Hi Prahlad,

FFT stands for Fast Fourier Transform. It is an algorithm whereby a function, either continuous or, as in your case, discreet, can be mapped from a time domain into a frequency domain. In other words, you process your data points and the result will be a set of amplitudes as a function of frequency instead of a set of amplitudes as a function of time.

Your resultant data set would show the largest amplitude (peak) at the fundamental frequency, with smaller peaks at the locations of all of the harmonics. If you calculate an FFT of a pure sine wave, you will get a frequency domain spectrum with only a single peak at the frequency of the sine wave.

You can also perform an inverse FFT on the frequency domain data to map it back into the time domain.

Unfortunately I can not give you the formulae for these transforms off the top of my head. I would have to look them up. On the other hand, if you search the internet for them I am sure you will find more information than you want or need. They are quite common.

When you search, you might want to dig just a bit deeper than looking for a set of formulae. Specifically, I would suggest that you search for ways to calculate THD from an FFT. It is an artifact of calculating an FFT of a set of discreetly sampled points that information from beyond the Nyquist frequency will be aliased back into the domain of the FFT. In you're case, this could be adventageous.

Good luck,

Joe