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					                                                                              International Journal of Computer Information Systems,
                                                                                                                    Vol.2, No.6, 2011

 Selective Restoration Indices Based Power System
Restoration Assessment Requirements in a Two-Area
        Reheat Interconnected Power System
                           R.Jayanthi                                                     Dr.I.A.Chidambaram
           Department of Electrical Engineering                                   Department of Electrical Engineering
                  Annamalai University                                                  Annamalai University
             Annamalai Nagar-608002, India                                          Annamalai Nagar-608002, India
                 rrjay_pavi@yahoo.co.in                                                  driacdm@yahoo.com


Abstract— To provide a high quality reliable and secured electric     response. Therefore, in an inter-area mode, damping on the
power supply to the customers is one of the most important            critical electromechanical oscillations is to be carried out in an
aspects in the power system restoration problems. To allow a          interconnected system. Moreover, system frequency
quick and orderly recovery from system disturbances within a          deviations should be monitored and remedial actions to
short period is very much essential to ensure restoration
capability. During system restoration after disturbance a
                                                                      overcome frequency excursions are more likely to protect the
reasonable balance must be maintained between generation and          system before it enters an emergency mode of operation [1].
load to avoid excessive frequency deviations. It is necessary to           Maintaining the standards of security and quality of
maintain system frequency within allowable limits in a short          supply of an electricity system are of utmost importance.
period of time. The operation strategy based on the proposed
methodology is applied to a two-area two-unit thermal reheat
                                                                      When an incident occurs on the system frequency within the
interconnected power system (TATURIPS) taking into account            stipulated limits is a major priority and if these limits are
Super Conducting Magnetic Energy Storage (SMES) device and            breached, then the magnitude of the excursion needs to be
Gas Turbine(GT) in either areas to meet out the system                restricted and the frequency returned within the limits as quick
uncertainties due to various disturbances. With these units as        as possible [2]. The controllers based on classical control
back-up energy suppliers Feasible Restoration Index (FRI) and         theories are insufficient because of changes in operating points
Complete Restoration Index (CRI) are computed for various             while the loads change continuously during daily cycle which
disturbances based on settling time of the output response of the     is an inherent characteristic of power system. Due to the
system. With the two Restoration Indices, the conditions required     requirement of perfect model, which has to track the state
to be satisfied in ensuring the restoration of the power system are
listed out and proper implementation will result in high quality
                                                                      variable and satisfy the system constraints, Proportional-
and reliable power supply.                                            Integral (PI) controller with addition of a small capacity
                                                                      SMES unit and GT units to the system significantly improves
Keywords- Restoration Index; Distribution Generation Capacity;        the transients of frequency, tie-line deviations and reduces the
Combined Cycle Plant; Settling Time; Proportional-Integral (PI)       settling time span against load changes in the proposed
Controller; Particle Swarm Optimization (PSO); Restoration            methodology[3].
Indices (RI); Feasible Restoration Index (FRI); Complete
Restoration Index (CRI).                                                   SMES and GT units with a minimum capacity can be
                                                                      applied not only as a fast energy compensation device for
                                                                      large loads but also to damp out the frequency and tie-line
                      I.         INTRODUCTION                         deviations, which makes it a cost-effective system. Moreover
                                                                      Gas Turbines become increasingly popular in different power
    Nowadays a significant growth of electric energy demand,          systems due to their green house emission as well as higher
in combination with financial and regulatory constraints has          efficiency especially when connected in a combined cycle
forced power utilities to operate system near stability limits.       setup [4].
Under an occurrence of uncertain load changes, a system
frequency may be considerably perturbed from a normal
operating value. Especially if frequencies of changing loads                        II.      MODELING OF THE TATURIPS
are in the vicinity of inter-area oscillation modes, a system
frequency may be heavily disturbed and oscillate, which                    An interconnected power system is considered with two-
results in a serious stability problems. Under this situation a       area having two-units in each where all generators are
conventional frequency control (i.e.) a governor may no longer        assumed to act as coherent group. These areas are connected
be able to compensate for such load changes due to its slow           to each other by tie-lines. The power system investigated in




       June Issue                                             Page 62 of 78                                    ISSN 2229 5208
                                                                                  International Journal of Computer Information Systems,
                                                                                                                        Vol.2, No.6, 2011


                                     1                                                                   3


                                     2                                                                   4

                                                       SMES                        GT
                                                                                                                                 PD2
                            PD1
                                                       Figure 1. TATURIPS with SMES and GT

