Evolutionary-programming-based optimization of reduced-rank adaptive filters for reference generation in active power filters

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 addition, practically no prior knowledge of the electricity distribution network and its loading characteristics is needed for designing the current reference generator. 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, on the other hand, is a constituent of soft computing (SC). Such open-minded 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.     

A digital signal processing approach to real-time AC motor modeling

Discrete-time computational models of squirrel-cage AC motors are derived and analyzed for the purpose of advanced motor control. The starting point is a continuous-time model defined by a pair of simultaneous complex-coefficient differential equations. Discrete-time models are derived using both the bilinear transformation and the forward-difference approximation. The exact dependence between stability and the minimum sampling rate in the forward-difference approach is shown. The responses of the two discretized models are compared using real-world motor parameters. Using the forward difference method, a more compact implementation is obtained (although a higher sampling rate is required) as compared to the bilinear transformed model of equivalent performance and 2.7 times higher computational complexity. A 16/24-b DSP-ASIC coprocessor prototype implementing the AC motor model is introduced