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Load Frequency Control of Multi-source Multi-areaHydro Thermal System Using Flexible AlternatingCurrent Transmission System DevicesVijaya Chandrakalaa, Balamurugan Sukumara & Krishnamoorthy Sankaranarayananb

a Department of Electrical and Electronics Engineering, Amrita School of Engineering, AmritaVishwa Vidyapeetham, Coimbatore, Indiab Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology,Coimbatore, IndiaPublished online: 28 May 2014.

To cite this article: Vijaya Chandrakala, Balamurugan Sukumar & Krishnamoorthy Sankaranarayanan (2014) Load FrequencyControl of Multi-source Multi-area Hydro Thermal System Using Flexible Alternating Current Transmission System Devices,Electric Power Components and Systems, 42:9, 927-934, DOI: 10.1080/15325008.2014.903540

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Electric Power Components and Systems, 42(9):927–934, 2014Copyright C© Taylor & Francis Group, LLCISSN: 1532-5008 print / 1532-5016 onlineDOI: 10.1080/15325008.2014.903540

Load Frequency Control of Multi-source Multi-areaHydro Thermal System Using Flexible AlternatingCurrent Transmission System Devices

Vijaya Chandrakala,1 Balamurugan Sukumar,1 and Krishnamoorthy Sankaranarayanan2

1Department of Electrical and Electronics Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham,Coimbatore, India2Department of Computer Science and Engineering, Sri Ramakrishna Institute of Technology, Coimbatore, India

CONTENTS

1. Introduction

2. Msma Hydro Thermal System

3. Tuning of Secondary Controller

4. Facts Devices for LFC

5. Simulation Results and Discussions

6. Conclusion

References

Appendix

Keywords: load frequency control, flexible alternating current transmissionsystem devices, static synchronous series compensator, superconductingmagnetic energy storage, fuzzy gain scheduling, genetic algorithm, hydrothermal plant, multi-source multi-area

Received 5 April 2013; accepted 9 March 2014

Address correspondence to Ms. Vijaya Chandrakala, K. R. M., Departmentof Electrical and Electronics Engineering, Amrita School of Engineering,Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India. E-mail:krm vijaya@cb.amrita.eduColor versions of one or more of the figures in the article can be found onlineat www.tandfonline.com/uemp.

Abstract—This article describes the load frequency control of amulti-area system. Each control area contains both a hydro and ther-mal power plant to form a multi-source multi-area hydro thermal sys-tem. The secondary proportional-integral controller has been tunedusing Ziegler–Nichols, genetic algorithm, and fuzzy gain schedulingmethods. On comparing the controller performance based on vari-ous performance indices, it is found that a fuzzy gain schedulingtuned proportional-integral controller is suitable for a multi-sourcemulti-area hydro thermal system. Further improvement on the loadfrequency dynamics has been achieved by connecting superconduct-ing magnetic energy storage unit in each control area and a staticsynchronous series compensator unit on a tie-line.

1. INTRODUCTION

Load disturbances highly influence the power system oper-ation. Day by day, high power consumption of load causesfrequency deviations leading to system instability [1, 2]. Loadfrequency control (LFC) connected to each generator helps toovercome the above problem by its fast-acting approach. TheLFC consists of two loops, namely the primary control loop,which matches the generation with demand, and the secondarycontrol loop, which does fine adjustment of frequency [1].

Most researchers analyzed and improved the LFC by con-sidering either thermal or hydro power plants as the isolatedcontrol area or as a multi-area by connecting the thermal sys-tem and hydro system using a tie-line [2–4]. Practically, bothhydro and thermal power plant will be present in each area. Thisconcept is considered in this article. A control area containingboth a hydro and thermal power plant connected to anothersuch area using a tie-line forms the multi-source multi-area(MSMA) hydro thermal system.

