final paper new

8/2/2019 Final Paper NEW

1/10

---

AbstractThis paper attempts to develop a linearized model ofautomatic generation control (AGC) for an interconnected two-

area reheat thermal power system under deregulated

environment. A comparison between a an conventional integral

controller, proportional integral derivative (PI) controller and a

fuzzy logic based controller is presented and the proposed fuzzy

based controller is shown to generate the best dynamic response

following a step load change. In addition, performance of

conventional integral controller, proportional integral derivative(PI) controller and a fuzzy logic based controller is examined

under various changes 30% in system parameters with various

bilateral contracts between control areas.

Keywords: Two area power system, load frequency control,

fuzzy logic controller, deregulated environment.

I. INTRODUCTIONIn a traditional electric power system, verticallyintegrated utility (VIU) owned generation,

transmission, and distribution, and supplies power

to the customers at regulated rates. In therestructured power systems the main concept is

transformation from vertically integrated utilities

(VIU) to open energy market system. Aim of this

was to enhance the economical efficiency of powersystem. With this market participants who are in

this open energy market to provide energy services

will be more competitive. The open market systemwill consist of generation companies (GENCOS),

distribution companies (DISCOS), and transmission

companies (TRANSCOS) and independent system

operator (ISO). Independent System Operator (ISO)is introduced to implement to achieve a secure and

economical operation of power systems in

restructured power system.In a power system, electricity is continuously

produced and consumed simultaneously and power

balance of demand-supply ratio must be maintained.

In open energy market particular DISCO has thefreedom to purchase the power with any GENCO, it

may be in intra or inter control area. ISO is

independent and disassociated agent for market

participants. In the open energy market, all thetransactions are done under the supervision of the

ISO. There are various ancillary services arecontrolled by IS0 to provide secure, reliable andeconomical power transmission. Automatic

generation control (AGC) is one of ancillary

services of ISO.

The DISCO participation matrix (DPM) is helps to

visualize the various contracts made between

GENCOs and DISCOs. The schematic blockdiagram of two area system in deregulated

environment is shown in Fig. 1. Each area is

containing two GENCOs and two DISCOs.

Block diagram of closed loop controlled system

model with fuzzy controller of reheat type two-area

thermal generating system is shown in Fig. 2.

When power systems are connected, tie-line flows

as well as frequency must be controlled.Maintaining frequency and power interchanges with

interconnected control areas at the scheduled values

are the two main primary objectives of a power

system AGC. The Automatic Generation control forinterconnected power system, achieved by

measuring deviation in frequency and tie-line power

flows, composite variable called the area controlerror (ACE).

This paper presents the performance of two area

interconnected reheat type turbine thermal systemwith conventional I, conventional PI and fuzzy logic

Frequency Stabilization using Fuzzy logic based

Controller for Multi-Area power system in

Deregulated Environment

First A. Author, Second B. Author, Jr., and Third C. Author,Member, IEEE


2/10

---

controller. The conventional I and PI controlstrategy does not give adequate control performance

when a 1% step load disturbance is given in either

area of the system. Fuzzy logic controller has been

proposed in this paper. By using conventionalcontroller it is difficult to obtain optimum value of

overshoot and settling time. Simulation results show

that the fuzzy logic controller greatly reduces theovershoots and settling time. Simulation results also

show better performance of fuzzy controller in

30% variation of system parameters in comparisonof conventional I and PI controller.

II. SYSTEM EXAMINEDThe system examined is consists of two control areaand two GENCO and two DISCO in each in

deregulated environment. The each GENCO is

reheat thermal system of equal capacity. Thissystem model is considered in continuous operation.The nominal system parameters are given in

appendix. The contracts between GENCO and

DISCO are shown in DPM matrix.

Fig.1 Block diagram representing a two area interconnected powersystem

The concept of contract participation factor

matrix (cpf_matrix) makes the visualization of

contracts. The number of rows indicates to the

number of GENCOs and the number of columnsindicates to the number of DISCOs. Here, the ijth

entry corresponds to the fraction of the total load

power contracted by DISCO j from a GENCO i. The cpf_matrix is:

Where, the sum of all the entries in a column in this

matrix is unity.

