Prediction of Vibration in the Discharge Ring of a River Type Hydroelectric Power Plant with Bulb Turbine Using Artificial Neural Networks and Support Vector Machine

Abstract

Cracks are formed around the manhole covers located in the discharge ring areas of the turbine units of a hydroelectric power plant with a river-type bulb turbine due to the vibration of the units. Determining the operating parameters for the low vibration zone of the units to reduce or eliminate these cracks is an important issue in terms of reducing plant operating efficiency and maintenance costs. To solve this problem and to determine the central operating parameters in the safe vibration zone, a vibration prediction model was created with artificial neural networks and support vector machine. Operating parameters of the hydroelectric power plant; artificial neural networks and support vector machine were created to predict vibrations for each turbine unit using the water inlet-outlet height, network pollution level, power of each unit, total unit power, and vibration data from the discharge rings of the units. Vibration estimates were made based on operating parameters and compared with actual vibration values. The results obtained showed that the operating parameters for reducing the vibration values of the turbine units of the hydroelectric power plant could be determined practically with the help of artificial neural networks and support vector machine.

Keywords

• artificial neural networks
• hydroelectric power plants
• support vector machine
• turbine vibrations, vibration prediction