A proportional-integral (PI) controller is used as sec-ondary controller in LFC of an MSMA system. The controllergain values are tuned using Ziegler–Nichols (ZN) [5], the

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928 Electric Power Components and Systems, Vol. 42 (2014), No. 9

genetic algorithm (GA) [6–8], and fuzzy gain scheduling(FGS) [9–11] methods. The controller performances are ana-lyzed by performance indices to identify the optimal controllerfor the MSMA hydro thermal system.

Further, flexible alternating current transmission system(FACTS) devices [12, 13], such as superconducting magneticenergy storage (SMES) and a static synchronous series com-pensator (SSSC), are used for improving LFC performance.SMES stores the energy and dissipates at a faster rate when-ever required [14–18]. It is connected in each control area toimprove frequency dynamics. The SSSC, which controls thepower flow, is connected on the tie-line [19–24]. The tie-linepower response can be improved by an SSSC.

In this article, Section 2 explains the development of thetransfer function model of an MSMA hydro thermal system.Tuning a secondary PI controller using ZN, GA, and FGSis explained in Section 3. Section 4 explains the functioningand mathematical model of the FACTS devices namely, theSSSC and SMES. The simulation of the MSMA hydro thermalsystem is carried out with various PI controller and FACTSdevices followed with a discussion in Section 5. Based on thesimulation results, the conclusion is derived in Section 6.

2. MSMA HYDRO THERMAL SYSTEM

In a power system, a control area has many power sources. Itis connected to another such control area using a tie-line. Inthis article, both a hydro and thermal power plant are availablein two areas, which are connected by a tie-line and form theMSMA hydro thermal system, as shown in Figure 1.

2.1. Thermal Power Plant

In a thermal power plant, the turbine is rotated by steam fromthe boiler and acts as the prime mover for the generator. The

FIGURE 1. MSMA hydro thermal system.

steam input is controlled using the speed governor when thereis a mismatch between the generation and demand. The mis-match is sensed by the governor in terms of change in fre-quency (�f 1). Based on change in frequency, using a hydraulicamplifier, the speed governor controls the valve position of thesteam input to the turbine [1–4]. The steam input can also becontrolled using the reference power setting (�Pref1) of thegovernor. The speed governor output (�Pg) is given by Eq.(1):

�Pg = �Pref 1 − 1

R1� f1. (1)

With the help of several stages of the hydraulic amplifier, thespeed governor controls the valve against the high-pressuresteam. The hydraulic amplifier output (�PH) is expressed inEq. (2):

�PH = 1

1 + sT H�Pg. (2)

The non-reheat turbine is considered in this article, the output(�PT) of which is furnished in Eq. (3):

�PT = 1

1 + sT T�PH . (3)

The turbine power output drives the generator. The transferfunction of the power system comprising of generator withload disturbance (�PD1) is given in Eq. (4):

�PT − �PD1 = K p1

1 + sTp1� f1. (4)

2.2. Hydro Power Plant

In hydro plants, water is the source for producing mechanicalenergy to drive the turbine. The functioning of the speed gov-ernor of the hydro power plant is similar to that of the steampower plant. In this work, the low head hydro power plant isconsidered [1–4]. The output of hydraulic valve (�PHV) andhydro governor (�PHg) are, respectively, given in Eqs. (5) and(6):

�PH V = 1 + sTR

1 + sT2�PHg. (5)

�PHg = K1

1 + sT1

(�Pref 2 − 1

R2� f2

). (6)

The reset time �TR is given in Eq. (7):

TR = [5.0 − (TW − 1.0)0.5]TW , (7)

where T1 is the transient droop time constant (in sec), whichis highlighted in Eq. (8):

T1 = RTD

RPDTR, (8)

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Chandrakala et al.: Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using FACTS Devices 929

where �RTD is the temporary droop, which is expressed in Eq.(9):

RTD = [2.3 − (TW − 1.0)0.15]TW

TM, (9)

in which �TM is equal to 2H , where H is the inertia constant.Water is used as an inlet to drive the turbine, which is

controlled by hydro governor. The output of hydro turbine(�PHT) is furnished in Eq. (10):

�PHT = (1 − sT W )

(1 + 0.5 sT W )�PHV . (10)

The transfer function of a generator connected to a powersystem with a provision to give load disturbance is similar tothat in thermal power plant as furnished in Eq. (4).

