Powers Ys 900

7/26/2019 Powers Ys 900

1/13

Corresponding author :J.Raja, Assistant Director, National Power Training Institute, Ministry of Power India, Faridabad 121 003,

Haryana, India. Email: [email protected], Cell No.: 8800124789.

Dr.C.Christober Asir Rajan, Associate Professor, Pondicherry Engineering College, Puducherry, India. Email:

[email protected]

Copyright JES 2011 on-line : journal.esrgroups.org/jes

J. Raja

C.Christober Asir RajanJ. Electrical Systems 7-2 (2011): 193-205

Regular paper

Improved Power System DynamicPerformance Using SMES For

Frequency Excursion

This paper analyses a comparative transient performance of six types of single machine power

system. The six types of system configurations are viz. i) AVR loop with Proportional

Integral controller PI) combined with AGC loop. ii) AVR loop with single input CPSS

combined with AGC loop. iii) AVR with PI combined with SMES unit based AGC loop. For

AGC loop both thermal unit and hydro unit are individually consider. iv) AVR with CPSS

combined with SMES unit based AGC loop. For AGC loop both thermal unit and hydro unit

are individually considered. v) AVR with FLC combined with FLC based AGC loop. For

AGC loop both thermal unit and hydro unit are individually considered. vi) AVR with FLC

combined with SMES unit based AGC with FLC loop. For FLC based AGC loop both thermal

unit and hydro unit are individually considered. The Thermal unit is either single or double

reheat turbine. Hydro unit is considered with mechanical or electrical governor. Proportional

Integral, Conventional power system stabilizer and fuzzy controller is provided in AGC loop.

It is shown that the SMES based fuzzy controlled AGC loop along w ith fuzzy controlled AVR

assist better transient performance of the power system in all cases under different operating

conditions.

Keywords: Automatic Generation Control (AGC), Automatic Voltage Regulator (AVR),

Conventional Power System Stabilizer (CPSS), Fuzzy Logic Controller (FLC), Superconducting

Magnetic Energy Storage (SMES).

1. Nomenclature

Incremental change in Rotor angle.

Incremental change in electrical angular frequency.M Inertia co-efficient.

D Damping co-efficient.1

qE Voltage proportional to direct axis flux linkages

K1 Change in electrical power for a change in rotor angle with constant flux linkage

K2 Change in electrical power for change in flux linkage with constant rotor angle.K3 Impedance factor

K4 Demagnetizing effect of change in rotor angleK5 Change in terminal voltage with change in rotor constant angle E

q1.

K6 Change in terminal voltage with change in Eq1 for constant 1

doT Direct axis open circuit time constant of the machine

Ka Amplifier gain

Ta Time constant of amplifierKe Exciter gain

Te Time constant of exciterKP Proportional Gain Constant

IK Integral Gain Constant

Kcf Gain constant of frequency

7/26/2019 Powers Ys 900

2/13

J. Electrical Systems 7-2 (2011): 193-205

194

Kpss Power System Stabilizer Gain

Td1,Td2 Time constant of the power system stabilizerTd3,Td4 Time constant of the power system stabilizer

Tdc Converter time constant

Tg Governor time constant

Trr Terminal voltage transducer time constantTt Non- reheat turbine time constant is S domainTw Washout time constant of PSS block on S domain

Tr Reheat turbine time constant

2.0 Introduction

Load Frequency control is an important issue in power system operation and control.The loading in power system is never constant. To ensure the quality of the power supply,

we need to design a load frequency control system which deals with control of generatorwith changing load. There has been continuing interest in designing strategy for load

frequency controls has been proposed since 1980 [1-3].

In [4], Nanda and Mangla discussed the AGC performance of interconnected system inthe continuous mode using conventional integral and fuzzy controllers, Nanda et al [5] also

considered an interconnected Hydro-Thermal system in the continuous discrete mode usingconventional integral controller or PI controller. It has been established that maximum

deviation and settling time are same for the controllers and the paper has established thefuzzy controller without considering SMES.

