Intelligent Information-Measuring System for Controlling the Operating Mode of Hydroelectric Power Plants

Problem statement One of the main problems in the operation of hydraulic structures of hydroelectric power stations is maintaining the water level in the reservoir. The inflow and consumption of water resources depends on many factors: meteorological and hydrological conditions, volumes of water resource consumption. The balance of water resources in the reservoir is monitored using sensors that measure water inflow and flow. The main problem of maintaining the water level in a reservoir over a long period is the optimal distribution of water resources. Exceeding a certain retaining value of the water level can lead to its overflowing over the dam, and a drop in the water level below this disrupts the normal operation of the hydroelectric power station. The flow of water resources is controlled by gates with electromechanical drives. Typically, the management strategy of a hydroelectric power plant is determined based on previous meteorological and hydrological conditions and information about the delay and current water level in the reservoir. It should be noted that accurately predicting the water level in a reservoir is a difficult task, since a hydraulic structure is an extended object that is affected by a large number of different random and unpredictable factors. Therefore, to solve this problem, it is proposed to use an intelligent information and measurement system (IMS) for controlling electromechanical drives of reservoir gates. Thus, the IMS for controlling electric gate drives for the tasks being solved is SEMS. Purpose of the study to solve the problem of optimal distribution of water resources in a reservoir, allowing to ensure normal operation of the hydroelectric power station and meet the needs of other consumers, taking into account the flow of water into the reservoir, it is proposed to use an artificial neural network (ANN). ANN allows you to solve the problem of predicting the flow of water into a reservoir from rivers, often flowing over a large area with different weather conditions. Results to implement the proposed solution, an intelligent information and measurement system for controlling electromechanical drives of reservoir gates (SEMS) has been developed. The structure of the neural network and the methodology for its training have been developed, taking into account the statistical database, geographical and weather factors. Practical value The developed intelligent information and measurement system for controlling electromechanical drives of reservoir gates (SEMS), which, when implemented, will reduce fluctuations in the water level in the reservoir and, therefore, ensure the normal operation of the hydroelectric power station. In addition, this system can be used to control water balance in other hydrological structures, such as irrigation systems.