Arise'14 Ijert
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Watermarking of Compressed imageswithImproved
Encryption
Deepa L C
Department of Computer Science, CUSAT
TKM Institute of Technology
Kollam,Kerala,India
Meerakrishna G H Department of Computer Science, CUSAT
TKM Institute of Technology
Kollam,Kerala,India
AbstractMedia data generally Handles In compressed and encrypted form. It is necessary To watermark these compressed
encrypted media Items in the compressed encrypted
domainitselffortamperdetectionorownershipdeclaration
orcopyrightmanagement purposes. It is a challenge to watermark
this media data in compressed and encrypted domain because of
security and visual quality problems. The watermarking in
encrypted domain gives double security. Thus it is necessary to
choose a watermark embedding and encryption scheme for
maintaining both security and visual quality. In this work, a
robust approach for watermarking images in compressed and
encrypted domain is presented. The encryption algorithm here
used is Rijndael encryption algorithm. While the proposed
technique embeds watermark in the compressed-encrypted
domain, the extraction of watermark can be done in the decrypted
domain. The watermark embedding technique used is Rational
Dither Modulation (RDM).
Keywords Compressed and Encrypted domain watermarking, copyright, Visual cryptography, RDM
I. INTRODUCTION
Watermarking has an important role in the digital media
content distribution. It is necessary to watermark these
compressed encrypted media items in the compressed
encrypted domain itself for tamper detection or ownership
declaration or copyright management purposes. Digital Right
management system is an example, where the owner of
multimedia content, distribute it in a compressed and encrypted
format to consumers through multilevel distributor network,
each distributor sometime needs to watermark the content for
media authentication, traitor tracing or proving the
distributorship. Watermarking has an important role in DRM
systems. It helps publishers; copyright protectors etc to keep
track their digital data after sale. It helps the developers to
transfer the media data securely in this domain. In DRM
systems there are multiple levels of distributers and consumers.
The distributors dont have access to the plain text. This paper focus on the watermarking of compressed encrypted images,
where the encryption refers to the ciphering of complete
compressed stream. Watermarking in compressed-encrypted
content saves the computational complexity as it does not
require decompression or decryption, and also preserves the
confidentiality of the content because it doesnt need decryption at the time of watermark embedding.A V
Subramanyam (2012) [1] proposed a robust watermarking
algorithm to watermark jpeg2000 compressed encrypted
images.The technique here used was spread spectrum. But the
problem was that this technique has only low number of bit
capacity. GaoHai-ying, Liu Guo-qiang, and XuYin(1993) [2]
proposed a new robust watermarking algorithm for JPEG2000
images. Here the watermark information is embedded by
modifying the wavelet coefficients in pairs after quantization of
the original image. The main problem of this work was image
quality degradation and the lack of ability to resist attacks. To
overcome this problem Kan Li and Xiao-Ping Zhang(2001) [3]
proposed a robust adaptive watermarking scheme .It was a
compression degree adaptive method .Here the watermark will
be embedded in to the middle frequency wavelet coefficients
after quantization. But this approach couldnt overcome the security problems. Roland Schmitz (2006) [4] proposed a
commutative watermarking encryption method. It was
designed by combining histogram based watermarking scheme
with a permutation cipher. Here the permutation cipher is used
toencrypt the multimedia data. The disadvantage of this work
was that it was not a secure method. Zhi Li and Yong Lian
(2007) [5] introduced a method for content dependent
watermarking and authentication. It had been proposed as a
solution to overcome the potential estimation attack aiming to
recover and remove the watermark from the host signal. A
watermarking scheme based on TCQ quantization scheme was
proposed by D.Goudia(2009) [6]. The main contribution is that
this system allows both quantization of wavelet coefficients
and watermark embedding by using the same quantization
module.
In this paper we focus on watermarking of compressed-
encryptedimages, where the encryption refers to the ciphering
of images in compressed stream. The aim of watermarking is to
provide the digital media content creator with the ability to
keep track of their media data after sale. Watermarking is a
data hiding method. This technique is mainly used in one to
many communications. Watermarking can be done in
encrypted domain or compressed domain. The problem of
watermarking in encrypted domain is that changing a single bit
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may lead to random decryption and there is no strong security
in compressed domain. So here we choose the compressed and
encrypted domain. In our algorithm the watermark embedder
only have compressed encrypted content. Also the watermark
embedders do not have the key to unencrypt and get the plain
text compressed values. However the proposed system faces
the following challenges.
1) Compressed Domain Watermarking: A small modification in the compressed data may lead to the
degradation of decoded image. Thus we have to find the place
for embedding the data very carefully, so we can reduce the
visual quality degradation.
2) Encrypted Domain Watermarking and Watermark Retrieval: In an encrypted piece of content, changing even a
single bit may lead to a random decryption; therefore the
encryption should be such that the distortion due to embedding
can be controlled to maintain the image quality. It should also
be possible to detect the watermark correctly even after the
content is decrypted. Also, the compression gain should not be
lost as encryption may lead to cipher text expansion.
This paper is organized as follows. Section II describes the
proposed scheme. In section III we discuss the encryption
algorithm, watermark embedding and extraction algorithm .The
experimental results are discussed in Section IV. Section V
concludes the paper. The theoretical analysis and derivations
are given in the Appendix.
II. PROPOSED SCHEME
Overview
At first blue region detection is performed on input image
using HSV color space.Secondly cover image is transformed
in frequency domain. (DWT) This is performed by DWT on
image leading to four subbands.Then payload (number of bits
in which we can hide data) is calculated. Then secret data
embedding is performed in one of the high frequency sub-
band by tracingblue area pixels in that band.Then extract it.
Plain Text ( In form of Image)
Encryption ( Creating shares)
Channel (Cover image , Dither Modulation)
Extraction
Decryption
A. Image Compression
The image compression is divided into five stages. In the
first stage the input image is preprocessed by dividing it into
non-overlapping rectangular tiles, the unsigned samples are
then reduced by a constant to make it symmetric around zero
and finally a multi-component transform is performed. In the
second stage, the discrete wavelet transform (DWT) is applied
followed by quantization in the third stage. Multiple levels of
DWT gives a multi-resolution image. The lowest resolution
contains the low-pass image while the higher resolutions
contain the high-pass image. These resolutions are further
divided into smaller blocks known as code-blocks where each
code-block is encoded independently. Further, the quantized-
DWT coefficients are divided into different bit planes and
coded through multiple passes at embedded block coding with
optimized truncation (EBCOT) to give compressed byte stream
in the fourth stage. The compressed byte stream is arranged
into different wavelet packets based on resolution, precincts,
components and layers in the fifth and final stage. Thus, it is
possible to select bytes generated from different bit planes of
different resolutions for encryption and watermarking.
B. Encryption Algorithm
The encryption method we are using here is Visual
cryptography&Rijndael. The secret image will be divided into
two shares
Share 1
Share2
Stacking the shares reveals the secret.
Fig 1:Visual cryptography
Visual cryptography scheme in computer representation using
nm matrix is as follows:
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Orginalpixel :
Share 1 (S1) :
Share 2 (S :
Using the permutated basis matrices, each pixel from the secret image will be encoded into two sub pixels on each participant's share. A black pixel on the secret image will be encoded on the ith participant's share as the ith row of matrix S1, where a 1 represents a black sub pixel and a 0 represents a white sub pixel. Similarly, a white pixel on the secret image will be encoded on the ithparticipant's share as the ith row of matrix S0.
