid and drawn extensive attentions both domestically and abroad. Take the typical faults in Jiuquan for examples, the basic situations are presented and the causes of the fault on February 24 th are analyzed. Then the corresponding solutions are put forward afterwards.
文摘This paper presents a real-time mechanism to tolerate faults occurring in a wind turbine （WT） system. This system is composed of a FAST coded simulator designed by the U.S. National Renewable Energy Laboratory. The demonstrated mechanism lies under the taxonomy of active fault-tolerant control （FTC） systems, namely online redesign based approach. In the proposed approach, we do not use any a priori information about the model of the turbine in real-time. In fact, we use online measurements generated by the WT. Based on the given control specifications, and the observed measurement an occurred fault is accommodated by reconfiguring the online controller such that the WT generates rated power even under faulty conditions. Second, no explicit fault diagnosis （FD） module is used in this approach. As a result, issues of model uncertainty, false alarms, etc. associated with an integrated FD and controller reconfiguration approach to FTC systems are not experienced here.
基金supported in part by the European Union’s Horizon 2020 research and innovation program under grant agreement No.691714
文摘The diode rectifier unit(DRU)-based high-voltage DC(DRU-HVDC) system is a promising solution for offshore wind energy transmission thanks to its compact design, high efficiency, and strong reliability. Herein we investigate the feasibility of the DRU-HVDC system considering onshore and offshore AC grid faults, DC cable faults, and internal DRU faults. To ensure safe operation during the faults, the wind turbine(WT) converters are designed to operate in either current-limiting or voltage-limiting mode to limit potential excessive overcurrent or overvoltage. Strategies for providing fault currents using WT converters during offshore AC faults to enable offshore overcurrent and differential fault protection are investigated. The DRU-HVDC system is robust against various faults, and it can automatically restore power transmission after fault isolation. Simulation results confirm the system performance under various fault conditions.
基金National Natural Science Foundation of China(No.61105114)the Key Technology R&D Program of Jiangsu Province,China(No.BE2010189)
文摘Quick detection of a small initial fault is important for an induction motor to prevent a consequent large fault.The mathematical model with basic motor equations among voltages,currents,and fluxes is analyzed and the motor model equations are described.The fault related features are extracted.An immune memory dynamic clonal strategy(IMDCS)system is applied to detecting the stator faults of induction motor.Four features are obtained from the induction motor,and then these features are given to the IMDCS system.After the motor condition has been learned by the IMDCS system,the memory set obtained in the training stage can be used to detect any fault.The proposed method is experimentally implemented on the induction motor,and the experimental results show the applicability and effectiveness of the proposed method to the diagnosis of stator winding turn faults in induction motors.
基金the National Natural Science Foundation of China(No.51205018)the Fundamental Research Funds for the Central Universities of China(No.FRF-TP-14-121A2)the Research Project of State Key Laboratory of Mechanical System and Vibration(No.MSV-2014-09)
文摘Wind energy is one of the widely applied renewable energies in the world. Wind turbine as the main wind energy converter at present has very complex technical system containing a huge number of components,actuators and sensors. However, despite of the hardware redundancy, sensor faults have often affected the wind turbine normal operation and thus caused energy generation loss. In this paper, aiming at the wind turbine hydraulic pitch system, data-driven design of process monitoring(PM) and diagnosis has been realized in the wind turbine benchmark. Fault tolerant control(FTC) strategies focused on sensor faults have also been presented here, where with the implementation of soft sensor the sensor fault can be handled and the performance of the system is improved. The performance of this method is demonstrated with the wind turbine benchmark provided by Math Works.
