This method, based on fuzzy logic principles, is intended to find the most likely solution of an over-determined system, in specific conditions. The method addresses typical problems encountered in gas turbine performance analysis and, more specifically, to the alignment of a synthesis model with measured data. Generally speaking, the relatively low accuracy of measurements introduces a random noise around the true value of a performance parameter and distorts any deterministic solution of a square matrix-based linear system. The fuzzy logic estimator is able to get very close to the real solution by using additional (pseudo-redundant) parameters and by building the most likely solution based on each of the measurement accuracies. The accuracy—or “quality”—of a measurement is encapsulated within an extra dimension which is defined as fuzzy and which encompasses the whole range of values, between 0 (false) and 1 (true). The value of the method is shown in two examples. The first simulates compressor fouling, the other deals with actual engine test data following a hardware modification. Both examples experience noisy measurements. The method is stable and effective even at high level of noise. The results are within the close vicinity of the expected levels (within 0.2% accuracy) and the accuracy is about ten times lower than the noise level.

The new algorithm provides closed-loop control of the LP Compressor working line in such a way as to maintain compressor stability and to provide increased power from the engine at the same firing temperature. This method is intended for the Trent 60, an aero-derivative engine designated for power generation and mechanical drive applications. The power benefit is achieved by operating at higher LPC pressure ratio and thereby increasing the inlet air flow and overall pressure ratio of the engine. Since the algorithm controls the working line, the threats to compressor stability related to the working line level are removed (including production scatter, deterioration and fouling) and the required surge margin can be safely reduced, providing a significant benefit in engine performance. The paper presents comparatively the structure of the current and new concepts, the main features of the controller and stresses the improved accuracy and reliability of new algorithms. The performance benefit is then assessed; the increase in power is about 3% at ISO, sea level conditions and varies with ambient temperature.

The new algorithm provides closed-loop control of the LP compressor working line in such a way as to maintain compressor stability and to provide increased power from the engine at the same firing temperature. This method is intended for the Trent 60, an aeroderivative engine designated for power generation and mechanical drive applications. The power benefit is achieved by operating at higher LPC pressure ratio and thereby increasing the inlet air flow and overall pressure ratio of the engine. Since the algorithm controls the working line, the threats to compressor stability related to the working line level are removed (including production scatter, deterioration, and fouling) and the required surge margin can be safely reduced, providing a significant benefit in engine performance. The paper presents comparatively the structure of the current and new concepts, the main features of the controller, and stresses the improved accuracy and reliability of new algorithms. The performance benefit is then assessed; the increase in power is about 3% at ISO, sea level conditions and varies with ambient temperature.

This method, based on fuzzy logic principles, is intended to find the most likely solution of an over-determined system, in specific conditions. The method addresses typical problems encountered in gas turbine performance analysis and, more specifically, to the alignment of a synthesis model with measured data. Generally speaking, the relatively low accuracy of measurements introduces a random noise around the true value of a performance parameter and distorts any deteministic solution of a square matrix-based linear system. The Fuzzy Logic Estimator (FLE) is able to get very close to the real solution by using additional (pseudoredundant) parameters and by building the most likely solution based on each of the measurement accuracies. The accuracy — or “quality” — of a measurement is encapsulated within an extra dimension which is defined as fuzzy and which encompasses the whole range of values, between 0 (false) and 1 (true). The value of the method is shown in two examples. The first simulates compressor fouling, the other deals with actual engine test data following a hardware modification. Both examples experience noisy measurements. The method is stable and effective even at high level of noise. The results are within the close vicinity of the expected levels (within 0.2% accuracy) and the accuracy is about ten times lower than the noise level.