SlideShare a Scribd company logo
1 of 45
Network Theory
Dr. Sivkumar Mishra
Dept of Electrical Engineering,
IIIT Bhubaneswar
•Sinusoidal functions occupy a unique position in engineering.
•They are easy to generate and the steady state response of a linear
system to dc and sinusoidal excitations can be found easily by using the
impedance concept.
•In practice input signals are of more complex nature being non
sinusoidal periodic and non periodic waveforms.
•The French mathematician, J.B.J. Fourier (1758-1830) showed that
arbitrary periodic functions could be represented by an infinite series of
sinusoids of harmonically related frequencies known as Fourier series.
•Non periodic waveforms can be represented by Fourier transform.
Fourier Series
•A signal f(t) is said to be periodic of period T if f(t)=f(t+T) for all t.
•Several signal waveforms that fulfill this requirement are shown in Fig.
below
t
•Fourier series can be represented either in the form of infinite
trigonometric series or infinite exponential series.
•Fourier series consists of dc terms as well as ac terms of all harmonics.
Fourier Series
Any periodic signal f(t) (satisfying certain conditions) can be expressed
as a summation of sine and cosine functions of frequencies 0,ω,2ω,3ω
…..kω, ω where refers to the fundamental frequency and time period.
Thus,
where the coefficients an and bn are given by
Trigonmetric Fourier Series
0
1
0 1 2 3
1 2 3
( ) [ cos( ) sin( )]
cos cos 2 cos3 ... cos ..
sin sin 2 sin 3 ..... sin ....
n n
n
n
n
f t a a n t b n t
a a t a t a t a n t
b t b t b t b n t
ω ω
ω ω ω ω
ω ω ω ω
∞
=
= + +
= + + + +
+ + + +
∑
0
2
( )cos
T
na f t nwt dt
T
= ∫ 0
2
( )sin
T
nb f t nwt dt
T
= ∫0
0
1
( )
T
a f t dt
T
= ∫
Combining the sine and cosine terms, a more compact representation of
the series, either in the sine or cosine form, may be obtained as follows:
Let us consider the nth harmonic term
where, the amplitude and phase, of the nth harmonic are given by
•Writing C0=a0
Trigonmetric Fourier Series
( )
( ) ( )
( ) [ ]
( )
2 2
2 2 2 2
2 2
( ) cos sin
.cos .sin
cos .cos sin .sin
cos
n n n
n n
n n
n n n n
n n n n
n n
f t a n t b n t
a b
a b n t n t
a b a b
a b n t n t
C n t
ω ω
ω ω
θ ω θ ω
ω θ
= +
 
 = + +
 + +
 
= + +
= −
( )2 2 -1
; n=tann n n n nC a b b aθ+=
0
1
( ) cos( )n n
n
f t C C n tω θ
∞
=
= + −∑
In a similar way, the series may be represented in the sine form as
where,
The plot of Cn as a function of n or nω is known as the amplitude
spectrum and the plot of θn as a function of n or nω is known as the
phase spectrum.
0
1
( ) sin( )n n
n
f t C C n tω φ
∞
=
= + +∑
( )1
tann n nb aφ −
=
Find the Fourier series for the saw-tooth wave shown in Fig.
The analytical form of the function f(t) is given by
The coefficients are evaluated as below:
1
( ) , 0 2
2
f t t tω ω π
π
= ≤ ≤
2 2
0
0 0
1 1
( ) ( ) ( ) ( ) 0.5
2 2 2
t
a f t d t d t
π π ω
ω ω ω
π π π
= = =∫ ∫
2
2
2 20
0
1 1 cos ( )
( )cos ( ) ( ) sin ( ) 0
2 2
n
t t n t
a n t d t n t
n n
π
π ω ω ω
ω ω ω
π π π
 
= = + =  
∫
2
2
2 20
0
1 1 sin ( ) 1
( )sin ( ) ( ) cos ( )
2 2
n
t t n t
b n t d t n t
n n n
π
π ω ω ω
ω ω ω
π π π π
 