                                                                                              .
this study is shown in figure 1.The detailed block diagram is                                X  Ax  Bu  d                                (1)
given in figure 2 and the system data is provided in the
appendix.
                                                                                                  Y    = Cx                                  (2)
Due to the inherent characteristics of changing loads, the
operating point of power system may change very much
during a daily cycle. The generation changes must be made to              Where, the system state vector X consists of the following
match the load perturbation at the nominal conditions, if the             variables as
normal state is to be maintained. The mismatch in the real
power balance affects primarily the system frequency but
leaves the bus voltage magnitude essentially unaffected. In a
power system, it is desirable to achieve better frequency
constancy than obtained by the speed governing system alone.                                                                   Pc1 
                                                                                        System control input vector is [u] =        
This requires that each area should take care of its own load                                                                 Pc 2 
changes, such that schedule tie power can be maintained. A
two-area interconnected system dynamic model in state                                                                             Pd 1 
                                                                                     System disturbance input vector is[d] =            
                                                                                                                                 Pd 2 
variable form can be conveniently obtained from the transfer
function model. The state variable equation of the minimum
realization model of the „N‟ area interconnected power system
is expressed as[5]




                                                                                                                       Tie-
                                                                                                                      line




                                  Figure 2. Transfer function model of a two-area thermal reheat power system




      June Issue                                               Page 63 of 78                                         ISSN 2229 5208
                                                                             International Journal of Computer Information Systems,
                                                                                                                   Vol.2, No.6, 2011
    A is system matrix, B is the input distribution matrix and Γ
disturbance distribution matrix, is the state vector, is the
control vector and is the disturbance vector of load changes
of appropriate dimensions. The typical values of system
parameters for nominal operation condition are given in
appendix. This study focuses on optimal tuning of controllers
for LFC and tie-power control, settling time based
optimization using PSO algorithm to ensure a better power
system restoration assessment. The PSO is adopted to search
for the optimum controller parameter setting that maximizes
the minimum damping ratio of the system.
    On the other hand in this study the goals are to control the
frequency and inter area tie-power with good oscillation
damping and also to obtain a good performance under all                             Figure 3. Schematic diagram of SMES unit
operating conditions with various loading conditions and
finally to design a low-order controller for easy                    In LFC operation, the dc voltage Ed across the
implementation. To achieve the above said conditions a               superconducting inductor is continuously controlled
Feasible Restoration Index (FRI) and Complete Restoration            depending on the sensed area control error (ACE) signal.
Index (CRI) based on the settling time has been formulated in        Moreover, the inductor current deviation is used as a
this proposed methodology with the SMES unit and GT unit in          negative feedback signal in the SMES control loop. So, the
Area-1and Area-2 respectively. Their Proportional (P) and            current variable of SMES unit is intended to be settling to its
Integral (I) gains has been optimized to achieve the restoration     steady state value. If the load is used as a negative feedback
index conditions.                                                    signal in the SMES control demand changes suddenly, the
                                                                     feedback provides the prompt restoration of current. The
                    III   SMES UNIT                                  inductor current must be restored to its nominal value
       The Superconducting Magnetic Energy Storage                   quickly after a system disturbance, so that it can respond to
(SMES) system is a fast acting device which can damp out             the next load disturbance immediately. As a result, the
these oscillations and help in reducing the frequency and            energy stored at any instant is given by
tie-line Power deviations for better performance of system
                                                                                                t
disturbances. It is designed to store electric energy in the                 Wsm  Wsmo   Psm ( )d                                  (4)
low loss superconducting coil. Power can be absorbed or                                        t0
released from the coil according to the system requirement.          Where,
A super conducting magnetic energy storage(SMES) is
                                                                             Wsmo=1/2 LIdo2,initial energy in the inductor.             (5)
capable of controlling active and reactive power
simultaneously and has been expected as one if the most
                                                                      Equations of inductor voltage deviation and current deviation
effective stabilizers of power oscillations[6,7].
                                                                             for each area in Laplace domain are as follows
                                                                                       KSMES                 K                 (6)
                                                                          Edi ( s)           U ( s)             I ( s)
                                                                                                                       id
                                                                                                           smesi                   di
The schematic diagram in Fig.3 shows the configuration of a                            1  sT     d ci    1  sT          d ci

