Tianzan Li’s research while affiliated with Changsha University of Science and Technology and other places

A neural network method combined with simulated annealing algorithm is proposed for power system harmonic analysis. This method is aimed at the system in which the sampling frequency cannot be locked on the actual fundamental frequency. By updating the relevant parameters including the learning rate of fundamental frequency, fundamental frequency, harmonic phases and amplitudes, the accurate harmonic estimating results can be obtained. The simulating results show that the harmonic estimation accuracy by the proposed approach is relatively better than that by the conventional harmonic analysis methods in the asynchronous case.

A batch back-propagation neural networks (BBPNN) approach was presented to design general two-dimensional (2-D) quasi-equripple zero-phase finite-impulse response (FIR) digital filters. By minimizing the frequency-domain weighted error function, the BBPNN design method was obtained. The solution was presented as a parallel algorithm to approximate the desired frequency response specification. Thus, the method makes a fast calculation of the filter's coefficients possible. It is shown that the method leads to an optimal solution for the filter coefficients. The implementation of the approach was described together with some design guidelines, and some optimal design examples were given to demonstrate the effectiveness of the proposed approach.