This paper addresses the stability of a hydro-turbine governing system under hydraulic excitations. During the operation of a hydro-turbine, water hammer with different intensities occurs frequently, resulting in the stochastic change of the cross-sectional area (A) of the penstock. In this study, we first introduce a stochastic variable u to the cross-sectional area (A) of the penstock related to the intensity of water hammer. Using the Chebyshev polynomial approximation, the stochastic hydro-turbine governing model is simplified to its equivalent deterministic model, by which the dynamic characteristics of the stochastic hydro-turbine governing system can be obtained from numerical experiments. From comparisons based on an operational hydropower station, we verify that the stochastic model is suitable for describing the dynamic behaviors of the hydro-turbine governing system in full-scale applications. We also analyze the change laws of the dynamic variables under increasing stochastic intensity. Moreover, the differential coefficient with different values is used to study the stability of the system, and stability of the hydro-turbine flow with the increasing load disturbance is also presented. Finally, all of the above numerical results supply some basis for modeling efficiently the operation of large hydropower stations.
This paper analyzes the dynamic response of a pumped-storage hydropower plant in generating mode. Considering the elastic water column effects in the penstock, a linearized reduced order dynamic model of the pumped-storage hydropower plant is used in this paper. As the power load is always random, a set of random generator electric power output is introduced to research the dynamic behaviors of the pumped-storage hydropower plant. Then, the influences of the PI gains on the dynamic characteristics of the pumped-storage hydropower plant with the random power load are analyzed. In addition, the effects of initial power load and PI parameters on the stability of the pumped-storage hydropower plant are studied in depth. All of the above results will provide theoretical guidance for the study and 2 analysis of the pumped-storage hydropower plant.
Bayesian Networks are a flexible and intuitive tool associated with a robust mathematical background. They have attracted increasing interest in a large variety of applications in different fields. Furthermore, the fast growing availability of computational power on the one side and the implementation of efficient inference algorithms on the other, have additionally promoted the success of this method. In
The present work introduces a novel predictive control strategy for the analysis of the dynamic performance of hydro-turbine governing systems based on fuzzy logic. Firstly, a six-dimensional non-linear dynamic model of the system is defined. The defined model is applied to a realistic case-study, aiming to investigate the dynamic behavior of the system. In order to deal effectively with the non-linearity of the system under study, the T-S fuzzy approach is adopted. The results demonstrated through the use of the discrete Lyapunov function and Schur complements of matrices suggest that the closed-cycle control system can achieve a global asymptotic stability state. The second part focuses on the quantification of the impact of sudden changes in operating conditions on the overall performance. The numerical results indicate that proposed predictive control method can ensure the performance of the system to be reliable and robust to external inferences. In addition to this, the approach proposed has unquestionable advantages over the traditional PID and model predictive controllers with regard to non-linear systems applications.
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