36 International Journal for Modern Trends in Science and Technology
Low frequency Oscillations Damping in SMIB System using Fuzzy based SSSC Controller
S.Ramarao1| K.Parasuram2| M.Venkata vinay3| G.Anji babu4| T.Krishna Mohan5
1,2,3,4Department of Electrical and Electronics Engineering, Andhra Loyola Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India.
5Assistant professor 1Department of Electrical and Electronics Engineering, Andhra Loyola Institute of Engineering and Technology, Vijayawada, Andhra Pradesh, India.
To Cite this Article
S.Ramarao, K.Parasuram, M.Venkata Vinay, G.Anji Babu and T.Krishna Mohan, “Low frequency Oscillations Damping in SMIB System using Fuzzy based SSSC Controller”, International Journal for Modern Trends in Science and Technology, Vol.
04, Special Issue 02, March 2018, pp. 36-43.
Power oscillations such as low frequency oscillations(LFO) are a frequent adverse phenomenon which increases the risk of instability for the power system and the reduce the total available transfer capability(TTC and ATC).This brief investigates damping performance of the static synchronous series compensator (SSSC) equipped with an auxiliary fuzzy logic controller (FLC) in flexible AC transmission system.At the outset , synchronous machine of single machine infinite bus (SMIB) system installed with SSSC is established in the following, well performance of an auxiliary FLC for SSSC is well designed to enhance the transient stability of power system. In order to evaluate the performance of proposed FLC in damping LFO.
The SMIB power system is subjected to a disturbance such as changes in electromechanical power. The complete digital simulations are performed in the MATLAB/SIMULINK. The simulation studies validate the effective performance of the developed FLC in damping electromechanical oscillations in comparison with conventional proportional-integral(PI)controller.
KEYWORDS: Low frequency oscillations (LFO),Synchronous machine model, Auxiliary fuzzy logic damping controller (FLDC),Single machine infinite bus (SMIB) power system, Static synchronous series compensator (SSSC)
Copyright © 2018 International Journal for Modern Trends in Science and Technology All rights reserved.
I. INTRODUCTION
Power frameworks are among the biggest, most complex frameworks made by individuals. They show different methods of motions because of connection among framework segments. By interconnecting the extensive power frameworks, utilities have accomplished greater unwavering quality and frameworks, utilities have accomplished greater unwavering quality and prudent feasibility. In any case, low recurrence motions (LFO) with the frequencies in the scope of 0.2 to 2 Hz are one of the immediate consequences
of the huge interconnected power frameworks.
Thepower motions may come up to whole appraising of a transmission line, as they are superimposed on consistent state line stream.
Subsequently, these motions would restrain the aggregate and accessible exchange ability (TTC and ATC) by requiring higher well being edges. These electromechanical methods of motions are typically inadequately damped which may expand the danger of flimsiness of energy framework. In this way, it is dire and imperative to sodden the electromechanical motions at the earliest opportunity keeping in mind the end goal to keep ABSTRACT
Available online at: http://www.ijmtst.com/ncee2018.html
Special Issue from 3rd National Conference on Emerging Trends in Electrical Technology, 17th March 2018, Vijayawada, Andhra Pradesh, India
ISSN:2455-3778 :: Volume 04, Special Issue No:02, March 2018
37 International Journal for Modern Trends in Science and Technology up the strength of the whole framework.Torelieve
the motions in the power framework a wide range of strategies have been proposed .For some years, control framework stabilizer (PSS) has been one of the generally gadgets used to sodden out the motions . It is accounted for that amid some working conditions, PSS may not relieve the motions successfully. In any case, there have been issues experienced with PSS throughout the times of task. Some of these were because of the constrained ability of PSS, in damping just nearby and not inter area methods of motions. Also, PSS s can cause incredible varieties in the voltage profile under extreme unsettling influences and they may even bring about driving force factor task and losing framework dependability . Thus, other compelling options are required notwithstanding PSS s . This circumstance has required an audit of the conventional power framework ideas and practices to accomplish a bigger security edge, more prominent working adaptability, and better use of existing force frameworks.
