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1
Synthesis and Applications of Conjugated Polymers
& it’s Supramolecular Self Assembly for Organic
Photovoltaics
Speaker : Dr. Duryodhan Sahu
Place: National Institute of Science
& Technology
Date : 30-11-2013
2
Outline
Introduction
-Background / Working Principles of Bulk Heterojunction Solar Cell
Motivation
Results and Discussion
Conclusion
3
Background
What is an Organic Solar cell ?
An organic solar cell is a photovoltaic cell that uses organic
polymers or small molecules to convert the Solar energy directly into
electricity by the photovoltaic effect.
Why Organic solar cells ?
Potential Renewable Source ( 1 h sunshine = 3.8×1023kW,
Highest energy demand for human in an entire year =1.6×1020 kW (2005)
Environment friendly
Low-cost synthesis
Easy solution processable
Light-weight flexible devices
Tailoring of electro-optical properties
Goswami, D. Y. Advances in Solar Energy: An annual Review of Research and Development; 2003, Vol. 15.
4
Research interest on solar cell
Li, C.; Liu, M.; Pschirer, N. G.; Baumgarten, M.; Müllen, K. Chem. Rev. 2010, DOI- 10.1021/cr100052z
Figure Solar cells publications by year (Via SciFinder)Figure Certified record power conversion
efficiencies of organic solar cells published in
Progress in Photovoltaics.
5
Applications
6
Classifications of Organic Solar Cells
Organic solar cells can be divided into two main categories:
Dye-Sensitized Solar Cell
Bulk-heterojunction Solar Cell
7
Figure. Procedures for DSSC device fabrication
Dye-Sensitized Solar Cells (DSSCs)
Chen, C. Y.; Wu, S. J.; Wu, C. G.; Chen, J. G.; Ho, K. C. Angew. Chem, Int. Ed. 2006, 45, 5822.
8
Dye-Sensitized Solar Cells (DSSCs)
Figure. Working principles of DSSCs
D + hv → D*
D* → D+ + e-
D+ + 3/2 I- → D + 1/2 I3-
I3- + 2e-→ 3 I-
9
single layer
organic
photovoltaic
cell
Problem
The electric field resulting from the
difference between the two conductive
electrodes is not sufficient to break up the
photogenerated excitons.
Often the electrons recombine with the holes
rather than reach the electrode
Double layer
organic
photovoltaic
cell
Problem
The diffusion length of excitons in
organic electronic materials is typically on
the order of 10 nm. However, a polymer
layer typically needs a thickness of at least
100 nm to absorb enough light. At such a
large thickness, only a small fraction of the
exactions can reach the heterojunction
interface
Glass
ITO
D/A Blend
Cathod
PEDOT/PSS
Bulk Heterojuncton Solar cell
Device Architecture for Bulk Heterojunction solar cell
10
1. Absorption
2. Excitation
3. Exciton Diffusion
4. Charge Transfer
Bulk Heterojunction Solar Cell
Heterojunction Image
P-Solar Cells - FILM PREPARATION
Spin Casting is a easy coating
technique for small areas.
Material loss is very high. Doctor Blade Technique
was developed for large
area coating
Doctor Blade
has no material loss
12
Current Status in the Development of Solar Cells
Green, M. A.; Emery, K.; Hishikawa, Y.; Warta, W. Prog. Photovolt. Res. Appl. 2010, 18, 346.
13
Determination of Organic Solar Cell
Performances
Isc: Short-circuit current
Voc: Open circuit voltage
FF: Fill factor
Pmax : Maximum electrical power
η: Power conversion efficiency Figure. Current -Voltage characteristics of a typical
organic solar cell.
Kietzke, T.; Adv. in OptoElectronics 2007, Article ID 40285, 1.
14
Design Considerations for Organic Solar Cell Materials
Ideal Donor
- Favorable overlap of the absorption spectrum
- Better charge carrier mobility
- Optimized relative positions of the energy levels
Günes, S.; Neugebauer, H.; Sariciftci, N. S. Chem. Rev. 2007, 107, 1324.
Thompson, B. C.; Fréchet, J. M. J. Angew. Chem. Int. Ed. 2008, 47, 58.
15
Literature Riview
Huang, J. H.; Li, K. C.; Chien, F. C.; Hsiao, Y. S.; Kekuda, D.; Chen, P.; Lin, H. C.; Ho, K. C.; Chu C. W. J. Phys. Chem. C 2010, 114, 9062.
Blouin, N.; Michaud, A.; Leclerc, M. Adv Mater 2007, 19, 2295.
Yue, W.; Zhao, Y.; Shao, S.; Tian, H.; Xie, Z.; Geng, Y.; Wang, F. J. Mater. Chem. 2009, 19, 2199.
Li, Y. W.; Xue, L. L.; Li, H.; Li, Z. F.; Xu, B.; Wen, S. P.; Tian, W. Macromolecules 2009, 42, 4491
16
Huang, F.; Chen, K.-S.; Yip, H.-L.; Hau, S. K.; Acton, O.; Zhang, Y.; Luo, J.; Jen, A. K.-Y. J. Am. Chem.
Soc. 2009, 131, 13886.
Duan, C.; Cai, W.; Huang, F.; Zhang, J.; Wang, M.; Yang, T.; Zhong, C.; Gong, X.; Cao, Y.
Macromolecules 2010, 43, 5262.
Conjugated Polymers for Bulk-heterojunction Solar Cells
Sahu, D.; Padhy, H.; Patra, D.; Huang, J. H.; Chu, C. W.; Lin, H. C.; Journal of
Polymer Science: Part A: Polymer Chemistry, 2010, 48, 5812
17
V. Gupta, A. K. K. Kyaw, D. H. Wang, S.Chand, G. Bazan, Alan J. Heeger.
PCE = 8.6 %
PCE = 1.8 %
D. Sahu, C.-H Tsai, H.-Y. Wei, K.-C. Ho, F.-C. Chang C.-W. Chu, J. Mater.
Chem., 2012, 22, 7945
Conjugated Small molecules for Bulk-heterojunction Solar Cells
18
Motivation
Yang, P. J.; Wu, C. W.; Sahu, D.; Lin, H. C. Macromolecules 2008, 41, 9692.