thyristor controlled SMES unit. The SMES unit contains DC
                                                                                                       1 
superconducting Coil and converter which is connected by                                 I di ( s)         Edi ( s)               (7)
Y–D/Y–Y transformer. The inductor is initially charged to                                              sLi 
its rated current Id0 by applying a small positive voltage.          Where
Once the current reaches the rated value, it is maintained
constant by reducing the voltage across the inductor to zero
                                                                      Edi (s) =         converter voltage deviation applied to
since the coil is superconducting [8]. Neglecting the                                    inductor in SMES unit
transformer and the converter losses, the DC voltage is              KSMES      =        Gain of the control loop SMES
given by                                                             Tdci       =        converter time constant in SMES unit
                                                                     Kid        =        gain for feedback ∆Id in SMES unit.
     Ed=2Vd0cosα-2IdRc                                 (3)            I di (s) =        inductor current deviation in SMES unit
                                                                               The deviation in the inductor real power of SMES
    Where Ed is DC voltage applied to the inductor (kV), firing      unit is expressed in time domain as follows
angle (α), Id is current flowing through the inductor (kA). Rc is
equivalent commutating resistance (V) and Vd0 is maximum                                 ∆PSMESi=∆EdiIdoi+∆Idi∆Edi                      (8)
circuit bridge voltage (kV). Charge and discharge of SMES
unit are controlled through change of commutation angle α.




       June Issue                                            Page 64 of 78                                         ISSN 2229 5208
                                                                               International Journal of Computer Information Systems,
                                                                                                                     Vol.2, No.6, 2011
Figure 4 shows the block diagram of the SMES unit. To
achieve quick restoration of the current, the current deviation
can be sensed and used as a negative feedback signal in the
SMES control loop.




                    Figure 4.   Block diagram of SMES unit                                     Figure 5.     Gas Turbine Model



     In a two-area interconnected thermal power system                    This gives approximately two-thirds of the total power of
under study with the sudden small disturbances which                 a typical combined cycle power plant. When the load is
continuously disturb the normal operation of power system.           suddenly increased the speed drops quickly but the regulator
As a result the requirement of frequency controls of areas           reacts and increases the fuel flow to a maximum of 100%,
beyond the governor capabilities SMES is located in area1            thereby improving the efficiency of the system.
absorbs and supply required power to compensate the load
fluctuations in area1. The inductor is initially charged to its
rated current Ido by applying a small positive voltage. Once                  V. CONTROLLER DESIGN USING PARTICLE SWARM
the current reaches the rated value it is maintained constant                OPTIMIZATION TECHNIQUE FOR THE POWER SYSTEM
by reducing the voltage across the inductor to zero since the                            RESTORATION PROBLEM
coil is superconducting. Tie-line power flow monitoring is
also required in order to avoid the blackout of the power                  This is a population based search technique. Each
system. The Input of the integral controller of each area is         individual potential solution in PSO is called particle. Each
             ACEi = βi∆fi+∆Ptie i                           (9)      particle in a swarm fly around in a multidimensional search
                                                                     space based on its own experience and experience by
Where βi            = frequency bias in area i                       neighboring particles. Let in search space „S‟ in n-dimension
      ∆fi           = frequency deviation in area i                  with the swarm consists of „N‟ particles. Let, at instant „t‟, the
      ∆Ptie I       = Net tie power flow deviation in area           particle „i‟ has its position defined by

     SMES unit has the advantages that the time delay during                   i
                                                                                
                                                                       X ti  x1 , x2 , . . .xn
                                                                                    i         i
                                                                                                           
charge and discharge is quite short, Capable of controlling
both active and reactive power simultaneously, loss of power         Velocity,    Vt  v , v , . . .vn
                                                                                         i
                                                                                                i
                                                                                                 1
                                                                                                     i
                                                                                                     2
                                                                                                          i
                                                                                                                      
is less, highly reliable and efficient also.                         In variable space ‟S‟
                                                                              Velocity and position of each particle in the next
                     IV.        GAS TURBINE UNIT                     generation (time step) can be calculated as
     Amid growing concerns about green house emissions, gas
turbines have been touted as a viable option, due to their
higher efficiency and the lower green house emissions
                                                                                                                                         
                                                                             Vt i1  Vt i  C1.rand ( ). Pt i  X ti  C2 .Rand ( ). Pt g  X ti   
compared to other energy sources and fast starting capability
which enables them to be often used as peaking units that                           X ti1  X ti  Vt i1
respond to peak demands. Many models representing the gas            Where N – number of particle in swarm
turbines have been developed over the years. A GAST model                   - Inertia weight
as shown in figure 5 which is one of the most commonly used
dynamic models has been used in this methodology [9-11].Gas                       C1 , C2 - Acceleration constant
Turbines have the advantages like Quick start-up/shut-down,                       rand ( ) Rand ( ) - Uniform random value in the
low weight and size, cost of installation is less, low capital
                                                                     range [0, 1]
cost, Black-start capability, high efficiency requires less
cranking power, pollutant emission control etc.,




