Katsunori Mizuno

Study of load frequency control for a microgrid

This paper describes a method to enhance the load frequency control in a power system with distributed generations (DG). In this paper, a power storage system is used with solar photovoltaic generations and wind power generations. The microgrid system at Aichi Institute Technology in Japan is used as a simulation model. For each simulation, which was carried out by using a power transmission simulator, the model of the microgrid included one DG and a storage system. The effectiveness of load frequency control using the storage system was examined. It was confirmed that the electric power quality improves when load frequency control is carried out with the storage system.

Study of Identification for the Inertia Constant in a Small Power System for LFC

Abstract This paper has tried the identification for the inertia constant in a small power system, for the betterment of the LFC control characteristic. The power demand estimation method was used for the method of identifying an inertia constant power system. Until now, the method that used as an output set-point control by the power demand estimation is improved to the identification method. The power demand estimation method is using the frequency variation, the tie line power flow deviation, the output deviation of power plant, the inertia coefficient and damping coefficient of power system. In this paper, the actual electric power demand, the output of power plant, etc. was observed and the inertia coefficient is calculated using this estimation method. Then, the inertia constant and the damping constant are not observed. In this paper, the inertia coefficient of the system of 2000kW small-scale system was calculated.

An estimation of runoff ratio by using artificial neural network with radar echo data

This paper describes an application of a neural network for estimating the runoff ratio from radar echo data. A neural network system is developed through a case study on a dam for hydro-power plant located the upper district of the Yahagi River in Central Japan. The authors use the two types of neural networks; one of the types is for estimating the ground rainfall distribution from radar echo data and another one is for estimating the runoff ratio from estimated ground rainfall. It is found from their investigations that estimating accuracy of the runoff ratio is improved by utilization the radar echo data.