STATCOM -Based Damping stabiliser to Improve power System

International Journal of Engineering, Management & Sciences (IJEMS)ISSN-2348 –3733, Volume-1, Issue-2, February 2014

8 www.alliedjournals.com

Abstract— Power system stability enhancement viaSTATCOM-based stabilizers is thoroughly investigated in thispaper. This study presents singular value decomposition (SVD)based approach to assess and measure the controllability of thepoorly damped electromechanical modes by different controlinputs of a STATCOM. The Coordination among the proposeddamping stabilizers and the STATCOM internal AC and DCvoltageA controller has been taken into consideration. The designproblem of STATCOM-based stabilizers is formulated as anoptimization problem. For coordination purposes, a timedomain-based multi objective function to improve the systemstability as well as AC and DC voltage regulation is proposed.Then, a real-coded genetic algorithm (RCGA) is employed tosearch for optimal stabilizer parameters. This aims to enhanceboth rotor angle stability and voltage regulation of the powersystem. The proposed stabilizers are tested on a weaklyconnected power system with different loading conditions. Thenonlinear simulation results show the effectiveness androbustness of the proposed control Schemes over a wide range ofloading conditions.

Index Terms—Statcom, Power Control, AGC.

I. INTRODUCTION

Since 1960s, low frequency oscillations have been observedwhen large power systems are interconnected by relativelyweak tie lines. These oscillations may sustain and grow tocause system separation if no adequate damping is available[1-3]. Although PSSs provide supplementary feedbackstabilizing signals, they suffer a drawback of being liable tocause great variations in the voltage profile and they may evenresult in leading power factor operation under severedisturbances. The recent advances in power electronics haveled to the development of the flexible alternating currenttransmission systems (FACTS) [4]. Generally, a potentialmotivation for the accelerated use of FACTS devices is thederegulation environment in contemporary utility business.Along with primary function of the FACTS devices, the realpower flow can be regulated to mitigate the low frequencyoscillations and enhance power system stability. This suggests

Manuscript received 20 February, 2014.Yogesh, M.Tech Scholar, Electrical Engineering Department. RIET

Jaipur Rajasthan, IndiaSwati Gupta Asst. Prof. Electrical Engineering Department,RIET Jaipur

, Rajasthan, IndiaMahesh Garg, Asst. Prof. Electrical Engineering Department,RIET

Jaipur , Rajasthan, India

that FACTS will find new applications as electric utilitiesmerge and as the sale of bulk power between distant and interconnected partners become more wide spread. Recently,several FACTS devices have been implemented and installedin practical power systems such as static VAR compensator(SVC), thyristor controlled series capacitor (TCSC), andthyristor controlled phase shifter (TCPS) [5-7].Theemergence of FACTS devices and in particular gate turnoff(GTO) thyristor-based STATCOM has enabled suchtechnology to be proposed as serious competitive alternativesto conventional SVC. From the power system dynamicstability viewpoint, the STATCOM provides better dampingcharacteristics than the SVC, as it is able to transientlyexchange active power with the system [8]. A little work hasbeen done on the investigation of the coordination problem ofSTATCOM-based damping stabilizers and the STATCOMinternal AC and DC voltage controllers. A multivariabledesign of STATCOM AC and DC voltage control waspresented in [9]. The coordination between the AC and DCvoltage PI controllers was taken into consideration. However,the structural complexity of the presented multivariable PIcontrollers with different channels reduces their applicability.Moreover, the utilization of damping capability of theSTATCOM has not been addressed. The STATCOMdamping characteristics have been addressed in [10-16].However, the coordination among the damping controllersand AC and DC voltage PI controllers has not beeninvestigated. In this study, a comprehensive assessment of theeffectiveness of the STATCOM damping stabilizers whenapplied in coordination with the STATCOM internal AC andDC voltage controllers has been carried out. At first, acontrollability measure based on singular valuedecomposition (SVD) is used to identify the effectiveness ofeach control input on the electromechanical mode ofoscillations. To enhance power system dynamic stability andvoltage regulation, the coordination among the proposedSTATCOM damping stabilizers and the its internal AC andDC voltage controllers is taken into consideration. Thecontroller design problem is transformed into an optimizationproblem where a real-coded genetic algorithm (RCGA) isemployed to search for the optimal settings of stabilizerparameters. The nonlinear simulation results are carried out todemonstrate the effectiveness and robustness of the proposedstabilizers to enhance system dynamic and transient stability.In addition, the potential of the proposed STATCOM-baseddamping stabilizers to enhance the power system transientstability is demonstrated.

