Prediction of operating characteristics for industrial gas turbine combustor using an optimized artificial neural network

Energy ◽  
2020 ◽  
Vol 213 ◽  
pp. 118769 ◽  
Author(s):  
Yeseul Park ◽  
Minsung Choi ◽  
Kibeom Kim ◽  
Xinzhuo Li ◽  
Chanho Jung ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Operating Characteristics ◽  
Gas Turbine Combustor ◽  
Artificial Neural ◽  
Industrial Gas Turbine

A Novel Approach for Gas Turbine Condition Monitoring Combining CUSUM Technique and Artificial Neural Network

2009 ◽  
Author(s):  
Magnus Fast ◽  
Thomas Palme´ ◽  
Magnus Genrup
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Gas Turbine ◽  
Condition Monitoring ◽  
Sequential Analysis ◽  
Ann Model ◽  
Novel Approach ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Operational Data

Investigation of a novel condition monitoring approach, combining artificial neural network (ANN) with a sequential analysis technique, has been reported in this paper. For this purpose operational data from a Siemens SGT600 gas turbine has been employed for the training of an ANN model. This ANN model is subsequently used for the prediction of performance parameters of the gas turbine. Simulated anomalies are introduced on two different sets of operational data, acquired one year apart, whereupon this data is compared with corresponding ANN predictions. The cumulative sum (CUSUM) technique is used to improve and facilitate the detection of such anomalies in the gas turbine’s performance. The results are promising, displaying fast detection of small changes and detection of changes even for a degraded gas turbine.

scholarly journals Artificial Neural Network–Based System Identification for a Single-Shaft Gas Turbine

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Hamid Asgari ◽  
XiaoQi Chen ◽  
Mohammad B. Menhaj ◽  
Raazesh Sainudiin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
System Identification ◽  
Gas Turbine ◽  
Gas Turbines ◽  
Real Data ◽  
Superior Performance ◽  
Minimum Mean Squared Error ◽  
Ann Models ◽  
Artificial Neural

During recent decades, artificial intelligence has been employed as a powerful tool for identification of complex industrial systems with nonlinear dynamics, such as gas turbines (GT). In this study, a methodology based on artificial neural network (ANN) techniques was developed for offline system identification of a low-power gas turbine. The processed data was obtained from a SIMULINK model of a gas turbine in matlab environment. A comprehensive computer program code was generated and run in matlab for creating and training different ANN models with feed-forward multilayer perceptron (MLP) structure. The code consisted of various training functions, different number of neurons as well as a variety of transfer (activation) functions for hidden and output layers of the network. It was shown that the optimal model for a two-layer network with MLP structure consisted of 20 neurons in its hidden layer and used trainlm as its training function, as well as tansig and logsid as its transfer functions for the hidden and output layers. It was also observed that trainlm has a superior performance in terms of minimum mean squared error (MSE) compared with each of the other training functions. The resulting model could predict performance of the system with high accuracy. The methodology provides a comprehensive view of the performance of over 18,720 ANN models for system identification of the single-shaft gas turbine. One can use the optimal ANN model from this study when training from real data obtained from this type of GT. This is particularly useful when real data is only available over a limited operational range.

scholarly journals Artificial Neural Network based Process History Data Model for Gas Turbine Compressor Systems

2019 ◽  
Vol 8 (4) ◽  
pp. 5069-5077
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Electric Power ◽  
Gas Turbine ◽  
Operating Conditions ◽  
System Modelling ◽  
History Data ◽  
Artificial Neural ◽  
Gas Turbine Compressor ◽  
Compressor Efficiency

Gas turbine-based power plants are found to play a vital role in electric power generation and act as spinning reserves for renewable electric power. A robust performance assessment tool is inevitable for a gas turbine system to maintain high operational flexibility, availability, and reliability at different operating conditions. A suitable simulation model of the gas turbine provides detailed information about the system operation under varying ambient and load conditions. This paper illustrates a systematic methodology for process history data-based modelling of a gas turbine compressor system. The ReliefF feature selection method is applied for the proper identification of the parameters influencing the compressor efficiency. Appropriate Artificial Neural Network (ANN) based models are developed for data classification and system modelling of the compressor. The model performance has been validated using actual plant operational data, and the standard deviation of the error in model output was found to be 0.38. A novel approach for suitable integration of data processing methods, machine learning tools and gas turbine domain knowledge has led to the development of a robust compressor model. The model has been utilized for the health assessment of an existing gas turbine compressor, demonstrated through an illustrative case study. The model has been found suitable for parametric analysis of compressor efficiency with operating hours, which is helpful for operational decision-making involving studies on the influence of part-load operation, compressor wash planning, maintenance planning etc

Forecasting breaks in cast iron water mains in the city of Kingston with an artificial neural network model

2014 ◽  
Vol 41 (10) ◽  
pp. 918-923 ◽  
Author(s):  
Michael Nishiyama ◽  
Yves Filion
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Confusion Matrix ◽  
Ann Model ◽  
Operating Characteristics ◽  
Data Set ◽  
Water Mains ◽  
Artificial Neural ◽  
Water Main ◽  
The City

Predictive water main break models can assist municipalities in prioritizing the replacement and rehabilitation of water mains. The aim of the paper is to develop an artificial neural network (ANN) model to forecast water main breaks in the water distribution network of the City of Kingston, Ontario, Canada. The ANN model includes variables of diameter, age, length, and soil type to forecast breaks. Historical break data from the 1998 to 2011 period is used to develop the ANN model and forecast pipe breaks over a 5 year planning period. The mean square error, receiver operating characteristics curves, and a confusion matrix are used to evaluate the ANN model training and testing. The trained neural network correctly classified 85% of the data set at the training, validation, and testing stages. Model forecasts showed lower pipe break rates in Kingston West, Kingston Central, and Kingston East. The reduction in break rate in the Kingston system was attributed to the removal of old pipes, and the favourable performance of pipes that are in the usage phase of their life cycle. The ANN model provided Utilities Kingston with a tool to assist them in the planning and management of their water main rehabilitation program.

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