       June Issue                                            Page 65 of 78                                                ISSN 2229 5208
                                                                               International Journal of Computer Information Systems,
                                                                                                                     Vol.2, No.6, 2011
          Pt i - best-position that particle ‟i‟ could find so far          VI      SIMULINK MODEL OF PSO BASED PI CONTROLLER
                                                                                The PSO algorithm is used to search an optimal
          Pt g - global best at generation „t‟                          parameter set containing Kp and Ki. The parameters (Table 1)
Performance of PSO depends on selection of inertia weight               used for tuning the PSO algorithm and simulink models (as
(  ), Max velocity Vmax and acceleration constant                      shown in Figure 6.) are given below:

(   C1 , C2 )
A. Effect of Inertia Weight ()

   Suitable weight factor helps in quick convergence.Large
weight factor facilitates global exploration (i.e. searching of
new area).While small weight factor facilitates local
exploration (so wise to choose large weight factor for initial
iterations and gradually smaller weight factor for successive
iterations).Generally,  0.9 at beginning and 0.4 at end.
B. Max velocity                                                          Figure 6.   Simulink model of plant with PSO Algorithm based PI
                                                                                                   Controller

   With no restriction on the max velocity of the particle,
velocity may become infinitely large. If Vmax is very low                  TABLE1. PARAMETER VALUES TUNED FOR PSO ALGORITHM
particle may not explore sufficiently. If Vmax is very high it
                                                                                          Parameters                     Value
may oscillate about optimal solution. Therefore, velocity                                 Population size                  5
clamping effect has to be introduced to avoid „swarm                                      Number of generations           10
explosion‟. Generally, max velocity is set as 10-20% of                                                  
                                                                                          Inertia weight (  )             0.8
dynamic range of each variable. Velocity can be controlled                                Cognitive coefficient (C1)      2.05
within a band.                                                                            Social coefficient (C2)         2.05
                       Vini  V fin
         Vmax  Vini               iter
                        itermax                                                                  VII RESTORATION INDICES
                                                                             The availability of units in each area with their storage
C. Acceleration constant (    C1 , C2 )                                 units, with enough margins to pick up the overload ensures
                                                                        whether the load disturbances or disturbance due to the outage
                                                                        of the units have to be given prime importance or not. In this
 C1 is called Cognitive Parameter which pulls each particle             section evaluation index namely Feasible Restoration Indices
towards local best position. C1 , C2 is called Social                   and Complete Restoration Indices are discussed. These
Parameter which pulls the particle towards global best                  restoration Indices indicate whether the system is in a
                                                                        condition to be restored which can be adjudged with various
position. Generally, C1 , C2 are chosen between 0 to 4.                 case studies.

         The design steps of PSO based PI controller is as
follows:                                                                    A. Feasible Restoration Indices
     1. The algorithm parameters like number of generation,                      The optimal Proportional plus Integral controller
         population, inertia weight and         constants are           gains are obtained using PSO technique for TATURIPS
         initialized.                                                   with/without distributed generations. The various case studies
     2. The values of the parameters KP and Ki initialized              that have been carried out for framing the Feasible Restoration
         randomly.                                                      Indices were obtained based on the settling time of the output
     3. The fitness function of each particle in each                   response of the frequency deviations in both areas as follows:
         generation is calculated.
     4. The local best of each article and the global best of           Case 1: In the TATURIPS with 1% step load disturbance in
         the particles are calculated.                                  area-1; the settling time (τs1) of the frequency deviation in
     5. The position, velocity, local best and global best in           area-1 is obtained and FRI1 is found as
         each generation is updated                                                        FRI1= τs1/Tp                          (10)
     6. Repeat the steps 3 to 5 until the maximum iteration
         reached or the best solution is found.                         Case 2: In the TATURIPS considering SMES and GT in Area-
                                                                        1 and Area-2 respectively with 1% step load disturbance in



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                                                                          International Journal of Computer Information Systems,
                                                                                                                 Vol.2, No.6, 2011
area1 and the settling time (τs2) of the frequency deviation in    Case8: In the TATURIPS considering SMES and GT in Area-
area-1 is obtained and FRI2 is found as                            1 and Area-2 respectively with 1% stepload disturbance in
                                                                   area1 and the settling time (τs8) of the frequency deviation in
            FRI2= τs2/Tp                                   (11)    area-1 is obtained and FRI8 is found as