The system output, which depends on the areacontrol error (ACE), is Where, is frequency bias constant, frequencydeviation and is change in tie line power.Coefficients that distribute area control error (ACE)to several GENCOs are termed as ACE

participation factors (apfs), shown in apf_matrix:

Where, all apfs addition is equal to 1, withincontrol area. The contracted scheduled loads in DISCOs in Area

1 are

and

and in Area 2 are

and

and these are shown in the

matrix. The uncontracted local loads inAreas 1 are shown in matrix.

=[]The total distributed power by j

thDISCO, +

Where is contracted can be shownthrough cpf_matrix but uncontracted power for j

th

DISCO is out of scope of cpf_matrix.

The total distributed power shown in matrix is:

=

+

Similar to this, total generated power through

GENCOs in Area 1 are and and in Area2 are and and these are shown in the matrix.


3/10

---

Fig. 2: Complete Simulink model

Fuzzy

Controller1

apf1

apf2

Speed

Governor

Speed

GovernorReheater

Reheater

Power

System1

1/R1

1/R2B1

Turbine

Turbine

Fuzzy

Controller2

apf3

apf4

Speed

Governor

Speed

GovernorReheater

Reheater

Power

System2

1/R3

1/R4

Turbine

Turbine

B2

a12a12

Scheduled Power Ptie12

DISCO4DISCO3

Cpf31

Cpf32

Cpf33

Cpf34

Cpf41

Cpf42

Cpf43

Cpf44+ +

++

++

++

DISCO2DISCO1

Cpf11

Cpf12

Cpf13

Cpf14

Cpf21

Cpf22

Cpf23

Cpf24

+ +

+

+

++

+

+

+

+

- +

++ -

-

-

++

+

-

+

-

+

+

+

+

+

+

+

+

-+

-+

Power Demand of Area 1

-

Power Demand of Area 2

-

2

1


4/10

---

Fig.3 represents the block diagram representation of scheduled_Ptie12 used in Fig.2.

The contracted generated powers in Areas 1 are

shown in

matrix.

[]

The uncontracted powers demanded under contract

violation required in Areas 1 and Area 2 are

referred is required powerby local GENCOs only in that area. That required

power from GENCOs shown in matrix. [

]

Where are uncontractedrequired power from GENCO1 and GENCO2 in

area 1 and areuncontracted required power from GENCO3 and

GENCO4 in area 2. Where i, referred to GENCOs within k

thcontrol

area.

And is calculated from eq. , as:=apfi* Or in matrix form,=apf_matrix* So, total required generation power in matrix formrepresented as:= +

= The total generation required of individual

GENCOs can be calculated also from equation, as: = * ) + apfi* So, total demanded power from GENCOs is shown

in matrix. [

]The scheduled tie line power flow between areas 1

and 2 can be represented as:

III. CONTROL STRATEGIES

There are different control strategies can be appliedin load frequency control in power system. In this

paper three controllers applied for load frequency

control for two-area thermal reheat type powersystem. These controllers are as the following:

A. Conventional Integral ControllerIn the system model in fig.2, in place of controller,integral controller replaced. The controller input is

cpf14

cpf13

cpf24

cpf23

Cpf32

Cpf31

Cpf42

Cpf41

+

+

+

+

+

+

+

+

+

+

+

+

+ -


5/10

---

ACEi, Ki is gain of controller. And ui is output ofcontroller, is: The integral controller is optimized using integralsquare error function. For ISE technique cost

function J is:

J= ( ) Where T is minimum simulation time, theresystem is stable. The optimized value of Ki is 1.4for I controller in model used in this paper.

