Improved generalized predictive control for hydro-turbine governing system integrating ensemble fault diagnosis

Xinjie Lai; Yue Zong; Dedi Li; Huimin Huang; Yuechao Wu

doi:10.1117/12.2681211

23 May 2023 Improved generalized predictive control for hydro-turbine governing system integrating ensemble fault diagnosis

Xinjie Lai, Yue Zong, Dedi Li, Huimin Huang, Yuechao Wu

Author Affiliations +

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126454I (2023) https://doi.org/10.1117/12.2681211
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Hydro-Turbine Governing System (HTGS) is a key component in hydropower station, which converse hydropower into electricity, the stable operation of HTGS is of vital importance to a hydropower station. This paper aims to study the fault diagnosis method under noisy environment and propose an Improved Generalized Predictive Control (IGPC) strategy under fault operation conditions. Firstly, the Extended Kalman Filter (EKF) is adopted to characterize the faults of HTGS based on the nonlinear mathematical model of HTGS. Each EKF corresponds to a certain fault mode, which composes a fault archive. Meanwhile, the EKFs are able to effectively filter the external noise. The detection and recognition of faults are accomplished by the generalized residual model, the probability of a certain fault is quantified by the deviation between the observation of EKFs and the state variables of real HTGS. Then, the probability of each fault is injected into IGPC to reconstruct the CARIMA of HTGS, hence the CARIMA always follow the real state of HTGS even under fault operation conditions. Based on the online reconstruction of CARIMA, the robustness of GPC is further strengthened. Simulation result indicates that the proposed diagnosis model is effective for the detection and recognition of known faults under noise contaminated environment. In addition, the proposed IGPC is able to effectively stabilize the HTGS under fault operation conditions.

Citation Download Citation

Xinjie Lai, Yue Zong, Dedi Li, Huimin Huang, and Yuechao Wu "Improved generalized predictive control for hydro-turbine governing system integrating ensemble fault diagnosis", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126454I (23 May 2023); https://doi.org/10.1117/12.2681211

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KEYWORDS

Mathematical modeling

Control systems

Complex systems

Tolerancing

Signal filtering

Turbines

System integration

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