Study On Control Strategy For Turbines In Small Hydropower Stations

With the vigorous Intelligent development,ensuring their reliability of power generation has become an important task.One of the important indicators to measure the reliability of power generation is the output frequency of the turbine control system.The commonly used PID turbine control system cannot balance among rapid,accurate and stable,and there are many limitations in control.To guarantee the more stable operation,the self-adjusting and parameter self-tuning abilities at small fluctuations,the fractional order control more suitable for the turbine control system and the single neuron adaptive control with the self-learning ability are introduced to provide better control for the stability of turbine system in small hydropower plants.After studying the PID control theories,the fractional order PI? control theories,the single neuron adaptive control theories,and the system simulation methods,the implementation method and process of three kinds of controllers were analyzed,and the corresponding mathematical model was established.Based on the working principles of the hydropower station and the principles of the turbine control system,the modular mathematical models and simulation models were established for the turbine diversion system,hydraulic system,hydraulic turbine,generator and controller.The model of the turbine control system was formed.Then in the MATLAB/SIMULINK environment,the simulation test of frequency disturbance and load disturbance were carried out for the turbine control system in a small hydropower plant.The simulation results show that the fractional order PI? controller has a certain degree of adaptability,the system can overcome the disturbance,the fluctuation amplitude is small,and the amount of overshoot is small;while the single neuron adaptive control system has a small amount of overshoot,strong robustness,smooth transition process,and it can stabilize even faster.These two controllers can not only provide a theoretical basis for the design of small hydropower plants but also can promote the intelligent development of small hydropower.