= = − + = −  
∫
So, the Fourier series can be written as:
Here, C0=a0=0.5 and as an=0 hence Cn=bn=1/nπ. So, the amplitude
spectrum is as shown in Fig.
1
1
( ) 0.5 sin
n
f t n t
n
ω ω
π
∞
=
= − ∑
Certain kinds of symmetries found in non sinusoidal periodic signal
waveforms leads to simpler Fourier coefficient calculations i.e. absence
of a0,an or bn coefficients from the series.
There are four types of waveform symmetries, which are discussed as
below.
•Even Symmetry
•Odd Symmetry
•Half Wave Symmetry
•Quarter Wave Symmetry
Wave Form Symmetry
Even Symmetry
A function f(x) is said to be even, if ,
Examples of even functions are x2
, cos x, cosh x, tanx2
, 1+2x2
+4x4
, etc.
It is to be noted that the even nature is preserved on addition of a
constant.
The sum of even functions remains even.
The waveforms of some even functions are shown in Fig.
It is found that these functions exhibit mirror symmetry about the
vertical axis, the negative half being the mirror image of the positive
( ) ( )f x f x= −
In Fourier series expansion of the even signal waveforms, the bn-terms
are absent i.e. only a0 and an terms are present.
2
0
4
( )cos
T
na f t nwt dt
T
= ∫
0nb =
2
0
0
2
( )
T
a f t dt
T
= ∫
Odd Symmetry
A function f(x) is said to be odd, if ,
Examples of odd functions are x, x3
, sin x, x cos 10x. etc.
The sum of odd functions remains odd.
The waveforms of some even functions are shown in Fig.
The addition of a constant removes the odd nature of the function.
( ) ( )f x f x= − −
The waveform symmetry associated with odd functions has the effect of
making the Fourier expression contain only the sine terms. Thus,
2
0
4
( )sin
T
nb f t nwt dt
T
= ∫
0na =
0 0a =
Half Wave Symmetry
A function f(x) is said to be half wave symmetric, if ,
The waveforms of some half wave symmetric functions are shown in
Fig.
In general, Fourier series expansion of these waveforms contains both an
and bn coefficients, i.e. odd harmonics sine and cosine terms are present
unless the function is even or odd as well.
( ) ( 2)f x f x T= − ±
Quarter Wave Symmetry
Waveforms showing half wave symmetry with either odd or even
symmetry are said to have quarter wave symmetry. Waveforms as shown
in Fig. a,b of slide, have both even as well as half wave symmetries,
similarly, waveforms as shown in Fig. 11.5-a,b and c , have both odd as
well as half wave symmetries. Hence, these waveforms have quarter
wave symmetry.
Find the Fourier series expansion of the periodic rectangular waveform
shown in Fig.
The function is even. Hence, only cosine terms will be present, i.e. bn=0.
The function f(t) is defined as
0 2 4
( ) 1 4 4
0 4 2
for T t T
f t for T t T
for T t T
− ≤ ≤ −

= − ≤ ≤
 ≤ ≤2 2
0
2 2
1 1 1
( ) (1)
2
T T
T T
a f t dt dt
T T− −
∴ = = =∫ ∫
2 4
2 4
2 2 2
( )cos cos
T T
n
T T
a f t n tdt n tdt
T T n
ω ω
π− −
= = =∫ ∫
Application of Fourier Series in Network Analysis
Consider a case, when a non sinusoidal voltage is applied to a linear
network. The Fourier series expansion of this waveform represents linear
combination of various harmonic voltages, which give rise to
corresponding harmonic currents in the circuit.
Each term of the Fourier series of voltage is considered to represent an
individual voltage source.
The equivalent impedance of the network at each harmonic frequency nω
is used to compute the current at that harmonic.
The sum of these individual responses provides the total response of the
circuit as per the principle of superposition.
Effective Value or RMS value
If,
by definition, the effective value or rms value can be written as
where c0 is the dc component and c1, c2, c3, … are the amplitudes of the
harmonics.
0
1
( ) [ cos( ) sin( )]n n
n
f t a a n t b n tω ω
∞
=
= + +∑
[ ]
[ ]
2 2 2 2 2 2 2
2 2 2 2
1 22
0
0
1
1 2
0 1 2 3 1 2 3
1 2
0 1 2 3
1
[ cos( ) sin( )]
1
........ .....
2
1
......
2
T
rms n n
n
F a a n t b n t dt
T
a a a a b b b
c c c c
ω ω
∞
=
   
= + +  
   
 
= + + + + + + + + 
 
 
= + + + + 
 
∑∫
In general, if the voltage and current are given by
0( ) sin( )n nv t V V n tω φ= + +∑ 0( ) sin( )n ni t I I n tω θ= + +∑
Then, the effective values of the voltage and current are given as
( )2 2
1 2
1 22 2 2 2 2 2
0 1 2 3 0 1 2 3
1
...... ......
2
rms rms rms rmsV V V V V V V V V
 
 = + + + = + + +   
( )2 2
1 2
1 22 2 2 2 2 2
0 1 2 3 0 1 2 3
1
...... ......
2
rms rms rms rmsI I I I I I I I I
 
