Abstract:
A method for monitoring a compressor comprising a rotor is presented. The method comprises obtaining a dynamic pressure signal of the rotor, obtaining a blade passing frequency of the rotor, using the blade passing frequency signal for filtering the dynamic pressure signal, buffering the filtered dynamic pressure signal over a moving window time period, and analyzing the buffered dynamic pressure signal to predict a stall condition of the compressor.

Description:
BACKGROUND 
   The subject matter disclosed herein relates generally to monitoring health of rotating mechanical components, and more particularly, to stall and surge detection in a compressor of a turbine. 
   In gas turbines used for power generation, compressors are typically allowed to operate at high pressure ratios in order to achieve higher efficiencies. During operation of a gas turbine, a phenomenon known as compressor stall may occur, when the pressure ratio of the turbine compressor exceeds a critical value at a given speed the compressor pressure ratio is reduced and the airflow that is delivered to the engine combustor is also reduced and in some circumstances may reverse direction. Compressor stalls have numerous causes. In one example, the engine is accelerated too rapidly. In another example, the inlet profile of air pressure or temperature becomes unduly distorted during normal operation of the engine. Compressor damage due to the ingestion of foreign objects or a malfunction of a portion of the engine control system may also cause a compressor stall and subsequent compressor degradation. If a compressor stall remains undetected and is permitted to continue, the combustor temperatures and the vibratory stresses induced in the compressor may become sufficiently high to cause damage to the turbine. 
   One approach to compressor stall detection is to monitor the health of a compressor by measuring the air flow and pressure rise through the compressor. Pressure variations may be attributed to a number of causes such as, for example, unstable combustion, rotating stall, and surge events on the compressor itself. To determine these pressure variations, the magnitude and rate of change of pressure rise through the compressor may be monitored. This approach, however, does not offer prediction capabilities of rotating stall or surge, and fails to offer information to a real-time control system with sufficient lead time to proactively deal with such events. 
   BRIEF DESCRIPTION 
   Briefly, a method for monitoring a compressor comprising a rotor is presented. The method comprises obtaining a dynamic pressure signal of the rotor, obtaining a blade passing frequency of the rotor, using the blade passing frequency signal for filtering the dynamic pressure signal, buffering the filtered dynamic pressure signal over a moving window time period, and analyzing the buffered dynamic pressure signal to predict a stall condition of the compressor. 
   In another embodiment, a system for monitoring a compressor comprising a rotor is presented. The system comprises a pressure sensor configured for obtaining a dynamic pressure signal of the rotor, a speed sensor configured for obtaining a speed signal of the rotor, a controller configured for using the rotor speed signal for filtering the dynamic pressure signal, buffering the filtered dynamic pressure signal over a moving window time period, and analyzing the buffered dynamic pressure signal to predict a stall condition of the compressor. 

   
     DRAWINGS 
     These and other features, aspects, and advantages of the present invention will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein: 
       FIG. 1  is a cross sectional view of a compressor with sensors in accordance with one aspect of the invention; 
       FIG. 2  illustrates a block diagram of a compressor monitoring and controlling system according to one embodiment of the invention; 
       FIG. 3  is a block diagram illustrating monitoring and controlling of compressor health in accordance with one embodiment disclosed herein; and 
       FIG. 4  is a Fast Fourier transform representation over a long time period. 
   

   DETAILED DESCRIPTION 
   As discussed in detail below, embodiments of the invention include a gas turbine system having a compressor and a system for monitoring the compressor. In an exemplary embodiment of the invention, an industrial gas turbine is used as part of a combined cycle configuration that also includes, for example, steam turbine and a generator to generate electricity from combustion of natural gas of other combustion fuel. The industrial gas turbine may be operated in combined cycle system or simple cycle system. However, in both the cycle systems it is a desirable goal to operate the industrial gas turbine at the highest operating efficiency to produce high electrical power output at relatively low cost. Typically, in a highly efficient industrial turbine system, a compressor should be operated to produce a cycle pressure ratio that corresponds to a high firing temperature. However, the compressor can experience aerodynamic instabilities, such as, for example, a stall and/or surge condition, as the compressor is used to produce the high firing temperature or the high cycle pressure ratio. It may be appreciated that the compressor experiencing such stall and/or surge may cause problems that affect the components and operational efficiency of the industrial gas turbine. Typically, to maintain stability, it is desirable to engage the industrial gas turbine within operational limits of cycle pressure ratio. 