STATCOM -Based Damping stabiliser to Improvepower System Stability Using Real coded Genetic

Algorithm

Yogesh, Swati Gupta, Mahesh Garg

STATCOM -Based Damping stabiliser to Improve power System Stability Using Real coded Genetic Algorithm


II. RESEARCH METHODOLOGY

The control (input) signals are the tie-line deviationΔPtie(measured from the tie line flows), and the frequencydeviation Δf(obtained by measuring the angle deviation Δδ).These error signals Δf and ΔPtie are amplified, mixed andtransformed to a real power signal, which then controls thevalve position. Depending on the valve position, the turbine(prime mover) changes its output power to establish the realpower balance. The complete control schematic is shown infig.

Fig- 1. The Schematic representation of ALFC system

For the analysis, the models for each of the blocks in Fig arerequired. The generator and the electrical load constitute thepower system. The valve and the hydraulic amplifierrepresent the speed governing system. Using the swingequation, the generator can be modeled by .

Expressing thespeed deviation inpu,

The load on the system is composite consisting of a frequency

independent component and a frequency dependent

component. The load can be written as

where, is the change in the load;

is the frequency independent load component;is the frequency dependent load component.

Where, D is called frequency characteristic ofthe load (also called as damping constant) expressed inpercent change in load for 1% change in frequency. IfD=1.5%, then a 1% change in frequency causes 1.5% changein load.The turbine can be modeled as a first order lag as shown in thefig.

is the TF of the turbine; is the change in valveoutput (due to action) . is the change in the turbineoutput.

The governor can similarly modeled as shown in Fig. Theoutput of the governor is by where

is the reference set power, and is the power given bygovernor speed characteristic. The hydraulic amplifiertransforms this signal into valve/gate positioncorresponding to a power . Thus

Fig -2 The block diagram representation of the governorAll the individual blocks can now be connected to representthe complete ALFC loop as shown in fi

Fig-3 – The block diagram representation of ALFC

Power Control in a Single Area SystemIn a single area system, there is no tie-line schedule to bemaintained. Thus the function of the AGC is only to bring thefrequency to the nominal value. This will be achieved usingthe supplementary loop which uses the integral controller tochange the reference power setting so as to change the speedset point. The integral controller gain KI needs to be adjustedfor satisfactory response (in terms of overshoot, settling time)of the system. Although each generator will be having aseparate speed governor, all the generators in the control areaare replaced by a single equivalent generator, and the ALFCfor the area corresponds to this equivalent generatorPower Control in a Multi Area System

In an interconnected (multi area) system, there will be oneALFC loop for each control area (located at the ECC of thatarea). They are combined as shown in Fig for theinterconnected system operation. For a total change in load of

, the steady state deviation in frequency in the two areas isgiven by, = = where, = (D1+1/R1); and

2 = (D2+1/R2).

(speed)

Governor

Steam

water

Valve/gate

turbine

generATORator

PowernetworkT G

Kt

1+sTt

Kg

1+sTg

(s) (s)

Powersystem

Turbine

1+ +-

1/R

Speeddroop

Governor

1++

-



Fig-4. The block diagram representation of the AGC

Algorithm

Step 1: Set the values of ω scheduled and Ptie.Step 2: Get the values of ω actual and Ptie actual and thencalculate Δω and ΔPtieStep 3: With the values of Δω and ΔPtie decide the controlaction to be taken.Step 4: Send the control action in the particular area and applyEconomic Load Dispatch using Genetic Algorithm.Step 5: Represent the problem variable domain as achromosome of a fixed length, choose the size of achromosome population N, the crossover probability pc andthe mutation probability pm.Step 6: Define a fitness function to measure the performance,or fitness, of an individual chromosome in the problemdomain. The fitness function establishes the basis for

selection of chromosomes that will be mated togetherduring reproduction.Step7: Randomly generate an initial population ofchromosomes of size N: x1, x2. . . , xN.Step 8: Calculate the fitness of each individual chromosome: f(x1), … . , f (xN)Step 9: Select a pair of chromosomes for mating from thecurrent population. Parent chromosomes are selected with aprobability related to their fitness. Highly fit chromosomeshave a higher probability of being selected for mating thanless fit chromosomes.Step 10: Create a pair of offspring chromosomes by applyinggenetic operators crossover and mutation.Step 11: Place the created offspring chromosomes in the newpopulation.Step12: Repeat Step 9 until the size of the new chromosomepopulation becomes equal to the size of the initial population,N.

Step 13: Replace the initial (parent) chromosome populationwith the new (offspring) population.Step 14: Go to Step 8, and repeat the process until thetermination criterion is satisfied. Check the feasibility of thesolution corresponding to the satisfaction of the equalityconstraint.