C..Embedding Algorithm
The embedding algorithm uses color image as cover and
grayscale image as watermark. The color image is decomposed
into Luminance, Intensity and Hue channels. The DWT is
applied on the Luminance channel of color image, which
produces the frequency subband coefficients. From these
subband coefficients the highest texture energy subband is
selected. On this subband apply DWT to obtain the second
level decomposition. From this again select a subband having
hightexture energy. Before embedding the watermark into
selected subbands, the watermark image is split into two shares
by applying (2, 2)V CS scheme using AOD . Out of these two shares one share is embedded into selected subband and other
share is kept secret.
The details of the algorithm is as follows:
Algorithm: Watermark Embedding Algorithm.
Input : Cover (Color) image, Watermark (gray-scale) image.
Output : Watermarked color image.
1) Read the cover (color) image I of size N N and watermark
(gray-scale)imageWof size M M
2) Decompose the color image into Luminance (Y ), Intensity (I)
and Hue (Q) channels of size M M
3) Split the watermark by applying V CS using AOD is kept
secret and S1 is used for embedding.
4) Apply DWT on Luminance (Y ) channel to get subband
coefficients (LL1, LH1, HL1 and HH1).
5) Extract the texture property Energy for each subband
coefficient
6) Select the subband frequency coefficients (LL1 or LH1 or
HL1 or HH1 ) which is having high energy.
7) Apply the DWT on selected subband to get second level
decomposition (LL2, LH2, HL2 and HH2).
8) Extract the vector of texture property Energy for each
subband of second level decomposition
9) Select the subband which is having high energy from second
level decomposition (LL2,orLH2 or HL2 or HH2).
10) Embed the share S1 produced in Step 3 into the selected
subband coefficients of Step 9 using following steps.
fori= 1 to M do
forj= 1 to M do
Y_(i, j) = (|Y (i, j)| + )S1(i, j) end for
end for
Where Y_(i, j) represents the modified frequency coefficient of
subband, Y (i, j) represents the original frequency coefficient of
subband, represents the watermark scaling factor. 11) The value of is adjusted such that the texture properties of embedded subband are changed by negligible value
12) Replace the modified subband coefficients into its initial
location and apply twice inverse DWT to get the watermarked
Luminance channel.
13) Combine the watermarked Luminance (Y ) channel with
Intensity (I) and Hue (Q) to get watermarked color image.
D. Extraction Algorithm
0 1
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Extraction algorithm is of type blind extraction which uses
only watermarked color image as input. The watermarked color
image is decomposed into Luminance, Intensity and Hue
channels. The DWT is applied on the Luminance channel of
watermarked color image, which produces the frequency
subband coefficients. From these subband coefficient the
highest texture energy subband is selected. On this subband
apply DWT to obtain the second level decomposition. From
this againselect a subbandhaving high texture energy. The
watermark is extracted from these selected subband
coefficients. After extracting the watermark, the watermark
image is superimposed with secret share using V CS scheme as
explained in Section 3. The output of superimposition produces
the extracted watermark. The details of the extraction
algorithm are explained below.
Algorithm: Watermark Extraction Algorithm.
Input : Watermarked (Color) image.
Output : Extracted watermark.
1) Read the watermarked color image I of size N N
2) Decompose the watermarked color image into Luminance
(Y ), Intensity (I) and Hue (Q) channels of size M M
3) Apply DWT on Luminance (Y ) channel to get subband (LL1,
LH1, HL1 and HH1).
4) Extract the texture property Energy for each subband
coefficients.
5) Select the subband frequency coefficients (LL1 or LH1 or
HL1 or HH1 ) which is having high energy.
6) Apply the DWT on selected subband to get second level
decomposition subbands(LL2, LH2, HL2 and HH2)
7) Extract the texture property Energy for each subband of
second level decomposition.
8 Select the subband frequency coefficients which is having
high energy from second level (LL2,orLH2 or HL2orHH2).
9) Extract the share S1 from selected subbandcoefficientsof
Step 9 using following steps.
fori= 1 to M do
forj= 1 to M do
ifY _ _ 0 then
S1(i, j) = 1;
else
S(i, j) = 0;
end if
end for
end for
10) Superimpose extracted share S1with secret share S0using V
CS
III. RESULTS AND DISCUSSION
Security of Encryption Algorithm
To verify the effectiveness of the proposed scheme, a series of
experiments were conducted. By keeping the cipher structure
simple, it becomes accessible to a larger set of people for
evaluation. The simplistic structure also plays a part in
performance and security. The security of the cipher is
amplified by the simple structure. For instance, the rate of
diffusion is improved by several simple steps in the round:
integer multiplication, the quadratic equation, and fixed bit
shifting. The data-dependent rotations are improved, as the
rotation amounts are determined from the high-order bits in f(x),
which in turn are dependent on the register bits. The security
has been evaluated to possess an adequate security margin; this rating is given with familiarity of theoretical attacks, which
were devised out of the multiple evaluations. The AES-specific
security evaluations provide ample breadth and depth to how
RC6 security is affected by the simplicity of the cipher.
Table 1 : Algorithm comparison
Algorithm Key Size Block
size
Algorithm
structure
Rounds Existing
cracks
Rijndael 128,192,256
bits
128 Substitutio
n ,permutat
ion
10,12 or
14
Side channel
attacks
Twofish 128,192,256
bits
128 Feistel
Network
16 Truncated
differential
cryptanalysis
Blowfish 32-448 bit 64 Feistel Network
16 Second order differential
attacks
RC4 Variable Variable
Stream Unknown
Weak key schedule
RC2 8- 128 bit 64 Heavy
Fiestel Network
16 Related key
attacks
TripleDES 112 or 168
bits
64 Feistel
Network
48 Theoritically
possible
DES 56 bits 64 Feistel Network
16 Brute force attacks
IV. CONCLUSION
This paper provides double security through encryption and
watermarking. Encryption provides security by hiding the
content of secret information; while watermarking hides the
existence of secret information. Earlier works were
concentrated on encrypted or compressed domain only.The
proposed system helps to embed a robust watermark in the
compressed encrypted images using the watermarking scheme
spread spectrum. The algorithm is simple to implement as it is
directly performed in the compressed-encrypted domain, i.e., it
does not require decrypting or partial decompression of the
content. This scheme also preserves the confidentiality of
content as the embedding is done on encrypted data. The
homomorphic property of the cryptosystem is exploited, which
allows us to detect the watermark after decryption and control
the image quality as well.
ACKNOWLEDGEMENT
This workwassupportedin partbythe Departmentof
ComputerScience&Engineering, TKMIT,andKollam.We
wouldliketoshow ourgratitudetoProfP.Mohamed
Shameem&Asst.Prof.Meerakrishna G Hfortheirvaluable
guidance.
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REFERENCES
[1] A.V.Subramanyam,SabuEmmanuel,Robustwatermarkingof compressed encrypted JPEG 2000images, IEEEtransactions on multimedia, vol. 14, no.3, june 2012.
[2] Guo-quang,LiuGuo-qiang and Xuyin,A New Robust watermarking algorithm for JPEG2000 images,.
[3] KanLiand Xiao-Ping Zhang, Reliable Adaptive Watermarking Scheme Integrated with JPEG2000,Proceedings of the 3rd International Symposium on Image and Signal Processing and Analysis
(2003).
[4] S. Lian, Z. Liu, R. Zhen, and H. Wang, Commutative watermarking and encryption for media data, Opt. Eng., vol. 45, pp. 13, 2006.
[5] Z. Li, X. Zhu, Y. Lian, and Q. Sun, Constructing secure content dependent watermarking scheme using homomorphic encryption, in Proc. IEEE Int. Conf. Multimedia and Expo, 2007, pp. 627630.