Case 3: In the TATURIPS considering SMES and GT in Area-                       FRI8= τs8/Tp                                    (20)
1 and Area-2 respectively with 1% step load disturbance in
area2 and the settling time (τs3) of the frequency deviation in    Case9: In the TATURIPS considering SMES and GT in Area-
area-2 is obtained and FRI3 is found as                            1 and Area-2 respectively with 1% stepload disturbance in
                                                                   area2 and the settling time (τs9) of the frequency deviation in
            FRI3= τs3/Tp                                   (12)    area-2 is obtained and FRI9 is obtained as

Case 4:In the TATURIPS with 4% step load disturbance in                        FRI9= τs9/Tp                                    (21)
area-1; the settling time (τs4) of the frequency deviation in
area-1 is obtained FRI4 is found as                                Case10: In the TATURIPS with one unit outaged in area-1and
                                                                   with 4% stepload disturbance; the settling time is (τs10) of the
           FRI4= τs4/Tp                                    (14)    frequency deviation in area-1 is obtained and FRI10 is found as

Case 5: In the TATURIPS considering SMES and GT in Area-                       FRI10= τs10/Tp                                  (22)
1 and Area-2 respectively with 4% step load disturbance in
area1 and the settling time (τs5) of the frequency deviation in    Case11: In the TATURIPS considering SMES and GT in
area-1 is obtained and FRI5 is found as                            Area-1 and Area-2 respectively with 4% stepload disturbance
                                                                   in area1 and the settling time (τs11) of the frequency deviation
            FRI5= τs5/Tp                                   (15)    in area-1 is obtained and FRI11 is found as

Case 6: In the TATURIPS considering SMES and GT in Area-                       FRI11= τs11/Tp                                  (23)
1 and Area-2 respectively with 4% step load disturbance in
area-2 and the settling time (τs6) of the frequency deviation in   Case12: In the TATURIPS considering SMES and GT in
area-2 is obtained and FRI6 is found as                            Area-1 and Area-2 respectively with 4% stepload disturbance
                                                                   in area-2 and the settling time (τs12) of the frequency deviation
            FRI6= τs6/Tp                                   (16)    in area-2 is obtained and FRI12 is obtained as

Where τs1, τs2, τs3 ,τs4, τs5,τs6 are the settling time of the                 FRI12= τs12/Tp                                  (24)
(frequency deviation) output response of the system for
various case studies respectively and Tp is the power system                 CRI = { FRI1, FRI2, FRI3, ……..,FRI11,FRI12 } (25)
time constant. The maximum and minimum Feasible
Restoration Indices are obtained as follows:                                 CRImax=Max{FRI1, FRI2, FRI3, …. ,FRI12 } and (26)

      FRImax=Max{FRI1, FRI2, FRI3, FRI4, FRI5 ,FRI6 } (17)                   CRImin=Min{FRI1, FRI2, FRI3, …. ,FRI12} and       (27)

      FRImin=Min{FRI1, FRI2, FRI3, FRI4 FRI5 ,FRI6}        (18)    Apart from the FRI and CRI computations which are based on
                                                                   the settling time of the output response of ΔF1 in TATURIPS
    B. Complete Restoration Indices                                with various case studies, the magnitude of ΔF1 can also be
Apart from the normal operating condition of the TATURIPS          used for finding FRI and CRI.
few other case studies like outage of one distributed
generation in TATURIPS are considered with 1% and 4% step              The following steps are adopted to find the restoration
load disturbances. The various case studies obtained based on      Indices  1 as:
their optimal gains and their performance index is designated
by CRI as follows:
                                                                            1. For various case studies obtain the output
Case7: In the TATURIPS with one unit outaged in area-1and                   responses of ΔF1, ΔF2
with 1% stepload disturbance; the settling time is (τs7) of the             2.    is obtained
frequency deviation in area-1 is obtained and FRI7 is found as
                                                                            as
            FRI7= τs7/Tp                                   (19)
                                                                   The    amount     of     max      peak      (or    percentage)
                                                                   overshoot/undershoot directly indicate the relative stability of




      June Issue                                           Page 67 of 78                                   ISSN 2229 5208
                                                                                    International Journal of Computer Information Systems,
                                                                                                                            Vol.2, No.6, 2011
the system. In the transient response specification the max                   Figures 7(a),7(b) to 12(a),12(b) represent the Complete
overshoot and the rise time conflict with each other. In other                Restoration responses. The results of the PSO tuned gain
words, both the max overshoot and rise time (which indicates                  values, their respective settling time, FRI, CRI and also the
rate of change of control input) cannot be made smaller                       peak overshoot value|1| of all the case study has been
simultaneously. If one of them is made smaller and the other                  presented in the table 2 and 3.The proposed methodology with
necessarily becomes larger [16].                                              respect to the settling has been analyzed and is presented as an
                                                                              index for normal operation with load changes and for outage
          VIII. SIMULATION RESULTS AND OBSERVATION                            condition also.