 = + + + = + + +   
Exponential Series:
• A compact way of expressing the Fourier series
•The sine and cosine forms of the trigonmetric series are represented
in exponential form using Euler’s identity.
Fourier Series
Exponential Series:
• A new coefficient cn is defined
• Now, f(t) becomes
• This is the complex or Exponential form of f(t)
Fourier Series
Exponential Series:
• This form is more compact than the sine-cosine form.
• The coefficient can be found as:
• The plots of the magnitude and phase of cn versus now are called
the complex amplitude spectrum and phase spectrum of f(t)
respectively.
Fourier Series
• Consider a periodic pulse train as shown. We have to find its phase
and amplitude spectra.
• The period of the pulse train T= 10
ω0= 2π/10=π/5
Fourier Series
• cn is the product of 2 and a function of the form sin x/x, which is
known as sinc function. Thus,
Some properties of the sinc function are:
For an integral multiple of π, the value of sinc is zero
• The sinc function always shows even symmetry
Fourier Series
Amplitude and Phase Spectra
• Fourier series enables us to represent a periodic function as a sum
of sinusoids and to obtain the frequency spectrum from the series.
• Fourier transform allows us to extend the concept of a frequency
spectrum to non periodic functions.
• The transform assumes that a non periodic function is a periodic
function with an infinite period.
• Thus the Fourier transform is an integral representation of a non
periodic function that is analogous to a Fourier series representation
of a periodic function.
Fourier Transform
• The Fourier transform is an integral transform like Laplace
transform, which transforms the function in time domain to
frequency domain.
•Fourier transform is useful in communication systems and digital
signal processing, in situations where Laplace transform does not
apply.
• While Laplace transform can only handle circuits with inputs for
t>0 with initial conditions, Fourier transform can handle circuits
with inputs for t<0 as well as those for t>0.
Fourier Transform
• Consider a non periodic function p(t) as shown below. Also
consider a periodic function f(t), whose shape for one period is same
as p(t).
•
• If we let period T→∞ , only a single pulse of width τ remains as all
other adjacent pulses move to ∞. The function f(t) is no longer
periodic.
Fourier Transform
Fourier Transform
Fourier Transform
Fourier Transform
The Inverse Fourier transform
0 0( ). ( ) ( )f t t t dt f tδ
∞
−∞
− =∫
Shifting Property of the impulse function:
When a function is integrated with impulse function, we obtain
the value of the function at the point where the impulse occurs
Fourier Transform
Fourier Transform
Fourier Transform
Properties of Fourier Transform
Properties ofmFourier Transform
Properties of Fourier Transform
Properties of Fourier Transform
Filters
A filter is a frequency selective electrical network that allows signal of
desired band of frequencies to pass freely whilst attenuates the signals at
other frequencies.
Ideally, a filter should produce no attenuation in the desired band, called
the transmission band or pass band.
It should provide infinite attenuation at all other frequencies, called
attenuation band or stop band.
The frequencies that separate the pass band and stop band are called cut-
off frequencies(fc).
Classification of Filters
Filters can be classified on the basis of the range of the pass band and
stop band frequencies as:
1. Low Pass Filters (LPF)
2. High Pass Filters (HPF)
3. Band Pass Filters (BPF)
4. Band Stop or Band Elimination Filters (BSF)
Classification of Filters
- Passive Filters, which are made of L and C components
Filters also can be classified on the basis of components they are made of
as:
-Active Filters are made of components such as operational amplifiers
Active Filters
In the design of passive filters, inductor is an integral part. Inductor
creates some problems and because of other advantages of using active
elements, passive filters are seldom used in practical. Some of the
limitations of passive filters are:
-The use of inductor as a network element is not desirable especially
at low frequencies(less than 1 kHz) as at these frequencies practical
inductors of reasonable Q tend to become bulky, and expensive. The
dissipative losses start increasing when an inductor is minimized.
-It is necessary to cascade many sections to form a composite filter.
A composite filter has a prototype filter, two terminating half
sections(one at each end) and an m-derived section. Due to
presence of so many components, it becomes bulky.
Active Filters
While cascading different sections of filters a buffer or
isolation amplifier is required to prevent loading of the
circuit.
There is a need for an external amplifier to the required gain.
-The active filter provides gain and frequency adjustment
flexibility.
-Due to high input resistance and low output resistance of the
Op-Amp, they do not cause loading of the source or load.
-Reduction in size and weight and increased equipment density.
Reduction in power consumption.
-More economical than passive filters due to the use of Op-
Amps and absence of inductors.
References
1.C.K. Alexander and M.N.O. Sadiku, “Fundamentals of Electric
Circuits,” 3rd Edition, Tata McGraw Hill, 2008.
2.M. E. Valkenburg, “Network Analysis,” 3rd Ed., Pearson Prentice
Hall, 2006.
3.W. H. Hayt, J. E. Kemmerly and S. M. Durbin, “Engineering Circuit
Analysis,” 6th Edition, Tata McGraw Hill, 2007.

More Related Content

What's hot

Fourier Series - Engineering Mathematics
Fourier Series -  Engineering MathematicsFourier Series -  Engineering Mathematics
Fourier Series - Engineering MathematicsMd Sadequl Islam
 
Fourier series 1
Fourier series 1Fourier series 1
Fourier series 1Faiza Saher
 
07 periodic functions and fourier series
07 periodic functions and fourier series07 periodic functions and fourier series
07 periodic functions and fourier seriesKrishna Gali
 
Lecture1 Intro To Signa
Lecture1 Intro To SignaLecture1 Intro To Signa
Lecture1 Intro To Signababak danyal
 
Fourier series example
Fourier series exampleFourier series example
Fourier series exampleAbi finni
 
Fourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamFourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamMd Nazmul Islam
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2slmnsvn
 
Fourier integral of Fourier series
Fourier integral of Fourier seriesFourier integral of Fourier series
Fourier integral of Fourier seriesChintan Mehta
 
Chapter4 - The Continuous-Time Fourier Transform
Chapter4 - The Continuous-Time Fourier TransformChapter4 - The Continuous-Time Fourier Transform
Chapter4 - The Continuous-Time Fourier TransformAttaporn Ninsuwan
 
Ist module 3
Ist module 3Ist module 3
Ist module 3Vijaya79
 
1531 fourier series- integrals and trans
1531 fourier series- integrals and trans1531 fourier series- integrals and trans
1531 fourier series- integrals and transDr Fereidoun Dejahang
 