III. THE PROPOSED APPROACH

(A) OBJECTIVE

The main objective of power system operation and control isto maintain continuous supply of power with an acceptablequality, to all the consumers in the system. The system will bein equilibrium, when there is a balance between the powerdemand and the power generated. As the power in AC formhas real and reactive components: the real power balance; aswell as the reactive power balance is to be achieved.There are two basic control mechanisms used to achievereactive power balance (acceptable voltage profile) and realpower balance (acceptable frequency values). The former iscalled the automatic voltage regulator (AVR) and the latter iscalled the automatic load frequency control (ALFC) orautomatic generation control (AGC).

(B) CHARACTERISTICS Load is always changing. To maintain power balance, generators need to produce

more or less to keep up with the load.When Gen < Load (Gen > Load), generator speed and

frequency will drop (rise).We use this generator speed and frequency as control

signals.

(C) REAL CODED GENETIC ALGORITHM

Genetic Algorithm always evaluate from population, so theycreate the number of parents required to form the population.The most tidies job in GA is to define the fitness function,depending upon the requirement and application we have todefine the function and calculate the fitness value of the entireparent, depending upon the fitness value the parent whosefitness value is low has to be replaced by the parent of higherfitness value and thus the population is called mating pool.

(1) STEPWISE PROCEDURE

1. Select a population2. According to fitness value generate the mating pool3. Select two parents randomly from pool to produced twochildren

I. Apply crossoverII Apply mutation

4. Check the No of child if No < population size got to step 3,else step 55. Check the fitness value of each child if 95% child having

same fitness value terminates the execution else go to step 6

6. Generated child will form a new population and continuewith step



-1 -0.137 -0.144 0

314.16 0 0 0

0 -0.194 -0.423 -0.136Ke 0 7.315-20.84 -50

Crossover: A blend crossover (BLX-α) has beenemployed in this paper .this method start by choosing

randomly a number from the interval.

IV. RESULT and ANALYSIS

Consider aSingle machine connected to infinite bus systemwith a simple exciter, having the parameters K1, K2, K3, K4, K5and K6 [1]. The following table describes all the differentcombinations of parameters and loading explored with thissystem. The system is here defined and analyzed in case ofover load and under load conditions. The parameters used inthis work are listed as under

The machine constants that were used are:WhereXdReactance of d-axis ,Xd’ - Transient Reactance of D AxisKA Gain Constant of Excitation System TA Time Constant ofExcitation System Vref Reference Voltage Xq- Reactance ofq-axis for generator. Consider a synchronous machineconnected to an infinite bus bar though an external reactancewith a simple exciter with operated at a load ofP+JQ=0.9+j0.3. Parameters of the system are as fallows.K1 = 0.7643, K2 = 0.8649, K3 = 0.3230. K4 = 1.4187, K5 =

-0.1463 K6 = 0.4168, H=3.

A=

Γ=

The work is here presented for 3 area system, The stability ishere been achieved for these systems by using genetic basedoptimization. We have implemented the genetics for 4500rounds and get the optimized value for Energy Factor K. K is0.0043 for all areas.

Fig. -5-Signal Stability for System

Here x-axis represented by the integrations and y axis is

representing the change in the angle

Parameters Values

Xd .3 and 1

M 4.5

D 4

KA 10

0.16

0

0



.Fig- 6. -Signal Stability for Overload Condition

Here x-axis represented by the integrations and y axis isrepresenting the change in the angle.

Fig- 7. : Signal Stability for Nominal Conditions

Here x-axis represented by the integrations and y axis isrepresenting the change in the angle.

Fig 8 : Genetics Iterative Process

Here x axis is showing the rounds for which genetic processis defined and y axis defines the objective

function

Fig-9. -Angle Effect respective to Load

In this figure, x axis represents the angle and y axis representsthe reactive power.

Fig-10 Rotor Angle Stability (Nominal Load Vs. Heavy

Load)

Here x-axis represented by the integrations and y axis isrepresenting the change in the angle. ConsumptionConclusion

The presented system is defined to achieve the stability evenif the fault occur over the system. The system is checkedunder three phase fault. In this presented work, the problem offault is been covered by using the statcom based stabilizer andthe voltage controller. The fault is associated with the busline. In general case, as the fault occur the voltage, completevoltage distribution over the system is affected and overallvoltage in the system set to 0. In this presented system, aGenetic improved statcom model is been presented to achievethe stability over the system.

But in this presented system, the statcom based controller isbeen defined to achieve the voltage stability. The presentedsystem provides the stable voltage distribution as fault occurover the system. The obtained results shows that the powerstability is been achieved by the system.

Future ScopeIn this presented work, Genetic based stability analysis isdefined under the statcom based controller for achieving thepower stability in case of three phase fault. The presentedsystem can be improved in different ways in future.



The Work can be implemented with some other kindof faults over the system.

The system is defined with genetic model in futuresome other optimization approach can also be usedalong with it.

REFERENCES

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STATCOM -Based Damping stabiliser to Improve power System