Abstract
A small-signal stability problem of a synchronous generator influencing a power grid was studied. A thorough theoretical analysis of a simplified linearized model of the synchronous generator connected to an infinite bus was carried out. The described analysis pointed out disadvantages of the conventional linear power system stabilizer. In contrast to the conventional, an original self-tuning power system stabilizer was developed. This stabilizer compacts a linear quadratic regulator and a recursive least square identification method. The study involving numerical simulations and laboratory experiments reveals encouraging results with displayed advantages and feasibility of this novel approach in considered applications. DOI: http://dx.doi.org/10.5755/j01.eee.21.4.12773
Highlights
One of the most sophisticated control problems of a synchronous generator (SG) is the problem of small-signal stability
To enhance damping of SG and to improve the power system stability, SG must be equipped with a power system stabilizer (PSS) [1]
The SG connected to an infinite bus is a multivariable non-linear dynamic system described with a 7th order nonlinear state-space model
Summary
One of the most sophisticated control problems of a synchronous generator (SG) is the problem of small-signal stability. The oscillations of older SG with conspicuous damper windings have relatively small amplitudes Due to cost-design construction optimization, modern SG have significantly less damper windings This can in turn increase the oscillations and lead to the instability. By varying an operating point, SG’s dynamic characteristics vary as well This is the fact due to which PSS determined in the nominal operating point does not assure the optimal damping in the entire operating range. The adaptive control has been used in order to assure the optimal damping through the entire SG operating range. The main original contribution of the presented work is description of the development and the advantages of new self-tuning PSS based on the linear quadratic regulator and the recursive least square identification method
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