Liang, T. C.; Chiang, I. H.; Yang, P. J.; Kekuda, D.; Chu, C. W.; Lin, H. C. J. Polym. Sci. Part A: Polym. Chem. 2009, 47, 5998.
1. Easy purification and functionalisation process
2. lack of problems in molecular weight
distributions
3. Poor solvent processability due to low solution
viscosity.
4. Irregular film morphology
Small Molecule Polymer
1. Not easy to purify or functionalise
2. Do have problems in molecular weight distributions
3. Better solvent processability and higher solution
viscosity.
4. Better film morphology
Therefore, in order to get the advantages of both oligomeric and polymeric properties, an attractive approach
would be: The well-defined supramolecular architectures of π -conjugated oligomers with the
processabilities of polymers.
19
Figure . Structure of H-Donor dyes (S1, S2) and H-Accepting Polymer
Result and Discussion
20
Result and Discussion
21
Figure. Synthesis of H-Bonded Polymer Networks
Synthetic procedures of H-Bonded Polymer Networks
22
Thermogravimatric Analysis
382o C
374o C
23
Optical Properties
24
Cyclovoltametry
Polymer Eox,onset (V)a Ered,onset (V)a HOMO (eV)b LUMO (eV)b Eg cv, (eV)
PFNA/S1 0.90 -0.91 -5.25 -3.44 1.81
PFNA/S2 1.01 -0.90 -5.36 -3.45 1.91
a Onset oxidation and reduction potentials measured by cyclic voltammetry in solid filmsb HOMO/LUMO = [-(Eonset - 0.45) - 4.8] eV, where 0.45 V is the value for ferrocene vs. Ag/Ag+ and 4.8 eV is the energy level of
ferrocene below the vacuum.
Table 2. Electrochemical Properties of H-bonded Polymer networks
25
Photovoltaic Properties
26
Conclusion
Organic solar cell do have the potential to achieve higher power
conversion efficiencies and cost effective than the existing
conventional Silicon based solar cells
In order to get the advantages of both oligomer and polymeric
properties in organic photovoltaic applications ,the concepts of
supramolecular architectures by complexation of H-donor dyes
with a side-chain H-acceptor homopolymer via hydrogen
bonding may be encouraging for the future research.
27
Thank you for your kind
attention
“We were born to unite with our fellow men, and to join in community with the human race”
(Marcus tullius cicero)
RECENT ADVANCEMENTS IN PROTECTION
OF SMART GRID
Presented by:
Rajlaxmi saha
Hi-tech college of Engineering
CONTENTS
INTRODUCTION
SMART GRID TECHNOLOGY
SMART GRID SECURITY REQUIREMENT
SMART GRID TRANSIENT ENVIROMENT
PROBLEM DETECTION AND MITIGATION
CONCLUSION
REFERENCES
INTRODUCTION
Smart grid is a method to decrease dependency on energy
sources, reduce emission of global warming components and
create a reliable sources of electricity.
It is two way flow as grid as in smart grid, electricity can also
be put back into grid by user.
Security means the cyber attacks and protection from transient.
Internet based IPV4 andIPV6 developed many years will
provide cost effective transport.
One way is by SCADA which have various capabilities and
securities and other one is transient environment of smart grid.
The lightning will continue to produce direct and coupled
transient that propagate in conductor until it grounded by surge
devices.
SMART GRID TECHNOLOGY
SECURITY REQUIREMENT AND
SOLUTION
It depends on :
• Authentication
• Authorization
• Privacy Technologies
Solution offering strong security and high performance are:
FIPS: Federal information processing standard.
AES: Advanced Encryption Standard
3DES: Triple Data Encryption Algorithm.
It is based on public key infrastructure(PKI) Technology.
PKI used as digital certificate which binds with certificate authority(CA).
Communication begins by sending certificate signing request(CSR) to registration authority(RA).
Then RA to CSR then to CA, which then issue certificate
It sends to relying party(RA)
RP validates the certificate by requesting the certificate statues from validation authority(VA).
PKI allow the chain of trust, when 1st CAs extend trust to second CA s, this enables RP to trusts the 1st CA.
When two CA issue each other certificate it is cross signing.
This way trust from one organization to other organization
Now it enable secure mode.
SMART GRID TRANSIENT ENVIROMENT
Applying SPDs( surge protective device) to the Smart Grid.
Two power sources are there primary power sources and
alternate power source.
Primary power supplied from utility.
Alternate power is supplied from on site resources.
It require automatic transfer switch (ATS) to ensure coordination
of both sources.
ATS allow alternate power sources to be networked into smart
grid.
SPDs devices used to protection from transients.
Protecting equipment from transient requires SPDs at both
sources.
SPDs(Accordance to National Electric Code)
It should not be used on undergrounded, impedance grounded,
or corner grounded system unless approved for use.
SPDs marked with short circuit current rating.
SPDs connected indoors or outdoors.
Conductors used to connect SPDs should be as short as
possible.
It should be permit between any two conductor.
PROBLEM DETECTION AND
MITIGATION
Blackout occurs.
Power Loss.
SCADA and other energy management systems have long
been used to monitor transmission systems, visibility into the
distribution system has been limited.
Dispatchers will require a real-time model of the distribution
network capable of delivering.
• Real-time monitoring
• Anticipation
• Isolation
CONCLUSION
The Smart Grid will revolutionize generation, distribution and
utilization of electrical energy similar to how the Internet has
revolutionized communications.
At lower costs and lower levels of pollutants.
More reliable
To protect the Smart Grid, and all the advanced electronic
which proven performance, demonstrated safety and reliability
require.
The need for a cohesive set of requirements and standards for
smart grid security.
REFERENCES[1]. Anthony R. Metke and Randy L. Ekl “Security Technology for Smart Grid
Networks” IEEE TRANSACTIONS ON SMART GRID, VOL. 1, NO. 1, JUNE
2010.
[2]. Draft smart grid cyber security strategy and requirements, NIST IR7628, Sep.
2009[Online]. Available:http://csrc.nist.gov/publications/drafts/nistir-
7628/draft-nist-7628.pdf
[3]. E.O. Lawrence Berkley National Laboratory (2001). Scoping Study on Trends
in the Economic Value of Electricity Reliability to the U.S. Economy. Available
[on-line] at http://certs.lbl.gov/pdf/47911.pdf. Retrieved 2009, July 31
[4]. Institute of Electrical and Electronics Engineers (2005). IEEE Recommended
Practice for Powering and Grounding Electronic Equipment. IEEE Standard
1100, Emerald Book. Piscataway, NJ, USA.
