Yasuharu Ohsawa

Development of an advanced power system stabilizer using a strict linearization approach

This paper presents a practical design for a power system stabilizer using the differential geometric linearization approach. This stabilizer uses the information at the secondary bus of the step-up transformer as input signals, enabling its application to multi-machine power systems. The stabilizer makes it possible to obtain all necessary information within its own power station without observing an equivalent reactance in other power systems. To evaluate the dynamic performance of this stabilizer, the authors tested it on a multimachine power system computer model. The simulation results showed that the stabilizer offered good dynamic performance and robustness compared with conventional power system stabilizers, thus confirming its very effective and practical application in power system stabilization.

Determination of installation location of SMES for power system stabilization

In the present paper, the method for determining the effective location of SMES (superconducting magnetic energy storage) for power system stabilization is reexamined. When the capacity of SMES is infinitesimal, the effective location can be determined from the eigenvector corresponding to the oscillation mode. When the SMES has a finite capacity however, the criterion may not be correct, because the eigenvector is affected by the SMES. The variation of eigenvectors with increasing capacity of SMES is examined, and the relationship between the SMES capacity and the system damping effect is investigated.

Modeling of power system dynamics using neural network

Hirofumi Fujita, Student Member, Yasuharu Ohsawa, Member (Kobe University) In this paper, examined is the modeling of power system dynamics through the direct approximation of power system input/output mapping by neural network (NN) instead of through the conventional method by differential equations. The NN used is of multilayer type with delayed signals, which is suitable for dealing with time series data, and it is trained by the error back propagation algorithm. Two sample systems are modeled by the NN; one is a numerical simulation model, and the other is an experimental system, both of which are a one-machine infinitebus system. The input signal and the output signal to the NN are the reference value of the generator terminal voltage and the terminal voltage itself, respectively. The parameters in the learning algorithm are adjusted so that the training develop smoothly and converge in 30,000 times for the numerical simulation model. Thus obtained values of parameters are used for the identification of the experimental system, and the development of training is evaluated. NNs with different input structure are trained for both sample systems, and high approximation accuracy was found achieved. The performance of the NN trained by the experimental system data is compared with that of the conventional differential equation model, and the possibility of the power system dynamics modeling by NN is shown.