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3D IMAGERY IN ROCKETRY
Soumya V. S M.Tech , Dept of ECE Marian Engineering College
Abstract - 3-D video will become one of the most
significant video technologies in the next-generation. In
rocketry bandwidth is an essential requirement. Due to
the ultra high data bandwidth requirement for 3-D
video, effective compression technology becomes an
essential part in the infrastructure. Thus multiview video
coding (MVC) plays a critical role. MVC is an extended
version of H.264/AVC that improves the performance of
multiview videos. The entire image is divided into
macro blocks. The size of macroblock depends on
codec used. Multi-view video coding (MVC) is an
ongoing standard in which variable size disparity
estimation (DE) and motion estimation (ME) are both
employed to select the best coding mode for each
macroblock (MB). A multidirectional spatial prediction
method is also employed for each macroblock to
reduce spatial redundancy. The multi-view video plus
depth (MVD) coding will give 3D video (3DV). Index Terms- 3D video coding (3DVC), multi-view
video plus depth (MVD), H.264/AVC, multiview video coding (MVC).
I. INTRODUCTION
WITH the development of the technology of 3DTV and free viewpoint TV (FTV), MVC attracts more and more
attention. In recent years, MVC technology is now being
standardized by the Joint Video Team (JVT) as an extension
to H.264 [1].
Subha Varier Scientist/Engineer SG Indian Space Research Organization (ISRO) Thiruvananthapuram.
The sensation of realism can be achieved by visual
presentations that are based on three-dimensional (3D)
im-ages. To generate even more vivid and realistic
informa-tion, it is possible to use two or more cameras
placed at slightly different view-points. This allows the
production of multiview sequences. The Multi-view video structure consists of several video
sequences, which are captured by closely located cameras
in most of the applications. The close location of cameras in
these applications results in a high redundancy between the
sequences from different cameras. 3D video provides a visual experience with depth per-
ception through the usage of special displays that re- pro-ject
a three-dimensional scene from slightly different dir-ections
for the left and right eye. Such displays include stereoscopic
displays, which typically show the two views that were
originally recorded by a stereoscopic camera system. Here,
glasses-based systems are required for mul-tiuser
audiences. Especially for 3D home entertainment, newer
stereoscopic displays can vary the baseline between the
views to adapt to different viewing distances. In addi-tion,
multi-view displays are available, which show not only a
stereo pair, but a multitude of views (typically 20 to more
than 50 views) from slightly different directions. Each user
still perceives a viewing pair for the left and right eye.
However, a different stereo pair is seen when the viewing
position is varied by a small amount. This does not only
improve the 3D viewing experience, but allows the
perception of 3D video without glasses, also for multi-user
audiences. As 3D video content is mainly produced as stereo
video content, appropriate technology is required for
generating the additional views from the stereo data for this
type of 3D displays. For this purpose, different 3D video
formats or representations have been considered. A straight forward method to encode the multi-view
se-quences is simulcast coding, in which each view is
en-coded independently with the state-of-art
H.264/AVC co-dec. Though the H.264/AVC can
achieve a very high cod-ing efficiency for each single
view, statistical results show that there are still
correlations left between different views [2].
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Fig 1: Overall structure of an MVC system
Stereoscopic vision is based on the projection of an object
on two slightly displaced image planes and has an extensive range of applications, such as 3-D television, 3-D
video applications, robot vision, virtual machines, medical surgery
and so on. Two pictures of the same scene taken from two nearby
points form a stereo pair and con-tain sufficient information for
rendering the captured scene depth. The above demanding
application areas re-quire the development of more efficient
compression tech-niques of a stereo image pair or a stereo image
sequence. In a monoscopic video system the compression is based
on the intra-frame and inter-frame redundancy. Typically the
transmission or the storage of a stereo image sequence re-quires
twice as much data volume as a monoscopic video system.
Nevertheless, in a stereoscopic system a more effi-cient coding
scheme may be developed if the in-ter-sequence redundancy is
also exploited. H.264 is the newest international video coding standard.
Compared to prior video coding standards, H.264 mostly enhances
the coding efficiency. So its more possible to resolve the problem of
stereoscopic storage and transmis-sion using coding based on
H.264.Since the multi video approach creates large amounts of data
to be stored or transmitted to the user, efficient compression
techniques are essential for realizing such applications. The
straight-forward solution for this would be to encode all the video
signals independently using a state-of-the-art video codec such as
H.264/AVC [2][4]. However, multiview video contains a large
amount of inter-viewstatistical dependen-cies, since all cameras
capture the same scene from differ-ent viewpoints. These can be
exploited for combined tem-poral/inter-view prediction, where
images are not only predicted from temporally neighboring images
but also from corresponding images in adjacent views, referred to
as Multiview Video Coding (MVC). The overall structure of MVC
defining the interfaces is illustrated in Fig. 1. In this paper, a typical stereoscopic video compression scenario
is mainly studied. The essential requirements are described in
Section II. Section III investigates coding of
stereo views. The prediction structures are presented
in Section IV. Here to obtain 3D view it requires a 3-D
depth impression of the observed scenery. Section V
ex-plains the depth coding approaches. Finally,
Section VI concludes this paper. II. REQUIREMENTS
The central requirement for any video coding standard is high
compression efficiency. In the specific case of MVC, this means
a significant gain compared to inde-pendent compression of
each view. Compression effi-ciency measures the tradeoff
between cost (in terms of bit-rate) and benefit (in terms of video
quality), i.e., the qual-ity at a certain bit-rate or the bit-rate at a
certain quality. However, compression efficiency is not the only
factor un-der consideration for a video coding standard. Some
re-quirements of a video coding standard may even be con-
tradictory such as compression efficiency and low delay in some
cases. Then a good tradeoff has to be found. General
requirements for video coding such as minimum resource
consumption (memory, processing power), low delay, er-ror
robustness, or support of different pixel and color res-olutions,
are often applicable to all video coding standards. III. CODING OF STEREO VIEWS The main difference between classic video coding and
multiview video coding is the availability of multiple cam-
era views of the same scene. As coding efficiency of hy-
brid video coding depends on the quality of the prediction
signal to a great extent, a coding gain can be achieved for
MVC by additional inter-view prediction. If there is no
such gain, independently encoding each camera view
with temporal prediction would already provide the best
pos-sible coding efficiency. A. Disparity-Compensated Prediction
The distance between two points of a superimposed
ste-reo pair that correspond to the same scene point is
called disparity. Disparity compensation is the process
that es-timates this distance (disparity vector or DV),
predicts the right image from the left one and produces
their difference or residual image (disparity compensated
difference or DCD). As a first coding tool for dependent views, the concept of
disparity-compensated prediction (DCP) has been ad-ded as
an alternative to motion-compensated prediction (MCP).
Here, MCP refers to inter-picture prediction that uses already
coded pictures of the same view at different time instance,
while DCP refers to inter-picture prediction
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that uses already coded pictures of other views at the same
time instance. B. Motion Homogeneity Determined
A region with homogeneous motion means that the mo-tions in
the region have homogenous spatial property, and the
corresponding motions in a spatial window are with consistence. A
uniform motion vector field at 4x 4 block level can be generated for
the calculation of motion homo-geneity in each MB. A Block
Matching Algorithm is a way of locating matching blocks in a
sequence of digital video frames for the purposes of motion estimation. The purpose
of a block matching algorithm is to find a match-ing block from a
frame i in some other frame j, which may appear before or after i.
This can be used to discover tem-poral redundancy in the video
sequence, increasing the ef-fectiveness of the interframe video
compression and tele- vision standards conversion. Block matching
algorithms make use of an evaluation metric to determine whether a
given block in frame j matches the search block in frame i. IV. PREDICTION STRUCTURES
To the fact that current existing prediction structures lack have
low coding efficiency a Diagonal Interview Pre-diction (DIP) is
presented in this paper, which performs the interview prediction
from the reference pictures of dif-ferent time slots to the encoding
picture. By introducing the DIP, a MVC prediction structure can
support the 3d view of rocketry, while raising the coding efficiency.
