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This paper presents a proposed technique for security constrained optimal dispatch (SCOD) problem under normal and emergency conditions. In this technique a modified version of the genetic algorithm (GA) is used. The SCOD problem is formulated using non-linear unit cost functions and solved by the proposed technique. The results obtained using this technique are compared with those obtained using a conventional linear programming technique and with those obtained using fuzzy modeling technique. The comparison studies are performed considering the changes in system constraints as: membership models in f i m y technique and chromosomes in GA technique. Numerical studies of fuzzy modeling are based on the fuzzy linear programming (FLP) technique with fuzzy constraints of different shapes for their membership functions. Simulation results show that the proposed GAbased technique for SCOD is more accurate and efficient, especially with increasing the system size.

This paper presents a self-tuning PID control algorithm using an adaptive networkbased fuzzy inference structure (ANFIS). In particular, the design of a self-tuning PID learningbased optimum controller is introduced which can be applied to nonlinear as well as linear systems. A recursive adaptation scheme is employed for on-line implementation of the self-tuning PID controller in which an exponential forgetting factor is used to weigh old data and both an error and control rate cost are used in the backwards pass. Results show that the method is a viable approach for tuning the parameters of a PID controller.