     The FRI and CRI indicates the possible restoration indices               Power System Restoration Assessment:-
for the TATURIPS with SMES and GT units for different case                    RI based on Settling Time
studies of the output response of the system. The case study                        (i) If FRI or CRI is greater than 1 then more amount
has been carried out for load change as well as with single and                         of distributed generation requirement is needed.
both units which can be considered as an uncertainty that
occurs in an interconnected power system. The Feasible                        RI based on peak undershoot of ΔF1
Restoration Index (FRI) implies a restoration index for
different load conditions and Complete Restoration Index                                 (i) If FRI or CRI in greater than 0.4 then the system
(CRI) based on the settling time of the output response of the                                 is vulnerable and the system      becomes unstable
system with outage of one unit and/or outage of Distribution                                   [16] and may result to blackout.
Generation Capacity to give a secure and reliable operation of                           (ii) If FRI or CRI is 0.02 ≤ |1|≥ 0.075 then more
TATURIPS under study. Figures 1(a),1(b) to 6(a),6(b)
                                                                                               amount of distribution generation requirement is
represent the respective frequency responses and control input
                                                                                               needed.
deviations of the case study 1 to 6 i.e Feasible Restoration
                                                                                         (iii) If FRI or CRI is greater than 0.075 not only more
responses.
                                                                                               amount of distributed generation is required but
                                                                                               also load shedding is preferable.


                                                     TABLE 2. RESTORATION INDEX VALUES
     Case study          System        ΔPd     KP      KI     ζs        ζs    RI based on settling time   RI based on peak under shoot of ΔF1
                                                             (Δf)     (ΔPC)     (ΔF1)         (ΔPC)
        CASE 1         TATURIPS        1%     0.75    0.34   17.5      19        0.88          0.95                       0.018
        CASE 2       TATURIPS+SMES     1%     0.39    0.15   14.8      15        0.74          0.75                       0.02
        CASE 3        TATURIPS+GT      1%     0.60    0.25    20       20         1             1                         0.019
        CASE 4         TATURIPS        4%     0.42    0.26   21.8      19        1.09          0.95                       0.074
        CASE 5       TATURIPS+SMES     4%     0.54    0.26    27       20        1.35           1                         0.073
        CASE 6        TATURIPS+GT      4%     0.70    0.26    27       25        1.35          1.25                       0.072
        CASE 7         TATURIPS        1%     0.75    0.34    18       10        0.9           0.5                        0.026
        CASE 8       TATURIPS+SMES     1%     0.39    0.15    29       25        1.45          1.25                       0.028
        CASE 9        TATURIPS+GT      1%     0.60    0.25    27       29        1.35          1.45                       0.026
        CASE 10        TATURIPS        4%     0.42    0.26   23.5      20        1.18           1                         0.104
        CASE 11      TATURIPS+SMES     4%     0.54    0.26    38       37        1.9           1.85                       0.102
        CASE 12       TATURIPS+GT      4%     0.70    0.26    38       35        1.9           1.75                        0.1

         CASE 1-6: Both Units in operation
         CASE 7-12: With one unit outaged

                  TABLE 3. FEASIBLE RESTORATION INDEX (FRI) BASED ON FREQUENCY DEVIATION REPRESENTED BY

                                                                                Range for
                                                                                   based on undershoot
                                             System
                                                                              FRI                          CRI
                                                                     1%             4%           1%           4%
                                        TATURIPS                    0.018        0.074          0.026         0.10
                                     TATURIPS + SMES                 0.02        0.073          0.03          0.10
                                      TATURIPS + GT                 0.019        0.072          0.026         0.10




        June Issue                                                  Page 68 of 78                                           ISSN 2229 5208
                                                                                            International Journal of Computer Information Systems,
                                                                                                                                  Vol.2, No.6, 2011




                                     1(a)                                                                     1(b)