     FIG. 1  illustrates a cross-sectional view of a compressor wherein sensors are installed at various locations within the compressor to sense compressor parameters. As illustrated the compressor system  10  includes a rotor  12  and a stator  14 . Further, the reference numeral  16  indicates the flow direction wherein working fluids are progressively compressed between  16  and  18 . Typically such compressors use multi-stage compression wherein the stator  14  may be configured to prepare and/or redirect the flow from the rotor  12  to a subsequent rotor or to the plenum. In one embodiment of the invention, location of sensors at  20  is better suited to sense the compressor parameters that indicate stall and/or surge condition. However, it may be noted that sensors are placed in various locations such as for example,  22  and  24  to sense the parameters. Sensors may include for example, speed sensors configured to detect rotational speed and pressure sensors configured to detect pressure dynamically. 
     FIG. 2  is a diagrammatic representation of a compressor monitoring and control system as implemented in the compressor system  10  of  FIG. 1 . The compressor monitoring and control system  30  includes a controller. In an exemplary embodiment, the controller includes a filter  32 , a storage medium  40 , a signal processor  42 , a comparator  44 , a lookup table  46 , and a stall indicator  48 . The system includes sensors for obtaining a dynamic pressure signal  36  and obtaining a blade passing frequency from the rotor speed signal  34  and using the blade passing frequency for filtering the dynamic pressure signal  36 . The filter  32  is coupled to sensors (not shown). Corresponding to the compressor parameters, the sensors generate signals such as rotor speed signal  34  and dynamic pressure signal  36 . In one embodiment of the invention, the filter  32  is configured to filter the sensed parameters of the compressor such as rotor speed signal  34  and dynamic pressure signal  36 . Further the filter is configured to remove undesired components such as for example, high frequency noise from the sensed parameters. According to a contemplated embodiment of the invention, the filter includes multiple configurations such as second order low pass, first order low frequency high pass, and sixth order Chebychev band pass filters. It may be appreciated by one skilled in the art, that such filters have configuration parameters such as pass band and cut off frequencies set appropriately depending on input parameters and desired output. 
   Buffering (or storing) of filtered data over a period of time is performed over a sample rate during a moving window. In one example, the moving window occurs over a period of at least four seconds. The storage medium  40  is configured to store the filtered data and/or buffered data. The controller is further configured, in one embodiment, to shift the buffered dynamic pressure signal to a lower frequency domain. Signal processor  42  is coupled to the storage medium  40  and configured to compute a fast Fourier transform of the buffered data. The comparator  44  is coupled to the signal processor  42  and configured to compare the computed Fast Fourier Transform data with a pre-determined baseline value. The pre-determined baseline value is stored in a look up table  46  that is coupled to the comparator. It may be appreciated that the pre-determined baseline value is calculated by way of stall likelihood measurements and constants. The system  30  further includes a stall indicator  48  coupled to the comparator  44  and configured to generate a stall indication signal  50  based upon the comparison. The stall indication signal  50  is coupled to the compressor for corrective action in case of stall likelihood. 
     FIG. 3  is a more detailed block diagram illustrating various steps of monitoring and controlling of compressor health in accordance with embodiments of the invention. In an exemplary embodiment, the compressor monitoring system  56  includes a low pass filter  58  that is configured to receive rotor speed signal  34  from sensors coupled to the compressor (not shown in  FIG. 3 ). The low pass filter is configured, in a more specific embodiment to filter the rotor speed signal via a second order low pass filter. Typically the cut-off frequency is about 0.1 Hz. However, the cut-off frequency is dependent on speed control topology. 
   A speed to frequency converter  60  is coupled to the low pass filter to convert the filtered rotor speed signal into a blade passing frequency  62 . It may be noted that the blade passing frequency is a product of the mechanical speed and number of rotor blades. 
   In a presently contemplated embodiment of the invention, the compressor parameter such as pressure is monitored dynamically. The dynamic pressure signal  36  is filtered via first order low frequency high pass filter to remove low frequency bias and may further be filtered via Chebychev band pass filter with both filters reference by filter element  66  with attenuation outside the pass-band of about 40 dB to obtain filtered dynamic pressure signal  68 . As will be appreciated by one skilled in the art, the band-pass should have a margin of few hundred hertz to factor in the variations in monitored parameter. Furthermore, the sampling rate of the dynamic pressure signal measurement is typically on the order of at least 2 or 3 times the band pass frequency. If the mechanical speed remains constant, the band pass filter constants may remain constant. If the location of the blade passing frequency changes, however, it is useful to update the band pass filter constants to reflect the new location of the blade passing frequency. 
