Systems and methods related to detecting blowout occurrences in gas turbines

1. A system for controlling an operation of a combustor in a gas turbine, the system comprising:

the combustor;

an acoustic sensor configured to periodically measure a pressure of the combustor and generate a a raw data stream that comprises pressure data points resulting from the periodic measurement of the pressure; and

a blowout detection unit configured to receive the raw data stream from the acoustic sensor;

wherein the blowout detection unit comprises a processor and a machine-readable storage medium on which is stored instructions that cause the processor to execute a procedure related to a detection of a blowout precursor; and

wherein the procedure comprises an ensemble approach in which the detection of the blowout precursor depends upon a plurality of outcomes generated respectively from a plurality of separate detection analytics, the plurality of separate detection analytics of the ensemble approach comprising at least two selected from the following detection analytics:

a first detection analytic that comprises a calculation as to whether a selected pressure data point of the pressure data points within the raw data stream varies from a steady state condition, wherein the steady state condition is defined by the pressure data points of the raw data stream that fall within a look-back period that precedes the selected pressure data point;

a second detection analytic that comprises a calculation of a standard deviation for the pressure data points of the raw data stream within the look-back period;

a third detection analytic that comprises calculating an entropy value related to an entropy feature of the pressure data points of the raw data stream within the look-back period; and

a fourth detection analytic that comprises a manifold anomaly detection approach that includes calculating a value of an anomaly indicator that is based on a pattern formed when the pressure data points of the raw data stream within the look-back period are projected onto a manifold shape.

2. The system according to claim 1 , wherein the blowout precursor comprises an indication of an increased risk of a flame blowing out in the combustor;

wherein the look-back period comprises a sliding temporal window that is defined relative to the selected pressure data point;

wherein the look-back period comprises a period of between 1 and 20 minutes; and

wherein the selected pressure data point comprises a most current one of the pressure data points received by the blowout detection unit from the acoustic sensor.

3. The system according to claim 1 , wherein the look-back period comprises a period of between 8 and 12 minutes; and

wherein:

the first detection analytic comprises calculating whether the selected pressure data point is outside of three standard deviations of a mean of the pressure data points that fall within the look-back period;

the second detection analytic comprises calculating whether a value for a standard deviation for the pressure data points that fall within the look-back period is equal to about zero;

the entropy feature of the third detection analytic comprises a permutation entropy, wherein the calculation of the entropy value of the permutation entropy is based on permutation patterns given a temporal ordering of values of the pressure data points that fall within the look-back period; and

the projection of the manifold anomaly detection approach o the fourth detection analytic comprises a kernel-based Laplacian projection.

4. The system according to claim 3 , wherein the outcome of each of the first detection analytic, the second detection analytic, the third detection analytic, and the fourth detection analytic comprises one of: a positive result, which indicates an increased likelihood of the blowout precursor; and a negative result, which indicates a decreased likelihood of the blowout precursor;

wherein:

in regard to the first detection analytic, the positive result corresponds to the selected pressure data point being outside of the three standard deviations of the mean, and the negative result corresponds to the selected pressure data point being inside of the three standard deviations of the mean;

in regard to the second detection analytic, the positive result corresponds to the standard deviation not equaling zero, and the negative result corresponds to the standard deviation equaling zero;

in regard to the third detection analytic, the positive result corresponds to the entropy value exceeding a predetermined threshold for the entropy value, and negative result corresponds to the entropy value not exceeding the predetermined threshold for the entropy value; and

in regard to the fourth detection analytic, the positive result corresponds to the value of the anomaly indicator exceeding a predetermined threshold value of the anomaly indicator, and negative result corresponds to the value of the anomaly indicator not exceeding the predetermined threshold value for the anomaly indicator.

5. The system according to claim 4 , wherein the plurality of separate detection analytics of the ensemble approach comprises at least three selected from the first detection analytic; the second detection analytic; the third detection analytic; and the fourth detection analytic.

6. The system according to claim 4 , wherein the plurality of separate detection analytics of the ensemble approach comprises each of the first detection analytic; the second detection analytic; the third detection analytic; and the fourth detection analytic;

wherein the ensemble approach further comprises:

determining an overall ensemble result given the plurality of outcomes generated respectively from the plurality of separate detection analytics, the overall ensemble result being based on:

which of the first detection analytic, the second detection analytic, the third detection analytic, and the fourth detection analytic produces the positive result; and

which of the first detection analytic, the second detection analytic, the third detection analytic, and the fourth detection analytic produces the negative result;

basing the detection of the blowout precursor on the overall ensemble result.

7. The system according to claim 6 , wherein the step of determining the overall ensemble result comprises weighting the outcomes of the first detection analytic according to a relative predictive strength.

8. The system according to claim 7 , wherein a frequency of the periodic measurement of the pressure comprises at least 13,000 hertz and the raw data stream includes each of the pressure data points generated by the acoustic sensor;

wherein the procedure performed by the blowout detection unit further comprises:

sampling the raw data stream via a sampling process to generate a sampled raw data stream, wherein the blowout detection unit comprises both the raw data stream and the sampled raw data stream;

feeding the raw data stream to one or more of the first detection analytic, the second detection analytic, the third detection analytic, and the fourth detection analytic for use in determining the outcomes; and

feeding the sampled raw data stream to one or more of the first detection analytic, the second detection analytic, the third detection analytic, and the fourth detection analytic for use in determining the outcomes.

