High accuracy of the load forecasting for power systems improves the security of the power system and generation cost. However, the forecasting problem is difficult to handle due to the nonlinear and the random-like behavior of system loads as well as weather conditions and variation of economical environments. So far. many studies on the problem have been made to improve the prediction accuracy using deterministic, stochastic, knowledge based and artificial neural net(ANN) method. In the conventional load forecasting method, the load forecasting maximum error occurred for the holidays on Saturday and Monday. In order to reduce the load forecasting error of the daily peak load for the holidays on Saturday and Monday, fuzzy concept and linear regression theory have been adopted into the load forecasting problem. The proposed algorithm shows its good accuracy that the average percentage errors are 2.11% in 1996 and 2.84% in 1997.

AVR parameter tuning for voltage control of generators has generally been done with the off-line open-circuit model of the synchronous generator. When the generator is connected on-line and operating with load the AVR operates in an entirely different environment from the open-circuit conditions. This paper describes a new method for AVR parameter tuning for on line conditions using SQP(Sequential Quadratic Programming) meshed with frequency response characteristics of linearized on-line system model. As the proposed method uses the un - line system model the tuned parameter sets show more optimal behavior in the on-line operating conditions. furthermore, as this method considers the performance indices that are needed for stable operation as constraints, AVR parameter sets that are tuned by this method could guarantee the stable performance, too.

This paper presents an efficient improvement of the iterative eigenvalue calculation method of the AESOPS algorithm. The intuitively and heuristically approximated iterative eigenvalue calculation method of the AESOPS algorithm is transformed to the Second Order Newton Raphson Method which is generally used in numerical analysis. The equations of second order partial differentiation of external torque, terminal and internal voltages are derived from the original AESOPS algorithm. Therefore only a few calculation steps are added to transform the intuitively and heuristically approximated AESOPS algorithm to the Second Order Newton Raphson Method, while the merits of original algorithm are still preserved.

This paper presents a new method for the protective relaying scheme in power transformer using wavelet based neural networks. This approach is as fellows. After approximation and detail information is extracted by daub wavelet transform from differential current of power transformer, the former is used for obtaining the rate of differential currents and restrain currents, the latter used as the input of artificial neural networks to avoid the Hiss-operation in over-exciting state and magnetizing inrush state of power transformer. The simulation of EMTP with respect to different faults, inrush conditions and over-exciting conditions in power transformer have been conducted, and the results preyed that the proposed method is able to discriminate magnetizing inrush states, over-exciting stales and internal faults.

Frequency is regarded as one of most important indices for the operating power systems. Several digital techniques for measuring frequency have been presented in the last decades. This paper proposes a design and implementation an adaptive scheme using phase angle difference calculation fort frequency measuring in power system. The advantages of the proposed technique are demonstrated by fault signals from EMTP simulation and user defined arbitrary signals by Excel program. The proposed technique is compared with the conventional methods. Performance teat results indicate that the proposed technique provides accurate measures in pretence of noise and harmonics and in case faults and is suitable for measurement near-nominal, nominal. and off-nominal frequencies. We can see that It will be useful in microprocessor based relays and digital metiers that need to measure power system frequency.

In this paper, we propose a Probabilistic Tabu Search(PTS) method for hydro-thermal scheduling. Hydro scheduling has many constraints and very difficult to solve the optical schedule because it has many local minima. To solve the problem effectively, the proposed method uses two procedures, one is Tabu search procedure that plays a role in local search, and the other is Restarting procedure that enables to diversify its search region. To adjust Parameters such as a reducing rate and initial searching region, search strategy is selected according to its probability after restarting procedure. Dynamic decoding method was also used to restrict a search region and to handle water balance constraints. In order to show the usefulness of the proposed method, the PTS is applied on two cases which have independent or dependent hydro plants and compared to those of other method. The simulation results show it is very efficient and useful algorithm to solve the hydro-thermal scheduling problem.