scholarly journals Enhanced Component Analytical Solution for Performance Adaptation and Diagnostics of Gas Turbines

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4356
Author(s):  
Binbin Yan ◽  
Minghui Hu ◽  
Kun Feng ◽  
Zhinong Jiang
Keyword(s):  
Sensitivity Analysis ◽  
Analytical Solution ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Particle Swarm Optimization Algorithm ◽  
Nonlinear Behavior ◽  
Swarm Optimization ◽  
Performance Adaptation ◽  
Nonlinear Behaviors

Accurate component analytical solution is very important to gas path prognostics and diagnostics of a gas turbine. However, due to the highly complex nonlinear behavior of component performance, the nonlinear relationships between various key parameters still should be further studied. For this purpose, a new component analytical solution is proposed to enhance the current adaptation and diagnostics scheme of gas turbines. First, the tuning factors are defined to construct the enhanced component analytical solution and identify the nonlinear behaviors more accurately. Second, a sensitivity analysis for tuning factors is performed to understand the effect of each factor on the shape of component maps. Then, a particle swarm optimization algorithm is used to capture the optimal tuning factors, and then the performance adaptation is implemented. Finally, the proposed method has been validated with normal field data and fouling fault field data of a PGT25+ gas turbine. Compared with two earlier off-design point adaptation methods, the proposed method shows some advantages in performance adaptation and diagnostics.

Detection and Classification of Sensor Anomalies in Gas Turbine Field Data

2018 ◽  
Author(s):  
Giuseppe Fabio Ceschini ◽  
Lucrezia Manservigi ◽  
Giovanni Bechini ◽  
Mauro Venturini
Keyword(s):  
Anomaly Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Time Correlation ◽  
Detection Algorithm ◽  
Anomaly Classification ◽  
Monitoring And Diagnostics ◽  
Faulty Sensors

Anomaly detection and classification is a key challenge for gas turbine monitoring and diagnostics. To this purpose, a comprehensive approach for Detection, Classification and Integrated Diagnostics of Gas Turbine Sensors (named DCIDS) was developed by the authors in previous papers. The methodology consists of an Anomaly Detection Algorithm (ADA) and an Anomaly Classification Algorithm (ACA). The ADA identifies anomalies according to three different levels of filtering. Anomalies are subsequently analyzed by the ACA to perform their classification, according to time correlation, magnitude and number of sensors in which an anomaly is contemporarily identified. The performance of the DCIDS approach is assessed in this paper based on a significant amount of field data taken on several Siemens gas turbines in operation. The field data refer to six different physical quantities, i.e. vibration, pressure, temperature, VGV position, lube oil tank level and rotational speed. The analyses carried out in this paper allow the detection and classification of the anomalies and provide some rules of thumb for field operation, with the final aim of identifying time occurrence and magnitude of faulty sensors and measurements.

The NOx Emission Levels of Unconventional Fuels for Gas Turbines

1977 ◽  
Vol 99 (4) ◽  
pp. 575-579
Author(s):  
W. S. Y. Hung
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Nox Emission ◽  
Heterogeneous Combustion ◽  
Emission Model ◽  
Unconventional Gas ◽  
Gas Turbine Combustors ◽  
Combustion Systems ◽  
Good Agreement

An experimentally verified NOx emission model for gas turbines has been reported previously. The model has been modified to determine the NOx emission levels of various fuels as compared to No. 2 distillate oil. The NOx emission levels of various conventional and unconventional gas turbine fuels of interest are predicted. The predicted NOx emission levels for these fuels, including methanol, ethanol, propane, and hydrogen, are in good agreement with available laboratory and field data from stationary, aircraft, and automotive gas turbine combustors. The predicted results should be applicable to other fuel-lean, heterogeneous combustion systems.

Part-Load Performance of Gas Turbines: Part II — Multi-Point Adaptation With Compressor Map Generation and GA Optimization

2012 ◽  
Author(s):  
E. Tsoutsanis ◽  
Y. G. Li ◽  
P. Pilidis ◽  
M. Newby
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Combined Cycle ◽  
Performance Model ◽  
Part Load ◽  
Design Performance ◽  
Turbine Performance ◽  
Map Generation ◽  
Compressor Map ◽  
Performance Adaptation

Accurate gas turbine performance simulation is a vital aid to the operational and maintenance strategy of thermal plants having gas turbines as their prime mover. Prediction of the part load performance of a gas turbine depends on the quality of the engine’s component maps. Taking into consideration that compressor maps are proprietary information of the manufacturers, several methods have been developed to encounter the above limitation by scaling and adapting component maps. This part of the paper presents a new off-design performance adaptation approach with the use of a novel compressor map generation method and Genetic Algorithms (GA) optimization. A set of coefficients controlling a generic compressor performance map analytically is used in the optimization process for the adaptation of the gas turbine performance model to match available engine test data. The developed method has been tested with off-design performance simulations and applied to a GE LM2500+ aeroderivative gas turbine operating in Manx Electricity Authority’s combined cycle power plant in the Isle of Man. It has been also compared with an earlier off-design performance adaptation approach, and shown some advantages in the performance adaptation.

