Evolutionary programming in the design of adaptive filters for power systems harmonics reduction

Abstract

We introduce an evolutionary programming based method for designing robust and computationally efficient adaptive bandpass filters. These predictive filters are optimized for generating current references in active power filters (APFs). The accuracy (phase/amplitude) of the reference current is crucial in current injection type systems, because it directly affects the harmonics reduction ability of the APF. Our digital filtering approach has the following advantages: selective bandpass response, efficient attenuation of specific harmonic components, capability to handle typical frequency alteration, small number of multiplications, and structural simplicity. In an illustrative example, the total harmonic distortion of an artificial current waveform was reduced from 36.7% to less than 3.7% within the line frequency range 49-51 Hz. The proposed scheme is a combination of the hard computing (HC) type multiplicative general parameter method and evolutionary programming that is a constituent of soft computing (SC). Such fusion thinking is emerging among researchers and engineers, and it can potentially lead to efficient combinations of HC and SC methodologies - both on the algorithm level and on the system level.