[5]. National Fire Protection Association (2008). National Electric Code. NFPA 70.
Quincy, MA, USA.
[6]. Litos Strategic Communication (2009). The Smart Grid: An Introduction.
Prepared for the U.S. Department of Energy under contract DE-AC26-
04NT41817, Subtask 560.01.04.
THANK YOU!!!
POWER QUALITY ISSUES IN DISPERSED
GENERATION
Anita ShialHI-TECH COLLEGE OF ENGINEERING
BPUT,ODISHAEmail:[email protected]
CONTENTS
Introduction
Dispersed Generation
Power Quality Issues
Possible Solutions
Conclusion
Reference
Introduction• The World today is moving toward smart distribution grids & dispersed generation.
one of the most important issues in future grids are power quality & supplyreliability issues.
• Power quality concerns the electrical interaction between the network and itscustomers which consists of two parts: the voltage quality concerns the way inwhich the supply voltage impacts equipment; the current quality concerns the wayin which the equipment current impacts the system & various power quality issuesare Voltage fluctuation, Voltage Sag, flicker etc.
• Dispersed generation has been recommended as one of the environmentallyfriendly solutions for improving the energy system, decreasing the losses andincreasing effectiveness.
• This presentation will focus on how the change to dispersed generation & whatare the main problems, that need quick & active solutions so that future gridswould be fully functional & reliable.
Dispersed Generation
• Dispersed generation is the production of electricity at or near the point ofuse. Most or part of consumed energy is produced at point of use and restof the electricity goes into the distribution grid.
• Dispersed Generation defines distributed generation as all generationunits with a maximum capacity of 50 MW to 100 MW, that are usuallyconnected to the distribution network & that are neither centrally plannednor dispatched.
Power Quality Issues
• Connection of dispersed resources and changing dispersed generation tothe distribution grid can affect the power quality in a great amount.
• The widespread use of nonlinear loads may implicate significant reactivepower and problems with higher harmonics in a grid.
• Harmonic currents produced by nonlinear loads are injected back into thesupply systems which can interact adversely with a wide range of powersystems equipment causing additional losses, overheating andoverloading.
Possible Solutions
• The approach is to implement additional functionality into powerelectronic equipment which is permanently connected to the grid, e.g.inverters to improve power Quality
• The combination of power electronics and communication technologyenables the control of a distributed system.
CONTD….
Fig. shows the possible voltage variation with the distance from the transformer for different load and generation conditions.
Increasing Voltage Quality and Grid Capacity by Reactive Power
The reactive power control structure consists of three different controls:
• Voltage limitation by reactive power consumption
• Smoothing of voltage fluctuations
• Reactive power compensation
Voltage limitation by reactive power consumption
•If reactive power is used for limiting the grid voltage additional power losses are generated in the inverter and in the grid lines due to the higher grid current. •But the benefit is that higher active power can be transmitted and a surplus of solar generated electrical power can be fed in to the grid.•Therefore it is appropriate to provide the reactive power not by a static characteristic of the inverters but to minimize the reactive power absorption by individually activating those inverters which have the most significant effect to the grid voltage.
Smoothing of voltage fluctuations
• Fluctuating power input due to passing clouds or highly fluctuating loads cause voltage fluctuations in the low voltage grid
• Provision of reactive power (capacitive) at negative voltage peaks and reactive power absorption (inductive) at positive voltage peaks by the distributed solar inverters can smoothen voltage fluctuations in the grid.
• The risk of flickers can be reduced by such an additional control that is implemented locally in the inverters
Reactive power compensation
• Reactive power compensation to this date requires additional equipment and associated installation and commissioning costs which should be compensated by greater efficiencies. So far, compensation is mainly used in large industrial plants.
• Therefore, generating decentralized reactive power for compensation significantly lowers the power losses due to short transmission distances of the reactive power
Conclusion
• If more and more dispersed generation is going to be installed all over thepower networks like it seems to go then it is most important to findmeasures for guarantying quality and security of supply.
• In the situation where generation as well as consumption producesdecrease of power quality in the grid which is essential to analyze bothgeneration and consumption in a very thorough way.
• Appropriate on-line diagnostics of dispersed generation units must beapplied to guarantee sufficient power quality, supply reliability and overallsafety of customers and different facilities connected to the grid.
Reference
[1]M. Bollen, Understanding power quality problems: Voltage Sags andInterruptions, 1st ed., Wiley-IEEE Press, 2000, p. 543.
[2]K.Purchala, R. Belmans, L. Exarchakos, A. D. Hawkes, “Distributedgeneration and the grid integration issue”, KULeuven, Imperial CollegeLondon
[3]T. Ackermann, G. Andersson, L. Söder, “Distributed generation: adefinition”, Electric Power Systems Research, vol. 57, pp. 195–204,2001.[Online].Available:http://dx.doi.org/10.1016/S03787796(01)00101-8
[4]T.Vaimann, J. Niitsoo, T. Kivipõld, “Dispersed generation accommodationinto smart grid”, in Proc. of the 52nd International Scientific Conference ofRiga Technical University. Section of Power andElectrical Engineering, RigaTechnical University Press, 2011, ID-42.
THANK U
VOLTAGE MODE CONTROL FOR IMPROVING MPPT PERFORMANCE IN
PV SYSTEM
Presented by:
Pedda Suresh Ogeti
Department Of Electrical Engineering
1
Outline2
Introduction
Algorithms of PV System
Block diagram of PV sytem
Simulation diagram
Results
Conclusions
Future Work
References
Introduction3
1. The growing demand for energy, together with the increased price of oil products and the attention paid to environmental pollution, have progressively increased the interest in renewable energy sources.
2. Solar power is an alternative technology that will hopefully lead us awayfrom our petroleum dependent energy sources.
3. Solar panels themselves are quite inefficient (approximately 30%) in theirability to convert sunlight to energy
4. However, the charge controllers and other devices that make up the solarpower system are also somewhat inefficient and costly.
5. . The maximum power point tracking (MPPT) of the PV output for all sunshineconditions, therefore, becomes a key control in the device operation forsuccessful PV applications.