In the context of AGC coordinated CPSS equipped with AVR model, it is quite

relevant to investigate the further improvement in the transient performance by the

application of SMES with fuzzy controller. In [5-6], transfer function model for singlestage, double stage reheat turbine, the transfer function of model mechanical and electricalgovernor are given. A literature survey reveals that mechanical governor has an integral

part of existing hydro units.

On the other hand, owing to their low power consumption while in operation, modernhydro units are being equipped with electric governors. Nanda et al [5] are the first to

compare the performance of electric and mechanical governors in the context of

interconnected hydrothermal AGC system. The uses of SMES and battery energy storagefor load leveling application and for improvement of the dynamic performance of power

system have been described [7-11]. The importance of control system using SMES has

been presented as one of the powerful stabilizers for undamped oscillations which tend tooccur in a long distance bulk power transmission system has been viewed and analyzed in

the literature [12]. In [13] the improvement in AGC with the addition of a small capacitySMES unit is studied, and time domain simulations are used to study the performance of

the power system dynamics are analyzed.

Fuzzy PI controller has some advantages: (i) it provides an efficient way of copyingwith imperfect information, secondarily, (ii) it offers flexibility in decision making process,

thirdly, (iii) it provides an interesting man / machine interface by simplifying ruleextraction from human experts and fallowing a simpler a posterior interpretation of the

system reasoning. Fuzzy logic controllers are knowledge base controllers usually derived

from a knowledge acquisition process or automatically synthesized from self organizingcontrol architecture. Fuzzy Logic Controllers have been used in both AGC and AVR.

Attempt has been made to examine suitable number of triangular membership functions(MFs)that can provide better dynamic response. The dynamic responses are obtained and

compared to those obtained with conventional integral controllers, conventional power

7/26/2019 Powers Ys 900

3/13

J.Raja et al: A Improved Power System Dynamic Performance Using SMES

195

system stabilizer and Fuzzy controller. Fuzzy Logic controllers are quite robust and the

Fuzzy rules for nominal condition need not be changed for 25% variations in systemparameters and 20% variations in operating load condition from their nominal values.

The rest of the paper is documented in the following headings. Section 3 provides test

power system investigation. Section 4 deals with Mathematical model SMES. Section 5 and6 incorporates fuzzy logic in AGC and AVR. Section 7 includes Simulation Results, .

Section 8 Result and followed by References in section 9.

3.0 System Investigation

A single machine connected to infinite bus system (SMIB) is considered [18]. TheMATLAB-SIMULINK representation of SMIB system with AVR, exciter, Synchronous

generator, CPSS loop, AGC loop and subsystem loop is shown in fig 1, subsystem SMES isshown in fig 3.The synchronous generator with AVR, IEEE STIA thyristor excitation

system along with generator and equivalent transmission line reactance are represented by atwo axis fourth order model.

3.1Automatic Generation Control

The main aim of AGC is making ACE zero for an interconnected hydro thermal system so

that the scheduled value of the system frequency and tie line power is maintained, In

general two control variables associated with AGC schemes and these two variable gives anidea about ACE [20] and they are related with the equation (1).

ACE= (P + Bf)

tie (1)

In the present work ACE is only, B*f bias setting of B=i is considered for both hydro and

thermal areas. The system model is considered for continuous mode operation. The nominal

system parameters are given in the Appendix 1.

3.2Automatic Voltage Regulator

AVR is about controlling the reactive power in a stable power system. The proposed AVRsystem block diagram for system simulating a fourth order model of synchronous generator

shown in figure 1. The AVR system response is studied and simulated with PI controller,

Conventional power system stabilizer (CPSS) and Fuzzy logic controller (FLC). First asimple system with first order generator and without PI controller is simulated. Second a

more realistic system has been simulated with PI and a fourth order generator. Third

conventional power system stabilizer is designed for a fourth order generator to suppressesthe voltage fluctuations, Fourth the same system is simulated with FUZZY controller

instead of conventional and PI controllers. Dynamic response shows that performance offixed gain CPSS is better for particular operating conditions. It may not yield satisfactory

results when there is a change in the operating point.

