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Energies 2015, 8(12), 13911-13927; doi:10.3390/en81212403

Gas Turbine Transient Performance Tracking Using Data Fusion Based on an Adaptive Particle Filter

1
Jiangsu Province Key Laboratory of Aerospace Power Systems, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
2
Collaborative Innovation Center of Advanced Aero-Engine, Beijing 100191, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Terese Løvås
Received: 29 September 2015 / Revised: 26 November 2015 / Accepted: 30 November 2015 / Published: 8 December 2015
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Abstract

This 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
Keywords: gas turbine; performance tracking; data fusion; particle filter (PF); probability density truncation; wavelet transform gas turbine; performance tracking; data fusion; particle filter (PF); probability density truncation; wavelet transform
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

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.

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