B. Conventional PI ControllerIn the system model in fig.2, in place of controller,proportional integral controller replaced. The

controller input is ACEi, Kp and Ki are gain of

controller. And ui is output of controller, is:

The proportional integral controller is also

optimized by using same integral square error

function. The optimized value ofKp is 1.1 and Kiis0.8 for PI controller in model used in this paper.

C. Fuzzy Logic ControllerNowadays fuzzy logic is widely used in engineering

problems. Fuzzy set theory and fuzzy logic establish

the rules of a nonlinear mapping. The fuzzy logiccontroller modeling consists of three steps of

fuzzification, determination of fuzzy control rules

and defuzzification. Fuzzy logic is a systematic and

easier way to implement control algorithm foruncertain and indefinite models in engineering.

Fuzzy logic based logical system is much closer in

spirit to human thinking than classical logicalsystems.

The load frequency control (LFC) controls the

frequency and the tie-line flows between theinterconnected power system areas. Many

investigations in the area of LFC of interconnected

power system using fuzzy logic controller havebeen reported in the past[5],[6],[7].

Due to complexity and multi-variable conditions of

the power system, conventional control methods

may not give satisfactory solutions. On other handconventional controller work on linear model and

fuzzy logic controller is work on nonlinear model,

so fuzzy logic controllers more suitable fornonlinear power system models. On the other hand,

fuzzy controllers are more robust and more reliable

in solving a wide range of control problems.The comparison among the proposed controller and

conventional I and PI controller shows that the two

important dynamic parameters i.e. overshoots andsettling time with the proposed controller are better

than conventional I and PI controllers.

Fig. : The MISO type fuzzy controller

The fuzzy controller for the two input and single

output type of systems MISO type is shown in Fig.4

. Kp and Ki are the proportional and integral gains

respectively. In this work derivative of ACE i i.e.

( ) together with ACEi is fed to the fuzzycontroller. The fuzzy controller block is formed by

fuzzification of ACEi and , the inferencemechanism and defuzzification. Therefore, Yi is a

crisp value and ui is a control signal for the system.

Mamdani fuzzy theory has been applied to

determining the gain of controller [8][9].

The block diagram of fuzzy logic controller is

shown in Figure 4 [4].Membership Functions (MF)specifies the degree to which a given inputs belongs

to set. Here, seven membership functions have been

used to explore best settling time namely, Negative

Very (NV), Negative Medium (NM), NegativeSmall (NS), Zero (Z), Positive Small (PS), Positive

Medium (PM) and Positive Very (PV).

The membership function sets of fuzzy logic for

ACE, dACE/dt, Kp and Ki (PI Gain) are shown in

Fig. 5.

Fuzzy Logic

ControllerACEi

+

+

Yi ui


6/10

---

Fig. Surface view of inputs and output

TableFUZZY RULES

ACE

ACE

NV NM NS Z PS PM PV

NV NV NV NM NM NS NS Z

NM NV NM NM NS NS Z PS

NS NM NM NS NS Z PS PS

Z NM NS NS Z PS PS PM

PS NS NS Z PS PS PM PM

PM NS Z PS PS PM PM PV

PV Z PS PS PM PM PV PV

Fig. Membership functions of inputs and output

variable

IV. TEST CASESCase A:(PoolCo based transactions)

In this first case where the all GENCOs in each area

participate equally in AGC, Each GENCO will supplies power

to DISCOs within its control area only and ACE participation

factors are,

apf1=0.5, apf2=1-apf1=0.5;

apf3=0.5, apf4=1-apf3=0.5.And cpf_matrix is:

,

=

As per equation to meet demanded power generated power is,

Assume that the load change occurs simultaneously in both

areas I and II. The load is demanded only by all DISCOs in

equal ratios and the value of this load demand is 0.01 pu MW

for each of them.

GENCO1 and GENCO2 are not contracted by any DISCOs in

Area 2 for a transaction of power and GENCO3 and GENCO4

are not contracted by any DISCOs in Area 1 for a transaction

of power; hence, their change in generated power is zero in the

steady state. So, for this case, from equation ( ) .In this case no GENCOs will supply uncontracted power to

any of DISCOs.