3.3. Combining LFC and AVR

Due to weak coupling relationship between the AVR and AGC, the voltage and frequency

are regulated separately. The study of coupling effects of the AVR and AGC can be foundin Kundur [2], where it is mentioned that a small change in the electrical power Peis the

product of the synchronizing power coefficient PS and the change in the power angle .

7/26/2019 Powers Ys 900

4/13


196

Taking in to account that the voltage is proportional to the main field winding flux Ed, the

following linearised equation is obtained (2).

'P K K E

e 2 1 = + (2)

Where K1is the change in the electrical power for a change in the direct axis flux linkage

with constant rotor angle and K2=PS. By modifying the generator field transfer functionand taking in to account the effect of rotor angle. The equation for the stator EMF can

expressed as (3).

.

K' GE (V K )f 3(1+ST )G

= (3)

Where K3is the demagnetizing effect of a change in the rotor angle (at steady state). Thesmall effect of this rotor angle upon the generator terminal voltage can be expressed as

equation (4)

Fig 2: AGC Loop Sub System

Fig.1: MATLAB SIMULINK Representation of SMIB with AVR, SynchronousGenerator, PSS Loop, AGC Loop, SMES Loop

7/26/2019 Powers Ys 900

5/13


197

'V K K Et 4 5

= + (4)

Where K4is the change in terminal voltage with the change in rotor angle for Ed, K5is the

change in terminal voltage with the change in Edfor a constant rotor angle. Therefore the

simulation model for a fourth order machine time constant is generated figure 1.The actualmodel may vary slightly in presentations due to some limitations in the MATLAB graphicsinterface nonetheless. Note that VLand PLare included to simulate the load change in

voltage and power respectively, which are effectively the change in real and reactive power.

4.0 Superconducting Magnetic Energy Storage

The operation of SMES units, that is, charging, discharging, the steady state mode and thepower modulation during dynamic oscillatory periods are controlled by the application of

the proper positive and negative voltage to the inductor. This can be achieved by

controlling the firing angle of the converter bridges. Neglecting the transformer and theconverter losses in [22] and DC voltage is given by in equation (5).

E 2 V COS - 2 I R d do d c

= (5)

Where, Ed= DC voltage applied to the inductor (KV), = firing angle (degree), Id= current

through the inductor (KA), Rc= equivalent commutating resistance (ohm), Vdo = maximumopen circuit bridge voltage of each 6-pulse converter at = 0 deg (KV). The inductor is

initially charged to its rated current, Ido by applying a small positive voltage. Once thecurrent has attained the rated value, it is held constant by reducing voltage ideally to zero

since the coil is superconducting. A very small voltage may require to over coming thecommutating resistance. The energy stored at any instant [8],

2LIdW MJ

L 2= ; Where, L=Inductance of SMES, H.

a) Frequency deviation as a control signal:

The frequency deviation f of the power system is sensed and used to control the SMESvoltage, Ed. When power is to be pumped back in to the grid in the case of fall in the

frequency due to sudden loading in the armature, the control voltage E d is to be negative

since the current through the inductor and thyristor cannot change its direction. Theincremental change in the voltage applied to the inductor is expressed as:

KfE fd (1 ST )dc

= + (6)

Where : Edis the incremental change in converter voltage; Tdcis the converter time delay;Kfis the gain of the control loop and S is the Laplace operator d/dt. If ACE is used as

control signal then

Ai IAi

K 1fE K ( f + P )- K Id i ij di(1 ST ) Bdc i

=

+ (7)

Where, i,j=1,2

b) Area control error (ACE) as control signal:

In case where tie line power deviation signals are available, it may be desirable to use areacontrol error as input to SMES control logic. This has certain disadvantages, which are

desirable later, compared to frequency deviation derived controls. The area control error of

two areas is defined in equation (1) and then.

7/26/2019 Powers Ys 900

6/13


198

Ai

d i i ij

dc i i

K 1E ( f + P )

(1 S T ) B =

+ (8)

i,j = 1,2.

