Abstract:In order to maintain the stability and security of the power system, the uncertainty and intermittency of wind power must be taken into account in economic dispatch (ED) problems. In this paper, a dynamic economic dispatch (DED) model based on chance constrained programming is presented and an improved particle swarm optimization (PSO) approach is proposed to solve the problem. Wind power is regarded as a random variable and is included in the chance constraint. New formulation of up and down spinning reserve constraints are presented under expectation meaning. The improved PSO algorithm combines a feasible region adjustment strategy with a hill climbing search operation based on the basic PSO. Simulations are performed under three distinct test systems with different generators. Results show that both the proposed DED model and the improved PSO approach are effective.

ABSTRACT

This paper presents an adaptive neural-fuzzy control scheme for a dual-level-structure flexible manipulator with variable dynamic payload. The dynamic moving model of the flexible manipulator is derived and the state-space equation is formulated first. A control scheme that consists of a neural-fuzzy controller in the feedback channel and an image-guided identification network (IGIN) in the forward configuration is then proposed. The IGIN is employed to locate the object (e.g. bimetal) to achieve the tracking function, while the dynamic neural network is used to learn the weighting factor of the fuzzy controller. Finally, simulations are run for various modes to describe the dynamic tracking system, and simulated results show a good performance of the control tracking system.

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