 Open Access
 Total Downloads : 5285
 Authors : A.Ananda Kumar, J.Srinivasa Rao
 Paper ID : IJERTV1IS4182
 Volume & Issue : Volume 01, Issue 04 (June 2012)
 Published (First Online): 01072012
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Power Quality Improvement of Grid Interconnected 3phase 4wire Distribution System using Fuzzy logic control
Power Quality Improvement of Grid Interconnected 3phase 4wire
Distribution System using Fuzzy logic control
A.Ananda Kumar, J.Srinivasa Rao.

INTRODUCTION
The greenhouse gases such as carbon dioxide absorb the infrared radiation and trap the heat in the Earth's atmosphere. These greenhouse gases emissions come primarily from the combustion of fossil fuels in energy use [1]. The impact of the traditional fossil fuels in our environment and the fact that these are non renewable sources, have encouraged the need to find alternative energy sources to the fossil fuel. Therefore, the renewable energy sources have been one of the most important topics of research in the last years. They are constantly replenished and will never run out [2].
Renewable energy source (RES) integrated at distribution level is termed as distributed generation (DG). The utility is concerned due to the high penetration level of intermittent RES in distribution systems as it may pose a threat to network in terms of stability, voltage regulation and powerquality (PQ) issues. Therefore, the DG systems are required to comply with strict technical and regulatory frameworks to ensure safe, reliable and efficient operation of overall network. With the advancement in power electronics and digital control technology, the DG systems can now be actively controlled to enhance the system operation with improved PQ at PCC.
However, the extensive use of power electronics based equipment and nonlinear loads at PCC generate harmonic currents, which may deteriorate the quality of power [3][5].
The widespread increase of nonlinear loads nowadays, significant amounts of harmonic currents are being injected into power systems. Harmonic currents flow through the power system impedance, causing voltage distortion at the harmonic currents frequencies. The distorted voltage waveform causes harmonic currents to be drawn by other loads connected at the point of common coupling (PCC). The existence of current and voltage harmonics in power systems increases losses in the lines, decreases the power factor and can cause timing errors in sensitive electronic equipments.
The harmonic currents and voltages produced by balanced 3 phase nonlinear loads such as motor drivers, silicon controlled rectifiers (SCR), large uninterruptible power supplies (UPS) are positivesequence harmonics (7th, 13th, etc.) and negativesequence harmonics (5th, 11th, etc.). However, harmonic currents and voltages produced by single phase nonlinear loads such as switchmode power supplies in computer equipment which are connected phase to neutral in a 3 phase 4wire system are third order zerosequence harmonics (triplen harmonics3rd, 9th, 15th, 21st, etc.). These triplen harmonic currents unlike positive and negativesequence harmonic currents do not cancel but add up arithmetically at the neutral bus. This can result in neutral current that can reach magnitudes as high as 1.73 times the phase current. In addition to the hazard of cables and transformers overheating the third harmonic can reduce energy efficiency. [6]
The traditional method of current harmonics reduction involves passive LC filters, which are its simplicity and low cost. However, passive filters have several drawbacks such as large size, tuning and risk of resonance problems. The increased severity of harmonic pollution in power networks has attracted the attention of power electronics and power system engineers to develop dynamic and adjustable solutions to the power quality problems. Such equipment, generally known as active filters (AFs) [7], Active power filters (APF) are extensively used to compensate the load current harmonics and load unbalance at distribution level. This results in an additional hardware cost. However, in this paper authors have incorporated the features of APF in the, conventional inverter interfacing renewable with the grid, without any additional hardware cost. That conventional inverter is called as a "grid interfacing inverter". The inverter is controlled to perform as a
Fig. 1.Schematic of proposed renewable based distributed generating system
multifunction device by incorporating active power filter functionality. The inverter can thus be utilized as: 1) power converter to inject power generated from RES to the grid, and
2) shunt APF to compensate current unbalance, load current harmonics, load reactive power demand and load neutral current. All of these functions may be accomplished either individually or simultaneously.

SYSTEM DESCRIPTION
In this paper, it is shown that using an adequate control strategy, with a fourleg fourwire grid interfacing inverter, it is possible to mitigate disturbances like voltage unbalance. The topology of the investigated grid interfacing inverter and its interconnection with the grid is presented in Fig. 1. It consists of a fourleg fourwire voltage source inverter. The voltage source inverter is a key element of a DG system as it interfaces the renewable energy source to the grid and delivers the generated power. In this type of applications, the inverter operates as a current controlled voltage source. Fourth leg is used for neutral connection. The RES may be a DC source or an AC source with rectifier coupled to dclink. In this paper wind energy is used as a RES, the variable speed wind turbines generate power at variable ac voltage. Thus, the power generated from these renewable sources needs to convert in dc before connecting on dclink [8][10]. The simulink model of wind farm is given in Fig2.Wind farm generates a variable ac supply; this variable ac supply is converted into dc by connecting a rectifier at output side.