5.0 The Fuzzy Logic Based PI Controller for AGC

Fuzzy set theory and fuzzy logic establish the rules of a non-linear mapping. The use offuzzy sets provides a basis for a systematic way for the application of uncertain and

indefinite models. Fuzzy set is based on a logical system called fuzzy logic controller.

Because of the complexity and multi-variable conditions of the power system, conventionalcontrol methods may not give satisfactory solutions. On the other hand, robustness and

reliability make the fuzzy logic controller useful in solving a wide range of control

problems. The fuzzy logic controller has two input signals, namely, ACE and ACE*, theoutput signal (y) of the fuzzy logic controller is the input signal of the conventional PI

controller. Finally, the output signal from the conventional PI controller called the control

signal (u) is used for controlling the LFC in the interconnected power system.

The fuzzy logic controller is comprised of four main components: the fuzzifier, theinference engine, the rule base, and the defuzzifier. By taking the system output, the control

signal for the FLPI controller is given by in designing the FLPI controller, important

procedures are how to obtain the PI gains, membership functions and control rules. The PIgains have been determined easily, but the membership functions and the control rules aredifficult. Thus, to simplify solving this problem, this paper presents a new approach to

determine the PI gains, membership functions and control rules based on the TS algorithmThe membership functions of the fuzzy logic controller presented in Table.1 consist of

three memberships functions (two-inputs and one-output). Each membership function has

five memberships, comprising five triangular memberships. All memberships are selectedto describe all linguistic variables. For the determination of the control rules, it can be more

complicated than membership functions, which depend on the designer experiences and

actual physical system referred [. The FLPI controller uses the membership functions andthe control rules as shown in Figure3 and table 1 [17-20].Conventional controllers are

derived from control theory technique based on mathematical models of the open loop

process to be controlled. For instance, a conventional Proportional Integral controller canbe described by the function.

TABLE - 1 KNOWLEDGE BASE

ACE

NL NS ZE PS PL

NL NL NL NS NS ZE

NS NB NL NS ZE ZE

ZE NS NS ZE PS PS

PS ZE PS PS PL PL

Change

in ACE*

PL ZE ZE PS PL PL

7/26/2019 Powers Ys 900

7/13


199

p iU K K e dt= + (9)According to the conventional automatic theory. The performance of the PI controller isdetermined by its proportional parameter KP and integral parameter KI. The proportional

controller provides control action equal to some of multiple of error, while the integral

controller term forces the steady state error to zero.

6.0 FUZZY-Logic Based Power System Stabilizer

Power system stabilizers (PSSs) are added to excitation system to enhance the dampingduring low frequency oscillations. This paper presents a study of fuzzy logic based PI

controller with power system stabilizer (PSS) for stability enhancement of a single machinepower system. In order to accomplish the stability enhancement, speed deviation () and

acceleration () of the rotor of synchronous generator were taken as the input to the fuzzylogic controller. This variable gives significant effects on damping of the generator shaft

mechanical oscillations. The stabilizing signals were computed using the fuzzy membershipfunctions depending on these variables.

TABLE-2 KNOWLEDGE BASE

The nonlinear model of single machine infinite bus system (SMIB) developed usingSimulink. After choosing proper variables as input and output of fuzzy controller, it is

required to decide on the linguistic variables. These variables transform the numericalvalues of the input of the fuzzy controller to fuzzy quantities. The number of these

linguistic variables specifies the quality of the control which can be achieved using the

fuzzy controller. As the number of the linguistic variables increases, the Computationaltime and required memory increase. Therefore, a compromise between the qualities of

control and computational time is needed to choose the number of linguistic variables. Forthe power system under study, five linguistic variables for each of the input and output

variables are used to describe them in Large Negative (LN), Medium Negative (MN),Zero(Z), Medium positive (MP), Large positive (LP).The two inputs; speed deviation andacceleration, result in 25 rules for each machine. Decision in table 2 shows the result of 25

rules, where a positive control signal is for the deceleration control and a negative signal is

for acceleration control.

