Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine

This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part of recent data in the training and validation sets accounting for the impact of the system degradation. R-Square values of the training and validation sets demonstrate the validity of the model. The residue plot, without any clear pattern, shows the model is appropriate. The ranking of the importance of the process variables are demonstrated and the prediction profile confirms the significance of the process variables. The model trained by using neural network algorithm manifests the optimal settings of the process variables to reach the minimum value of NOx emissions from the degrading gas turbine system.

Wind Turbine Gearbox Condition Based Monitoring

The main objective of this paper is finding effective gearbox condition monitoring methods by using continuously recorded monitoring SCADA (Supervisory Control and Data Accusation) data points. Typically for wind turbine gearbox condition monitoring; temperature readings, high frequency sounds and vibrations in addition to lubricant condition monitoring have been used. However, collection of such data, require shutting down equipment for installation of costly sensors and measuring lubricant quality. Meanwhile, operational data usually collected every 10 minutes, comprised of wind speed, power generated, pitch angle and similar performance parameters can be used for monitoring health of wind turbine components such as blades, gearbox and generator. This paper uses gear rotational speed for monitoring health of gearbox teeth; since gearbox teeth deterioration can be measured by monitoring rotor to generator rotation ratios over extended period of time. As nature of wind is turbulent with rapid fluctuations, a wind turbine may operate in variety of modes within relatively short period of time. Monitoring rotational speed ratio over time, requires consistent operational conditions such as wind speed and torques within the gearbox. This paper also introduces the concept of clustering such as Normal Mixture algorithm for dividing operating datasets into consistent subgroups, which are used for long term monitoring.