6.The MPPT control is, in general, challenging, because the sunshine conditionthat determines the amount of sun energy into the PV array may change allthe time, and the current voltage characteristic of PV arrays is highlynonlinear.
Problem Formulation
4
Since the PV array has to be operated at
maximum power output at all radiations and
temperature, it is necessary to track the voltage
corresponding to the maximum power point.
In almost all the linear controllers, this method is
generally used and MPPT algorithms are generally
used for tracking purpose.
But still the MPPT efficiency is not upto the required
mark.
Block diagram of single phase grid
connected PV system5
ALGORITHMS FOR MPPT6
1.Fractional Voc
2.Fractional Isc
3.Perturb and observe
4.Hill Climbing
5.Incremental Conductance
and many algorithms are proposed in literaturefor improving MPPT, but mainly 3 algorithms areprominently used, like P&O algorithm, Hillclimbing algorithm and Incremental conductancealgorithm.
Fractional Open-circuit Voltage
Method 7
Since there is near relationship between VMPP and Voc of the PVarray, under varying irradiance and temperature levels, has givenrise to the fractional Voc method.
VMPP = K1 Voc
Where K1 is a constant of proportionality. Since K1 is dependent onthe characteristics of PV array being used, it usually has to becomputed beforehand by emperically determining VMPP and Voc forthe specific PV array at different irradiance and temperature levels.The factor K1 has been taken is inbetween 0.71 to 0.78.
Once K1 is known, VMPP can be computed using the above equationby momentarily shutting down the power converter.
The main disadvantage in this method is temperary loss of power isincurred.
Fractional Short circuit Current Method8
Fractional Isc results from the fact that, under varyingatmospheric conditions, IMMP is approximately linearlyrelated to ISC of the PV array as given below
IMMP = K2 ISC .
Where K2 is the proportionality constant. The value of K2 isgenerally found to be between 0.78 and 0.92.
Measuring Isc during operation is problematic.
An additional switch usually has to be added to the powerconverter to periodically short the PV array so that ISC canbe measured using a current sensor.
This increases the number of components and cost. So, theswitch in boost converter itself is used to short the PV array.
P&O Algorithm9
1) Increasing the voltage increases the
power on the left of MPP
2) Decrementing the voltage decreases the
power on right of MPP
3) The process is repeated until the MPP is
reached. The system then oscillates at
MPP. The oscillation can be minimized
by reducing the perturbation step size.
4) Disadvantage in P&O and Hill climbing
method is, under rapidly changing
environmental conditions, this algorithm
fails to track the MPP.
Hill Climbing Algorithm10
Hill climbing algorithm is similar
to P&O algorithm, but instead
of Voltage, duty cycle ratio is
perturbed.
Incremental Conductance Algorithm11
( )
at MPP
left of MPP
right of MPP
dP d IV II V
dV dV V
I I
V V
I I
V V
I I
V V
0,
0,
0,
dpleft of MPP
dv
dpat MPP
dv
dpright of MPP
dv
Comparison of Parameter Performance
of PV Algorithms12
Algorithm /
Parameter
Perturb & Observe Hill-Climbing Incremental
Conductance
Dependence Voltage variation Duty ratio variation Conductance
variation
Complexity Low Low Medium
Analog/Digital Both Both Digital
Periodic tuning No No Yes
Sensed parameters Voltage, current Voltage, current Voltage, current
Convergence speed Relatively low Relatively low Relatively high
Control techniques proposed in
literature for improving MPPT13
Voltage mode control
Current mode control
New MPPT tracker using Sliding Mode observer forestimation of Solar Array Current in the Gridconnected Photovoltaic system
Extremum seeking control
Fuzzy logic control
Feedforward control
Neural Network approach control
Characterestics of PV Cell14
Characteristics of PV cell15
For PV cell, the characteristics has been plotted for different temperatures by keeping
the insolation constant.
Current, Power Vs Voltage of PV cell16
It is observed that Maximum power is 3.5W at 0.54V. So, for tracking this
maximum power point voltage at different insolation and temperature, algorithms
are being used.
Proposed Model for MPPT using Error
Amplifier17
Dynamics of Error Amplifier18
2 2e ref A
1 1
Z ZV = 1+ V - V (1)
Z Z
VeD = (2)
Vcarrier
1 1Z =R (3)
21 2
2
21 2
1 1+R
sC sCZ = (4)
1 1+ +R
sC sC
2 1
2 1 22
2 1
1+
R C= (5)
(C +C )C +
R C
s
s s
2 12
21
2
1+
R CZ = (6)
2sC +
R
s
s
21
1 +ω = (7)
C +2sω
s
s
2 1
1where ω= (8)
R C
2 2 21
1 +ω Z = (9)
C (s+ω) -ω
s
-ω t2
1
1 Z = e sinωt (10)
C
-ω t -ω te Ref A
1 1 1 1
1 1V = 1+ e sinωt V - e sinωt V (11)
R C R C
Design of error Amplifier in Boost
Converter for increasing MPPT efficiency19
Output, carrier and switching pulses Vs
time20
The input voltage considered is 10V,20V, 30V for showing the variation of switching
the power devices in dc-dc converter.
Output, carrier and switching pulses Vs
time21
The boost converter output voltage is compared with the referencevoltage in the error amplifier and the error voltage Ve is noted,which again is compared with the carrier voltage of fixed frequency(20KHz) and fixed carrier voltage (15V) in the PWM comparator.
The pulses obtained from the PWM comparator are used forswitching power devices.
When error voltage is intersecting with the fixed carrier frequency,switching pulses are generated which gives switching pulses asshown in previous slide.
So the switching is very important to get the required outputaccording to the load variations.
Error amplifier and PWM comparators in which the first order poleand zero is used to obtain the error voltage and PWM switchingpulse.
The error amplifier is designed in such a way that the poles andzeros are nearer to origin which improves the stability of the system.
Output, carrier and switching pulses Vs
time22
Output, carrier and switching pulses Vs
time23
Output, carrier and switching pulses Vs
time24
Conclusions25
All the techniques discussed improve the maximumpower point tracking in PV system.
voltage control mode discussed above are trackingthe maximum power by using the converters(boostconverter and inverter).
Different algorithms are proposed and voltagemode control along with error amplifier is alsoexplained for improving MPPT.
Voltage control mode given better results comparedto MPPT algorithms.