In comparison, the traditional interview prediction, in which the
reference picture of the coding picture, is noted as Normal Interview
Prediction (NIP). Figure 2 gives ex-amples of different prediction
structures. Figure 2(a) shows a simple DIP case, in which the en-coding
picture is predicted from two reference pictures of the previous time
slot, in which one is a temporal refer-ence picture, and another one
is an spatial reference picture. Figure 2(b) shows a NIP case, in
which the encod-ing picture is then predicted from a temporal
reference picture and a spatial prediction reference picture but at
the same time slot to the encoding picture. In figure 2(c), the coding
picture is predicted from only one temporal refer-ence picture, and
views are encoded independently, such a coding structure is called
Simulcast coding. In Figure 2(b) structure, the decoding of the current view has
one picture decoding delay compared with the reference view,
i.e. the decoding of picture (T,V) has to wait until the decoding of
picture (T,V-1) is finished.
Figure.2 Diagonal Inter-View Prediction Test Mode. (a) The Diagonal inter-view prediction test mode. (b) Nor -
mal inter-view prediction test mode. (c) Simulcast test But for the structure of DIP in Figure 2(a), the two views
can be decoded simultaneously, as the DIP reference pic-
tures are always been decoded at the previous time slot.
When the number of views becomes very large, the NIP
will cause large decoding delay. As a result, the DIP or
the Simulcast coding mentioned above is a good structure
on the point of decoding delay removing and parallel
comput-ing. Besides the fast algorithm described above, the
motion estimation process in the prediction stage can
be further speed up based on the motion correlation of
different frames. By considering two consecutive
frames of same view motion estimation can be done. V. DEPTH PERCEPTION In the MVC reference software JMVC, different mode
sizes including 16 16, 16 8, 8 16, 8 8, 8 4, 4 8,
and 4 4 are used in the prediction procedures. Large
sizes are usually selected for the macroblocks (MB) in the
regions with homogeneous motion, while small sizes are
selected for the MBs with complex motion. This technique
achieves the highest possible coding efficiency, but
results in extremely large encoding time which obstructs it
from practical use. A depth map represents a relative distance from a cam-
era to an object in the 3D space, it can be regarded as a
grayscale image using dark and bright values to represent far
and close object, and the object depth not only repres-ents
the physical object position in 3D space but also in-dicates
the motion activity of the object itself on the image plane.
Under the condition that cameras are set up in a close
parallelized structure, the depth maps are correlated to the
texture video motion fields. People can see depth because they look at the 3D world
from two slightly different angles (one from each eye). Our
brains then figure out how close things are by determ-ining
how far apart they are in the two images from our
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eyes. The idea here is to do the same thing with a com-puter.
The algorithm is based on Segment-Based Stereo Matching Using Dissimilarity Measure.
The first step is to get an estimate of the disparity at each
pixel in the image. A reference image is chosen, and the other
image slides across it. As the two images slide over one another we subtract their intensity values. Addi-tionally, we
subtract gradient information (spatial derivat-ives). We record
the offset at which the difference is the smallest, and call that
the disparity. Next we combine image information with the pixel dis-parities
to clean up the disparity map. First, we segment the reference
image .Then, for each segment, we look at the associated pixel
disparities. Here assign each segment to have the median
disparity of all the pixels within that segment. This gives depth. VI. CONCLUSION
In rocketry bandwidth is an essential requirement. To achieve good coding efficiency redundancy within a frame and redundancy between views are exploited. Here DE is utilized to exploit inter-view dependencies in MVC.
Although temporal prediction is on average the most efficient
mode in MVC system, there are many reasons for using both DE
and ME to achieve better predictions than using only ME. One
main reason is due to complex motion. In general, the temporal
motion cannot be char-acterized in an adequate way, especially
when there is non-rigid motion (such as zooming, rotational
motion, and deformations of non-rigid objects) or motion edge.
For the former, the ME based on the translational rigid motion
model of blocks fails for zooming, rotational motion and
deformation of non-rigid objects, and thus it produces poor
prediction results. For the latter, the re-gion with motion edges is
usually predicted using small block sizes with large motion
vectors and high residual energy, and thus it has low coding
efficiency. On the other side, usually the disparity which is mainly
determ-ined based on the relative positions of the objects and
cameras is more structured than the temporal motion in complex
motion region. MBs in region with complex motion are more likely
to choose the inter-view predic-tion mode. Thus, the
region with homogeneous motion is more likely to select
temporal prediction mode where inter-view prediction is
not needed, and the region with complex motion is more
likely to select inter-view pre-diction mode. The
comparative experimental results show that the proposed
algorithm not only significantly reduces the complexity of
MVD coding while improves the coding performance, but
also maintain the rendering quality. REFERENCES ISO/IEC/JTC1/SC29/WG11, Multiview Coding Us-ing AVC, Bangkok, Thailand, Jan. 2006. [1] U. Fecker,and A. Kaup, Statistical Analysis of Multi-Reference Block Matching for Dynamic Light Field Cod-ing, Proc. 10th International Fall Workshop Vision, Mod-eling, and Visualization, pp. 445-452, Erlangen, Germany, Nov. 2005. [2] Advanced Video Coding for Generic Audiovisual Services, Version 3, ITU-T Rec. & ISO/IEC 14496-10 AVC, 2005. [3] T. Wiegand, G. J. Sullivan, G. Bjntegaard, and A. Lu-thra, Overview of the H.264/AVC video coding standard, IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 7, pp. 560560, Jul. 2003. [4] G. Sullivan and T. Wiegand, Video compression
From concepts to the H.264/AVC standard, Proc. IEEE,
Special Issue on Advances in Video Coding and
Delivery, vol. 93, no. 1, p. 18, Jan. 2005.
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Enhanced Grid Synchronization of a DG system
based on Positive Sequence Estimation and Current
Control
S.Manoharan Dr.K.Gnanambal R.Girija
Department of EEE, Department of EEE, Department of EEE,
K.L.N College of Engineering, K.L.N College of Engineering, K.L.N College of Engineering,
Madurai,Tamil Nadu, India Madurai,Tamil Nadu, India Madurai,Tamil Nadu, India
[email protected] [email protected]
AbstractDistributed Generation (DG) System is a small scale electric power generation which encompasses a wide
range of technologies such as wind energy, fuel cell, solar
power, micro turbines etc. Grid synchronization has been
identified as the most significant barrier to the control of
inverters connected to the grid. A Wind turbine based on
direct drive permanent magnet synchronous generator
(PMSG) is connected to the grid. The proper operation of grid
connected inverter system is determined by grid voltage
conditions such as phase, amplitude and frequency. A phase-
locked loop (PLL) is used to track the phase angle in order to
improve the synchronization systems response in adverse grid
conditions. Using the enhanced synchronization structure the
fundamental positive-sequence component of grid voltages in
asymmetric and distorted three-phase systems is estimated.
The - stationary frame is used to obtain the pulsation for grid inverter using a space vector pulse width modulation
(SVPWM) technique. The performance of the proposed
structure is verified through simulations using a grid set of
ideal and non-ideal grid conditions (three-phase voltage
unbalance, variation in frequency, variation in amplitude and
phase shift).The simulation results demonstrates that the
proposed method is very effective in digital structure
synchronization .
KeywordsDistributed Generation (DG), permanent magnet
synchronous generator (PMSG), discrete phase-locked loop (PLL),
synchronization systems, positive-sequence component, SVPWM,
non-ideal grid conditions.
I. INTRODUCTION
The Distributed Generation (DG) systems are highly
sporadic power generation system and their power output
depends heavily on the natural conditions. Wind power
generation based on direct drive permanent magnet
synchronous generator has received much attention due to its
self excitation capability and high efficiency operation [1].