The Benefits ofFlexible AC Transmission Systems(FACTs) usages to improve power systems stability are well known . The growth of the demand for electrical energy leads to loading the transmission system near their limits. Thus, the occurrence of the LFO has increased. FACTs Controllers has capability to control network conditions quickly and this feature of FACTs can be used to improve power system stability.On the other hand, the advent of flexible ac transmission system (FACTS) devices has led to a new and more adaptable way to deal with control the power framework desire . Realities controllers give an arrangement of intriguing capacities, for example, control stream control, receptive power remuneration, voltage direction, damping of motions, transient solidness upgrade et c. The static synchronous arrangement compensator (SSSC) is one of the arrangement FACTS gadgets in light of a strong state voltage source inverter which produces a controllable air conditioning voltage in quadrature with the line current . By along these lines, the SSSC copies as an inductive or capacitive reactants and subsequently controls the power stream in the transmission lines. In , creators The linearised synchronous machine model of a power system installed with SSSC is used to investigate the impact of SSSC on damping oscillations in power systems. This section is dedicated to extract an exact linearised synchronous machine model for the investigated power system. As depicted in Fig, a single
machine infinite bus (SMIB) system installed with SSSC is considered as the sample power system.
In this figure, XT is the transformer reactance and XL corresponds to the reactance of the transmission line. Also, VT and Vb represent the generator terminal voltage and infinite bus voltage respectively. A simple SSSC consisting of a three-phase GTO-based voltage source converter (VSC) is incorporated in the transmission line. It is assumed that the SSSC performance is based on the well-known pulse width modulation (PWM) technique. For the SSSC, XSCT is the transformer leakage reactants; VINV is the series injected voltage; CDC is the DC link capacitor; VDC is the voltage at DC link; m is amplitude modulation index and ø is the phase angle of the series injected voltage. have built up the damping capacity for the SSSC. By legitimately planning an assistant power swaying damping (POD) controller, the SSSC would be fit for stifling the vacillations as an ancillary duty. Distinctive techniques have been proposed in the writing to outline a POD controller for SSSC. For instance, in creators have utilized the stage pay strategy to build up a supplementary damping controller for SSSC. The fundamental issue related with these techniques is that the control procedure depends on the linearised machine display. execution falls apart when the framework conditions differ generally or expansive he other regularly utilized approach is the proportional integral (PI) controller. In spite of the fact that the PI controllers offer effortlessness and simplicity of plan, their unsettling influences happen. In this unique circumstance, some new balancing out control answers for control framework have been displayed. As of late, fluffy rationale controllers (FLC s) have developed as an effective apparatus to go around these disadvantages. The qualitative and quantitative knowledge about the system operation through some hierarchy is integrated by FLC. Fuzzy logic provides a general concept for description and measurement of systems. Most of fuzzy logic systems encode human reasoning into a program in order to arrive at decisions or to control a system. Fuzzy logic comprises fuzzy sets, which is a way of representing nonstatistical uncertaintyyalong with approximate reasoning and in fact includes the operations used to make inferences. There are some manuscripts which have demonstrated the successful application of FLC for transient stability enhancement of a power system. In , Channelling et al. have used a fuzzy supplementary controller with the aim of
38 International Journal for Modern Trends in Science and Technology achieving low frequency oscillations the initial
step, a non-linear dynamic model forthe inspected framework is inferred by disregarding the protection of the considerable number of segments including generator, transformer, transmission line, and arrangement converter transformer. The conditions determining the dynamic execution of the SSSC can be composed as takes after damping The investigation is carried out for a single machine infinite bus (SMIB) power system installed with a SSSC. In the sequel, the linearisedsynchronous model of the examined plant is evolved. An auxiliary FLC is utilized to modulate the amplitude modulation index during the transients to enhance the stability of the power system. Subsequently, aiming to provide a fruitful investigation, a comparative study is developed where the FLC is compared with a conventional PI controller.
Simulation results using MATLAB/SIMULINK exhibits the superior damping of LFO obtained with FLC than PI controller.
II. POWER SYSTEM MODELLING
”The linearisedsynchronous model model of a power system installed with SSSC is used to investigate the impact of SSSC on damping oscillations in power systems. This section is dedicated to extract an exact linearisedsynchronous machine model for the investigated power system. As depicted in Fig1. a single machine infinite bus (SMIB) system installed with SSSC is considered as the sample power system. In this figure, XT is the transformer reactants and XL corresponds to the reactants of the transmission line. Also, VT and Vb represent the generator terminal voltage and infinite bus voltage respectively. A simple SSSC consisting of a three-phase GTO-based voltage source converter (VSC) is incorporated in the transmission line. It is assumed that the SSSC performance is based on the well-known pulse width modulation (PWM) technique. For the SSSC, XSCT is the transformer leakage reactance; VINV is the series injected voltage; CDC is the DC link capacitor; VDC is the voltage at DC link; m is amplitude modulation index and ø is the phase angle of the series injected voltage.
III. NON-LINEAR DYNAMIC MODEL OF POWER SYSTEM
WITH SSSC
As the initial step, a non-linear dynamic model forthe inspected framework is inferred by disregarding the protection of the considerable number of segments including generator, transformer, transmission line, and arrangement converter transformer. The conditions determining the dynamic execution of the SSSC can be composed as takes after .