                                     2(a)                                                                      2(b)




                                     3(a)                                                                        3(b)




                                      4(a)                                                                       4(b)




                                     5(a)                                                                        5(b)




                                      6(a)                                                                      6(b)


Fig ure 7. Figures 1(a),1(b) to 6(a),6(b) - Frequency rsponses and control input deviations of FRI case study; X-axis ---- Settling Time in Seconds,Y-axis(a) ----
                                          Frequency Deviation in HZ, Y-axis(b) ---- Control Input Deviation in p.u MW




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                                         7(a)                                                                   7(b)




                                            8(a)                                                                  8(b)




                                                9(a)                                                              9(b)




                                                   10 (a)                                                              10(b)




                                                   11 (a)                                                                11 (b)




                                                                                                                       12 (b)
                                                   12 (a)

Figure 8. Figures 7(a),7(b) to 12(a),12(b)-Frequency rsponses and control input deviations of CRI case study; X-axis ---- Settling Time in Seconds,Y-axis(a) ----
                                         Frequency Deviation in HZ, Y-axis(b) ---- Control Input Deviation in p.u MW




        June Issue                                                     Page 70 of 78                                                ISSN 2229 5208
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                                                                                                                            Vol.2, No.6, 2011
                                                                                [11] Nagpal M., A.Moshref, G.K.Morison, P.Kundur, “Experience with
                           IX. CONCLUSION                                            Testing and Modeling Of Gas Turbine”, Proceedings of the
    The proposed methodology has been implemented on                                 IEEE/PES, Winter Meeting, Columbus USA, pp. 652-656, 2001.
                                                                                [12] ChaoOu, Weixing Lin, “Comparison between PSO and GA for
TATURIPS without/with SMES and GT units in Area-1 and
                                                                                     Parameters Optimization of PID Controller”, Proceedings of the
Area-2 respectively to meet out even under peak load
                                                                                     IEEE International Conference on Mechatronics and Automation,
conditions and to restore the system within a short span of
                                                                                     pp.2471-2475, June 2006.
time during inter-area oscillations thereby damping out the
                                                                                [13] Ghoshal S.P., “Optimizations of PID gains by particle swarm
peak overshoots and frequency deviations due to a                                    optimizations in fuzzy based automatic generation control”, Electrical
considerable limit of load changes and also during a outage                          power system Research, Vol.2, pp.203-212, June 2004.
condition. A better stability margin is obtained with both the                  [14] Kennady, J. and R.C. Eberhart “Particle swarm optimization”, IEEE
SMES and GT units compared to that of conventional unit                              . Int Conf. on Neural Network, Perth, Australia, pp:1942-1948,1995.
alone.Comparitively, SMES is having a better restoring                          [15] Seong-II Lim, Seung-Jae-Lee, Myeon-Song Choi, Dong-Jin Lim and
capability. Applying the proposed methodology to the                                 Dong-Ho Park “Restoration Index in Distribution Systems and its
system operation, enhances the operating efficiency, securing                   [16] Application to System Operation”, IEEE Transactions on Power
a full restoration capability, thereby reducing the addition of                       Systems, Vol.21.No.4, November 2006.
new power facilities to meet out the load changes. It is clear                  [16] Katsuhiko Ogata, Modern control Engineering, Prentice Hall of India,
that the restoration capability can be maintained at high                            New Delhi, 1986.
level, thereby increasing the service reliability.
                        ACKNOWLEDGMENT
The authors wish to thank the authorities of Annamalai                                                          APPENDIX
University, Annamalai Nagar, TamilNadu, India for the
facilities provided to prepare this paper.                                             Data for the two-area interconnected thermal power system
                                                                                                  with reheat turbines (TATURIPS) [5]
                            REFERENCES
                                                                                      Pr1=Pr2 =2000MW
                                                                                      Kp1=Kp2=120Hz/p.u
[1]   E.K.Nielsen, M.M.Adibi, D.Barrie, M.E.Cooper, K.W.Heussner,                     Tp1=Tp2 =20sec.
      M.E.RobertSon, J.L.Scheidt and D.Scheurer, “System Operation                    Tt1=Tt2 =0.3 sec.
     Challenges”, IEEE Transactions on Power Systems, Vol.3, No. 1,                   Tg1=Tg2 =0.08sec.
     pp.118-124,February 1988.                                                        Kr1=Kr2 =0.5
[2] M.M.Adibi, R.J.Kafka, “Power System Restoration Issues”, IEEE                     Tr1=Tr2 =10 sec.
                                                                                      R1=R2 =2.4Hz/p.u MW.
     Computer Applications in Power, Vol. 4, No. 2,pp. 19-24, April 1991.
                                                                                      a12      =-1
[3] Adibi M.M., R.J Kafka, D.P Milanic, “Expert system requirements                   T12      =0.545 p.u MW/Hz
     for power system Restoration”, IEEE Transactions on Power Systems,               β 1= β2 =0.425 p.u. MW/Hz
     Vol.9, pp.1592-1598, Aug 1994.
 [4] Aldeen, M. and J.F. Marah, “Decentralized PI design method for
     interconnected power system”. IEE Proc.-C, Vol. 138, No. 4, pp.263-              Data for the SMES unit [8]
     274, 1991.
[5] Chidambaram, I.A and S.Velusami, “Design of Decentralized Biased                  L           =    2H
     Controllers for Load Frequency Control of Interconnected Power                   Tdc         =    0.026sec
                                                                                      Ido         =    4.5KA
     Systems” Electric Power Components and Systems, Vol. 33, No.12,
                                                                                      Kid         =    0.2 KV/KA
     pp. 1313-1331, 2005.                                                             KSMES       =    100KV/unit MW
[6] Demiroren A., “Application of a self-tuning to power system with                  Idmin       =    4.05 KA
     SMES”, European Transactions on Electrical Power (ETEP), Vol.                    Idmax       =    6.21KA
     12, N0.2, pp. 101-109, 2002.
[7] Y.Mitani,      K.Tisuji    and    Y.Murakami,      “Application    of             Data for GT unit [9]
     Superconducting Magnetic Energy Storage to Improve Power System
     Dynamic Performance”, IEEE Transaction on Power System, Vol.3,                   T1            = 10sec
     No.4, pp. 1418-1425, 1988.                                                       T2            = 0.1sec
[8] S.C.Tripathy, R.Balasubramania and N.P.S.Chandramohanan,                          T3            = 3sec
                                                                                      Kt            = 1
     “Adaptive Automatic Generation Control with Super Conducting
                                                                                      Kr            = 0.04
     Magnetic Energy Storage in Power System”, IEEE Trans. On Energy                  Dturb         = 0.03
     Conversion, Vol.7, No.3, pp. 134-141, 1992.                                      Max and Min Valve Position =1 and -0.1
[9] Soon Kiat Yee, Jovica V.Milanovic, F.M.Michael Hughes,
     “Overview and comparative Analysis of Gas Turbine Models for
     System Stability Studies”, IEEE Trans. On Power Systems Vol.23,
     No.1, pp. 108-118 February 2008.
[10] Barsali,S.,D.Poli,A.Pratico,R.Salvati,M.Sforna,R.Zaottini,
     “Restoration Islands Supplied by Gas Turbine”, Electric Power
     System Research, Vol.78, pp. 2004-2010 August 2008.