Various grid code requirements must be met to connect the
DG systems with the utility grid. To ensure safe and reliable
operation of power system based on DG system [2], usually
power plant operators should satisfy the grid code
requirements such as fault ride through, power quality
improvement, grid synchronization, grid stability and power
control etc.
The grid synchronization techniques can be adversely
affected by the application of a disturbing influence (influence
quantity) on the electrical input signals. Due to the increase in
number of Distributed Generation (DG) Systems has lead to
complexity in control while integrating into grid. As a result
requirements of grid connected inverters have become stricter
to meet very high power quality standards.
Grid voltage conditions such as phase, amplitude and
frequency determine the proper operation of a grid connected
system. In such applications, a fast and accurate detection of
the phase angle, frequency and amplitude of the grid voltage is
essential. These factors, together with the implementation
simplicity and the cost are all important when examining the
credibility of a synchronization scheme. Therefore an ideal
phase-detection scheme must be used to promptly and
smoothly track the grid phase through various short-term
disturbances [3] [4] and long term disturbances to set the
energy transfer between the grid and the power converter.
One of the earliest methods used for tracking the phase
angle is Zero Crossing Detector (ZCD) method [5], but the
performance of ZCD is badly affected by power quality
phenomena [6]. The Linear PLL is mainly used to detect phase
for single phase supply. Use of voltage controlled oscillators
(VCOs) resulted in more rigid controllers such as the Phase
Locked Oscillator systems and the Charge-Pump PLLs.
However with the development of discrete devices such as
microcontrollers, various high performance synchronization
methods have been introduced.
The most recently proposed technique that can be used for
grid synchronization is the phase-locked loop (PLL); it is a
control system that generates an output signal whose phase is
related to the phase of an input "reference" signal [7].Some
significant applications are active power filters [8] [9],
uninterruptible power supplies [10], power-factor control [11],
[12], distributed power generation [13] and flexible ac
transmission systems [14]. Synchronous reference framephase-locked loops (SRF-s) are the most widely used systems
for synchronizing signals [15].
A fast and accurate estimation of fundamental positive-
sequence component [16] of grid voltage is essential for
different applications involving FACTS, power devices and
grid connected power converters. It is essential to estimate for
both monitoring and control in order to satisfy grid codes, and
to obtain high performance response.
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This paper proposes an enhanced synchronization structure based on PLL and fundamental positive-sequence components which is used to synchronize the component of grid voltages in three-phase systems that can include distorted and asymmetric voltage terms. Finally, the performances of the digital synchronization structure are investigated in the presence of both ideal and non-ideal grid conditions such as amplitude variation, frequency variation, phase jump and improper phase shift. The analysis is carried out in MATLAB/SIMULINK environment and the obtained results are discussed for effectiveness of the study.
II. OVERVIEW OF PROPOSED SYSTEM
A. Wind Turbine based DG
The PMSG-based wind turbine is fed to an ac-dc-ac
converter so as to maintain the ac output voltage at specified
frequency and amplitude. One of the main challenges is to
provide inverter control to present the customers with
balanced supply voltage. The wind speed is maintained
constant so as to keep the modulation index 1 in the load side
inverter.
B. Synchronous reference frame based PLL
A phase-locked loop is a control system that generates an
output signal whose phase is related to the phase of an input
"reference" signal [6]. Frequency is the time derivative of
phase. Keeping both the input and output phase in lock step
implies keeping the input and output frequencies in lock step.
Consequently it can track an input frequency or it can generate
a frequency that is a multiple of the input frequency. At present Synchronous Reference Frame PLL (SRF-PLL)
is the one of the most employed PLL topology. If the single-
phase voltage input V, is an internally generated signal that is a 90 degrees shifted version of V .The transformation blocks changes the reference frame, bringing the voltages system
from an - stationary reference frame to a d-q rotating synchronous reference frame.
The feedback loop controls the angular position of this d-q
reference frame. In particular the utility voltage vector is
totally lined up to the q-axis. In this way it coincides with all
its q-component; consequently the d-component is made equal
to zero. The q-component describes the voltage vector
amplitude course.
After studying the various Phase Locked Loop schemes
used today in modern power system, we observe that the
Synchronous Reference Frame PLL method provides a simple
yet effective way to measure the phase angle. In case of a
single phase system we obtain the quadrature signal by
delaying the available sinusoid or adopting some other similar
structure, however in 3 phase system this problem is greatly
reduced due to the availability of three phase shifted signals.
Hence by using arithmetic manipulation we obtain the
required orthogonal signal necessary for SRF-PLL
implementation.
C. Current control with PI Regulator:
The PI controller is a linear controller and one of the most
common controllers used in control system. It is based on the
principal of control loop feedback. The error of the measured
and reference output signal is the function of the control
response which will produce an output until it matches the
value of reference. There are two actions to be performed
namely proportional and integral action in the controller. The
proportional term control action is to simply proportional to
the control error. The proportional term output is given by
multiplying the error by a constant Kp(0.08) which is called
the proportional gain constant. The integral term objective in
PI controller is to eliminate control error in steady state. It
calculates and accumulates a continuous sum of the error
signal. The accumulated error is then multiplied by constant
Ki(200) which is called the integral gain constant and gives
the integral control output.
D. Space vector pulse width modulation:
It is used for the control of pulse width modulation. To
implement space vector modulation a reference signal is
sampled with fundamental frequency. The reference signal
needed is generated from the Clarke transformation from three
phase voltage source.
Fig. 1. Synchronization sytem structure
III. PROBLEM FORMULATION
The operation of the proposed synchronized structure is
implemented by considering three phase supply voltage source
Va, Vb and Vc. In order to track the phase angle a discrete
three phase PLL is used. It controls the internal voltage
source. The output consists of estimated phase synchronous
angle and (sin , cos ) for the dq transformation blocks. In steady state sin will be in phase with the fundamental positive sequence of the -component. The PLL also measures the frequency and generates a signal t locked on the variable frequency of system voltage. The sin , cos values estimated using the PLL are used to obtain d, q and zero components
using Park transformation.
))3/2sin()3/2sin(sin(3/2 tItItII cbad
(1)
))3/2cos()3/2cos(cos(3/2 tItItIIq cba
(2)
)(3/10 cba IIII (3)
The current control is usually performed in a d-q synchronous
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reference frame. A distortion free, balanced, constant magnitude three-phase voltage has d components only, while the q and 0 components will be zero. Hence a reference current for Iq is set zero. The controller gains calculated for the PI regulator are Kp=0.08 and Ki=200. The output obtained is again converted into Iabc using Park transformation. The dq0_to_abc Transformation is commonly used in three-phase electric machine models. It transforms three quantities such as direct axis, quadratic axis and zero-sequence components. It is also expressed in a two-axis reference frame back to phase quantities. The following transformation is used:
0)cos()sin( ItItII qda (4)
0)3
2cos()
3
2sin( ItItII qdb
(5)
0)3
2cos()
3
2sin( ItItII qdc
(6)
Fig. 2. Simulation model for positive sequence estimation and current control.
A SVPWM is used to obtain the pulsation for the DC-AC
inverter with U and U as the reference signal obtained by using Clark transformation.
)5.05.0(*3/2 VcVbVaU (7)
)*2/3*2/3(*3/2 VcVbU (8)
IV. RESULT AND DISCUSSION
To verify the effectiveness of the proposed synchronization
system structure, some significant cases have been simulated
are performed using Matlab-Simulink software.
