Application and Limitation of Fourier Analysis

In summary: I think I understand what you are trying to say now.The harmonics in a sine wave have no real significance when it comes to losses. They are simply a result of the waveform being nonlinear and dispersive. When calculating power lost in a system, it is simply more accurate to sum the infinitesimal values of energy over a cycle to get the total energy. This is easily done with digital signal processing (DSP) at high frequencies, which is why it is a trivial problem to do when the system is nonlinear and dispersive.
  • #1
I_am_learning
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Suppose I have a non-linear load in my home (A half wave rectifier supplied DC load, say).
Since it will consume current from the source only during +ve cycle of the Voltage, the current will be half-wave too. The current isn't sinusoidal.
We can mathematically say that
Distored Current = Algebraic Sum of Various Sine Wave currents with various Frequencies.
Which we call the Fourier Analysis.
But where can we apply this?
I don't think we can say,
Power Consumed By load = Sum of (Irms ^ 2 ) * R of all sine wave components.
(This we can't do because, superposition principle don't work when we square the quantity)
We can however do,
Poser Consumed by load = Sum of (Vsource rms * Irms * Cos(phi)) of all sine wave components.
(Because superposition holds here, because the current isn't squared)

Now, How can this harmonic analysis aid me in finding the losses in the source and cables.
For the same reason, I don't think we can say,
Power lost in source and cable = Sum of (Irms ^ 2)* (Resistance of source+cable) for all sine wave components.
How do we find the losses then?
To summarize,
Two load current waveforms can have same RMS value, but one with lots of harmonics and other with very less harmonics.
From the point of minimizing the losses in source and cables, which one would be better.
Why and how?
 
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  • #2
Not a trivial theoretical problem when the system is non linear and dispersive unless you can characterise the system accurately..
The best answer would be to sample V and I, instantaneously over a cycle and integrate V times I. That would be doing what you really want - i.e. summing the infinitesimal values of energy over the cycle, to give the total energy. Actually, that would be one of the easiest solutions, too because DSP at that sort of frequency is a piece of cake.
 
  • #3
sophiecentaur said:
Not a trivial theoretical problem when the system is non linear and dispersive unless you can characterise the system accurately..
The best answer would be to sample V and I, instantaneously over a cycle and integrate V times I. That would be doing what you really want - i.e. summing the infinitesimal values of energy over the cycle, to give the total energy. Actually, that would be one of the easiest solutions, too because DSP at that sort of frequency is a piece of cake.

Thanks for the reply. I agree, that's the best method to find the true power.
But my real question here is quite different. Perhaps It would be better if I rephrase it like this,
I have a resistive DC load.
I want to dissipate say 10 KW in it.
If I directly hook this resister to AC or Full wave bridge rectifier, it will dissipate way lot more power than 10KW.
So I want controlled voltage source, that will reduce effective Vrms across this load so that I can control its power.
I can use Power Electronics to switch the device on/off. The switching can be done in various patterns yet all of them giving the same Vrms.
For eg. see this
harmonics%2520scheme.jpg

By carefully designing the controlled rectifier, it can be so made that both scheme 1 and scheme 2 provide the same Irms or Vload rms,
In the point of view of effects on source and cables, which scheme is better?
It appreas that, the losses in source and cables resistance should be equal in both scheme because, Irms is same.
But, if we do Fourier analysis harmonics contents are different on those two schemes.
Has harmonics got anything to do here regarding the losses?
If they have nothing to do, what's their significance, and why reduce them?
Thanks
 

What is Fourier analysis?

Fourier analysis is a mathematical technique used to decompose a complex signal into its individual frequency components. It is based on the idea that any signal can be represented as a sum of simple sine and cosine functions.

What are the applications of Fourier analysis?

Fourier analysis has numerous applications in various fields such as signal processing, image analysis, data compression, and solving differential equations. It is used to analyze and filter out noise from signals, extract features from images, compress large data sets, and model physical systems.

What are the limitations of Fourier analysis?

One of the main limitations of Fourier analysis is that it assumes signals are periodic and stationary. This means that they do not change over time and have a repeating pattern. In reality, most signals are non-stationary and may have time-varying characteristics, making them difficult to analyze using Fourier techniques.

How is Fourier analysis related to the Fourier transform?

The Fourier transform is a mathematical tool used in Fourier analysis to convert a signal from the time domain to the frequency domain. It is a more general form of Fourier analysis and can be applied to non-periodic and non-stationary signals.

What are some common misconceptions about Fourier analysis?

One common misconception is that Fourier analysis can only be applied to periodic signals. As mentioned earlier, the Fourier transform can also be applied to non-periodic signals. Another misconception is that Fourier analysis can accurately analyze signals with infinite bandwidth, which is not possible as it requires an infinite amount of data.

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