Chapter3 - Fourier Series Representation of Periodic Signals
Chapter3 - Fourier Series Representation of Periodic SignalsChapter3 - Fourier Series Representation of Periodic Signals
Chapter3 - Fourier Series Representation of Periodic SignalsAttaporn Ninsuwan
 
Fourier series Introduction
Fourier series IntroductionFourier series Introduction
Fourier series IntroductionRizwan Kazi
 
Chapter 2 signals and spectra,
Chapter 2   signals and spectra,Chapter 2   signals and spectra,
Chapter 2 signals and spectra,nahrain university
 

What's hot (20)

Fourier series pgbi
Fourier series pgbiFourier series pgbi
Fourier series pgbi
 
Fourier integral
Fourier integralFourier integral
Fourier integral
 
Fourier Series - Engineering Mathematics
Fourier Series -  Engineering MathematicsFourier Series -  Engineering Mathematics
Fourier Series - Engineering Mathematics
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Fourier series 1
Fourier series 1Fourier series 1
Fourier series 1
 
07 periodic functions and fourier series
07 periodic functions and fourier series07 periodic functions and fourier series
07 periodic functions and fourier series
 
Lecture1 Intro To Signa
Lecture1 Intro To SignaLecture1 Intro To Signa
Lecture1 Intro To Signa
 
Fourier series example
Fourier series exampleFourier series example
Fourier series example
 
Fourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islamFourier series and its applications by md nazmul islam
Fourier series and its applications by md nazmul islam
 
Fourier series and transforms
Fourier series and transformsFourier series and transforms
Fourier series and transforms
 
160280102001 c1 aem
160280102001 c1 aem160280102001 c1 aem
160280102001 c1 aem
 
Introduction to Communication Systems 2
Introduction to Communication Systems 2Introduction to Communication Systems 2
Introduction to Communication Systems 2
 
Fourier integral of Fourier series
Fourier integral of Fourier seriesFourier integral of Fourier series
Fourier integral of Fourier series
 
Chapter4 - The Continuous-Time Fourier Transform
Chapter4 - The Continuous-Time Fourier TransformChapter4 - The Continuous-Time Fourier Transform
Chapter4 - The Continuous-Time Fourier Transform
 
Ist module 3
Ist module 3Ist module 3
Ist module 3
 
1531 fourier series- integrals and trans
1531 fourier series- integrals and trans1531 fourier series- integrals and trans
1531 fourier series- integrals and trans
 
Chapter3 - Fourier Series Representation of Periodic Signals
Chapter3 - Fourier Series Representation of Periodic SignalsChapter3 - Fourier Series Representation of Periodic Signals
Chapter3 - Fourier Series Representation of Periodic Signals
 
Fourier series Introduction
Fourier series IntroductionFourier series Introduction
Fourier series Introduction
 
Chapter 2 signals and spectra,
Chapter 2   signals and spectra,Chapter 2   signals and spectra,
Chapter 2 signals and spectra,
 
Signal & system
Signal & systemSignal & system
Signal & system
 

Similar to Nt lecture skm-iiit-bh

4. a Find the compact trigonometric Fourier series for the periodic s.pdf
4. a Find the compact trigonometric Fourier series for the periodic s.pdf4. a Find the compact trigonometric Fourier series for the periodic s.pdf
4. a Find the compact trigonometric Fourier series for the periodic s.pdfjovankarenhookeott88
 
fourier-method-of-waveform-analysis msc physics
fourier-method-of-waveform-analysis msc physicsfourier-method-of-waveform-analysis msc physics
fourier-method-of-waveform-analysis msc physicsRakeshPatil2528
 
Fourier series and Fourier transform in po physics
Fourier series and Fourier transform in po physicsFourier series and Fourier transform in po physics
Fourier series and Fourier transform in po physicsRakeshPatil2528
 
03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdfROCIOMAMANIALATA1
 
Find the compact trigonometric Fourier series for the periodic signal.pdf
Find the compact trigonometric Fourier series for the periodic signal.pdfFind the compact trigonometric Fourier series for the periodic signal.pdf
Find the compact trigonometric Fourier series for the periodic signal.pdfarihantelectronics
 
Aplicación de la serie Fourier en un circuito electrónico de potencia)
Aplicación de la serie Fourier en un circuito electrónico de potencia)Aplicación de la serie Fourier en un circuito electrónico de potencia)
Aplicación de la serie Fourier en un circuito electrónico de potencia)JOe Torres Palomino
 
Ch1 representation of signal pg 130
Ch1 representation of signal pg 130Ch1 representation of signal pg 130
Ch1 representation of signal pg 130Prateek Omer
 
Circuit Network Analysis - [Chapter3] Fourier Analysis
Circuit Network Analysis - [Chapter3] Fourier AnalysisCircuit Network Analysis - [Chapter3] Fourier Analysis
Circuit Network Analysis - [Chapter3] Fourier AnalysisSimen Li
 
Fourier Specturm via MATLAB
Fourier Specturm via MATLABFourier Specturm via MATLAB
Fourier Specturm via MATLABZunAib Ali
 
The Fourier Series Representations .pptx
The Fourier Series Representations .pptxThe Fourier Series Representations .pptx
The Fourier Series Representations .pptxEyob Adugnaw
 
