A new method for the simulation of gas turbine fuel systems based on an intercomponent volume method has been developed. It is able to simulate the performance of each of the hydraulic components of a fuel system using physics-based models, which potentially offers more accurate results compared with those using transfer functions. A transient performance simulation system has been set up for gas turbine engines based on an inter-component volume (ICV) method. A proportional-integral (PI) control strategy is used for the simulation of engine controller. An integrated engine and its control and hydraulic fuel systems has been set up to investigate their coupling effect during engine transient processes. The developed simulation system has been applied to a model aero engine. The results show that the delay of the engine transient response due to the inclusion of the fuel system model is noticeable although relatively small. The developed method is generic and can be applied to any other gas turbines and their control and fuel systems.
Cyber and physical attacks threaten the security of distribution power grids. The emerging renewable energy sources such as photovoltaics (PVs) introduce new potential vulnerabilities. Based on the electric waveform data measured by waveform sensors in the distribution power networks, in this paper, we propose a novel high-dimensional data-driven cyber physical attack detection and identification approach (HCADI). Firstly, we analyze the cyber and physical attack impacts (including cyber attacks on the solar inverter causing unusual harmonics) on electric waveforms in distribution power grids. Then, we construct a high dimensional streaming data feature matrix based on signal analysis of multiple sensors in the network. Next, we propose a novel mechanism including leverage score based attack detection and binary matrix factorization based attack diagnosis. By leveraging the data structure and binary coding, our HCADI approach does not need the training stage for both detection and the root cause diagnosis, which is needed for machine learning/deep learning-based methods. To the best of our knowledge, it is the first attempt to use raw electrical waveform data to detect and identify the power electronics cyber/physical attacks in distribution power grids with PVs.
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