2.3. Tie-line

The power systems consist of many control areas. These con-trol areas are interconnected by means of a tie-line to improvethe reliability and stability of the system. When the areas areinterconnected, the load change in an area will be compensatedby all areas. The power flow in the tie-line (�Ptie12) is givenin Eq. (11):

�Ptie 12 = 2�T

s(� f1 − � f2). (11)

The transfer function model of the MSMA hydrothermalsystem shown in Figure 1 is developed using the thermal modeldiscussed in Section 2.1, hydro model in Section 2.2, and tie-line model in Section 2.3, and shown in Figure 2.

3. TUNING OF SECONDARY CONTROLLER

The PI controller has been widely used in the field of controlas a secondary controller due to its simplicity and flexibility.The “P” part of the controller helps to improve the transientperformance, whereas, the “I” part of the controller improvesthe steady state. In this work, a derivative controller is not con-sidered. Though, a derivative controller reduces the magnitudeof overshoot produced by the I controller, it amplifies the noiseand can cause a system to become unstable because of its highsensitivity. Since a power system is subjected to continuousload change, the derivative controller can make the systemunstable. The mathematical expression of the PI controller isexpressed in Eq. (12):

u(t) = K pe(t) + Ki

∫ t

0e(t), (12)

where Kp and Ki are the gains of the PI controller, which aretuned by the methods explained in the following sections.

FIGURE 2. Transfer function model of MSMA hydro thermalsystem.

3.1. ZN Method

In this method [5], the process is kept under closed-loop Pcontrol; the gain of the P controller at which the loop oscillateswith constant amplitude is referred to as the ultimate gain(Kcu). The ultimate period (Tu) is the period of these sustainedoscillations. The higher the ultimate gain is, it is easier tocontrol the process loop. The PI controller of the MSMA istuned using Kcu and Tu.

3.2. GA

The GA works with a population of strings. By searchingmany peaks simultaneously, the GA reduces the possibilitiesof trapping into a local minimum. The variables are coded inbinary, and the string length based on accuracy. The fitness ofeach string is evaluated to guide the search. The number ofgenerated individuals in the population pool and the numberof generations are experimentally decided. All individuals ineach generation will be evaluated. Based on the fitness value,parents are selected. Crossover and mutation are performed be-tween the parents to produce offspring. If all offspring have thesame fitness, the variable has reached the global minima [6–8].

For the MSMA, the number of variables is eight. Consider-ing a population size of 40, maximum generations of 150, theintegral squared error (ISE) as the fitness function, cross-over

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930 Electric Power Components and Systems, Vol. 42 (2014), No. 9

ACE

LN MN SN Z SP MP LP

ACE1 LN LP LP LP MP MP SP ZMN LP MP MP MP SP Z SNSN LP MP SP SP Z SN MNZ MP MP SP Z SN MN MN

SP MP SP Z SN SN MN LNMP SP Z SN MN MN MN LNLP Z SN MN MN LN LN LN

TABLE 1. Fuzzy rules for scheduling Kp and Ki

fraction of 0.8, and mutation probability of 0.05, the controllergains are optimized using GA.

3.3. FGS

FGS is a set of linguistic control rules related by the dual con-cepts of fuzzy implication and the compositional rule of infer-ence. It provides an algorithm that can convert the linguisticcontrol strategy based on expert knowledge into an automaticcontrol strategy. The PI controller discussed in Sections 3.1and 3.2 have a fixed gain value. But the system condition willbe changing. Practically, the gain value of the PI controllershould change based on system condition. Only such an adap-tive controller can make precise and accurate control. In thisarticle, the fuzzy system decides the gain value of the PI con-troller based on system condition. It is used in many practicalcontrol applications, such as speed control, frequency control,temperature control, and drives control. Since fuzzy is usedfor scheduling the gain values of the PI controller; it is calledan FGS PI controller. The inputs to FGS are area control error(ACE) and derivative of ACE (ACE1). The outputs of FGS areKp and Ki values of the PI controller [9–11]. A Mamdani-typefuzzy inference system is used.

