Orthogonal least squares learning algorithm based radial basis function (RBF) network adaptive power system stabilizer

The paper presents a systematic approach for designing a radial basis function (RBF) network based adaptive power system stabilizer using orthogonal least squares learning algorithm. The training patterns are generated over a wide range in machine real/reactive power output and terminal voltage using linearized model of the system. Investigations reveal that the required number of RBF centers heavily depend on the spread factor β and tolerance expressed as sum of squared errors. Studies reveal that the dynamic performance of the system with radial basis function network adaptive power system stabilizer (RBFAPSS) is superior to that with a conventional PSS. Moreover, RBFAPSS provides optimum performance for a wide range in loading conditions and large perturbations