Future Work26
Immediate future work is to develop a prototype for Hybridenergy system and interfacing with the MPPT controller forextracting maximum efficiency.
Further future works include:
• To design Boost Converter and Inverter incorporatingMPPT.
• In NIT, Rourkela, 5KW Hybrid PV-Wind Energy Systemhas been installed. Experiments has to be carried forimplementing the MPPT controllers and improving theefficiency .
• Implementing the developed results on FPGA set up forverification.
References 27
[1] B. M. T. Ho and H. S.-H. Chung, ―An integrated inverter with maximum power tracking for grid-connected PV systems,‖ IEEE Trans. Power Electron., vol. 20, no. 4, pp. 953–962, Jul. 2005.
[2] M. Calais, J. Myrzik, T. Spooner, and V. Agelidis, ―Inverters for single-phase grid connectedphotovoltaic systems—an overview,‖ in Proc. IEEE Power Electronic Specialists Conf., Jun. 2002,pp.1995–2000.
[3] S.B. Kjaer, J.K. Pedersen, and F. Blaabjerg, ―A review of single-phase grid-connected invertersfor photovoltaic Modules,‖ IEEE Trans. Ind.Appl., vol. 41, no. 5, pp. 1292–1306, Sep./Oct.2005.
[4] S. Saha and V. P. Sundarsingh, ―Novel grid-connected photovoltaic inverter,‖ Proc. Inst. Elect.Eng., vol. 143, no. 2, pp. 143–56, 1996.
[5] B.K.Bose, ―Energy, environment, and advances in power electronics,‖ IEEE Trans. Power Electron.,vol. 15, no. 4, pp. 688-701, Jul. 2000.
[6] A.J.Forsyth and S.V.Mollov,‖Modelling and control of DC-DC converters,‖ Power EngineeringJournal, Vol.12,issue 5,pp.229-236,1998.
[7] Juing-Huei Su, Jiann-Jong Chen Dong-Shiuh Wu, ―Learning Feedback Controller Design ofSwitching Converters Via MATLAB/SIMULINK‖ in IEEE TRANSACTIONS ON EDUCATION, VOL.45, NO. 4, NOVEMBER.
THANK YOU
28
National Seminar on
DISPERSED GENERATION AND SMART GRID
DGSG-2013
Presented byMs Sasmita Padhy, NIST, Berhampur
Mr B. Rajanarayan Prusty, NIST, Berhampur
• What is Grid?
• Why it is needed to integrate renewable energy to Electric Grids?
• What are the challenges/ barriers in integrating the renewable energy sources with electric Grid?
Need to integrate renewable Energy with Existing Grid
• Lack of fossil fuel.
• Increasing demand for electricity.
• Harmful effect of carbon dioxide on the climate.
• To reduce emissions and conserve available fossil sources.
Contd…
• Need a technological change from a generation dominated, security and reserve thinking centralized grid to demand oriented, economically/ecologically optimized decentralized grid.
• Allocation of technologies to store the excess electricity and control different processes with adequate communication port.
How RE can be integrated with grid?
Integrating RE storage including solar PV into
electricity Grid.
Grid security and modernization can be done by accelerating the integration of solar power to national grid investigating modern storage techniques.
RE Grid integration challenges
Wind and solar generation experiences
• Intermittency
• Non controllable variability
• Partial unpredictability
• Depends on resources that are location dependent
• Wind and solar output variation can not be controlled.
• Causes the power output variation.
• Need an external energy to balance supply and demand on the grid.
• Need frequency regulation and voltage support
Non controllable variability
Continuation
• Hourly wind power output on 29 different days in the month of April at the Tehachapi ind plant in California
• Availability of wind & sunlight.
• Can be managed through improved weather & generation forecasting.
• Reserves should ready when RE generation produces less energy.
• Despathable load should available when RE generation produces more energy
Partial unpredictability
Continuation
• Example of a day-ahead forecast scenario tree for the wind power forecast in US.
• Wind and solar resources are based in specific location
• Can not be transported to a generator site
• New transmission capacity required to connect wind and solar resources to the grid
Location dependence
Transmission Technology
Large capacity RE plants are located far away from loadcentres.
For large capacity RE power transmission –
AC Transmission
Offshore wind power integration –
VSC-HVDC
* CSC-UHVDC are planned for large capacity RE.
AC Transmission
Power transmission capacity =
For small-to-medium scale RE power plants,transmission lines below 330 kV are usually used.
For large scale, long-distance RE power,transmission lines above 500 kV are usuallyneeded.
AC transmission above 500 kVfor RE integration in China and the USA
USA Improvements
Three major 500 kV transmission projects (inCalifornia) are under construction for RE powertransmission.
China Improvements
In November 2010, a 2 398 km double-circuit 750kV transmission line was commissioned.
- Xinjiang and the Northwest
Also the Transmission of the Phase I Jiuquan windpower base (installed capacity of 5 160 MW).
A small portion is locally consumed.
*A second 750 kV transmission corridor is nowunder construction.
Transmission of the phase I Jiuquan Wind power base, Northwest China
Why VSC-HVDC Transmission is desirable for RE Integration?
Advantages of IGBT-based VSC-HVDC over Thyristor
based CSC-HVDC:
Rapidly control of both real and reactive power(independently, within its rated MVA capacity).
VSC-HVDC terminals can generate or absorb a givenamount of reactive power as instructed oraccording to the voltage level of the connected ACgrid.
Contd…
It does not require support from theconnected AC grid for commutation and cantherefore be connected to weak AC grids.
VSC-HVDCprojects commissioned for RE integration
There were more than 12 VSCHVDCtransmission projects under construction allover the world.
Contd…
HVDC projects under construction in theworld has reached 10 GW, which is four timeshigher than that of the projects built before2009.
Operational Technologies
• Operation of the power system with highpenetration of charge capacity RE generation,RE power forecasting is critical for Gridoperators.
Power forecasting methods
Short-term forecasting is currently used with a time scale up to 48 to 72 hr.
Physical method.
Statistical method.
ConclusionRenewable energies, driven by climate change, fuelsecurity and other motives, will be providing more andmore of our electricity in the future. They represent anopportunity and a risk.
It is assumed simply that excellent reasons exist for theshare of renewable in the energy mix to grow considerably,and that they will therefore do so.