Ch4 (1)_fourier series, fourier transform
Ch4 (1)_fourier series, fourier transformCh4 (1)_fourier series, fourier transform
Ch4 (1)_fourier series, fourier transformShalabhMishra10
 
Signal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsSignal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsArvind Devaraj
 
SP_SNS_C2.pptx
SP_SNS_C2.pptxSP_SNS_C2.pptx
SP_SNS_C2.pptxIffahSkmd
 
Eece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformEece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformSandilya Sridhara
 
Optics Fourier Transform Ii
Optics Fourier Transform IiOptics Fourier Transform Ii
Optics Fourier Transform Iidiarmseven
 
Ss important questions
Ss important questionsSs important questions
Ss important questionsSowji Laddu
 

Similar to Nt lecture skm-iiit-bh (20)

4. a Find the compact trigonometric Fourier series for the periodic s.pdf
4. a Find the compact trigonometric Fourier series for the periodic s.pdf4. a Find the compact trigonometric Fourier series for the periodic s.pdf
4. a Find the compact trigonometric Fourier series for the periodic s.pdf
 
fourier-method-of-waveform-analysis msc physics
fourier-method-of-waveform-analysis msc physicsfourier-method-of-waveform-analysis msc physics
fourier-method-of-waveform-analysis msc physics
 
Fourier series and Fourier transform in po physics
Fourier series and Fourier transform in po physicsFourier series and Fourier transform in po physics
Fourier series and Fourier transform in po physics
 
03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf03 Cap 2 - fourier-analysis-2015.pdf
03 Cap 2 - fourier-analysis-2015.pdf
 
Find the compact trigonometric Fourier series for the periodic signal.pdf
Find the compact trigonometric Fourier series for the periodic signal.pdfFind the compact trigonometric Fourier series for the periodic signal.pdf
Find the compact trigonometric Fourier series for the periodic signal.pdf
 
Aplicación de la serie Fourier en un circuito electrónico de potencia)
Aplicación de la serie Fourier en un circuito electrónico de potencia)Aplicación de la serie Fourier en un circuito electrónico de potencia)
Aplicación de la serie Fourier en un circuito electrónico de potencia)
 
Ch1 representation of signal pg 130
Ch1 representation of signal pg 130Ch1 representation of signal pg 130
Ch1 representation of signal pg 130
 
Circuit Network Analysis - [Chapter3] Fourier Analysis
Circuit Network Analysis - [Chapter3] Fourier AnalysisCircuit Network Analysis - [Chapter3] Fourier Analysis
Circuit Network Analysis - [Chapter3] Fourier Analysis
 
Fourier Specturm via MATLAB
Fourier Specturm via MATLABFourier Specturm via MATLAB
Fourier Specturm via MATLAB
 
The Fourier Series Representations .pptx
The Fourier Series Representations .pptxThe Fourier Series Representations .pptx
The Fourier Series Representations .pptx
 
Lecture 9
Lecture 9Lecture 9
Lecture 9
 
Ch4 (1)_fourier series, fourier transform
Ch4 (1)_fourier series, fourier transformCh4 (1)_fourier series, fourier transform
Ch4 (1)_fourier series, fourier transform
 
unit 4,5 (1).docx
unit 4,5 (1).docxunit 4,5 (1).docx
unit 4,5 (1).docx
 
Signal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier TransformsSignal Processing Introduction using Fourier Transforms
Signal Processing Introduction using Fourier Transforms
 
SP_SNS_C2.pptx
SP_SNS_C2.pptxSP_SNS_C2.pptx
SP_SNS_C2.pptx
 
signals and system
signals and systemsignals and system
signals and system
 
Eece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transformEece 301 note set 14 fourier transform
Eece 301 note set 14 fourier transform
 
Optics Fourier Transform Ii
Optics Fourier Transform IiOptics Fourier Transform Ii
Optics Fourier Transform Ii
 
Fourier series
Fourier seriesFourier series
Fourier series
 
Ss important questions
Ss important questionsSs important questions
Ss important questions
 

Recently uploaded

Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxPoojaBan
 
Churning of Butter, Factors affecting .
Churning of Butter, Factors affecting  .Churning of Butter, Factors affecting  .
Churning of Butter, Factors affecting .Satyam Kumar
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...VICTOR MAESTRE RAMIREZ
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learningmisbanausheenparvam
 
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024hassan khalil
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxwendy cai
 
Introduction to Microprocesso programming and interfacing.pptx
Introduction to Microprocesso programming and interfacing.pptxIntroduction to Microprocesso programming and interfacing.pptx
Introduction to Microprocesso programming and interfacing.pptxvipinkmenon1
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfme23b1001
 
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSAPPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSKurinjimalarL3
 
Artificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxArtificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxbritheesh05
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx959SahilShah
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxpurnimasatapathy1234
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )Tsuyoshi Horigome
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerAnamika Sarkar
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEroselinkalist12
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024Mark Billinghurst
 

Recently uploaded (20)

★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
★ CALL US 9953330565 ( HOT Young Call Girls In Badarpur delhi NCR
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptx
 
Churning of Butter, Factors affecting .
Churning of Butter, Factors affecting  .Churning of Butter, Factors affecting  .
Churning of Butter, Factors affecting .
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...
 
