International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

Load frequency Control of Hydro and Nuclear PowerSystem by PI & GA Controller

Mohit Kumar Pandey Mahesh Kumar Meena Omveer Singh Prashantjeet TyagiDepartment of EEE Department of EEE Department of EN Department of ENACME Coleege of Engg,Ghaziabad , IET,Alwar,Rajashtan , Galgotias college of Engg. Gr.Noida , IIMT.Meerut

Abstract- There has been continuing interest indesigning load-frequency controllers with betterperformance from last many years. Many controlstrategies for LFC have been proposed since the1970s. In the recent time the PI controller andGenetic Algorithm based Controller to control anonlinear process has received wide attention. Thispaper presents the behavior of genetic algorithmbased controller for interconnected power systemand compares the results with conventional PIcontroller. A two-area interconnected power systemconsisting of non-identical power plants withEHVAC transmission link as interconnection isconsidered for investigations. The hydro and nuclearpower plant comprises of area-1 and area-2respectively and the dynamic response plot ischecked for 1% load disturbance in area-1.

Keywords: Genetic Algorithm, AGC, Controlprototyping, hydro-nuclear interconnected powersystem.

I. INTRODUCTION

In real situations, the power systems consist ofconventional forms of electrical power generationslike, thermal, hydro, and nuclear as a major share ofelectrical power. The configuration of today’sintegrated power system becomes more complex dueto these power plants with widely varyingdynamic characteristics. Nuclear units owing totheir high efficiency are usually kept at base loadclose to their maximum output with no participationin system automatic generation control (AGC) [1-3].Gas power generation is ideal for meeting varyingload demand. However, such plants do not play verysignificant role in AGC of a large power system,since these plants form a very small percentage oftotal system generation. Gas plants are used to meetpeak demands only. Thus the natural choice forAGC falls on either thermal or hydro units. But with

integration of nuclear power plants in the powersystem, it is also required to study the behavior ofAGC for the interconnected power systemconsidering nuclear power plant. A literaturesurvey shows that most of the earlier

works in the area of AGC pertain to interconnectedthermal systems and relatively lesser attention hasbeen devoted to the AGC of interconnected hydronuclear system [4-5]. The generatingcharacteristics of low- head hydro units such as usedin run-of-river plants have excellent responsecapabilities. Many can be cycled over their entireoperating range in under a minute. Most are notcurrently controlled by AGC, but there areexceptions. BWR units operate under AGCtypically can respond at 3% per minute for 10minutes or so within their regulating range. Theseunits are capable of making 20% excursions at ratesof nearly 3% per minute. The objectives traditionallydefined for AGC appear to be vague and incomplete.Only comparison of attributes of AGC strategiesfrom different aspects and for each attribute, thepreferred strategy is indicated. The conceptsdeveloped for the single control area case are thenextended to that of an interconnectioncomprising several control areas. Supplementarycontrollers are designed to regulate the area controlerrors to zero effectively. Rapid control prototyping(RCP) is a control design method where testing of anew controller is done first in simulationenvironment. The simulation models are thenconverted with aid of automatic code generation toa prototype controller that can be used in fieldtesting. With this approach, the time needed fordeveloping new functions is reduced significantlybecause the manual computer codeimplementation phase is left out of the design andtesting process. Consequently, it is possible toevaluate different control solutions withoutadditional delay.

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

It is known that a linear controller is notnecessarily good for controlling a nonlinear process.A simple generator unit, which feeds a power line tovarious users whose power demand can vary overtime, is a nonlinear process. When there is anelectric load perturbation then there is a frequencyfluctuation. To bring the steady state frequency backto its nominal value a PI controller is used. In viewof this, new areas for load frequency control areproposed. Genetic Algorithm(GA) controller is oneof them. With the help of GA gain scheduling of acontroller can be developed which shows a bettertransient response and reduce settling time. Its mainadvantage is that controller parameters can bechanged very quickly in response to change insystem dynamics.

II. POWER SYSTEM MODEL UNDERINVESTIGATION

Investigations have been carried out on aninterconnected Hydro-Nuclear system as shown infigure1, neglecting the generation rate constraints.