7.0 Simulation Results

Simulation study has been carried out by varying active and reactive power. The presentsystem model is new one and totally different form reference [5-8]. Fuzzy based SMES

system results are newly computed and compared with CPSS, PI controllers. The majorobservations of the present works are as documented:(i) Sensitivity Analysis

In this paper, the investigations are carried out to study the effect of power system by

varying system parameters like H, Tg, Tt, Kr, Tr, real and reactive power. the parameters are

LN MN Z MP LN

LP Z Z MP MP LP

MP MN Z Z MP MP

Z MN Z Z Z MP

MN MN MN Z Z MP

LN LN MN MN Z Z

7/26/2019 Powers Ys 900

8/13


200

varied by 25% from the nominal values. From reference [5-8] results, the dynamic

response hardly changes by 25% variations in system parameters, for correspondingoptimum PI gains. But the Fuzzy controllers are quite robust for system parameter

variations. The table 3 clearly shows parameter variation should not affect the dynamic

response of the system.

Table -3 Sensitivity Analysis

Parameter variation Load KP KI

All nominal 100% 5.326 0.050

30% 6.431 0.085Load Parameter

70% 6.213 0.073

75% 6.332 0.045H parameter125% 7.236 0.086

75% 7.523 0.048TgParameter

125% 7.653 0.087

75% 4.882 0.046TtParameter

125% 7.123 0.066

75% 4.775 0.043Krparameter 125% 7.865 0.088

75% 3.851 0.049Trparameter

125% 7.995 0.091

30% 3.261 0.025P & Q Variations

70% 4.163 0.043

(ii)Comparison of single stage and double stage reheat turbine for thermal unit

From figure 4(a) & (b), the optimal transient performance of the power system for thermal

unit with single stage reheat and double stage reheat turbine in the AGC loop coupled withCPSS & PI plus AVR loop is portrayed both for 0.01 p.u. step perturbation in load torque,

operating condition being P=0.9, Q=0.1, Xe=0.4752, Et=1.0. From this figure, it is againobserved that while comparing the transient response profile of the power system for single

stage and double stage reheat turbine, it is clear that, both the turbine models yield the same

performance .Thus, it may be inferred that a double stage turbine can be modeled as asingle stage one

(iii) Comparison of electric and mechanical governor for hydro unit:From figure 5 (a) and (b), a close look at the comparative performance characteristics ofelectric governor and mechanical governor for hydro unit unveil that electric governor is

better than mechanical governor when system transient performance is of interest. In the

recent work carried out by Nanda et al.[2],a very fast oscillation in the performance ofmechanical governor while settling down the frequency deviation in the AGC loop is

noticed. However, in the present work, due to the assistance of PSS loop that very fast

oscillatory response is not present in mechanical governor .However, a very fast initial

oscillation for a short duration in electric governor is noticed due to system transient.

7/26/2019 Powers Ys 900

9/13


201

(iv) Performance evaluation and comparative analysis of AGC (PI) with different AVR systems

(PI, CPSS, FUZZY).

Figure 6 (a), (b), (c) and (d) shows the optimal transient performance of power system withvarious combination of AVR, corresponding to an operating condition of P=0.9,Q=0.1,

Xa=0.4752, E

t=1.0 for thermal with single stage reheat turbine. Simulated results are

displayed in figure (a) to (d). From these results , it is noted that the importance of CPSS

action for either 0.01P.U step perturbation in reference voltage, the system transientperformance is better than PI controller, CPSS improves the dynamic performance and it

reduces settling time, peak over shoot. But compared to AVR with CPSS, fuzzy controller

reduces settling time faster manner, the Fuzzy with power system transient performance isbetter than PI and CPSS.

(v)Performance evaluation and comparative analysis of Power System with Conventional

Controller and FUZZY controller.

Figures 7(a), (b) shows the optimal transient performance of power system with various

combination of AVR AGC with PI and Fuzzy controllers, corresponding to an operatingcondition of P=0.9,Q=0.1, Xa=0.4752, Et=1.0 for thermal with single stage reheat turbine.