POWER SYSTEMS-1 Complete notes examples
POWER SYSTEMS-1 Complete notes  examplesPOWER SYSTEMS-1 Complete notes  examples
POWER SYSTEMS-1 Complete notes examples
 
chaitra-1.pptx fake news detection using machine learning
chaitra-1.pptx  fake news detection using machine learningchaitra-1.pptx  fake news detection using machine learning
chaitra-1.pptx fake news detection using machine learning
 
Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024Architect Hassan Khalil Portfolio for 2024
Architect Hassan Khalil Portfolio for 2024
 
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCRCall Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
Call Us -/9953056974- Call Girls In Vikaspuri-/- Delhi NCR
 
What are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptxWhat are the advantages and disadvantages of membrane structures.pptx
What are the advantages and disadvantages of membrane structures.pptx
 
Introduction to Microprocesso programming and interfacing.pptx
Introduction to Microprocesso programming and interfacing.pptxIntroduction to Microprocesso programming and interfacing.pptx
Introduction to Microprocesso programming and interfacing.pptx
 
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
9953056974 Call Girls In South Ex, Escorts (Delhi) NCR.pdf
 
Electronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdfElectronically Controlled suspensions system .pdf
Electronically Controlled suspensions system .pdf
 
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICSAPPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
APPLICATIONS-AC/DC DRIVES-OPERATING CHARACTERISTICS
 
Artificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxArtificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptx
 
Application of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptxApplication of Residue Theorem to evaluate real integrations.pptx
Application of Residue Theorem to evaluate real integrations.pptx
 
Microscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptxMicroscopic Analysis of Ceramic Materials.pptx
Microscopic Analysis of Ceramic Materials.pptx
 
SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )SPICE PARK APR2024 ( 6,793 SPICE Models )
SPICE PARK APR2024 ( 6,793 SPICE Models )
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
 
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETEINFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
INFLUENCE OF NANOSILICA ON THE PROPERTIES OF CONCRETE
 
IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024IVE Industry Focused Event - Defence Sector 2024
IVE Industry Focused Event - Defence Sector 2024
 

