Kernel Recursive Least Square Approach for Power System Harmonic Estimation.

Harmonic frequencies existing in power systems produce harmful effects. Signals containing harmonic effect produce power quality problems such as heating, premature wear, motor pulsations, and so on. Commonly, to cancel the harmonic effects in power systems is based on traditional filtering methods. However, such methodologies tend to fail in the presence of high convexity, which produces undesirable results. Under such circumstances, the appropriate harmonic identification is an important task for removing and reducing the harmonic effects, avoiding the potential damage in electrical power systems. Several published research in the literature has addressed this issue using traditional techniques as Recursive Least Square (RSL), Evolutionary Computation Techniques (ECT), or even the hybridization of both achieving interesting results. However, such techniques present some shortcomings, such as low accuracy and principally high computational time. In this paper, a Kernel Recursive Least Square (KRLS) approach for harmonic estimation is proposed as an alternative methodology. The KRLS uses the least-square formulation in feature space by using kernel mapping for determining the harmonic signals, different experimental simulations demonstrate the high accuracy and robustness, such as the considerably low computational time in different noisy conditions of the proposed method regarding similar approaches. [ABSTRACT FROM AUTHOR]