Where k is the settled proportion between the converter AC and DC voltages and is subject to the inverter structure. For a basic three-stage voltage source converter k is equivalent with 3/4 . The majority of the circumstances, SSSC executes as an unadulterated capacitor or inductor; thus, the main primary controllable parameter for SSSC is the abundance adjustment record m .
For the current work, the IEEE Type-ST1A excitation framework is considered. Fig.2 shows the square graph of the excitation framework where the terminal voltage Vt and the reference voltage Vref are the information signals. KA and TA are the pick up and time consistent of the excitation framework separately.
Fig. 2 IEEE Type-ST1A excitation system
) sin cos
( 90
) sin (cos
1
Iq Cdc Id
mk dt
dVdc
mkVdc j
mkVdc inv
V
I JIq Id I
39 International Journal for Modern Trends in Science and Technology The dynamic model of the power system in Fig. 1
wouldbe as follows .
whereδ: Rotor angle of synchronous generator in radians
Ù : Rotor speedin rad/sec
P m : Mechanical power input to the generator
P e : Electrical power of the generator P D = D ( ù -1 ),
D : Damping coefficient
E q1: Generator internal voltageEfd:2Generator field voltage
I d : d-axis current I q : q-axis current
IV. LINEARDYNAMICMODELOFPOWER SYSTEMWITHSSSC
The linearn synchronous machine model of an SMIB system including SSSC can be extracted by linearizing the nonlinear model around a nominal operating point .
Where
Fig. 3 exhibits the transfer function model for the modified synchronous machine model of the SMIB system with SSSC
Fig3: synchronous machine
V. CALCULATION OF SYNCHRONOUS MACHINE MODEL
Bus power system with SSSC. Where X and U are defined as the state control vector respectively.
With respect to the corresponding system matrix namely A,and the control matrix B,are obtained for the investigated powersystem.
The ostensible working point for the power framework is set to the given esteems.
Constants
Pe = 0.8pu, Qe =0.144pu, Vb = 1pu
6 ) sin cos
4 ( 3
) 5 _ ( 0 4
3 2
) 1 ( 0
iq Cdc id
vdc m
Ta
Vt Vref KA Efd Efd
Td Efd Eq Eq
M Pd Pe Pm
14
6 5
13 3
4
12 2
1
11 9
8 7
10 0 9
) (
8 7
0
m Kvm Vdc
Kvdc Eq
K K
Vt
m Kqm Vdc
Kqdc Vdc
K K
Eq
M Kpm Vdc
Kpdc Eq
k K
Pe
m Kdcm Vdc
K Eq K K
Vdc
Ta
Vt Vref Ka Efd Efd
TD Efd Eq Eq
M D Pe Pm
189 0
8 0
7
1 0 6
5
' '
1 '
0 3 '
4
2 0 1
0 0
0 0 0
k k
k
Ta KaKvDC Ta
Ta KaK Ta
KaK
do T KqDC do
T do T
K do
T K
M KpDC M
K M
D M
K
40 International Journal for Modern Trends in Science and Technology The synchronous machine display constants are
figured in light of the given esteems for the ostensible working point and some other information which are accounted for in the Appendix A. Likewise the parameters of SSSC are given in the Appendix B. In the long run; Appendix C assembles the greater part of the constants figured for the framework display portrayed in Fig.
3.
VI. DESIGN OF DAMPING CONTROLLERS
Intending to moist the low recurrence motions, two sorts of damping controllers are outlined and contrasted and each other. In the researched framework, as specified prior, the SSSC arrangement converter adequacy adjustment file to be specific m, gives a control flag to yield better damping of motions
provides a control signal to yield better damping of oscillations[1]. In thesubsequent sections, each controller is individually discussed in detail.
VII. CONVENTIONAL PROPORTIONAL INTEGRAL (PI)
CONTROLLER
The damping controllers are outlined to give an additional electrical torque in stage with the speed deviation keeping in mind the end goal to upgrade the damping of motions [1]. Fig. 4 demonstrates the regular PI controller structure. As for this figure, it can be watched that the primary square contrasts the generator rotor speed and the reference speed.
In the continuation, the mistake is encouraged to a PI controller to create the best possible sufficiency balance record for the SSSC converter. There are distinctive techniques to outline PI controllers, for example, attempt and blunder strategy, post position, Ziegler-Nichols et c. In this review, attempt and blunder strategy is utilized to set appropriate esteems for PI controller picks up.