Seven linguistic variables, namely large negative (LN),medium negative (MN), small negative (SN), zero (Z), smallpositive (SP), medium positive (MP), and large positive (LP),are used for both the inputs and outputs. LN and LP are oftrapezoidal membership functions, whereas the remainder isof a triangular shape. The rules of FGS are furnished in Table 1.

4. FACTS DEVICES FOR LFC

FACTS devices have gained its importance as load frequencystabilizers [12, 13]. For improving system frequency and tie-line power variations, SMES and SSSC devices are considered,respectively, in the MSMA hydro thermal system, as shown inFigure 3.

FIGURE 3. MSMA system with SMES and SSSC.

4.1. SMES

SMES is commonly used as an energy storage device in apower system. SMES, a FACTS device member, effectivelydamps out the frequency oscillations in the system due toits fast response and high efficiency. On the occurrence ofthe system load disturbance, SMES effectively suppresses thefrequency oscillations by discharging its stored magnetic en-ergy. On the whole, the active power is controlled effectivelyby SMES in the respective areas for short duration until thegovernor and its secondary controller take its action [14–18].This is possible only because of the fast-acting nature ofSMES. In Figure 3, SMES is connected in each area of theMSMA hydro thermal system to achieve effective transient fre-quency response, and its mathematical model based frequencystabilizer is shown in Figure 4.

4.2. SSSC

Another FACTS device, namely the SSSC, is very effectivein damping tie-line power oscillations [19–24]. It controls theactive power of the tie-line by connecting in series with thetie-line of the system, as shown in Figure 3. The capabilityof high-speed control of the SSSC helps to damp oscillationsat a faster rate by coordinating with the slow speed of thegovernor and FGS controller. The mathematical model of anSSSC-based damping controller is shown in Figure 5.

FIGURE 4. Transfer function model of SMES.

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Chandrakala et al.: Load Frequency Control of Multi-source Multi-area Hydro Thermal System Using FACTS Devices 931

FIGURE 5. Transfer function model of SSSC.

FIGURE 6. Frequency response of area 1 in MSMA hydrothermal system with various PI controllers.

FIGURE 7. Tie-line power response of MSMA hydro thermalsystem with various PI controllers.

FIGURE 8. Frequency response of area 2 in MSMA hydrothermal system with various PI controllers.

FIGURE 9. Frequency response of area 1 in MSMA hydrothermal system with and without SMES and SSSC.

The economics of FACTS devices is decided by the MW andMJ rating. The MW capacity of SMES and SSSC is determinedby the output; it is between –0.01 and 0.01 p.u. MW on thesystem base. MJ capacity of SMES is obtained by integratingthe MW output of SMES. For the SSSC, MJ capacity is notnecessary, so the SSSC is economically advantageous overSMES.

5. SIMULATION RESULTS AND DISCUSSIONS

The transfer function model of the MSMA hydro thermal sys-tem shown in Figure 2 is developed in MATLAB/Simulink[25]. The system is subjected to a unit step load disturbancein area 1. The frequency response of area 1, tie-line powerresponse, and frequency response of area 2 are shown inFigures 6, 7, and 8, respectively.

From the responses, it is evident that the drooping char-acteristics of the governor creates offset in �f 1, �Ptie12, and�f 2. To remove the offset, the secondary PI controller is used,and its gain values are tuned using ZN, GA, and FGS methods,as explained in Sections 2.1, 2.2, and 2.3, respectively.

FIGURE 10. Tie-line power response of MSMA hydro thermalsystem with and without SMES and SSSC.