The renewable energies in question are wind and solar –both photovoltaic and thermal – and the risk is that if theyare present on a large scale their variability andunpredictability will prevent the correct functioning of thewhole electricity supply grid.
References• Richard Piwko, et al.: A Blast of Activity: Wind Power at
the IEEE Power & Energy Society, IEEE Power & EnergyMagazine, 9(6), p26-35, Nov/Dec 2011.
• Mark Ahlstrom, et al.: Atmospheric Pressure: Weather,Wind Forecasting, and Energy Market Operations, IEEEPower & Energy Magazine, 9(6), p97-107, Nov/Dec2011.
• Mark G. Lauby, et al.: Balancing Act: NERC’s Integrationof Variable Generation Task Force Plans for a LessPredictable Future, IEEE Power & Energy Magazine,9(6), p75-85, Nov/Dec 2011.
Demand Response In Smart
Micro-grids
National Seminar on
Dispersed Generation and Smart Grid
Presented by
G. Sivaranjani
Smitanjali Bhukta
TALK FLOW
Introduction
Demand response
Micro-grids
Agent Based Strategy
Intelligent Agents
CDA Market
Bidding Strategy
Micro-grid Architecture
An Example
Future Work
References
INTRODUCTION
Mismatch between supply and demand can be overcome
by effectively utilizing DERs or encouraging demand
side management.
Demand response is one of the technique to demand side
management.
An agent based architecture is used to simulate virtual
markets enabling customers of the market to participate
in demand response and trade power using intelligent
trading strategy.
DEMAND RESPONSE
Demand Response (DR) is defined as “Changes in
electric usage by end-use customers from their normal
consumption patterns in response to changes in the price
of electricity over time”.
This technique is verified here with two microgrids.
MICROGRIDS
A microgrid is an aggregation of DERs and loads.
Distributed Energy Resources (DER) is defined as
smaller-scale power generation or storage system.
DERs include distributed generation (DG) and
distributed storage (DS).
AGENT BASED STRATEGY
• This strategy requires three basic entities :
Intelligent agents (IA) for setting up a Multi-agent system
(MAS).
An environment or market to place these IA‟s.
A strategy to survive in the market.
Intelligent Agents (IA)
An agent is “a software (or hardware) entity that is placed in some
environment and is able to autonomously react to the changes in that
environment”.
An intelligent agent is an agent who exhibits :
◦ pro-activity (goal-directed behaviour)
◦ social ability (able to interact with other intelligent agents)
◦ reactivity (react to changes in the environment in a timely fashion).
The application of MAS is to construct robust, flexible and extensible
systems or as a modeling approach.
Continuous Double Auction (CDA) Market
A CDA is a market place with agents selling goods called sellers and
agents buying goods called buyers.
The sellers and buyers in any CDA market trade single type of goods like
power or energy.
An “ask” is the price placed by a seller to sell one unit of the goods.
A “bid” is the price placed by a buyer to purchase a unit of goods.
At any time in the market, the current lowest “ask” is called outstanding
ask and is generally represented by oa.
Similarly, the current highest bid in the market is called outstanding bid
and is represented by ob.
A valid ask is lower than the present oa and a valid bid is a bid higher
than present ob.
CDA Market (Contd..)
CDA progresses in rounds and in each round invalid asks and bids are
neglected by the market.
In each round at most one unit of goods will be cleared and hence a CDA
run will have multiple rounds and it terminates when all possible matches
have made.
The match price is equal to the average of oa and ob.
In competitive grid connected energy markets this range will be Grid
buying price (GBP), Grid selling price (GSP).
The role of agents in agent based CDA markets is to represent their owners,
who may be buyers or sellers to achieve a good profit.
Bidding Strategy
• Each trading agent in CDA follows a bidding strategy which allows
him/her to squeeze maximum profit from the market.
• The bidding strategy followed by any trading agent merely requires two
types of data, the global and local data.
• The global data includes acceptable market price range, present oa and ob,
winner of the last round, matching price history and supply to demand
ratio.
• The local data includes expected profit margin, how many units of goods to
trade, the fore cast of future market and risk attitude.
• In this paper, trading agents follow the intelligent bidding strategy.
• If „p‟ is the bid/ask price placed by a trading agent, then the price should
always be better than or equal to a price called limit price (LM) of the
trader.
• The relative difference between p and LM is called profit margin (PM).
• Qualitatively the intelligent strategy followed by trading agent is as follows
in CDA market.
– An intelligent selling agent raises its profit margin whenever the last ask was
accepted.
– An intelligent buying agent raises its profit margin whenever the last bid was
accepted and is less than LM of the buying agent.
• The proposed intelligent strategy computes the target price Ti(t) as
Ti (t) = Ai (t) *S +Bi (t) * e(t)
where Ai(t) , Bi(t) are the random values.
S is standard target
e(t) is called eagerness and is calculated as the present
ratio of supply to demand.
MICRO-GRID ARCHITECTURE
MICROGRID ARCHITECTURE FOR DEMAND RESPONSE
A. Load and Generation Agents :
• The agents in the bottom level of the architecture are Lxy and Gxy which represents
Load and Generation entities.
• The term „xy‟ in „Lxy‟ indicates load agent‟s location and association.
The agents „Lxy‟ and „Gxy‟ have intelligence to bid in the auction conducted
either by GAA or LAA.
Upon requested by MIA, each load and generation agent collects owner choice
and informs back to the MIA.
Apart from collecting owner choice „Gxy‟ has intelligence to sign bilateral
contracts and to remind the owner regarding the signed contracts.
B. Micro-grid Intelligent Agent (MIA):
It is responsible for conducting auction among local agents by
maintaining equal supply and demand in the local market.
MIA makes local market cheaper and provides a privilege of
„participation‟ to high priority loads.
The PMA of each MIA is responsible for maintaining priorities of the loads
having association with MIA.
The PMA calculates priority index of each load agent.
PMA issues gate pass to high priority loads to enter local market.
The LAA keeps track of the trading in the local market and also calculates
the average price.
C. Demand Response Agent (DRA):
Its role is to receive and serve the demand response options by load
agents. It has two internal agents BCMA and FBMA.
The BCMA is responsible recognizing the bi-lateral contracts with
other local agents and it helps to allocate the load agents to the
generation agents.
