A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP). A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM) with Wavelet Decomposition (WD) were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN)-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

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Reduction of nitric oxides (NO x ) in aircraft engines and in gas turbines by lean combustion is of great interest in the design of novel combustion systems. However, the stabilization of the flame under lean conditions is a main issue. In this context, the present work investigates the effects of sinusoidal dielectric barrier discharge (DBD) on a lean inverse diffusive methane/air flame in a Bunsen-type burner under different actuation conditions. The flame appearance was investigated with fixed methane loading (mass flux), but with varying inner airflow rate. High-speed flame imaging was done by using an intensified (charge-coupled device) CCD camera equipped with different optical filters in order to selectively record signals from the chemiluminescent species OH*, CH*, or CO 2 * to evaluate the flame behavior in presence of plasma actuation. The electrical power consumption was less than 33 W. It was evident that the plasma flame enhancement was significantly influenced by the plasma discharges, particularly at high inner airflow rates. The flame structure changes drastically when the dissipated plasma power increases. The flame area decreases due to the enhancement of mixing and chemical reactions that lead to a more anchored flame on the quartz exit with a reduction of the flame length.

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In this paper an integrated heath monitoring platform is proposed and developed for performance analysis and degradation diagnostics of gas turbine engines. In a first approach the numerical tool is able to predict engine measurable data from flight data, in order to create a dataset of expected values. Then, in the case of a mismatch between expected values and measured data coming from a real engine, a second part of the tool can be activated to detect the component under degradation. In order to evaluate the performance prediction artificial neural networks (ANN) have been implemented. The tool is able to recognize the degradation due to compressor fouling and turbine erosion. Synthetic data generation has been carried out to show how the degradation effects can affect the engine performance. The used data have been generated with a model based on gas path analysis. The training of the model is focused on components deterioration due to a combination of fouling and erosion. Different scenarios have been compared in order to carry out a sensitivity analysis and to choose the best parameters for the network input and output. Obviously the knowledge of the real engine health status can be crucial for maintenance and fleet management decisions.