Case B

(Combination of Poolco and bilateral based transactions)

In this case all the DISCOs contract power with the GENCOs

for power as per the following DPM. It is assumed that each

DISCO demands 0.01 pu MW power from GENCOs as

defined by cpfs in cpf_matrix and each GENCO participates

in AGC as defined by following apf:

apf1=0.5, apf2=1-apf1=0.5;

apf3=0.5, apf4=1-apf3=0.5.

And cpf_matrix is:

=

In this case demanded power is within contract limit, As per

equation to meet demanded power generated power is,


7/10

---

As Fig. 3(c) shows, the generated powers of the each

GENCOs to reach the desired values in the steady state.

Case C

(Contract violation)In this case, any DISCO may violate contracts by

demanding more power than those specified in the

contracts. It will be not shown in cpf_matrix. This

excess demanded power is local load of the particular

control area (un-contracted demand).apf1=0.5, apf2=1-apf1=0.5;

apf3=0.5, apf4=1-apf3=0.5. is uncontracted demanded load by eachDISCO.And cpf_matrix is:

, And =

As per equation to meet demanded power generated power is,

The total generated power

required by individual

GENCO, composed of allcontracted and un-contractedloads. Each GENCO shares the un-contracted load of its

own control area according to its own ACE participation

factor.

V. SIMULATION RESULTA fuzzy logic controller has been applied to a twoarea thermal with reheat power system.

Matlab/Simulink version 7 is used for simulation

purpose. The values of system parameters given in

appendix are used for all controllers for a

comparative study. Figure 3 presents the view ofrules for fuzzy logic controller utilized to design

controller. In rule base 49 rules are designed to getthe response. There are 7 triangular membership

functions are considered for inputs (ACEi and

dACEi/dt) and one output (ui) as shown in Fig. 4.

Frequency deviations of both areas and tie line

deviation after sudden load change in each area

for test cases A, B and C are shown in Fig. 5, 6

and 7 respectively.Two performance criteria wereselected in the Simulation, settling time and peak

overshoot.Peak overshoots and settling time for

5% band of both areas and tie line deviation

after sudden load change also 30 % with

change in system parameters in each area for

test cases A, B and C are shown in Fig. 5, 6 and 7

respectively. Effect of 30 % change inparameter values for values of , T12 and Tp is

examined. Table shows different values of

system parameters. The comparison of dynamicperformances of various controllers with the

proposed controller shows better results in terms of

lesser settling time and peak overshoot. In Fig. 5,

6and 7, it indicates that change in frequency in

area 1, area 2 and change in tie line power are

getting settled within reasonably good time.

(a)

(b)

0 10 20 30 40 50 60 70 80 90 100-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

Time in seconds-->

ChangeinFreq1-->

using Fuzzy Controller

using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

Time in seconds-->

ChangeinFreq2-->


using PI Controller

using I Controller


8/10

---

(c)

Fig: Comparison of conventional I, conventional PI and Fuzzy

Controller for two area thermal reheat power system with

1% step load change by each DISCO as Case A: Poolco

(a)frequency deviation in area 1, (b) frequency deviation in

area 2, (c) tie-line power deviation

(a)

(b)

(c)

Fig: Comparison of conventional I, conventional PI and Fuzzy

Controller for two area thermal reheat power system with

1% step load change by each DISCO as Case B: Poolco and

Bilateral Contracts

(a) frequency deviation in area 1, (b) frequencydeviation in area 2, (c) tie-line power deviation

(a)

(b)

(c)

0 10 20 30 40 50 60 70 80 90 100-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

