Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter
AbstractThis paper considers the problem of gas turbine transient performance tracking in a cluttered environment. To increase the accuracy and robustness of state estimation, a data-fusion nonlinear estimation method based on an adaptive particle filter (PF) is proposed. This method needs local estimates transmitted to a central filtering unit for data fusion, and then global data feedback to the local PF for consensus propagation. The computational burden is shared by the local PF and central filtering unit in the data-fusion architecture. Furthermore, the PF algorithm used for the data fusion is embedded with the prior knowledge of engine health condition and adaptive to the measurement noise, and hence is called the adaptive PF. The heuristic information of state variables represented by inequality constraints tunes the local estimates by a probability density truncation method. The covariance of measurement noise is calculated by wavelet transform and utilized to update the particle importance function of the real time PF. The performance improvements of the proposed method are indicated through extensive experiments for gradual and abrupt shift performance tracking under conditions of gas turbine transient operation. View Full-Text
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Lu, F.; Wang, Y.; Huang, J.; Huang, Y. Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter. Energies 2015, 8, 13911-13927.
Lu F, Wang Y, Huang J, Huang Y. Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter. Energies. 2015; 8(12):13911-13927.Chicago/Turabian Style
Lu, Feng; Wang, Yafan; Huang, Jinquan; Huang, Yihuan. 2015. "Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter." Energies 8, no. 12: 13911-13927.