Figure 1. Transfer function model of aninterconnected two-area Hydro- Nuclear system

III. SYSTEM MODEL

Mathematical model is developed and step loadperturbation of 1% of nominal loading has beenconsidered in area-1. to study the dynamicbehavior of the system [6-9].Below is themathematical of each of the components needed tobuild the power system model.

A. GeneratorThe Generator dynamics is modeled by swing

equation and is given in equation. 1

* d2 ∆δ /dt2 = ∆Pm - ∆ Pe (1)

For small perturbation the above relation can berepresented by a block diagram shown in figure2.

Figure 2. Transfer function diagram of generator

Similarly the composite load is considered and thecorresponding transfer function for the load modelis given as-

∆Pe = ∆PL + D∆ω (2)

Where ΔPL is the non frequency-sensitive loadchange and DΔω is the frequency sensitive loadchange. D is expressed as percentage change in loaddivided by the percentage change in frequency.Therefore the combined transfer function for thegenerator load model is shown in figure 3.

Figure 3. Combined transfer function diagram ofgenerator and load

B. Tie LineThe Tie line power is represented in equation

3 and the corresponding block diagram developedfrom equation 3,4,5 is given in figure 4.

Where δ1, δ2 = Power angles of equivalent machinesof the two areas.For incremental changes in δ1 and δ2 the incrementaltie line power can be expressed as

ΔPtie ,1 ( pu) = T12 (Δδ1 − Δδ2 ) (4)

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

Since incremental of power angles are integral ofincremental frequencies, can be written as

ΔPtie,1 ( pu) = 2πT12 (∫ Δf1dt − ∫ Δf2 dt) (5)

Figure 4. Isolated model of hydro power system forLFC

C. Hydraulic Turbine Governor SystemThe transfer function for the mechanical

hydraulic governor is given by eq. 6 and the turbineis given by eq. 7:T ( H- governer) =

(6)

T(H) = 1- sTh2 (7)1+ 0.5 sTh4

Where, K and T are the gain and time constant of thehydraulic system (h, 2 and 4 are suffix for Hydroarea and g for governor).The combined model for isolated hydro powersystem is given in figure 5.

Figure 5. Isolated model of hydro power system forLFC.

D. Nuclear Turbine Governor SystemThe Mathematical model considered for Nuclear

unit with tandem-compound turbines, one HPsection and two LP sections with HP reheater isshown in fig 6 [10-11].

Figure 6. Isolated model of Nuclear Power Systemfor LFC

IV. CONTROLLER MODEL AND TUNING

In this paper a two area interconnected powersystem with area 1 comprises of hydro power systemand area 2 comprises of nuclear power systemand conventional PI Controller is considered.

1. The output of conventional PI controller isgiven in equation 8.

U (t) = Kp (e(t) +1/Ti ∫e(t)dt) (8)

The control signal depends upon error signal e(t). Inthis case e(t) is area control error (ACE). Where Kpis proportional gain, Ti is the integral times andArea Control Error (ACE) is expressed as linearcombination of incremental frequency and tie linepower. Thus ACE for control Area-1 and for controlArea-2 are given as:-

ACE1 (s) = ΔPtie ,1 (s) + B1Δf1 (s) (9)

ACE2 (s) = ΔPtie , 2 (s) + B2 Δf2 (s) (10)

For tuning the gain of the controller, theinvestigated system is first considered withoutAGC controllers and then 1% step load perturbationis given in both the areas and Integral Square Errorcriterion is used to obtain optimum gainparameters for Controllers. In the beginning theintegral gains are assumed zero and optimum valueof proportional gain (Kp) is obtained. With thisvalue of Kp, integral gain is optimized using ISEcriterion. The dynamic performance of the AGCsystem depends upon controllers gain setting henceoptimized gains are calculated by the ISE techniqueswhose performance index is given as

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

(11)A value of 0.65 is used for α and β . and afterseveral iteration the optimized value of gain 0.03 iscalculated.

V. SIMULATION STUDIESThe simulations are implemented using MATLAB/SIMULINK and the comparison due to loadperturbation is observed. Simulation of belowsystems are done and dynamic behavior is observedand compared.(i) PI controller with interconnected power system.(ii) GA controller with interconnected power system.