OPERATION OF BCMA FOR BI-LATERAL CONTRACTS
The FBMA examines the results communicated by BCMA and
identifies the number of virtual buyers to be created.
The key role of these virtual buyers or FBA is to purchase power
from the global market on behalf of the local agent participating in
demand response and after finishing this job they are automatically
terminated by the FBMA.
D. Global Intelligent Agent (GIA):
This agent is responsible for initiating all the local markets and
conducting the auctions with global scope.
It also records the successful contracts and thereby informs
corresponding traders.
The GAA conducts auction among the buyer agents and seller
agents willing to buy/sell power from global market.
The RA plays an instrumental role in recording all the successful
transactions among global markets.
AN EXAMPLE
The demonstration for the above mentioned architecture is
implemented on a two micro-grid system and its operation is
indicated through the flowchart.
TWO MICRO-GRID SYSTEM
FLOW CHART FOR THE ABOVE CONSIDERED SYSTEM
FUTURE WORK
In this work an agent based architecture for trading and power
management in micro-grids are presented.
The proposed system uses continuous double auction algorithm for
trading.
Priority for customers participating in demand response at trading
level is novel.
The concept mentioned above has to be simulated with two micro-
grids system using JADE framework.
This work will be extended for a multiple micro-grid system.
REFERENCES
H.S.V.S. Kumar Nunna and Suryanarayana Doolla, “ Demand
Response in Smart Microgrids”, IEEE PES Innovative Smart Grid
Technologies- India, 2011.
H.S.V.S. Kumar Nunna and Suryanarayana Doolla, “Demand
Response in Smart Distribution System With Multiple Micro-grids”,
IEEE Transactions on Smart Grid, Vol.3, No. 4, pp 1641-1649, Dec
2012.
J. M. Guerrero, F. Blaabjerg, T. Zhelev, K.Hemmes, E. Monmasson,
S. Jemeï, M. P. Comech, R. Granadino, and J.I. Frau, “Distributed
generation: Toward a new energy paradigm ,” IEEE Ind.
Electron.Mag., pp. 52-64, Mar. 2010.
Application of Fuzzy Logic for Reduction of
Current Harmonic in Single-Phase Grid–
Connected PWM Inverter
Presented By
PREETIRANJAN SAHU
Department of Electrical and Electronics Engineering
Roland Institute of Technology , Berhampur
OUT LINE OF PRESENTATION
1. INTRODUCTION
2. DPGS:A VIABLE SOLUTION FOR THE ENERGY CRISIS
3. CONSTRAINTS FOR IMPLEMENTATION OF DG
4. DPGS INTEGRATION WITH UTILITY GRID
5. SINGLE PHASE GRID-CONNECTED VSI
6. FUZZY WITH HYSTERESIS CURRENT CONTROLLER
7. SIMULATION RESULTS
8. DISCUSSION
9. REFERENCES
The increasing demand of energy that has developed across the globe
at the beginning of 21st century has been further complicated due to
rapidly shrinking of conventional sources, like oil and coal.
The potential solution to the energy crisis in a realistic, reasonable,
secure and environmentally accountable fashion is ‘Distributed
Generation’ (DG) system. DG sources are small scale (Up to 20 MW)
renewable power generation system which is not only helps in
enhancing the existing capacity of the utility system. But also it
unravels the problem of rural electrification.
INTRODUCTION
DPGS:A Viable Solution For The Energy Crisis
DPGS does not mean only renewable generation. According to Ackermann .
DG is defined as the installation and operation of electric power generation
units connected directly to the distribution network or connected to the
network on the customer site of the meter.
DG is also referred to as dispersed generation or embedded generation, on site
generation. DG technologies include both renewable and non renewable
sources.
The renewable sources include:
solar, photovoltaic
Wind
Geothermal
Ocean.
Biomass
Nonrenewable technologies include:
Internal combustion engine
combined cycle
combustion turbine
micro turbines
Fuel cell.
DPGS:A Viable Solution For The Energy Crisis
The typical structure of DPGS with grid is shown in the fig.4.
CONSTRAINTS FOR IMPLEMENTATION OF DG
Is there Any Problem for implementation
Distributed generation systems and their interconnection should
meet certain requirements and specifications when interconnecting
with existing electric power systems (EPS) .
The main objectives of the control of grid connected PWM-VSI is
1) to ensure grid stability
2) active and reactive power control through voltage and frequency
control
3) power quality improvement (i.e. harmonic elimination) etc.
The main grid code requirements are highlighted below
CONSTRAINTS FOR IMPLEMENTATION OF DG
1. Low Voltage-Ride Through
2. Active power /frequency control
3. Voltage level and frequency range
4. Reactive power control and voltage regulation
5. Power quality
The electricity generation technology and grid connection of DG technologies can
be significantly different from traditional centralized power generation
technologies. Large power units use synchronous generators which are capable of
controlling the reactive power and active power.
The DGs present a relatively unusual and challenging picture due to the
intermittency nature of the input power. For example, the voltage generated by
variable speed wind turbine, fuel cell and PV generator cannot be directly
coupled to the utility grid.
The power electronic technology plays a vital role to match the characteristics of
the distributed generation unit and the requirement of the grid connection,
including frequency, voltage, control of active power and reactive power,
harmonic minimization etc.
Hence in order to increase the usefulness of DGs systems and reduce potential
impacts, power electronic can be used as efficient interfaces to integrate DGs
with the existing electrical power system
CONSTRAINTS FOR IMPLEMENTATION OF DG
Voltage source inverters have been widely used in many distributed
generation systems. VSIs are inherently efficient, compact and economical
devices, which are used to control power flow and the quality of power
supply.
The VSIs can be further categorized as Voltage Controlled VSIs (VCVSIs)
and Current controlled VSIs (CCVSIs), depending on their control
mechanism.
In most of the cases CCVSISs are used because of the following advantages:
It can provide current support (the VSI operating as a current source) to
the load.
It has faster response as compared to the VCVSI.
Active and reactive power can be controlled independently in the
CCVSI .
DPGS INTEGRATION WITH UTILITY GRID
The power quality and robustness to the grid voltage and frequency
variations are some vital point’s demanded in the latest issues of grid codes.
This part of the research deals with current controller technique of the grid
side inverter only.
In this part of the research work the Source side converter is taken as an AC
to DC diode Rectifier and the grid side converter is a current controlled
PWM-VSI. Here emphasize is only given on the current control technique of
the grid side converter which is a as marked in fig.5.