The main objective is to estimate the fundamental positive-
sequence component from the three phase supply voltages
which contains distortion asymmetries. The fundamental grid
frequency is 50 Hz and the sampling frequency used is 5 kHz. 1) Case test-1: The proposed structure is initially tested
with ideal condition without considering any distortion. The
grid positive sequence amplitude is set as 380V.
2) Case test-2: A three-phase voltage unbalance is applied
with a voltage reduction of 50V in each phase.
3) Case test-3: A three-phase frequency unbalance is
produced by a variation of 5Hz from the fundamental grid
frequency of 50Hz.
4) Case test-4: The amplitude is varied by 50V in phase-A
of the grid supply voltage.
Case test-5: Improper phase-shift is produced by having
constant frequency but not the proper phase shift of 120
relative to each other. A phase shift of 5 variation is applied
to the balanced three phase voltage. If you have an odd
number of affiliations, the final affiliation will be centered on
the page; all previous will be in two columns.
Fig. 3. Simulated results for PLL output, grid voltage and current.
Fig. 4. Simulated results for current control.
Waveforms presented in Figs. 5-9 show the simulated
output for the cases described. All of the cases first include
(top plot) the V and V that corresponds to the grid voltages in the - frame. The central plots show the fundamental positive sequence grid voltages V+ and V+ in the - frame. The bottom plots show the phase angle of the
fundamental positive sequence of grid voltages.
Fig. 5. Simulated result for case 1 (ideal condition).
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Fig. 6. Simulated results for case 2 (Three phase voltage unbalance).
Fig. 7. Simulated results for case 3 (Three phase frequency unbalance).
Fig. 8. Simulated results for case 4(voltage unbalance).
Fig. 9. Simulated results for case 5(Improper phase shift).
Different non-ideal conditions were simulated and most were handled well by the system. Unbalances in the three phase input signals were overall handled well by the system. The estimation of fundamental positive sequence component and phase angle tracking was performed well by the system. Although the system could handle the non-ideal cases fairly well it was sometimes slow.
V. CONCLUSION
A PLL can be used to obtain magnitude, frequency and
phase information for estimation of fundamental positive-
sequence component of grid voltage. Accurate and fast
estimation of these quantities can be used for control and
protection of the system. Overall the wind turbine integrated
grid synchronization system based on positive-sequence
estimation is able to handle non-ideal conditions well. The
positive-sequence phase angle is tracked within acceptable
margins and therefore the PLL system as given with the
positive sequence estimation could indeed operate in a real life
application.
ACKNOWLEDGMENT
The authors are grateful to the principal and management of K.L.N college of Engineering, Sivagangai for providing all facilities for the research work
REFERENCES
[1] C.N. Bhende, S.Mishra and Siva Ganesh Malla, Permanent magnet synchronous generator-based standalone wind energy supply system, IEEE Transactions on Sustainable Energy, vol. 2, no. 4, October 2011.
[2] F. Blaabjerg, R. Teodorescu, M. Liserre, and A. V. Timbus, Overview of control and grid synchronization for distributed power generation systems, IEEE Trans. Ind. Electron., vol. 53,no. 5, pp. 1398-1409,Oct. 2006.
[3] J. Svensson, Synchronisation methods for grid-connected voltage source converters, Proc. Inst. Elect. Eng., vol. 148, no.3, pp.229-235,May 2001.
[4] M. Karimi-Ghartemani and M. Iravani, A method for synchronization of power electronic converters in polluted and variable-frequency environments,IEEE Trans. Power syst., vol.19, no. 3,pp. 1263-1270, Aug.2004.
[5] F. M. Gardner, Phase Lock Techniques. New York:Wiley, 1979.
[6] Francisco D. Freijedo, Jesus Doval-Gandoy, Oscar Lopez, Carlos Martinez-Penalver, Alejandro G. Yepes, Pablo Fernandez-Comesana, Andres Nogueiras, JanoMalvar, Nogueiras, Jorge Marcos and Alfonso Lago, Grid-synchronization methods for power converters, Proc. Of IEEE 35th Annual Conference on Industrial Electronics, IECON 2009, pp. 522-529.
[7] FANG Xiong, WANG Yue, LI Ming, WANG Ke and LEI Wanjun,A novel PLL for grid synchronization of power electronic converters in unbalanced and variable-frequency environment, Proc. of IEEE International Symposium on Power Electronics for Distributed Generation Systems: pp. 466-471, 2010.
[8] C. Lascu, L. Asiminoaei, I. Boldea, and F. Blaabjerg, High performance current controller for selective harmonic compensation in active power filters, IEEE Trans. Power Electron., vol. 22,no. 5,pp. 1826-1835,Sep. 2007.
[9] M. Routimo, M. Salo, and H. Tuusa, Comparison of voltage-source and current-source shunt active power filters, IEEE Trans. Power Electron.,vol. 22, no. 2,pp. 636-643, March 2007.
[10] J. M. Guerrero, L. Hang, and J. Uceda, Control of dis tributed uninterruptible power supply systems, IEEE Trans. Ind. Electron., vol. 55, no. 8,pp. 2845-2859, Aug. 2008.
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[11] A. I. Maswood and F. Liu, A unity-power-factor converter using the synchronous-reference-frame-based hysteresis current control, IEEE Trans. Ind. Appl., vol. 43,no. 2, pp. 593-599, Mar./Apr. 2007.
[12] B.Wang, G. Venkataramanan, and A. Bendre, Unity power factor control for three-phase three-level rectifiers without current sensors, IEEE Trans. Ind. Appl., vol. 43, no. 5,pp.1341-1348, Sep./Oct.2007.
[13] T. Ahmed, K. Nishida, and M. Nakaoka, A novel stand-alone induction generator system for AC and DC power applications, IEEE Trans. Ind Appl., vol. 43, no. 6, pp. 1465-1474, Nov./Dec.2007.
[14] H. Awad, J. Svensson, and M. J. Bollen, Tuning software phase-locked loop for series-connected converters, IEEE Trans. Power Del., vol. 20, no. 1,pp. 300-308, Jan.2005.
[15] A. Timbus, M. Liserre, R. Teodorescu, P. Rodriguez, and F. Blaabjerg, Evaluation of current controllers for distributed power generation systems, IEEE Trans. Power Electron., vol. 24,no. 3,pp. 654-664,Mar. 2009.
[16] Pedro Roncero-Sanchez, Xavie del Toro Garcia, Alfonso Parreno Torres, and Vinvente Feliu, Fundamental positive-and negative-sequence estimator for grid synchronization under highly disturbed operating coditions, IEEE Trans. Power Electronics., vol. 28, no.8, August. 2013.
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Reduction of Lower Order Harmonics in a Grid-
connected Single-phase PV Inverter Using Adaptive
Harmonic Compensation Technique
#1
Ananda Raj. A.J
Final Year Student,Department of EEE,
Valliammai Engineering College,
Chennai, India
#2Pratheebha. J,
Assistant Professor,Department of EEE,
Valliammai Engineering College,
Chennai, India
Abstract This paper proposes a novel inverter current control method to mitigate lower order harmonics in a single-phase grid-
connected photovoltaic (PV) inverter. The circuit under
consideration is composed of a PV array, a boost section, a single-
phase inverter with an inductive filter and a step-up transformer
interfacing the grid or the load. The lower order harmonics,
which may be caused by non-ideal factors such as distorted
magnetizing current in transformer due to core saturation, dead
time of inverter, on-state voltage drops in switching etc., need to
be eliminated in order for the PV inverter to meet IEEE
standards. An inverter current control technique, wherein a
modification to the conventional PR controller (proportional-
resonant controller) is done is put forward. This novel controller,
named as proportional-resonant-integral (PRI) controller,
eliminates the dc component in the control system, which
introduces even harmonics in the grid current. An adaptive
harmonic compensation technique, which makes use of an LMS
adaptive filter to eliminate a particular harmonic component in
the output current, is proposed for the lower order harmonic
compensation. The complete design has been validated with
simulation results and the THD of the output voltage/ current
waveforms has been found to be in conformance with the IEEE
standards.
