Abstract:
A computer implemented method and system includes using information provided from sensors to monitor a wind turbine generator and provide signals representative of operation of the wind turbine generator, extracting signal level features from the signals, extracting model based features from the signals, calculating signal based conclusions, model based conclusions and spectral feature reinforcement based conclusions, and 
     fusing the conclusions to provide a fault detection indication.

Description:
RELATED APPLICATION 
       [0001]    This application claims priority to U.S. Provisional Application Ser. No. 61/228,320 (entitled WIND TURBINE GENERATOR FAULT DIAGNOSTIC AND PROGNOSTIC DEVICE AND METHOD, filed Jul. 24, 2009) which is incorporated herein by reference. 
     
    
     BACKGROUND 
       [0002]    Unscheduled generator failure is a major contributor to wind turbine down time. While monitoring the condition of an induction generator is similar to that of an induction motor, generator current may be governed by an external load. It is thus, difficult to apply monitors designed for induction motors to determine electrical and mechanical faults that may occur. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0003]      FIG. 1  is a block flow diagram illustrating extraction of signal level features according to an example embodiment. 
           [0004]      FIG. 2  is a block flow diagram illustrating extraction of model based features according to an example embodiment. 
           [0005]      FIG. 3  is a block flow diagram illustration low level fusion for fault detection according to an example embodiment. 
           [0006]      FIG. 4  is a block diagram of a computer system that executes programming for performing methods and procedures regarding extraction and fusion according to an example embodiment. 
       
    
    