Improved Method for Gas-Turbine Off-Design Performance Adaptation Based on Field Data

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Shiyao Li ◽  
Zhenlin Li ◽  
Shuying Li
Keyword(s):  
Gas Turbine ◽  
Error Control ◽  
Field Data ◽  
Operating Conditions ◽  
Performance Parameters ◽  
Improved Method ◽  
Design Performance ◽  
Average Prediction Error ◽  
Comparison Results ◽  
Performance Adaptation

Abstract Obtaining accurate components' characteristic maps has great significant for gas-turbine operating optimization and gas-path fault diagnosis. A common approach is to modify the original components' characteristic maps by introducing correction factors, which is known as performance adaptation. Among the existing methods, total average prediction error of measurable parameters (MPTAPE) at specified conditions is used to evaluate the adaptation accuracy. However, when a gas turbine undergoes a field operation, the performance parameters of each component are zonally distributed under the operating conditions. Under such circumstances, randomly selecting a few data points as the error control points (ECPs) for performance adaptation may lead to an inappropriate correction of the characteristic maps, further lowering the prediction accuracy of the simulation model. In this paper, a genetic-algorithm-based improved performance adaptation method is proposed, which provides improvements in two aspects. In one aspect, similarity between the components' predicted performance curves and the performance regression curves is used as the criterion with which to evaluate the adaptation accuracy. In the other aspect, in the process of off-design performance adaptation, the performance parameters at the design point are recalibrated. The improved method has been verified by using rig test data and applied to field data of a GE LM2500+SAC gas turbine. The comparison results show that the improved method can obtain more accurate and stable adaptation results, while the computational load can be significantly reduced.

Development and Validation of a General and Robust Methodology for the Detection and Classification of Gas Turbine Sensor Faults

2020 ◽  
Vol 142 (2) ◽  
Author(s):  
Lucrezia Manservigi ◽  
Mauro Venturini ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini ◽  
Enzo Losi
Keyword(s):  
Sensitivity Analysis ◽  
Fault Detection ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Sensor Fault ◽  
Fault Detection And Classification ◽  
Sensor Fault Detection ◽  
Optimal Setting ◽  
Monitoring And Diagnostics ◽  
Development And Validation

Abstract Sensor fault detection and classification is a key challenge for machine monitoring and diagnostics, since raw data cleaning represents a key process in the gas turbine industry. To this end, this paper presents a comprehensive approach for detection, classification, and integrated diagnostics of gas turbine sensors (named DCIDS), which was previously developed by the authors and has been substantially improved and validated by means of field data. For a single sensor or redundant/correlated sensors, the improved diagnostic tool, called improved-DCIDS (I-DCIDS), can identify seven classes of faults, i.e., out of range, stuck signal, dithering, standard deviation, trend coherence, spike, and bias. First, this paper details the I-DCIDS methodology for sensor fault detection and classification. The methodology uses basic mathematical laws that require some user-defined configuration parameters, i.e., acceptability thresholds and windows of observation. Second, a sensitivity analysis is carried out on I-DCIDS parameters to derive some rules of thumb about their optimal setting. The sensitivity analysis is performed on four heterogeneous and challenging datasets with redundant sensors acquired from Siemens gas turbines (GTs). The results demonstrate the diagnostic capability of the I-DCIDS approach in a real-world scenario. Moreover, the methodology proves to be suitable for all types of datasets and physical quantities and, thanks to its optimal tuning, can also identify the exact time point of fault onset.

Modeling and Analysis of Gas Turbine Performance Deterioration

1994 ◽  
Vol 116 (1) ◽  
pp. 46-52 ◽  
Author(s):  
A. N. Lakshminarasimha ◽  
M. P. Boyce ◽  
C. B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Field Observations ◽  
Modeling And Analysis ◽  
Turbine Performance ◽  
Common Causes ◽  
Performance Deterioration ◽  
Simulation Results ◽  
Compressor Map

The effects of performance deterioration in both land and aircraft gas turbines are presented in this paper. Models for two of the most common causes of deterioration, viz., fouling and erosion, are presented. A stage-stacking procedure, which uses new installed engine field data for compressor map development, is described. The results of the effect of fouling in a powerplant gas turbine and that of erosion in a aircraft gas turbine are presented. Also described are methods of fault threshold quantification and fault matrix simulation. Results of the analyses were found to be consistent with field observations.

Modelling and Analysis of Gas Turbine Performance Deterioration

1992 ◽  
Author(s):  
A. N. Lakshminarasimha ◽  
M. P. Boyce ◽  
C. B. Meher-Homji
Keyword(s):  
Gas Turbine ◽  
Gas Turbines ◽  
Field Data ◽  
Field Observations ◽  
Turbine Performance ◽  
Common Causes ◽  
Performance Deterioration ◽  
Simulation Results ◽  
Compressor Map

The effects of performance deterioration in both land and aircraft gas turbines are presented in this paper. Models for two of the most common causes of deterioration viz. fouling and deterioration are presented. A stage stacking procedure which uses new installed engine field data for compressor map development is described. The results of the effect of fouling in a powerplant gas turbine and that of erosion in a aircraft gas turbine are presented. Also described are methods of fault threshold quantification and fault matrix simulation. Results of the analyses were found to be consistent with field observations.

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