CONTROL STRATEGY
The controller requires the threephase grid current (Ia,,Ib, Ic), the threephase voltage at the PCC (Va, Vb, Vc) and the DClink voltage (VDC). As shown in Fig. 3, the sinusoidal waveform and the phase of the grid current reference (Ia*, Ib*, Ic*) comes from the line voltage thanks to a PLL.
Fig. 2. simulink diagram of wind farm.
Fig. 3.Representation of grid interfacing inverter control
Ua sin( )
(1)
Ub sin(
Uc sin(
2 3)
2 3)
(2)
(3)
The magnitude Im
of the same current is obtained by passing
the error signal between the DClink voltage (VDC) and a reference voltage (VDC*) through a fuzzy logic controller. Using this magnitude and phase displacement of 120Â° and 240Â° respectively, the reference threephase grid currents ia*,ib* and ic* can be expressed as:
Fig.6.Membership functions of input
TABLE1.FLC RULE BASE
Ia * Ib * Ic *
Im sin( )
Im sin( 2 3)
Im sin( 2 3)
(4)
/tr>
/
NB
NM
NS
ZE
PS
PM
PB
NB
NB
NB
NB
NB
NM
NS
ZE
NM
NB
NB
NM
NM
NS
ZE
PS
NS
NB
NM
NS
NS
ZE
PS
PM
ZE
NB
NM
NS
ZE
PS
PM
PB
PS
NM
NS
ZE
PS
PS
PM
PB
PM
NS
ZE
PS
PM
PM
PB
PB
PB
ZE
PS
PM
PB
PB
PB
PB
(5)
(6)

FUZZY LOGIC CONTROLLER (FLC)
The disadvantage of PI controller is its inability to react to abrupt changes in the error signal, , because it is only capable of determining the instantaneous value of the error signal without considering the change of the rise and fall of the error, which in mathematical terms is the derivative of the error denoted as . To solve this problem,[11][12] Fuzzy logic control as it is shown in Fig 4 is proposed.
Fi g. 4 . B a si c rep resen t a t i on of FLC
The determination of the output control signal, is done in an inference engine with a rule base having ifthen rules in the form of
"IF is ……. AND is ……., THEN output is …….."
With the rule base, the value of the output is changed according to the value of the error signal , and the rateof error . The structure and determination of the rule base is done using trialanderror methods and is also done through experimentation.
Fig.5.Membership functions of input
All the variables fuzzy subsets for the inputs and are defined as (NB, NM, NS, Z, PS, PM, PB). The membership function of inputs is illustrated in fig.5&6. The fuzzy control rule is illustrated in the table I.

SWITCHING CONTROL
As shown in Fig. 3, the hysteresis control has been used to keep the controlled current inside a defined band around the references. The status of the switches is determined according to the error. When the current is increasing and the error exceeds a certain positive value, the status of the switches changes and the current begins to decrease until the error reaches a certain negative value. Then, the switches status changes again. Compared with linear controllers, the nonlinear ones based on hysteresis strategies allow faster dynamic response and better robustness with respect to the variation of the nonlinear load. A drawback [13] [14] of the hysteresis strategies is the switching frequency which is not constant and can generate a large side harmonics band around the switching frequency.


SIMULATION RESULTS
An extensive simulation study is carried out using MATLAB/Simulink in order to verify the proposed control strategy. To achieve balanced sinusoidal grid currents at unity power factor, the 4leg grid interfacing inverter is actively controlled under varying renewable generating condition. The
Fig.7. simulation results:(a)grid voltages, (b) grid currents, (c) load currents,(d) inverter currents
wave forms of grid voltages, grid currents, unbalanced load current and inverter currents are shown in Fig.7. The corresponding active and reactive of grid (PQ grid), load (PQ load) and inverter (PQ inv) are shown in Fig.8. Positive values of grid activereactive powers and inverter activereactive powers imply that these powers flow from grid side towards PCC and from inverter towards PCC, respectively. The active and reactive powers absorbed by the load are denoted by positive signs.
Before t=0.72s, the grid interfacing inverter is not connected to network, hence the grid currents in Fig.7 (b) are same as unbalanced nonlinear load currents Fig.7(c).
At t=0.72s, the grid interfacing inverter is now connected to network. The grid current starts changing to sinusoidal balanced from unbalanced nonlinear current shown in Fig.7 (b). At this instant active power injected by the inverter from RES. From Fig.8. The load power demand is less than the generated power and the additional power in fed back to the grid. The grid is receiving power from RES after 0.72s and it is indicated by ve sign.
At t=0.82s, considering the load power demand as constant. The power generated from RES is increased to verify the system performance under variable power generation and hence it increases the magnitude of inverter current.
At t=0.92s generation of power from RES is reduced. The active and reactive power flows between the inverter, load and grid during increase and decrease of energy generation from RES can be noticed from Fig. 8. Observing fig.8&9. It is clear that the fuzzy controller has high accuracy and fast response to load parameter variation.
Fig.8.simulation results: (a) PQ grid, (b) PQ load, (c) PQ inverter, (d) dc link voltage using fuzzy controller
Fig.9.simulation results: (a) PQ grid, (b) PQ load, (c) PQ inverter using PI controller

CONCLUSION
This paper has presented a novel method to improve the power quality at point of common coupling (PCC) for a 3 phase 4 wire DG system using fuzzy logic control for grid interfacing inverter. The grid interfacing inverter is effectively utilized for power conditioning. This approach eliminates the additional power conditioning equipment to improve power quality at PCC. Simulation results analysis has shown that the proposed controller has fast response, high accuracy of tracking the DCvoltage reference, and strong robustness to load sudden variations
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Annavarapu Ananda Kumar born in 1987, in India. He received B.Tech degree in Electrical & Electronics Engineering from Acharya Nagarjuna University, India in 2008.
Currently he is persuing M.Tech degree from JNTU University in Power System Control & Automation. His research interest includes Power Systems, Power System Operation & Control, Power System Stability& Analysis.
SRINIVASARAO.J is an associate professor in QIS College of Engineering & Technology at ongole. He is a Ph.D candidate. He got his
MTech degree from JNTU Hyderabad and his BTech degree from RVRJC Engineering College, Guntur. His current research interests are power systems, power systems control and automation, Electrical Machines, power systems