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The paper describes a mid-term daily peak load forecasting method using recurrent artificial neural network (RANN). Generally, the artificial neural network (ANN) algorithm is used to forecast shortterm load pattern and many ANN structures have been developed and commercialized so far. Otherwise, learning and estimation for long-term and mid-term load forecasting are hard tasks due to lack of training data and increase of accumulated errors in long period estimation. The paper proposes a mid-term load forecasting structure in order to overcome these problems by input data replacement for special days and a recurrent-type NN application. Also, the proposed RANN gives good performances on estimating sudden and nonlinear demand increase during heat waves. The results of case studies using load data of South Korea are presented to show performances and effectiveness of the proposed RANN. INDEX TERMSIntelligent system, mid-term load forecasting, nonlinear load response, recurrent artificial neural network.

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-This paper deals with parameter tuning of the Power System Stabilizer (PSS) for 612 MVA thermal power plants in the KEPCO system and its validation in a field test. In this paper, the selection of parameters, such as lead-lag time constants for phase compensation and system gain, is optimized using linear and eigenvalue analyses. This is then verified through the time-domain transient stability analysis. In the next step, the performance of PSS is finally verified by the generator's on-line field test. After the field test, measured and simulated data are also compared to prove the effectiveness of the models used in the simulations.