Simulated results are displayed, it is noted that the importance of Fuzzy action for either

0.01P.U step perturbation in reference voltage, the system transient performance is better

7/26/2019 Powers Ys 900

10/13


202

than PI controller, CPSS improves the dynamic performance and it reduces settling time,

peak over shoot. But compared to AVR with CPSS, fuzzy controller reduces settling timefaster manner, the Fuzzy with power system transient performance is better than PI and

CPSS.

(vi)

Performance evaluation of SMES with single reheat thermal unit:

Figure 8 shows the simulation results, where the SMES is located at the generator terminal,the optimal transient performance of the power system corresponding to an operating

condition of P=0.9, Q=0.1, Xe=0.4752, Et =1.0. For thermal unit with single stage reheat

turbine in the AGC loop. From this figure it is noticed that the fluctuation of voltagesuppressed although the damping of power oscillations are hardly improved. The damping

is improved when the active power control is used. In contrast with these, the fluctuation ofvoltage is suppressed and damping is improved as well when the simultaneous control of

active and reactive power is applied. Due to the application of SMES action for either 0.01p.u. step perturbation in reference voltage or that in load torque the system transient

performance is considerably improved and settling time Ts, Mp, Peak Time Tpare listed intable 4 and corresponding controller gains are listed in table 5. The values of settling time

Ts, Mp, Peak Time Tp are reduced with the application of SMES.In figure 8 the dynamic performance has been compared with and without SMES under

different types of controllers. The main task of SMES may be attributed from the action ofa sudden rise in the demand of load. Under this contingency condition, the stored energy inSMES is almost immediately released through the PCS to the grid as line quality AC. As

the governor and other control mechanisms start working to set the power system to the

7/26/2019 Powers Ys 900

11/13


203

new equilibrium condition. Thus, improved transient performance is gained with the

application of SMES in the interactive with AVR and AGC with different controllers.

Table -4 coordinated performance of AGC, AVR & SMES with Different Controllers.

Settling time Peak over shoot Rise time

AGC(PI)+AVR(PI) 10.0 0.0066 1.00AGC(PI)+AVR(PI)+SMES 5.0 0.0085 0.05

AGC(PI)+AVR(CPSS) 8.0 0.0025 0.05

AGC(PI)+AVR(CPSS)+SMES 4.5 0.0095 0.06

AGC(FUZZY)+AVR(PI) 6.0 0.0015 0.10

AGC(FUZZY)+AVR(PI)+SMES 4.8 0.0018 0.10

AGC(FUZZY)+AVR(CPSS) 5.4 0.0016 0.10

AGC(FUZZY)+AVR(CPSS)+SMES 4.6 0.0020 0.04

AGC(FUZZY)+AVR(FUZZY) 3.0 0.0026 0.05

AGC(FUZZY)+AVR(FUZZY)+SMES 2.5 0.0035 1.00

.

8.0 Conclusion

(a) Performance of thermal single stage and double stage reheat turbines are close to each

other in the context of transient analysis. Thus a single stage reheat may replace in a double

stage reheat for all the practical purposes. Similarly Electrical governor is superior tomechanical governor and it yields better dynamic response with SMES.

(b)

Superconducting magnetic energy storage units are successfully implemented forimproving small signal dynamic performance of the power system. Inclusion of SMES unit

in coordinated with AVR, CPSS and AGC loop with either single stage or double stagereheat turbine improves the transient performance considerably. Due to inclusion of SMES

unit in the coordinated system configuration, same enhancement in the transient response

Table 5: Coordinated Performance of CPSS, AGC, CPSS, AGC & SMES SystemPerformance Under 0.01P.U Step perturbation in Reference Voltage with different

operating condition

0.01 P.U SteP.U Type Of AGC System Model KI KP

CPSS & AGC 5.3261 0.052Thermal unit with single stagereheat turbine CPSS , AGC & SMES 0.7995 0.193