-4

Time in seconds-->

Ch

angeinP12tieline-->


using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

Time in seconds-->

ChangeinFreq1-->


using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

Time in seconds-->

ChangeinFreq2-->


using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-12

-10

-8

-6

-4

-2

0

2x 10

-3

Time in seconds-->

ChangeinP12tieline-->


using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

Time in seconds-->

ChangeinFreq1-->


using PI Co ntrollerusing I Controller

0 10 20 30 40 50 60 70 80 90 100-0.06

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

Time in seconds-->

ChangeinFreq2-->


using PI Controller

using I Controller

0 10 20 30 40 50 60 70 80 90 100-9

-8

-7

-6

-5

-4

-3

-2

-1

0

1x 10

-3

Time in seconds-->

ChangeinP12tieline-->


using PI Controller

using I Controller


9/10

---

Fig: Comparison of conventional I, conventional PI and

Fuzzy Controller for two area thermal reheat power

system with 1% step load change by each DISCO as Case

C: Contract Violation

(a) frequency deviation in area 1, (b) frequencydeviation in area 2, (c) tie-line power deviation

Fig. Peak undershoot comparison at variation 30% variation

in system parameters for Case A.

Fig. Settling Time comparison at variation 30% variation in

system parameters for Case A

Fig. Peak overshoot comparison at variation 30% variation in

system parameters for Case B


system parameters for Case B

-0.06000

-0.05000

-0.04000

-0.03000

-0.02000

-0.01000

0.00000

f1(NominalV.)

f1(+30%up)

f1(-30%down)

f2(NominalV.)

f2(+30%up)

f2(-30%down)

Case A:

Peak UnderShoot

Peak Undershoot

I Controller

PID Controller

Fuzzy L.

Controller

0.00

5.00

10.00

15.00

20.00

25.00

30.00

Settling Time (5%)

I Controller

PID Controller

Fuzzy L.

Controller

-0.06000

-0.05000

-0.04000

-0.03000

-0.02000

-0.01000

0.00000

f1

(NominalV.)

f1

(-30%down)

f2(+30%up)

Ptie12

(NominalV.)

Ptie12

(-30%down)

Case B:

Peak Undershoot

I Controller

PID Controller

Fuzzy L.

Controller

0.00

5.00

10.00

15.00

20.00

25.00

30.00

f1(NominalV.)

f1(+30%up)

f1(-30%down)

f2(NominalV.)

f2(+30%up)

f2(-30%down)

Ptie12(NominalV.)

Ptie12(+30%up)

Ptie12(-30%down)

Settling Time (5%)

I Controller

PID Controller

Fuzzy L.

Controller


10/10

---

0.00

5.00

10.00

15.00

20.00

25.00

30.00

f1(NominalV.)

f1(+30%up)

f1(-30%down)

f2(NominalV.)

f2(+30%up)

f2(-30%down)

Ptie12(NominalV.)

Ptie12(+30%up)

Ptie12(-30%down)

Settling Time (5%)

I Controller

PID Controller

Fuzzy L.

Controller

Fig. Peak overshoot comparison at variation 30% variation in

system parameters for Case CThe simulation was repeated with various

instantaneous of load changes and always found

that results from proposed controller are better. Thesimulations results show that proposed method of

fuzzy logic controller for load frequency control inderegulated environment is giving distinguish

reduction in settling time and in peak overshoot in

compare of conventional I and PI controller.

VI. CONCLUSIONIn this paper, fuzzy logic controller is proposed forload frequency control of interconnected power

systems in deregulated environment. The controller

performance is observed on the basis of dynamic

parameters i.e. settling time and peak overshoot.Results of simulation shows that proposed

controller provides a better performance when

compared conventional I and conventional PI

controller in settling time and peak overshoot.Robustness of the proposed controller is also

checked with changing system parameters. This

justifies that Fuzzy logic controller provides a stableoperation for an interconnected thermal-thermal

with reheat type power system.


system parameters for Case C

-0.08000

-0.07000

-0.06000

-0.05000

-0.04000

-0.03000

-0.02000

-0.01000

0.00000

f1

(NominalV.)

f1

(-30%down)

f2(+30%up)

Ptie12

(NominalV.)

Ptie12

(-30%down)

Case C:

Peak Undershoot

I Controller

PID Controller

Fuzzy L.

Controller

final paper new

Documents