Nt lecture skm-iiit-bh

  • 1. Network Theory Dr. Sivkumar Mishra Dept of Electrical Engineering, IIIT Bhubaneswar
  • 2. •Sinusoidal functions occupy a unique position in engineering. •They are easy to generate and the steady state response of a linear system to dc and sinusoidal excitations can be found easily by using the impedance concept. •In practice input signals are of more complex nature being non sinusoidal periodic and non periodic waveforms. •The French mathematician, J.B.J. Fourier (1758-1830) showed that arbitrary periodic functions could be represented by an infinite series of sinusoids of harmonically related frequencies known as Fourier series. •Non periodic waveforms can be represented by Fourier transform. Fourier Series
  • 3. •A signal f(t) is said to be periodic of period T if f(t)=f(t+T) for all t. •Several signal waveforms that fulfill this requirement are shown in Fig. below t •Fourier series can be represented either in the form of infinite trigonometric series or infinite exponential series. •Fourier series consists of dc terms as well as ac terms of all harmonics. Fourier Series
  • 4. Any periodic signal f(t) (satisfying certain conditions) can be expressed as a summation of sine and cosine functions of frequencies 0,ω,2ω,3ω …..kω, ω where refers to the fundamental frequency and time period. Thus, where the coefficients an and bn are given by Trigonmetric Fourier Series 0 1 0 1 2 3 1 2 3 ( ) [ cos( ) sin( )] cos cos 2 cos3 ... cos .. sin sin 2 sin 3 ..... sin .... n n n n n f t a a n t b n t a a t a t a t a n t b t b t b t b n t ω ω ω ω ω ω ω ω ω ω ∞ = = + + = + + + + + + + + ∑ 0 2 ( )cos T na f t nwt dt T = ∫ 0 2 ( )sin T nb f t nwt dt T = ∫0 0 1 ( ) T a f t dt T = ∫
  • 5. Combining the sine and cosine terms, a more compact representation of the series, either in the sine or cosine form, may be obtained as follows: Let us consider the nth harmonic term where, the amplitude and phase, of the nth harmonic are given by •Writing C0=a0 Trigonmetric Fourier Series ( ) ( ) ( ) ( ) [ ] ( ) 2 2 2 2 2 2 2 2 ( ) cos sin .cos .sin cos .cos sin .sin cos n n n n n n n n n n n n n n n n n f t a n t b n t a b a b n t n t a b a b a b n t n t C n t ω ω ω ω θ ω θ ω ω θ = +    = + +  + +   = + + = − ( )2 2 -1 ; n=tann n n n nC a b b aθ+= 0 1 ( ) cos( )n n n f t C C n tω θ ∞ = = + −∑
  • 6. In a similar way, the series may be represented in the sine form as where, The plot of Cn as a function of n or nω is known as the amplitude spectrum and the plot of θn as a function of n or nω is known as the phase spectrum. 0 1 ( ) sin( )n n n f t C C n tω φ ∞ = = + +∑ ( )1 tann n nb aφ − =
  • 7. Find the Fourier series for the saw-tooth wave shown in Fig. The analytical form of the function f(t) is given by The coefficients are evaluated as below: 1 ( ) , 0 2 2 f t t tω ω π π = ≤ ≤ 2 2 0 0 0 1 1 ( ) ( ) ( ) ( ) 0.5 2 2 2 t a f t d t d t π π ω ω ω ω π π π = = =∫ ∫ 2 2 2 20 0 1 1 cos ( ) ( )cos ( ) ( ) sin ( ) 0 2 2 n t t n t a n t d t n t n n π π ω ω ω ω ω ω π π π   = = + =   ∫ 2 2 2 20 0 1 1 sin ( ) 1 ( )sin ( ) ( ) cos ( ) 2 2 n t t n t b n t d t n t n n n π π ω ω ω ω ω ω π π π π   = = − + = −   ∫
  • 8. So, the Fourier series can be written as: Here, C0=a0=0.5 and as an=0 hence Cn=bn=1/nπ. So, the amplitude spectrum is as shown in Fig. 1 1 ( ) 0.5 sin n f t n t n ω ω π ∞ = = − ∑
  • 9. Certain kinds of symmetries found in non sinusoidal periodic signal waveforms leads to simpler Fourier coefficient calculations i.e. absence of a0,an or bn coefficients from the series. There are four types of waveform symmetries, which are discussed as below. •Even Symmetry •Odd Symmetry •Half Wave Symmetry •Quarter Wave Symmetry Wave Form Symmetry
  • 10. Even Symmetry A function f(x) is said to be even, if , Examples of even functions are x2 , cos x, cosh x, tanx2 , 1+2x2 +4x4 , etc. It is to be noted that the even nature is preserved on addition of a constant. The sum of even functions remains even. The waveforms of some even functions are shown in Fig. It is found that these functions exhibit mirror symmetry about the vertical axis, the negative half being the mirror image of the positive ( ) ( )f x f x= −
  • 11. In Fourier series expansion of the even signal waveforms, the bn-terms are absent i.e. only a0 and an terms are present. 2 0 4 ( )cos T na f t nwt dt T = ∫ 0nb = 2 0 0 2 ( ) T a f t dt T = ∫
  • 12. Odd Symmetry A function f(x) is said to be odd, if , Examples of odd functions are x, x3 , sin x, x cos 10x. etc. The sum of odd functions remains odd. The waveforms of some even functions are shown in Fig. The addition of a constant removes the odd nature of the function. ( ) ( )f x f x= − −
  • 13. The waveform symmetry associated with odd functions has the effect of making the Fourier expression contain only the sine terms. Thus, 2 0 4 ( )sin T nb f t nwt dt T = ∫ 0na = 0 0a =
  • 14. Half Wave Symmetry A function f(x) is said to be half wave symmetric, if , The waveforms of some half wave symmetric functions are shown in Fig. In general, Fourier series expansion of these waveforms contains both an and bn coefficients, i.e. odd harmonics sine and cosine terms are present unless the function is even or odd as well. ( ) ( 2)f x f x T= − ±
  • 15. Quarter Wave Symmetry Waveforms showing half wave symmetry with either odd or even symmetry are said to have quarter wave symmetry. Waveforms as shown in Fig. a,b of slide, have both even as well as half wave symmetries, similarly, waveforms as shown in Fig. 11.5-a,b and c , have both odd as well as half wave symmetries. Hence, these waveforms have quarter wave symmetry.
  • 16. Find the Fourier series expansion of the periodic rectangular waveform shown in Fig. The function is even. Hence, only cosine terms will be present, i.e. bn=0. The function f(t) is defined as 0 2 4 ( ) 1 4 4 0 4 2 for T t T f t for T t T for T t T − ≤ ≤ −  = − ≤ ≤  ≤ ≤2 2 0 2 2 1 1 1 ( ) (1) 2 T T T T a f t dt dt T T− − ∴ = = =∫ ∫ 2 4 2 4 2 2 2 ( )cos cos T T n T T a f t n tdt n tdt T T n ω ω π− − = = =∫ ∫
  • 17. Application of Fourier Series in Network Analysis Consider a case, when a non sinusoidal voltage is applied to a linear network. The Fourier series expansion of this waveform represents linear combination of various harmonic voltages, which give rise to corresponding harmonic currents in the circuit. Each term of the Fourier series of voltage is considered to represent an individual voltage source. The equivalent impedance of the network at each harmonic frequency nω is used to compute the current at that harmonic. The sum of these individual responses provides the total response of the circuit as per the principle of superposition.