CPSS & AGC 6.3216 0.045Thermal unit with double stage

reheat turbine CPSS , AGC & SMES 0.6569 0.168

CPSS&AGC 6.3261 0.0453

Hydro unit with electric governor CPSS, AGC & SMES 0.5992 0.219

CPSS&AGC 5.9261 0.085

P=0.2

Q=-0.2

V=1.0

Hydro unit with mechanicalgovernor CPSS, AGC & SMES 0.6595 0.168

CPSS & AGC 7.3261 0.052Thermal unit with single stage


CPSS & AGC 6.3261 0.066Thermal unit with double stage


CPSS&AGC 6.3261 0.234Hydro unit with electric governor

CPSS, AGC & SMES 0.8895 0.234

CPSS&AGC 6.3261 0.198

P=0.6

Q=0.4

V=1.0

Hydro unit with mechanicalgovernor CPSS, AGC & SMES 0.5992 0.056

7/26/2019 Powers Ys 900

12/13


204

occurs for the electric or mechanical governor installed in the hydro automatic generation

loop.(c) Investigation reveal that the proposed Fuzzy controllers gains are quite robust , i.e

25% variation in system parameters, operating condition, etc from their nominal values

do not affect the system responses appreciably. Also this study reveals that the improved

dynamic stabilization action of SMES is always consistent under increase or decrease ineither reference voltage and /or load torque irrespective of the AGC configuration.(d) In this study the fuzzy logic power system stabilizer is designed for Single Machine

Power System along with AGC. The performance of the power system with fuzzy logicpower system stabilizer, AGC loop is better one since it is effective for all test conditions. It

is shown that an excellent performance of the fuzzy control over the conventional one forthe excitation control of synchronous machines could be achieved.

9.0 References

[1]

ANSI/IEEE std 122-1985, IEEE recommended practice for functional and performancecharacteristics of control system for steam turbine generator units, The institute of EEE

USA,1985.

[2]P.Kundur,Power System Stability and Control, McGraw-Hill Inc, 1994.[3]P.M.Anderson and A.A.Faurd, Power System Control & Stability, IEEE, Pres,

Revised printing, 1994.

[4]J.Nanda,A.Mangala,Automatic generation control of an interconnected hydro thermalsystem using conventional integral and fuzzy logic controller, in: proceeding of the IEEE

international conference on electric utility deregulation, Restructuring and powertechnologies, Hong Kong, 2004.

[5]J.Nanda,A.Mangala,S.Suri, Some New findings on Automatic generation control of an

interconnected hydro thermal system with conventional controllers, IEEE trans. EnergyConvers. 21 (March(1)) (2006) 187 -194.[6]P.Ajay.D.Vimal Raj,J.Raj, S.Senthil Kumar, R.C.Bansal, Automatic Generation

Control of a Hydro-Thermal and Thermal-Thermal system in a Deregulated

Environment,Journal of electrical systems Volume 5,2009.[7]H.J.Kunish, K.G.Krammer and H.Domonic Battery Energy Storage-Another Option

for Load Frequency Control & Instantaneous reserve, IEEE Trans on Energy conversionVol E-1, No.3.pp41-46,Sep 1986.

[8]H.J.Boeniig & J.F.Haur, Commissioning tests of the Bonneville power Administration

30 MJ super conducting Magnetic storage unit, IEEE Trans on power apparatus &

systems. Vol.PAS-104,No.2.PP 302-312. Feb 1982.[9]Banarjee.J.K, Chatterjee and S.C.Tripathy, Application of Magnetic energy storage

units as Load Frequency Control Stabilizers, IEEE Trans, on energyconversion,Vol.5.No.1,pp481-501,March 1990.

[10]Kwa-Sur Tam and Prem kumar,Application of super magnetic energy storage in an

asynchronous link between power system,IEEE Trans on Energy conversion,vol.5,No.3.Sep1971.

[11]H.A. Peterterson, N.Mohan and R.W.Boom, Super Conductive energy storageInductor-Converter Units for power system, IEE Trans. On Power apparatus and systems,

Vol. PAS-94,No.4,PP 1337-1348, July/August 1975.[12]R.J. Loyd, J.D.Rogers et al., A feasibility utility scale superconducting Magnetic

Storage Plant, IEEE Trans. On Energy conversion, Vol. EC-1, No.4, PP 63-64, Dec 1986.[13]O.I Elgerd, Electric Energy System Theory: An Introduction, McGraw Hill, 1982.