DPGS INTEGRATION WITH UTILITY GRID
DPGS INTEGRATION WITH UTILITY GRID
Fig.5.General Structure DPGS With Power Electronics Converter
SINGLE PHASE GRID-CONNECTED VSI
Fig.3. Single phase inverter
connected to utility grid
Fig.4. Hysteresis –Band Current
Controller
o refe i i (1)
(2)ref gi kv
2
2 L
m
Pk
V(3)
The switching frequency of the system can be calculated as
2 2
4
dc g
s
dc f
V Vf
V L HB (4)
[1],[2]
[3],[4]&[5]
FUZZY WITH HYSTERESIS CURRENT CONTROLLER
Fig.5. Block diagram for fuzzy with hysteresis current control for single-phase grid-
connected VSI
SIMULATION RESULTS
A. Steady State Analysis
Fig.9. Simulation result of (a) Grid current
and load curren (b) Active power &
reactive power
Fig.8. Simulation result of the fuzzy with
hysteresis current controller for steady state (a)
grid voltage (Vg) (b) reference current, actual
current and current error.
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-300
-200
-100
0
100
200
300
Time (Sec)
Vo
ltag
e (
V)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Actual Current
Reference Current
Error
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rrn
t (A
)
Grid current
Load Current
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-500
0
500
1000
1500
2000
Time(Sec)
Acti
ve P
ow
er(
Watt
)R
eacti
ve P
ow
er(
var)
Active Power
Reactive Power
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
0.5
1
1.5
Time (Sec)
Po
wer
Facto
r
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
2
4
6
8x 10
4
Time (Sec)
Sw
itch
ing
Fre
qu
en
cy
(H
z)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-300
-200
-100
0
100
200
300
Time (Sec)
Vo
ltag
e (
V)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Actual Current
Reference Current
Error
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rrn
t (A
)
Grid current
Load Current
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-500
0
500
1000
1500
2000
Time(Sec)
Acti
ve P
ow
er(
Watt
)R
eacti
ve P
ow
er(
var)
Active Power
Reactive Power
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
0.5
1
1.5
Time (Sec)
Po
wer
Facto
r
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
2
4
6
8x 10
4
Time (Sec)
Sw
itch
ing
Freq
uen
cy
(H
z)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-300
-200
-100
0
100
200
300
Time (Sec)
Vo
ltag
e (
V)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Actual Current
Reference Current
Error
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rrn
t (A
)
Grid current
Load Current
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-500
0
500
1000
1500
2000
Time(Sec)
Acti
ve P
ow
er(
Watt
)R
eacti
ve P
ow
er(
var)
Active Power
Reactive Power
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
0.5
1
1.5
Time (Sec)
Po
wer
Facto
r
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
2
4
6
8x 10
4
Time (Sec)
Sw
itch
ing
Fre
qu
en
cy
(H
z)
SIMULATION RESULTS CONTD…
B. Transient Analysis (Step change in load)
Fig.10. Simulation result of reference current, actual
current and error for change in load (a) hysteresis (b)
fuzzy with hysteresis current controller.
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Actual Current
Reference current
Error
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Actual current
Reference Current
Error
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-15
-10
-5
0
5
10
15
Time (Sec)
Cu
rren
t (A
)
Grid Current
Load Current
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
0
500
1000
1500
2000
Time (Sec)
Acti
ve P
ow
er
(watt
)R
eacti
ve p
ow
er(
var)
Active Power
Reactive power Power
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
0.5
1
1.5
Time (Sec)
Po
wer
Facto
r
Fig.11. Simulation result of (a) load
current and grid current (b) active
power and reactive power.
SIMULATION RESULTS CONTD…
Fig.12. Simulation result of grid current ,error
and switching frequency (a) for hysteresis
controller (b) fuzzy with hysteresis controller.
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-10
0
10
Cu
rren
t (A
)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
2
4
6
8x 10
4
Time (Sec)
Fre
qu
en
cy
(H
z)
HB=1
HB=1
HB=3
HB=3
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-10
0
10
Cu
rren
t (A
)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.20
2
4
6
8x 10
4
Time (Sec)
Frq
uen
cy
(Hz)
HB=3
HB=3
HB=1
HB=1
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-10
-5
0
5
10
Selected signal: 10 cycles. FFT window (in red): 1 cycles
Time (s)
0 500 1000 1500 2000 25000
50
100
Frequency (Hz)
Fundamental (50Hz) = 10.74 , THD= 2.18%
Mag
(%
of F
un
dam
en
tal)
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
-10
-5
0
5
10
Selected signal: 10 cycles. FFT window (in red): 1 cycles
Time (s)
0 500 1000 1500 2000 25000
50
100
Frequency (Hz)
Fundamental (50Hz) = 10.76 , THD= 1.66%
Mag
(%
of F
un
dam
en
tal)
Fig.13. THD of grid current (a) Hysteresis
current controller (b) fuzzy with hysteresis
current comptroller
CONCLUSIONS
The paper presents the control grid connected PWM VSI using fuzzy with
hysteresis controller in the control loop. From the study we observed that, fuzzy
with hysteresis current controller can able to enhance the power quality of the
grid system as it is enable to reduce switching frequency even if the band width
increased without any significant increase in the current error. As a result, the
THD level of grid current is considerably reduced as compared to conventional
hysteresis current controller. More over, switching frequency of the inverter
system has been reduced, in that in turn, switching losses are also reduced to
certain extent.
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Electronics, vol.19, no.5, pp. 1305- 1314, Sept. 2004.
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Issue: 5,2006 , Page(s): 1398 – 1409.
3. Ho, C.N.-M.,Cheung, V.S.P.,Chung, H.S.-H.” Constant-Frequency Hysteresis Current Control of Grid-
Connected VSI without Bandwidth Control”, IEEE Trans. on Power Electronics, TPEL,2009 Volume:
24, no. 11 , 2009, Pp:2484 – 2495.
4. D. Torrey and A. Al-Zamel, "Single-phase active power filters for multiple nonlinear loads," IEEE
Transactions on Power Electronics, vol. 10, no. 3, pp. 263-272, May 1995.
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Grid Connected Inverter” International. Conference on Power Electronics and Drive Systems, 2007.
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REFERENCES