  • 18. Effective Value or RMS value If, by definition, the effective value or rms value can be written as where c0 is the dc component and c1, c2, c3, … are the amplitudes of the harmonics. 0 1 ( ) [ cos( ) sin( )]n n n f t a a n t b n tω ω ∞ = = + +∑ [ ] [ ] 2 2 2 2 2 2 2 2 2 2 2 1 22 0 0 1 1 2 0 1 2 3 1 2 3 1 2 0 1 2 3 1 [ cos( ) sin( )] 1 ........ ..... 2 1 ...... 2 T rms n n n F a a n t b n t dt T a a a a b b b c c c c ω ω ∞ =     = + +         = + + + + + + + +      = + + + +    ∑∫
  • 19. In general, if the voltage and current are given by 0( ) sin( )n nv t V V n tω φ= + +∑ 0( ) sin( )n ni t I I n tω θ= + +∑ Then, the effective values of the voltage and current are given as ( )2 2 1 2 1 22 2 2 2 2 2 0 1 2 3 0 1 2 3 1 ...... ...... 2 rms rms rms rmsV V V V V V V V V    = + + + = + + +    ( )2 2 1 2 1 22 2 2 2 2 2 0 1 2 3 0 1 2 3 1 ...... ...... 2 rms rms rms rmsI I I I I I I I I    = + + + = + + +   
  • 20. Exponential Series: • A compact way of expressing the Fourier series •The sine and cosine forms of the trigonmetric series are represented in exponential form using Euler’s identity. Fourier Series
  • 21. Exponential Series: • A new coefficient cn is defined • Now, f(t) becomes • This is the complex or Exponential form of f(t) Fourier Series
  • 22. Exponential Series: • This form is more compact than the sine-cosine form. • The coefficient can be found as: • The plots of the magnitude and phase of cn versus now are called the complex amplitude spectrum and phase spectrum of f(t) respectively. Fourier Series
  • 23. • Consider a periodic pulse train as shown. We have to find its phase and amplitude spectra. • The period of the pulse train T= 10 ω0= 2π/10=π/5 Fourier Series
  • 24. • cn is the product of 2 and a function of the form sin x/x, which is known as sinc function. Thus, Some properties of the sinc function are: For an integral multiple of π, the value of sinc is zero • The sinc function always shows even symmetry Fourier Series
  • 26. • Fourier series enables us to represent a periodic function as a sum of sinusoids and to obtain the frequency spectrum from the series. • Fourier transform allows us to extend the concept of a frequency spectrum to non periodic functions. • The transform assumes that a non periodic function is a periodic function with an infinite period. • Thus the Fourier transform is an integral representation of a non periodic function that is analogous to a Fourier series representation of a periodic function. Fourier Transform
  • 27. • The Fourier transform is an integral transform like Laplace transform, which transforms the function in time domain to frequency domain. •Fourier transform is useful in communication systems and digital signal processing, in situations where Laplace transform does not apply. • While Laplace transform can only handle circuits with inputs for t>0 with initial conditions, Fourier transform can handle circuits with inputs for t<0 as well as those for t>0. Fourier Transform
  • 28. • Consider a non periodic function p(t) as shown below. Also consider a periodic function f(t), whose shape for one period is same as p(t). • • If we let period T→∞ , only a single pulse of width τ remains as all other adjacent pulses move to ∞. The function f(t) is no longer periodic. Fourier Transform
  • 31. Fourier Transform The Inverse Fourier transform 0 0( ). ( ) ( )f t t t dt f tδ ∞ −∞ − =∫ Shifting Property of the impulse function: When a function is integrated with impulse function, we obtain the value of the function at the point where the impulse occurs
  • 39. Filters A filter is a frequency selective electrical network that allows signal of desired band of frequencies to pass freely whilst attenuates the signals at other frequencies. Ideally, a filter should produce no attenuation in the desired band, called the transmission band or pass band. It should provide infinite attenuation at all other frequencies, called attenuation band or stop band. The frequencies that separate the pass band and stop band are called cut- off frequencies(fc).
  • 40. Classification of Filters Filters can be classified on the basis of the range of the pass band and stop band frequencies as: 1. Low Pass Filters (LPF) 2. High Pass Filters (HPF) 3. Band Pass Filters (BPF) 4. Band Stop or Band Elimination Filters (BSF)
  • 41. Classification of Filters - Passive Filters, which are made of L and C components Filters also can be classified on the basis of components they are made of as: -Active Filters are made of components such as operational amplifiers
  • 42. Active Filters In the design of passive filters, inductor is an integral part. Inductor creates some problems and because of other advantages of using active elements, passive filters are seldom used in practical. Some of the limitations of passive filters are: -The use of inductor as a network element is not desirable especially at low frequencies(less than 1 kHz) as at these frequencies practical inductors of reasonable Q tend to become bulky, and expensive. The dissipative losses start increasing when an inductor is minimized. -It is necessary to cascade many sections to form a composite filter. A composite filter has a prototype filter, two terminating half sections(one at each end) and an m-derived section. Due to presence of so many components, it becomes bulky.
  • 43. Active Filters While cascading different sections of filters a buffer or isolation amplifier is required to prevent loading of the circuit. There is a need for an external amplifier to the required gain.
  • 44. -The active filter provides gain and frequency adjustment flexibility. -Due to high input resistance and low output resistance of the Op-Amp, they do not cause loading of the source or load. -Reduction in size and weight and increased equipment density. Reduction in power consumption. -More economical than passive filters due to the use of Op- Amps and absence of inductors.
  • 45. References 1.C.K. Alexander and M.N.O. Sadiku, “Fundamentals of Electric Circuits,” 3rd Edition, Tata McGraw Hill, 2008. 2.M. E. Valkenburg, “Network Analysis,” 3rd Ed., Pearson Prentice Hall, 2006. 3.W. H. Hayt, J. E. Kemmerly and S. M. Durbin, “Engineering Circuit Analysis,” 6th Edition, Tata McGraw Hill, 2007.