9. The system according to claim 8 , wherein the sampling process comprises selecting one of the pressure data points for every 100 to 200 of the pressure data points in the raw data stream;

wherein weight comprises the predictive strength of the outcomes of each of the third detection analytic and the fourth detection being greater than the predictive strength of the outcomes of each of the first detection analytic and the second detection analytic; and

wherein the procedure performed by the blowout detection unit further comprises:

the feeding the raw data stream to the first detection analytic and the second detection analytic for use thereby in determining the outcomes; and

feeding the sampled raw data stream the third detection analytic and the fourth detection analytic for use thereby in determining the outcomes.

10. The system as in claim 1 , further comprising a controller that controls the operation of the combustor;

wherein the blowout precursor comprises an indication of an increased risk of a flame blowing out in the combustor;

wherein a first communication link connects the blowout detection unit to the controller;

wherein the procedure comprises the step of transmitting a blowout precursor notification to the controller via the first communication link upon the detection of the blowout precursor;

wherein the controller is configured to control the operation of the combustor at least partially in response to receiving the blowout precursor notification; and

wherein the controller is configured to take preemptive control action upon receiving the blowout precursor notification, the preemptive control action comprising one expected to decrease the increased risk of the flame blowing out in the combustor.

11. The system according to claim 10 , further comprising a data processing system configured to receive the raw data stream from the acoustic sensor and, once received, materially transform the raw data stream into a processed data stream;

wherein:

the preemptive control action comprises modifying at least one of: a fuel supply to the combustor; an air supply to the combustor; and a load output of the gas turbine;

a second communication link connects the data processing system to the controller, wherein the data processing system is configured to transmit the processed data stream to the controller via the second communication link; and

the controller is configured to receive the processed data stream and control the operation of the combustor in response to the processed data stream.

12. The system according to claim 11 , wherein the acoustic sensor comprises a pressure transducer;

wherein a frequency of the periodic measurement of the pressure comprises at least 13,000 hertz, and the raw data stream includes each of the pressure data points generated by the acoustic sensor.

13. The system according to claim 11 , wherein the acoustic sensor comprises a pressure transducer;

wherein a frequency of the periodic measurement of the pressure comprises at least 10,000 hertz and the raw data stream includes each of the pressure data points generated by the acoustic sensor;

wherein the data processing system comprises a signal amplifier, an analog-to-digital converter, and an input-output module;

wherein the data processing system materially transforms the raw data stream into the processed data stream via at least one of: a data aggregation; a data sampling; and a data conversion from a function of time to a function of frequency; and

wherein a third communication link connects the blowout detection to the data processing system, wherein the third communication link connects to the data processing system such that a data stream received by the third communication link comprises the raw data stream.

14. The system according to claim 13 , wherein the second communication link connects to the data processing system via one of the signal amplifier and the analog-to-digital converter.

15. The system according to claim 13 , wherein a fourth communication link connects the analog-to-digital converter to the input-output module; and

wherein the second communication link connects to the data processing system via one of the fourth communication link and the input-output module.

16. The system according to claim 11 , further comprising a user input device configured to interface with the blowout detection unit;

wherein the procedure comprises modifying at least one criterion by which the blowout precursors are detected according to a received input from a user via the user input device.

17. The system according to claim 16 , wherein the input from the user comprises a risk factor that relates to a choice made by the user as to a nearness to blowout conditions the user wants to the combustor to operate; and

wherein the blowout detection unit comprises an event stream processing unit.

18. A system for controlling an operation of a combustor in a gas turbine, the system comprising:

the combustor;

an acoustic sensor configured to periodically measure a pressure of the combustor and generate a raw data stream that comprises pressure data points resulting from the periodic measurement of the pressure; and

a blowout detection unit configured to receive the raw data stream from the acoustic sensor, wherein the blowout detection unit comprises a processor and a machine-readable storage medium on which is stored instructions that cause the processor to execute a procedure related to a detection of a blowout precursor; and

wherein the procedure comprises a plurality of separate detection analytics, including at least:

a first detection analytic that comprises a calculation as to whether a selected pressure data point of the pressure data points within the raw data stream varies from a steady state condition, wherein the steady state condition is defined by the pressure data points of the raw data stream that fall within a look-back period that precedes the selected pressure data point;

a second detection analytic that comprises a calculation of a standard deviation for the pressure data points of the raw data stream within the look-back period;

a third detection analytic that comprises calculating an entropy value related to an entropy feature of the pressure data points of the raw data stream within the look-back period; and

a fourth detection analytic that comprises a manifold anomaly detection approach that includes calculating a value of an anomaly indicator that is based on a pattern formed when the pressure data points of the raw data stream within the look-back period are projected onto a manifold shape;

wherein:

the blowout precursor comprises an indication of an increased risk of a flame blowing out in the combustor;

the look-back period comprises a sliding temporal window of between 8 and 12 minutes that is defined relative to the selected pressure data point; and

a frequency of the periodic measure of the pressure comprises at least 13,000 hertz and the raw data stream includes each of the pressure data points generated by the acoustic sensor.