Abstract:
A method for predicting an anomaly in a combustor (16) is presented. The method includes receiving signals representative of parameters in one or more combustion cans (22, 24) of the combustor, generating a plurality of patterns based on a permutation entropy window and the signals, identifying a plurality of pattern categories in the plurality of patterns, determining a permutation entropy based on the plurality of patterns and the plurality of pattern categories, and predicting an anomaly in the combustor based on the permutation entropy. The method further includes comparing the plurality of pattern categories to determined permutations of pattern categories if the anomaly is present in the combustor, and predicting a category of the anomaly based on the comparison of the plurality of pattern categories to the determined permutations of pattern categories.
Abstract:
A cooling tower simulation system may receive a measurement from a cooling tower sensor and generate a predicted Noutput of a cooling tower system based on a model of the cooling tower system. The simulation system may generate an estimated output using an extended Kalman filter with the measurement and the predicted output as inputs, wherein the estimated output represents a characteristic of the cooling tower system.
Abstract:
A method for predicting an anomaly in a combustor (16) is presented. The method includes receiving signals representative of parameters in one or more combustion cans (22, 24) of the combustor, generating a plurality of patterns based on a permutation entropy window and the signals, identifying a plurality of pattern categories in the plurality of patterns, determining a permutation entropy based on the plurality of patterns and the plurality of pattern categories, and predicting an anomaly in the combustor based on the permutation entropy. The method further includes comparing the plurality of pattern categories to determined permutations of pattern categories if the anomaly is present in the combustor, and predicting a category of the anomaly based on the comparison of the plurality of pattern categories to the determined permutations of pattern categories.