7/26/2019 Powers Ys 900

13/13


205

[14]Y.Mitani, K.Tsuji, Y.Murakami, Application of Superconducting Magnetic Energy

storage to improve power system dynamic performanceIEEE trans. On power system, Vol3,No.4-1988.

[15]S.C. Tripathy, R.Balasubramaniam, P.S. Chandramohan Nair, Effect of

Superconducting Magnetic Energy Storage on Automatic Generation Control Considering

Governer Dead band and Boiler dynamics.IEEE tran. On power system Vol 7,Aug 1992.[16]S.C. Tripathy, R.Balasubramanian,P.S. Chandramohan Nair , Small rating capacitiveenergy storage for dynamic performance improvement of automatic generation control,

IEE proceedings, Vol 138,Jan 1991.[17]J.Nanda and Mangala,AGC of an Interconnected Hydro- Thermal using conventional

and Fuzzy logic controller, 2004 IEEE international conference on Electric utilityderegulation April 2004,Hong Kong.

[18]D. K. Sambariya, R. Gupta, A. K. Sharma fuzzy applications to single machine powersystem stabilizers Journal of Theoretical and Applied Information Technology, 2005

2009 JATIT.

[19]

G.A.Chown, R.C.Hartman, Design and expcrience with a Fuzzy Logic Controllcr forAutomatic Generation Control (AGC), IEEE Transaction on Power System, Vol.-13, No.

3, Aug. 1998, pp 965-970.

[20]C.S.Indulkar, Baldev Raj, Application of Fuzzy Controllcr to Automatic GenerationControl, Electric Machine and Power System, 23:209-220, 1995.

[21]V.Mukherjee,S.P.Ghoshal, Applications of Capactive energy storage for transient

performance improvement of power system,Electric power systems research 2008.[22]E.W.Kimbark Direct current transmission,Vol. I,(book), john wiley,1971.

[23]Manoranjan Parida, J.Nanda, Automattic Generation control of a Hydro Thermalsystem in Deregulated environment.

10.0 Appendix

SMIB data: Xd=0.973, Xd=0.19, Ka=50, Ta=0.05s.

AGC loop time constant: R= 2.4 Hz/per unit MW, B = 0.275, D=0.0, Xq=0.55,Tdo=7.765s, H=4.63,

Re=0, Xe=0.997.

Single Stage Reheat turbine data: Tg=0.08s, Tt=0.3s, e=0.35.

Double Stage Reheat turbine data: Tr1=10s, Tr2=10s, Tt=0.3s, Kr1=0.2, Kr2=0.2.

Stabilizer datas at Different Operating Conditions (P, Q & V Variations)

CASE-I: P=0.9 P.U, Q=0.1 P.U, V=1.0 P.U.

K1=0.6067, K2=1.0065, K3=0.6025, K4=0.3625, K5=-0.1076 , K6=0.7604.CASE-II: P=1.0 P.U, Q=0.8 P.U, V=1.0 P.U.

K1=-0.3140, K2=-0.8455, K3=0.6025, K4=-0.3045, K5=0.1712, K6=0.7846.

CASE-III: P=0.25 P.U, Q=0.8 P.U, V=1.0 P.U.

K1=0.1803, K2=0.2252 , K3=0.6025, K4=0.0811, K5=-0.0366, K6=0.8361.

Conventional Power System Stabilizer fixed data : Tww=10s, Td2=0.05s, Td4=0.05s. The OptimizedGain parameter for a step load of 0.01P.U in area1.Refered in [2], [16].

SMES Data: When the Frequency deviation is used as the SMES contol signal- Ki1=0.875, KF1=35

Kw/Hz, Kid=0.20 Kw/KA

When ACE is Used as the SMES Control Signal: KI1=0.70, Kb1=50Kv/Unit ACE, Kid=0.20Kv/KA.

Powers Ys 900

Documents

Transcript of Powers Ys 900