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This paper investigates digital modeling and control strategies applied to the voltage regulation of a microgenerator system, placed in the Electric Energy Generation Laboratory of Federal University of Para. Identification parametric techniques are used in order to obtain a representative model for the controller design. With a suitable model, controllers of proportional integral type are designed, based on the root locus and fuzzy systems strategies, in order to improve the voltage regulation of the microgenerator system.

Tucurui's hydroelectric power plant, the second largest Brazilian electric plant, is one proper site to deal with bad damping electromechanical oscillations problem, because of its interconnection with two others large electrical systems (CHESF and FURNAS). The most usual way to minimize this problem, increasing power system dynamic stability margins, is to apply power system stabilizers, which are responsible for creating an additional damping signal in the generator excitation system. Tucurui's power system stabilizers, although fixed-parameter controllers, must be tuned properly to have good performance in any system operating condition. This work objective is to obtain updated linear system models from local signals measured in Tucurui's generators. System identification techniques, recursive and nonrecursive, have been used to obtain these linear models, that will be used later in fixed-parameter or even adaptive power system stabilizers design.

This work presents a digital adaptive power system stabilizer (PSS) which operates in a gain scheduling scheme. Its parameters are designed for several differently selected operating points in a PxQ plane (active and reactive powers), and saved offline under the form of a look-up table in the microcomputer that implements the control action. The complete set of all designed PSSs is called a local compensator network (LCN). During working, the a fuzzy logic based supervisor identifies the current operating point, and synthesizes the PSS parameters from the LCN. The test presented shows good performance for this PSS, when compared to a conventional (nonadaptive) one.

This work presents a digital adaptive power system stabilizer (PSS) which operates in a self-tuning structure. The parametric models are identified off line for several differently selected operating points in a P/spl times/Q plane (active and reactive powers) to form a local model network (LMN), and saved off-line under the form of a look-up table in the microcomputer that implements the control action. During working, a fuzzy logic based supervisor identifies the current operating point and determines the plant model from the LMN. Then, using this plant model, the PSS control law is designed based on the generalized predictive control (GPC) method. The tests presented show good performance for this PSS, when compared to a conventional (nonadaptive) one.