In this paper, a robust H$\infty$ optimal tuning problem of a structure-specified PSS is investigated for power systems with parameter variation and disturbance uncertainties. Genetic algorithm is employed for optimization method of PSS parameters. The objective function of the optimization problem is the H$\infty$-norm of a closed loop system. The constraint of the optimization problem are based on the stability of the controller, limits on the values of the parameters and the desired damping of the dominant oscillation mode. It is shown that the proposed H$\infty$ PSS tuned using genetic algorithm is more robust than conventional PSS.

The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

This paper presents an efficient algorithm for loss reduction and load balancing by sectionalizing switch operation in large scale distribution system of radial type. We use Genetic algorithm and Kruskal algorithm to solve distribution system reconfiguration. Genetic algorithm is used to minimize objective function including loss and load balancing items. Kruskal algorithm is used to satisfy the radial condition of distribution system. The experimental results show that the proposed method has the ability to search a good solution regardless of initial configuration and size of system.

This paper presents reasonable evaluation technique to determine system configuration of underground distribution system. To set up reasonable evaluation for a certain object region, it is necessary for methodology to access accurately factors such as economics, reliability, customer interruption cost, implementation and flexibility about proposed various configuration of underground distribution system. But it is difficult to evaluate factors as above-mentioned since there is no criterion to assess them in our country. Accordingly, having completed the technical and economical analysis and customer interruption cost of various types of system, this paper presents the most reasonable results to get the optimal configuration of underground distribution system. Also, this paper presents possible methodology which can be applied other areas having a different load characteristics by application actually making a selection Myungdong as sample area.

This paper presents the design of an H$\infty$ based robust Static Synchronous Compensator (STATCOM) supplementary controller to suppress the subsynchronous resonance (SSR) in the series-compensated system. The IEEE second benchmark, System-l model is employed for this study. In order to design the effective controller, the modal controllability and observability indices to the oscillation modes are considered. Comprehensive time domain simulations using a nonlinear system model that the proposed STATCOM supplementary controller can suppress the SSR efficiently in spite of the variations of power system operating conditions.