Minesh Ashok Shah

Modeling and Control of Combustion Dynamics in Industrial Gas Turbines

The thermoacoustic response of an industrial-scale gas turbine combustor to fuel flow perturbations is examined. Experimental measurements in a laboratory combustor along with numerical modeling results are used to identify the dynamic behavior of the combustor over a variety of operating conditions. A fast-response actuator was coupled to the fuel system to apply continuous sinusoidal perturbations to the total fuel mass flow rate. The effects of these perturbations on the combustor pressure oscillation characteristics as well as overall operability of the system are described. The results of this work suggest that persistent excitation of the fuel system may present a viable means of controlling combustion dynamics in industrial gas turbine and, in turn, enhance their performance.Copyright

GE Brilliant wind farms

Since the Brilliant wind platform was launched in early 2013, GE has developed and commercialized a host of hardware and software features to increase wind farm Annual Energy Production, improve services productivity, and open up new customer revenue streams. By utilizing advanced control algorithms and analytics coupled with deep domain expertise in power electronics and grid integration, GE is creating more with less - getting more power and efficiency out of existing hardware, taking an inherently variable wind resource and integrating it more smoothly onto the grid, managing the complexity of multiple turbines within a farm and multiple farms within a grid system, and making wind predictable and reliable even in areas with weak infrastructure. By harnessing the power of the industrial internet GE will continue to develop innovative features within the Brilliant wind platform, transforming the wind industry as we tackle the next generation of challenges and opportunities.

Acoustics Based Prognostics for DLE Combustor Lean Blowout Detection

Physics based algorithm that uses acoustic precursors indicating a Lean Blowout (LBO) is proposed for lean blowout detection in combustors. The proposed technology is presented for a typical multi-nozzle Dry Low Emission (DLE) combustor. Three narrow band dynamic pressure tones, namely LBO (low frequency) tone, high fuel to air (F/A) ratio (hot tone) and low F/A ratio (cold) tones are identified as strong precursors to behavior consistent with combustion instability as LBO event evolves. The likelihood of LBO is computed using a statistical model operating on the RMS value of the LBO tone. Two additional pieces of evidential information are built respectively using the relative change of the RMS values of these three tones and the frequency shift of the high F/A tone. A data fusion algorithm then uses these two evidential signatures to enhance the LBO probability based on the LBO tone. Field test results on GE’s commercial multi-nozzle combustor gas turbines showed that the algorithm is practical and effective giving enough lead-time to take corrective action to avoid a blowout. A closed loop controller that modulates global manifold fuel splits for all the combustors or total fuel flow of individual combustor in a multi-combustor (multi-can) turbine to avoid the incipient blowout upon detection using the method presented in this paper can then be easily designed.Copyright

Probabilistic Methodology for Combustor Airflow Surrogate Development

Operability of Dry-Low-NOx (DLN) combustors in heavy-duty gas turbines is dependent in part on satisfying boundaries or constraints associated with lean blow out (LBO), NOx , and combustion dynamics. To satisfy each of these boundaries or constraints, the determination of combustor airflow is critical. Since combustor airflow is difficult to measure directly, a surrogate or an estimator of combustor airflow that can be implemented in the control system is required. Presented in this paper is a methodology for determining the combustor airflow surrogate based on measurable parameters. The approach relies on developing a nonlinear equation of combustor airflow based on cycle simulations over a wide range of ambient (temperature, pressure and humidity) and parameter (e.g., inlet guide vane and load) variations. To understand the robustness of the combustor airflow estimate, sensitivity analysis with regard to sensor uncertainty, machine degradation, and machine-to-machine variation is presented. In each of these cases, the uncertainty/parameter variation is investigated via Monte Carlo simulations to quantify performance in terms of mean shift (i.e, bias) and standard deviation. It is shown that the resulting surrogate, which is comprised of compressor discharge pressure, compressor discharge temperature and specific humidity, is capable of addressing the variations listed above and is capable of representing a compressor map at different IGV settings via a simple relationship.Copyright