     DETAILED DESCRIPTION 
       [0007]    In the following description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration specific embodiments which may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized and that structural, logical and electrical changes may be made without departing from the scope of the present invention. The following description of example embodiments is, therefore, not to be taken in a limited sense, and the scope of the present invention is defined by the appended claims. 
         [0008]    The functions or algorithms described herein may be implemented in software or a combination of software and human implemented procedures in one embodiment. The software may consist of computer executable instructions stored on computer readable media such as memory or other type of storage devices. Further, such functions correspond to modules, which are software, hardware, firmware or any combination thereof. Multiple functions may be performed in one or more modules as desired, and the embodiments described are merely examples. The software may be executed on a digital signal processor, ASIC, microprocessor, or other type of processor operating on a computer system, such as a personal computer, server or other computer system. 
         [0009]    A hybrid approach to wind turbine generator fault diagnostics and prognostics utilizes a model and spectral signature analysis based methods of fault detection, along with advanced trending to generate a generator prognostics indicator. This approach for condition monitoring for a wind turbine induction generator system covers both electrical and mechanical faults. 
         [0010]    Wind turbine generator current is governed by external loads. In one embodiment, both signature and model based methods are used to provide coverage for electrical and mechanical faults via a computer implemented diagnostics system. Data collected from generator terminal currents and voltages, vibration signals from the generator bearing accelerometers, and thermocouples monitoring of critical bearings, generator exciter, and the generator windings are sensed progressively and transformed into information. The information is processed to determine a system level health indicator. 
         [0011]    Within this construct, the presence of multiple sensors; various sensing modalities along with known physics of failure or mechanistic models are exploited to calculate heal indicators for an actuator system. For example, in the case of generator eccentricity information redundancy may be exploited by using one or both of a bearing accelerometer signal and generator voltage signature to detect an underlying fault. The use of multiple sensor modalities improves the detection accuracy and false alarm rate of the diagnostics system. The monitored condition indicators may be further trended to generate information to support the overall wind turbine prognostics. 
         [0012]      FIG. 1  is a flow diagram  100  illustrating extraction of signal level features. At spectral analysis module  110 , electrical signals including voltage and current are received. Shaft speed may also be provided. The spectral analysis module includes signal processing may be based on fast Fourier transforms, time frequency analysis, or multimodal resolution analysis, or combinations thereof to provide a normalized spectrum. The electrical signals may also be provided to a transient detection module  112  and a load table  114  to aid in the spectral analysis. 
         [0013]    Vibration signals along with shaft speed are provided to a further spectral analysis module  120 . The spectral analysis module includes signal processing may be based on fast Fourier transforms, time frequency analysis, or multimodal resolution analysis, or combinations thereof to provide a normalized spectrum. The electrical signals may also be provided to a transient detection module  112  and a load table  114  to aid in the spectral analysis. 
         [0014]    Temperature signals along with shaft speed are provided to a transient detection module  130  and load table  132 . The normalized spectrum of electrical signals is provided to a signature analysis module  140  for feature extraction, and to a lower level function module  142  for providing spectral reinforcement. The normalized spectrum based on the vibration signals is also provided to the lower level function module  142  as well as a signature analysis module  144  for feature extraction. Temperature signals are analyzed in a spectral analysis module  146  for feature extraction. 
         [0015]    Each of the modules  140 ,  142 ,  144 , and  146  are respectively coupled to anomaly detection modules  150 ,  152 ,  154 , and  156 , which identify faults from the signals collected. Note that anomaly detector  150  detects faults as a function of the electrical signals, detector  152  detects faults based on both the electrical signals and the vibration signals, detector  154  operates on the vibration signals, and detector  156  on the temperature signals. The identified faults by the detectors are provided to a higher level fusion module  160 , which performs decision fusion and provides actual notices of faults and trends. 
         [0016]    In one embodiment, higher level fusion is the fusion of all the diagnostics/anomaly detection outputs from  150 ,  152 ,  154  and  156 . Probabilistic, heuristics and knowledge-based methods of fusion are used to fuse the inputs from different anomaly detection schemes. 
         [0017]    The extraction of features is commonly performed for induction motors. Many of the same analysis techniques may be used for induction generators, with a major difference being that the induction generator may be governed by an external load. 
         [0018]      FIG. 2  is a flow diagram illustrating extraction of model based features generally at  200 . A speed extraction module  210  receives electrical signals representative of voltage and current of the electrical generator. A generator model  212  is receives information from the speed extraction module  210  and the electrical signals and operates as a current/voltage predictor. Residual information from the generator model  212  is provided to a spectral analysis module  216 . The spectral analysis module  216  includes signal processing based on fast Fourier transforms, time frequency analysis, or multimodal resolution analysis, or combinations thereof to provide a normalized spectrum. The electrical signals may also be provided to a transient detection module  218  and a load module  220  to aid in the spectral analysis. 
         [0019]    Vibration signals along with shaft speed are provided to a further spectral analysis module  230 . The spectral analysis module includes signal processing may be based on fast Fourier transforms, time frequency analysis, or multimodal resolution analysis, or combinations thereof to provide a normalized spectrum. The electrical signals may also be provided to a transient detection module  232  and a load model  234  to aid in the spectral analysis. 
         [0020]    Temperature signals along with shaft speed are provided to a transient detection module  240  and load model  242 . The normalized spectrum of electrical signals is provided to a signature analysis module  244  for feature extraction, and to a lower level function module  246  for providing spectral reinforcement. The normalized spectrum based on the vibration signals is also provided to the lower level function module  246  as well as a signature analysis module  248  for feature extraction. Temperature signals are analyzed in a spectral analysis module  250  for feature extraction. 
         [0021]    Each of the modules  244 ,  246 ,  248 , and  250  are respectively coupled to anomaly detection modules  252 ,  254 ,  256 , and  258 , which identify faults from the signals collected. Note that anomaly detector  252  detects faults as a function of the electrical signals, detector  254  detects faults based on both the electrical signals and the vibration signals, detector  256  operates on the vibration signals, and detector  258  on the temperature signals. The identified faults by the detectors are provided to a higher level fusion module  260 , which performs decision fusion and provides actual notices of faults and trends at  265 . 
         [0022]    Low level fusion is then performed for fault detection as illustrated in block form at  300  in  FIG. 3 . The low level fusion is performed using the signal based conclusions, model based conclusions and spectral feature reinforcement based conclusions provided by the procedures identified at  142  in  FIGS. 1 and 246  in  FIG. 2  in one embodiment. The generator, bearing technical data is read, including the number of poles, type of bearing, gears, etc. A spectrum of generator current, voltage, and vibration data is computed. The generator speed and load is then measured or estimated. The spectrum is the normalized using the estimated load for a number of features W 1 , W 2 , and W 3 . Many more features may be handled in further embodiments, but for simplicity of description, three are used as an example. 
         [0023]    At  310 , the normalized current spectrum for each of the features is illustrated as Wi 1 , Wi 2 , and Wi 3 . Similarly, at  320 , normalized voltage spectrum information is represented as Wv 1 , Wv 2 , and Wv 3 . Normalized vibration spectrum is illustrated at  330  as Wa 1 , Wa 2 , and Wa 3 . Each of the normalized values are respectively combined at  340 , where W 1 =Wi 1 +Wv 1 +Wa 1 , at  345 , W 2 =Wi 2 +Wv 2 +Wa 2 , and at  350 , where W 3 =Wi 3 +Wv 3 +Wa 3 . Signals may be routed via direct connections, or via a bus. 
         [0024]    Using physical knowledge of the generator, windows of normalized spectral information are extracted from each of the current, voltage, and vibration spectrum. Knowledge based transforms are then used to combine, such as by adding the information together. Since evidence from these multi-modality sensors are collaborative, the spectral signal natures of true/present faults are enhanced, whereas those due to un-modeled dynamics and false alarms will be cancelled. Diagnostics may then be finalized based on detection from the reinforced spectral signature. Trend signatures over time along with the normalized failure peak strengths are used to estimate the fault progression for prognostics health management. 
         [0025]    A block diagram of a computer system that executes programming for performing the above algorithms of the diagnostics system is shown in  FIG. 4 . A general computing device in the form of a computer  410 , may include a processing unit  402 , memory  404 , removable storage  412 , and non-removable storage  414 . Memory  404  may include volatile memory  406  and non-volatile memory  408 . Computer  410  may include—or have access to a computing environment that includes—a variety of computer-readable media, such as volatile memory  406  and non-volatile memory  408 , removable storage  412  and non-removable storage  414 . Computer storage includes random access memory (RAM), read only memory (ROM), erasable programmable read-only memory (EPROM) &amp; electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD ROM), Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium capable of storing computer-readable instructions. Computer  410  may include or have access to a computing environment that includes input  416 , output  418 , and a communication connection  420 . The computer may operate in a networked environment using a communication connection to connect to one or more remote computers. The remote computer may include a personal computer (PC), server, router, network PC, a peer device or other common network node, or the like. The communication connection may include a Local Area Network (LAN), a Wide Area Network (WAN) or other networks. 
         [0026]    Computer-readable instructions stored on a computer-readable medium are executable by the processing unit  402  of the computer  410 . A hard drive, CD-ROM, and RAM are some examples of articles including a computer-readable medium. 
         [0027]    The Abstract is provided to comply with 37 C.F.R. §1.72(b) is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.