   Root mean square (RMS) converter  70  computes root mean square of the dynamic pressure signal  36 . Then, the blade passing frequency  62  and filtered dynamic pressure signal  68  are combined at multiplier  72  and fed as input  73  to a low pass filter  74 . Resulting filtered signal  75  and root mean square of the dynamic pressure signal  70  are fed into a signal processor  76  configured to normalize the filtered signal  75 . In one embodiment of the normalization process, the normalization gain, which multiplies the filtered signal  75 , is an inverse of the RMS dynamic pressure signal  70  multiplied by 2.3. In an exemplary embodiment, the block  60  is configured to compute a cosine of the band pass frequency minus a frequency that represents the new center frequency of the dynamic pressure signal measurement in the low frequency regime. The difference  62  is further multiplied with filtered dynamic pressure signal  68  at the multiplier  72 . The resultant product  73  is filtered via a sixth order (meaning sixth or high order) Chebychev low pass filter to obtain a shifted dynamic pressure signal  77  that represents a low frequency transformation of the original, high frequency, and dynamic pressure signal after the normalization at  76 . In one embodiment, the pass band of the Chebychev low pass filter is twice the new center frequency of the frequency shifted dynamic pressure signal measurement (so as to reduce noise associated with frequency shifting). 
   A data collector  78  buffers the shifted low frequency regime dynamic pressure signal  77  to facilitate further analysis. A storage medium may be configured to store the buffered dynamic pressure signal. An example of storage medium may include memory chip. Such buffered data (obtained from down sampling the shifted low frequency regime dynamic pressure signal) represents an appropriate time period of a dynamic pressure signal with frequency content centered around the blade passing frequency. In one embodiment, the time period is from a quarter of a second to eight seconds. In another embodiment, the time period is of the order of four seconds. A signal processor  80  computes a Fast Fourier Transform of the down sampled buffered data stored in data collector  78 . The blade passing frequency is filtered out from the transformed signal  81  at filter block  84 . Power associated with a frequency range of about ±15 Hz around the blade passing frequency is set to zero at source power block  86  and further multiplied by the transformed signal  81 . Power computer  88  calculates an average value of power and further calculates a square root of the average power value. Such average power typically represents a stall measure  90  about the blade passing frequency. In an exemplary embodiment, such stall measure  90  indicates un-scaled stall likelihood. 
   The un-scaled stall likelihood  90  and inlet guide valve scaling  94  are multiplied at  92 . Inlet guide valve measurements  87  are used in computing the inlet guide valve scaling  94 . In one embodiment, a look up table  97  includes stall likelihood and stall measure. The stall likelihood  96  is obtained via the look up table  97 . As will be appreciated by one skilled in the art, a pre-determined value of stall likelihood is computed by multiple measurements. Such look table includes computational constants as applied to the measurements indicating constraints around which the look up table is built. Constants may be used in computation while using look up table. In one embodiment of the invention, a scaled stall likelihood  99  is obtained via scaling factor such as inlet guide valve scaling  94  and un-scaled stall likelihood  90 . In another embodiment of the invention, computation of the scaled stall likelihood measure includes referring look up table having a stall margin remaining  98  as a scaling factor which is multiplied with the stall likelihood  96 . It may be noted that stall margin remaining  98  may be obtained via compressor pressure ratio  85 . The stall indicator  48  is configured to compute the stall indication signal  50  based upon the scaled stall likelihood  99 . The stall indication signal is further coupled to the compressor. Based upon the stall indication signal  50 , corrective action may be implemented on the compressor to prevent any stall and/or surge condition that may occur. 
     FIG. 4  is graphical representation of a long term fast Fourier transform  100 , having frequency on the horizontal axis  102  and power on the vertical axis  104 . The Fourier transform  100  includes various power spikes such as  106 ,  108 , and  110  as illustrated. This long term fast Fourier transform is obtained after the signal processor  80  has processed the buffered data over a long time period as referenced in  FIG. 3 . Further the power spike  106  that is representative of a blade passing frequency may be filtered at block  84  as referenced in  FIG. 3 . In about ±100 Hz around the blade passing frequency, certain power spikes such as  108  and  110  may be recorded. Such power spikes ( 108  and  110 ) typically are indicative of conditions that are deviating from the normal operating conditions and may indicate a potential stall and/or surge condition. The power computer  88  as referenced in  FIG. 3  is configured to detect and calculate such power spike deviations. 
   Advantageously, long term fast Fourier transform analyses of compressor parameters alleviate shortcomings in present day analysis. Furthermore, Fourier transform analysis helps in capturing accurately the abnormal pressure perturbations and hence minimizes false pressure surges by way of using scaling factor and stall margin remaining in the analysis. Moreover, aforementioned advantages helps in predicting onset of stall and/or surge condition accurately, before the compressor stalls and/or surges, and protect the compressor from damages by way of controlling the operating parameters suitably based on the prediction. 
   While only certain features of the invention have been illustrated and described herein, many modifications and changes will occur to those skilled in the art. It is, therefore, to be understood that the appended claims are intended to cover all such modifications and changes as fall within the true spirit of the invention.