Fig. 4 Conventional PIdamping controller
Fig5:CDC performance in low frequency damping oscillations
VIII. AUXILIARY FUZZY LOGIC DAMPING CONTROLLER
As clarified in the former segments, despite the fact that the PI controllers offer straightforwardness and simplicity of outline, their execution weakens when the framework conditions differ broadly or vast aggravations happen. Thus, to guarantee the powerful execution of damping controller over extensive variety of framework activities and furthermore to build the transient soundness of the framework, a supplementary fluffy rationale controller (FLC) in light of the Mamdani's fluffy surmising technique is intended for the SSSC input. FLC produces the required little change for abundancy tweak file to control the size of the infused voltage. The centroid defuzzyfication method was utilized as a part of this fluffy controller.
Fig. 5 exhibits the FLC structure. For this situation, a two– information, one– yield FLC is considered. The info signals are precise speed deviation (Δω ) and stack point deviation (Δδ ) and the resultant yield flag is the plentifulness balance file for SSSC converter.The presented FLC has a very simple structure.
Fig:6 FUZZY LOGIC DAMPING CONTROLLER STRUCTURE
41 International Journal for Modern Trends in Science and Technology fig7:FUZZY logic damping controller structure
The membership functions of the inputand output signals are shown in Fig. 6. There are two linguistic variable for eachinput variable, including,
“Positive” (P), and “Negative” (N). On the other hand, for the output variable there are three
linguistic variables, namely, “Positive” (P), “Zero”
(Z), and “Negative” (N).
The rules used for the FLC are chosen as follows:
If is P and is P, then m is P.
If is P and is N, then m is Z.
If is N and is P, then m is Z.
If is N and is N, then m is N.
Fig. 7 exhibits the yield of fluffy controller versus its information sources. As it can be found in Fig.
7, the standards surface is smooth which is an alluring alternative in outline system.
IX. SIMULATION RESULT AND DISCUSSION
Keeping in mind the end goal to look at the proposed fluffy rationale damping controller execution with the customary PI damping controller, some valuable reproductions are given.
The possibility re enacted is a stage change in mechanical power (∆Pm = 0.01 ) which happens at t=5sec and goes on for 0.1 sec.
Toward the start, the SSSC has no damping controller. For this case, the precise speed deviation and furthermore the heap point deviation reactions are shown in Fig. 8. This figure uncovers that when there is no damping controller, the LFO damping is extremely poor; thus a helper damping controller is basically required to enhance the transient soundness of the framework.
In the second case, simulations are performed with the same contingency in mechanical power but the SSSC has been equipped with a damping controller. Simulation results are shown in fig 9. with respect to this figure ,it is deduced that the fuzzy logic damping controller exhibits better damping than the conventional pi controller. Likewise,the power system transient stability is increased when the SSSC is equipped with fuzzy logic damping controller simulation results validate the efficiency of the proposed fuzzy logic damping controller and its better performance is emphasized.
42 International Journal for Modern Trends in Science and Technology Fig10: Comparison of CDC and FLDC in Low
frequency oscillations damping
Fig: Performance of STFLDC in low frequency oscillations damping
X. CONCLUSION
This original copy serves a correct examination to get a total SYNCHRONOUS MACHINE display for a solitary machine limitless transport control framework furnished with a SSSC to think about LFO damping with an assistant FLC. It was demonstrated that a possibility in control framework will cause to start control motions. In the continuation, two kinds of controllers, to be specific, the regular PI and the FLC were intended to clammy the framework motions. A relative report between the FLC and PI controller demonstrates that the proposed FLC has un-revealed execution and impact in transient solidness upgrade and motions damping. Re-enactment comes about approve the effectiveness of the proposed fluffy rationale damping controller and its better execution is stressed. Thu sly, the fluffy rationale controller would be a superior alternative in the plan of damping controllers.
Appendix a
Power System Parameter Generator: M=2H=6 MJ/MVA, D=0 T'do=5.044 s Xd=0.1 pu, Xq=0.06 pu, X'd=0.025 pu f0=60 Hz, ω0=2πf0 Excitation System: KA=5, TA=0.005 s Transmission Line and transformer reactances: XLine=0.2pu, Xts=0.2pu
APPENDIX B
SSSCPARAMETERS CDC=1 PU;VDC=0.5 PU; M=0.15;
XSCT=0.1PU
APPENDIX C
SYNCHRONOUS MODEL CONSTANTS K1=1.9014;
K2=0.6735; K3=1. 1429 K4=0.0498;
K5=-0.0127; K6=0. 9517 K7=-0.1759;
K8=0.0302; K9= 1.402×10-4 KDCM=-0.4255;
KPDC=0.0244; KQDC=0.0106; KVDC= 0.0035 KPM=0.0839;KQM=0.0354;KVM=-0.008
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