Keywordsodd and even harmonics, MPPT algorithm, boost converter, PRI controller, THD
I. INTRODUCTION
In recent years, distributed generation (DG) systems have
started making use of renewable energy sources owing to the
depletion of conventional energy sources. Distributed
generation allows collection of energy from many sources and
may give lower environmental impacts and improved security
of supply. In this paper, a system utilizing solar energy as the
source and a photo-voltaic inverter to supply the power
generated to the grid is elucidated. The topology of the solar
inverter system[1]
consists of the following three power circuit
stages:
1) a boost converter stage to perform maximum power
point tracking (MPPT)
2) a low-voltage 2-bridge VSI inverter
3) an inductive filter and an RL load
The objective of the paper is to mitigate the lower order
harmonics in this system. The system will not have any lower
order harmonics in the ideal case. However, harmonics are
generated due to the following aspects: distorted magnetizing
current drawn by the transformer due to the nonlinearity in the
BH curve of the transformer core, the dead time introduced between switching of devices, on-state voltage drops on the
switches, distortion in the grid voltage etc.
Harmonics have a negative impact on distribution networks
and influence the behaviour of system components and loads:
For example, conductors suffer from losses and skin effects,
eddy current losses can have detrimental effects on
transformers, with consequent equipment overheating,
capacitors may be affected by resonance phenomena with
potential breakdown, and machines can suffer from vibration
phenomena.
These harmonics need to be mitigated so that the PV
inverter meets standards such as IEEE 519-1992 and IEEE
1547-2003. This paper focuses on the design of an inverter
current control to achieve a good attenuation of the lower
order harmonics.
Fig.1: Schematic diagram of the circuit
Fig.1 shows the circuit block diagram of a single phase grid
connected PV inverter. The DC output from the solar array is
boosted using MPPT scheme. The goal of MPPT technique is
to automatically find the voltage VMPP or current IMPP at which
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a PV array should operate to obtain the maximum power
output PMPP under a given temperature and irradiance. The
boost converter stage employs duty ratio control during
MPPT.
Fig. 2 Power Circuit Topology of single-phase PV System
Fig.2 shows the power circuit topology of a single-phase
PV inverter connected to a grid. The controller employed here
is a PRI (proportional-resonant-integral) controller. This is a
modification to the conventional PR (proportional-resonant)
controller wherein any dc offset in a control loop will
propagate through the system and results in drawing of even
harmonics from the grid. Thus, an integral block is used along
with the PR controller to ensure that there is no dc in the
output current of the inverter. This would automatically
eliminate the even harmonics. The complete scheme is
verified experimentally and the results show a good
correspondence with the analysis.
The organization of this paper is as follows: Section II
discusses the sources of lower order harmonics in the system.
Section III explains the MPPT algorithm used, Section IV
about the design of fundamental current control using a PRI
controller. In Section V, design of the system using MATLAB
and the simulation results are elucidated. In Section VI, the
hardware details are provided. Conclusions are given in
Section VII.
II. LOWER ORDER HARMONICS
A. Harmonics
Harmonics are electric voltages and currents that appear
on the electric power system as a result of non-linear electric
loads. When a non-linear load is connected to the system, it
draws a current that is not sinusoidal. These result in
distortions, termed as harmonics. Harmonic frequencies in the
power grid are a frequent cause of power quality problems.
Some of the major effects of power system harmonics are:
increases the current in the system.
causes poor power factor
transformer and distribution equipment overheating
sensitive equipment failure
B. Lower order harmonics
Harmonics are steady-state distortions to current and
voltage waves and repeat every 50 hertz or 60 hertz cycle.
They occur as integral multiples of the fundamental frequency.
As the frequency increases, the magnitude decreases
gradually, thus making the lower order harmonics the most
predominant and harmful.
For instance, the third harmonic causes a sharp increase
in the zero sequence current, and therefore increases the
current in the neutral conductor. This effect can require special
consideration in the design of an electric system to serve non-
linear loads.
The origin of odd and even harmonics is discussed below:
1) Odd Harmonics: The following are the primary causes for
the lower order odd harmonics:
Distorted magnetizing current drawn by the transformer due to the nonlinear characteristics of the
BH curve of the core
Inverter dead time[2] (proportional to the dead time, switching frequency, and the dc bus voltage)
Semiconductor device voltage drops
Distortion in the grid voltage
Voltage ripple in the dc bus
2) Even Harmonics: The system is susceptible to the presence
of dc offset in the inverter terminal voltage. The dc offset is
caused by one or more of the following factors:
Varying power reference given by a fast MPPT block
Offsets in the A/D converter and the sensors.
C.Evaluation of harmonics:
Harmonics can be quantified using the Fourier series. It
provides a mathematical analysis of distortions to a current or
voltage waveform. Based on Fourier series, harmonics can
describe any periodic wave as summation of simple sinusoidal
waves which are integer multiples of the fundamental
frequency.
The harmonic voltage amplitude for a hth harmonic can
be expressed as
where td is the dead time,
Ts is the device switching frequency, and
Vdc is the dc bus voltage
III. MPPT ALGORITHM
Maximum power point tracking (MPPT) is a technique
that grid connected inverters, solar battery chargers and
similar devices use to get the maximum possible power from
one or more photovoltaic devices, typically solar panels. Solar
cells have a complex relationship between solar irradiation,
temperature and total resistance that produces a non-linear
output efficiency which can be analyzed based on the I-V
curve. It is the purpose of the MPPT system to sample the
output of the cells and apply the proper resistance (load) to
obtain maximum power for any given environmental
conditions. MPPT devices are typically integrated into
an electric power converter system that provides voltage or
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current conversion, filtering, and regulation for driving various
loads. Tracking the maximum power point (MPP) of a photovoltaic (PV) array is a crucial part of a PV system. Many
MPP tracking (MPPT) algorithms have been developed and
implemented. In this paper, the Perturb and Observe (P&O)
algorithm is made use of. That is, in this system, a PV array is
connected to a power converter. Thus, perturbing the duty
ratio of power converter perturbs the PV array current and
consequently perturbs the PV array voltage.
The graphical representation of the algorithm is shown in
Fig 3. It is clear from the graph that incrementing the voltage
increases the power when operating on the left of the MPP
(maximum power point) and decreases the power when on the
right of the MPP. Therefore, if there is an increase in power,
the subsequent perturbation should be maintained to reach the
MPP and if there is a decrease in power, the perturbation must
be reversed.
Fig. 3: P&O algorithm graphical representation
The process is repeated periodically until the MPP is
reached. The system then oscillates about the MPP. The
oscillation can be minimized by reducing the perturbation step
size.The block of MPPT used in the MATLAB simulink is
shown in Fig.4
Fig. 4: MPPT block in MATLAB
IV. DESIGN OF PRI CONTROLLER
This controller uses three blocks- a proportional controller,
a resonant controller and an integral controller.
A proportional control system is a type of
linear feedback control system. In the proportional control
algorithm, the controller output is proportional to the error
signal, which is the difference between the set point and
the process variable. In other words, the output of a
proportional controller is the multiplication product of the
error signal and the proportional gain.
The addition of a resonant block results in a PR controller.
For low order harmonic compensation, PR controllers are
good alternatives to PI(proportional-integral) controller,
especially in grid-connected distributed generation systems.
PR filters can be used for generating the harmonic command
reference precisely in an active power filter and for
implementing selective harmonic compensation.
