An application of neural networks for harmonic coefficients and relative phase shifts detection

The varying the phase shifts will completely change the shape of the distorted wave, and may thus greatly affect the ability of the neural network to recognize harmonics. In this study, feed forward neural networks were used for the detection of the harmonic coefficients and relative phase shifts. The distorted wave including uniform distributed 5th, 7th, 11th, 13th, 17th, 19th, 23rd, 25th harmonics with up to 20° relative phase shifts were simulated and used. Two neural networks were used for this purpose. One of the neural networks was used for the detection of the 5th, 7th, 11th, 13th harmonic coefficients and the other was used for the detection of the relative phase shifts of these harmonics. Scaled conjugate gradient algorithm was used as training algorithm for the weights update of the neural networks. The results show that these neural networks are applicable to detect each harmonic coefficient and relative phase shift effectively.

Research highlights
► Neural network structures trained by scaled conjugate gradient algorithm are applicable to detect each harmonic coefficient and relative phase shift effectively.
► Average THD value is 14.58% before compensation and obtained average THD values are less then 5% after compensation for all neural networks.
► Average THD values obtained after compensation are suitable to the recommendation IEEE 519.