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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 i1 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 ti1 X ti Vt i1 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 June Issue Page 66 of 78 ISSN 2229 5208 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 June Issue Page 69 of 78 ISSN 2229 5208 International Journal of Computer Information Systems, Vol.2, No.6, 2011 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 International Journal of Computer Information Systems, 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. June Issue Page 71 of 78 ISSN 2229 5208 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 June Issue Page 72 of 78 ISSN 2229 5208

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