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                                                                              International Journal of Computer Information Systems,
                                                                                                                    Vol.2, No.6, 2011
                 AUTHORS PROFILE                                                             Dr. I.A.Chidambaram (1966) received Bachelor of
                                                                                             Engineering in Electrical        and Electronics
                                                                                             Engineering (1987) Master of Engineering in Power
             R. Jayanthi received her B.E and M.E degrees from                               System Engineering (1992) and Ph.D in Electrical
             Faculty of Engineering and Technology, Annamalai                                Engineering (2007) from Annamalai University,
             University, Annamalai Nagar, Chidambaram, India                                 Annamalainagar. During 1988 - 1993 he was
             in 1994 and 2007 respectively. Currently working as                             working as Lecturer in the Department of Electrical
             an ASSISTANT PROFESSOR in Department of                                         Engineering, Annamalai University and from 2007
             Electrical and Electronics Engineering since 2007.                              he is working as PROFESSOR in the Department of
             She is currently working towards the Ph.D. degree.                              Electrical Engineering, Annamalai University,
             Her research interest include power system operation       Annamalai Nagar. He is a member of ISTE and Indian Science Congress
             and control.
                                                                        (ISC). His research interests are in power systems, l system(Electric al
                                                                        Measurement Laboratory, control systems. (Electrical Measurements
                                                                        Laboratory, Department of Electrical Engineering, Annamalai University,
                                                                        Annamalainagar 608002, Tamilnadu, India, Tel: -91-04144-238501, Fax:-
                                                                        91-04144-238275) driacdm@yahoo.com/ driacdm@gmail.com




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