AGC model with PI controller as shown in fig 7.0and the same model with GA controller as shown infig 8.0 is simulated with load perturbation havingamplitude of 0.01 p.u.MW to area 1,the frequencyoscillations and tie-line power flows are observedand the comparison are made.The comparison obtained from the optimum valueof PI controller gains and the GA controller areshown in fig.11.0 and result from the optimumvalue of PI controller gains and the GA controllerare shown in table 1 . Examining the responses itis seen that PI controller take more settling timeand max peak deviation than the GA controller whenperturbation of 1% occurs in the hydro area.

-s+1

0.5s+1Turbine

0.01

s

Transfer Fcn6

1

9s+1

1

7s+1

1

2.4

0.425

1

1

2.40.425

1

ACE

To Workspace2

Step

Scope4

Scope2

Scope

120

20s+1Power System1

120

20s+1

Power System

5s+1

10s+1LP Turbine

0.3

0.5s+1LP Turbine

5s+1

0.5750s +28.95s+12

Hydraulic Governer

2

0.5s+1HP Turbine

1

0.08s+1Governer

-1 Gain2-1Gain1

Disturbance

In1Out1

Out2

Controller1

In1Out1

Out2

Controller

Figure 7. Model with PI Controller

Subsystem 1 (Hydro)

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

-s+1

0.5s+1Turbine

0.01

s

Transfer Fcn6

1

9s+1

1

7s+1

1

2.4

0.425

1

1

2.40.425

1

ACE

To Workspace2

Step

Scope4

Scope2

Scope

120

20s+1Power System1

120

20s+1

Power System

5s+1

10s+1LP Turbine

0.3

0.5s+1LP Turbine

5s+1

0.5750s +28.95s+12

Hydraulic Governer

2

0.5s+1HP Turbine

1

0.08s+1Governer

-1 Gain2-1Gain1

Disturbance

In1Out1

Out2

Controller1

In1Out1

Out2

Controller

Figure 8. Model with GA Controller

0 5 10 15 20 25 30 35 40-0.15

-0.1

-0.05

0

0.05

0.1

Time(Second)

frequ

ency

dev

iatio

n(hz

)

Hydro Plant PI ControlledNuclear Plant PI Controlled

Figure 9. Frequency Deviation of Twoarea hydro- nuclear power system(PI control)

0 5 10 15 20 25 30 35 40-0.14

-0.12

-0.1

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04Area1 GA ControlledArea 2 GA Controlled

Figure 10. Frequency Deviation of Two areahydro- nuclear power system (GA control)

0 5 10 15 20 25 30 35 40-0.15

-0.1

-0.05

0

0.05

0.1

Time(second)

frequency d

evia

tion(h

z)

Hydro plant PI ControlledNuclear plant PI ControlledHydro plant GA ControlledNuclear plant GA Controlled

Figure.11. Frequency Deviation of Two areahydro-nuclear power system(Comparison betweenPI and GA PI control)

As shown in figure 11.0 for the frequency deviation,PI controller take more settling time than the GAController.

Subsystem 2(Nuclear)

International Journal of Scientific Research Engineering & Technology (IJSRET)Volume 2 Issue 6 pp 326-331 September 2013 www.ijsret.org ISSN 2278 – 0882

IJSRET @ 2013

VI. SUMMARY OF PARAMETER OBTAINEDFROM RESULTS

Table-1

PeakOvershoot

PeakUndershoot

SettlingTime

Two areaHydroArea

NuclearArea

HydroArea

NuclearArea

HydroArea

NuclearAreaHydro -

NuclearPIControlled

0.065

0.010 -0.02 -0.016

33.0 31.0

Hydro -NuclearGAControlled

0.02 0.009 -0.123

-0.015

22.0 18.0

Table-1 Sows the values of Peak overshoot, peakunder & settling time for both Hydro-Nuclear PIControlled Model & GA Controlled Model.

VII. CONCLUSION

Examining the responses it is clear that the GeneticAlgorithm controller improves effectively thedamping of the Oscillations after the load deviationin one of the areas in the interconnected powersystem as compared to PI controller and also hasbeen observed, the first peak, and settling time areless for the GA Controller as compared to PIController.

REFRENCES

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