Yet another development has been made in the controller
by the inclusion of an integral block. If the main controller
used is a PR controller, any dc offset in a control loop will
circulate through the system and the inverter terminal voltage
will have a nonzero average value. The integral block ensures
that there is no dc in the output current and eliminates the even
harmonics.
Fig.5: Block diagram of the fundamental current
control with the PRI controller.
The transfer function of the PR controller is:
The plant transfer function is formed as
where Vdc is the gain of inverter to the voltage reference
generated by the controller impedance (Rs + sLs ) is the
impedance offered by the controller given in s-domain.
Rs and Ls are the net resistance and inductance referred to the
primary side of the transformer, respectively.
Ls includes the filter inductance and the leakage inductance of
the transformer.
Rs is the net series resistance due to the filter inductor and the
transformer.
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First, a PR controller is designed for the system assuming
that the integral block is absent, i.e., KI = 0. Design of a PR
controller is done by considering a PI controller in place of the
PR controller.
With the PI controller as the compensator block in Fig. 4 and
without integral block, the forward transfer function will be
The closed-loop transfer function for Fig. 4 is given by
Without the integral block, the closed-loop transfer function
would be
Now the plant transfer function is,
where M = Vdc/Rs and T=Ls/Rs
The model of PRI controller used in the simulation is
shown in Fig.6.A discrete virtual PLL controller is used in
addition to the PRI controller for the sinusoidal waveform.
Fig.6: PRI Controller block using MATLAB
V. SIMULATION RESULTS
TABLE I
PV INVERTER PARAMETERS
Parameter Meaning Value
Vdc DC bus voltage 40 V
1:n Transformer turns ratio 1:15
wbw Bandwidth of current
controller 84.8 X 103 rad/s
Rs Net series resistance referred
to primary 0.28
Ls Net series inductance referred
to primary 1.41 mH
S1-S4, Sboost Power MOSFETs
IRF
Z44(VDS,max=60V,
ID,max=50A)
Cdc DC bus capacitance 6600 F, 63V
fsw Device switching frequency 40 kHz
Kp Proportional term 3
Kr Resonant term 594
KI Integral term 100
Kadapt Gain in harmonic
compensation block 25.6
Ta Time constant 0.03s
The circuit topology was built in laboratory for a max
power rating of 150W. The various power circuit and control
circuit parameters are listed in Table II. All the design related
plots and the simulation result have the parameters as listed in
Table II.
Fig.7. shows the grid connected single-phase PV inverter
using MATLAB Simulink
Fig.7. Grid connected single-phase PV Inverter
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From the simulation the output voltage waveform from
the solar panel is shown in Fig.8.And by implementing the
Maximum Power Point Tracking Techinque the output voltage
from the inverter is boosted to the maximum voltage and is
shown in Fig.9.
Fig.8. PV voltage with the load .This is the dc output
voltage from the solar panel.
Fig 9. Boosted output DC voltage waveform.
This boosted (i.e.,) Maximum power is passed to the filter
to remove the harmonic content. Harmonic of high frequency
will be eliminated using the filter. The ac voltage from the
transformer is shown in Fig.10.The r.m.s voltage of the output
voltage is 230V .is connected to the grid.This voltage is free
from lower order harmonics.
Fig.10. AC output voltage with the load connected to
grid.
A. FFT Analysis with load
This is the fast fourier transform analysis for the
given circuit. Here it is seen that the harmonics value is
reduced and the THD is only 1.30%
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Fig. 11: FFT Analysis of output voltage.
The above Fig.11 shows the FFT analysis for the output
voltage waveform in the grid where the load is connected. The
Total Harmonic Distortion is found to be 1.30% for 5 cycles
and this is within the IEEE standard. Thus the quality of
power is improved and the lower order harmonics are reduced.
VI. HARDWARE DETAILS
A. Specifications
Transformer 230/15v step-down transformer
MOSFET switches IRF840 (400v, 5A)
Inductor 47microH, 10mH, 100microH
Capacitors 1000F, 2200 F, 10 F, 0.01 F
PN junction diodes 1N4007
Microcontroller dsPIC33FJ64MC802
Voltage sensors 15v/5v (potential divider type)
Current sensors ACS714(hall effect sensor)
MOSFET driver&
Optocoupler IRS2110
B. Hardware snapshots
The hardware setup of a single-phase PV inverter
connected to RL load is shown in Fig. 12. The MOSFET
IRF840 of voltage rating 400V and current rating 5A is taken.
Peripheral Integral Controller of 33FJ64 family is used. In the
driver circuit, IRS2110 has been used. The value of the
resistance is 50 Ohm and inductor 1 mH respectively. The
output voltage across the load RL is shown in Fig. 13.
Fig 12. Hardware setup
Fig 13. Output Voltage waveform
VII. CONCLUSION
Modification to the inverter current control for a grid
connected single-phase photovoltaic inverter has been
proposed in this paper, for ensuring high quality of the current
injected into the grid. For the power circuit topology
considered, the dominant causes for lower order harmonic
injection are identified as the distorted transformer
magnetizing current and the dead time of the inverter. It is also
shown that the presence of dc offset in control loop results in
even harmonics in the injected current for this topology due to
the dc biasing of the transformer. A novel solution is proposed
to attenuate all the dominant lower order harmonics in the
system. The estimated current is converted into an equivalent
voltage reference using a proportional controller and added to
the inverter voltage reference. The design of the gain of a
proportional controller to have an adequate harmonic
compensation has been explained. To avoid dc biasing of the
transformer, a novel PRI controller has been proposed and its
design has been presented. The interaction between the PRI
controller and the adaptive compensation scheme has been
studied.
It is shown that there is minimal interaction between
the fundamental current controller and the methods
responsible for dc offset compensation and adaptive harmonic
compensation. The PRI controller and the adaptive
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compensation scheme together improve the quality of the
current injected into the grid. The complete current control
scheme consisting of the adaptive harmonic compensation and
the PRI controller has been verified experimentally and the
results show good improvement in the grid current THD once
the proposed current control is applied.
The transient response of the whole system is studied
by considering the startup transient and the overall
performance is found to agree with the theoretical analysis. It
may be noted here that these methods can be used for other
applications that use a line interconnection transformer
wherein the lower order harmonics have considerable
magnitude and need to be attenuated.
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Rapid Tracking of MPPT with Buck-Boost
Converter #1
Subash.T,
#Department of EEE, Paavai Engineering College,
Namakkal, Tamilnadu,India [email protected]
#2Thinesh.S,
#Department of EEE, Paavai Engineering College,
Namakkal, Tamilnadu,India
Abstract The power obtained from the sun through the solar
panel is the research work performed in this paper, to extract the
power effectively i.e up to the benchmark of the solar panel
capacity, the effective maximum power point technique (MPPT)
needs to be implemented. there are three types of algorithms
available they are Po, Incremental conductance algorithm . In
this proposed work, a combination of linear approximation and PO Algorithm to achieve maximum-power-point tracking (MPPT)
for PV arrays is proposed. The LA is based on that the
trajectories of maximum power point varying with temperature
are approximately linear. With theLA a maximum power point
can be determined very closer. Moreover,. In the paper a
corresponding LA is made by coding in the panel design which is
simple. As a result, the proposed circuit is cost-effective and can
be with PV arrays easily. Therefore the fluctuations in the steady
state can be minimized .And by using Buck-Boost converter the
voltage has been maintained in the desired level, by having both
combination of step-up and step-down process. The proposed
MPPT method has advantages of faster tracking fewer fluctuation
and higher accuracy over the conventional methods.
Key words:PVarray,MPPT, LA, Buck-Boost Converter, Mosfet
I. INTRODUCTION
Photovoltaic is the technology that uses solar cells or an array
of them to convert solar energy directly into electricity .The
power produced by the array of depends directly from the