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932 Electric Power Components and Systems, Vol. 42 (2014), No. 9

Controller ISE ITAE ITSE

ZN-tuned PI 0.001077 1.185 0.002858GA-tuned PI 0.0009056 0.5412 0.00123FGS 0.0001611 0.1605 0.0001548

TABLE 2. Comparison of various controller performances inMSMA hydro thermal system

Kp and Ki values tuned using the ZN method of thermal andhydro power plants in area 1 and area 2 are 0.714 and 0.612,respectively.Since the plants are identical, the tuned vales arethe same. This disadvantage is overcome by the GA. Using theGA, the tuned values of Kp and Ki of area 1 having a thermalpower plant are 0.3141 and 1.0532, and those of the hydropower plant are 0.6222 and 1.1153. Similarly, for area 2, Kp

and Ki values of the hydro power plant are 1.4242 and 0.0031,and those of the thermal power plant are 1.9178 and 0.0434.

The comparison response of �f 1, �Ptie12, and �f 2 of theMSMA hydro thermal system with ZN-, GA-, and FGS-tunedPI controller for a unit step load disturbance in area 1 areshown in Figures 6, 7, and 8, respectively.

The comparison response shows that FGS controller yieldsa better response for te MSMA hydro thermal system in termsof less overshoot and settling time when compared to the ZN-and GA-tuned PI controller.

For further analysis of controller performance, the perfor-mance index ISE, integral time absolute error (ITAE), andintegral time squared error (ITSE) are calculated using Eqs.(13), (14), and (15) and furnished in Table 2.

I SE =∫ t

0

(� f 2

1 + �P2tie12 + � f 2

2

)dt, (13)

I T AE =∫ t

0

( |� f1| + |�Ptie12| + |� f2|)t.dt, (14)

I T SE =∫ t

0

(� f 2

1 + �P2tie12 + � f 2

2

)t.dt . (15)

FIGURE 11. Frequency response of area 2 in MSMA hydrothermal system with and without SMES and SSSC.

From Table 2, it is quite evident that FGS is the best suitablecontroller for the MSMA hydro thermal system. For furtherimprovement, SMES and SSSC are included in the hydro ther-mal system, as shown in Figure 3. The transfer function modelof Figure 3 is developed in MATLAB/Simulink [25] usingFigures 2, 4, and 5. The system is subjected to a unit step loaddisturbance in area 1, and �f 1, �Ptie12, and �f 2 responses arefurnished in Figures 9, 10, and 11. The comparison responseshows that SMES connected in each area improves the respec-tive area frequencies, and the SSSC on the tie-line improvesthe power flow between the areas.

6. CONCLUSION

The MSMA hydro thermal system has been developed by con-necting both hydro and thermal power plants in each controlarea. Due to the drooping characteristics of the governor, offsethas been created in both frequency and tie-line power response.The offset has been removed by connecting a PI controllertuned using ZN, GA, and FGS. Based on performance indexcomparison, the FGS-tuned PI controller has been found tobe a suitable controller for the MSMA hydro thermal system.SMES connected in each control area improved the frequencyresponse. The SSSC connected in the tie-line improved thetie-line power response. For the FGS-tuned secondary PI con-troller, SMES on each control area and the SSSC on the tie-lineprovide the best LFC for the MSMA hydro thermal system.

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APPENDIX

Thermal power plant [1–4]:

R1 = speed regulation of governor = 2 Hz/p.u. MWTH = turbo governor time constant = 0.08 secTT = non-reheat turbine time constant = 0.3 secK P1 = K P2 = power system gain constant of area 1 and area

2 = 120TP1 = TP2 = power system time constant of area 1 and area 2

= 20 sec�PD1 = �PD2 = change in load demand power in area 1 and

area 2 = 0.01p.u.B1 = B2 = frequency bias constant of area 1 and area 2 =

0.425 p.u.MW/Hzp f11 = p f22 = participation of thermal power plant in area 1

and area 2, respectively, = 0.5 p.u.

Hydro power plant [1–4]:

R2 = speed regulation = 2.4 Hz/p.u. MWK1 = hydro governor gain = 1T1 = hydro governor time constant = 48.7 secTR , T2 = hydro power plant time constants = 5.0 sec, 0.513

secTW = water time constant = 1.0 secp f12 = p f21 = participation of hydro power plant in area 1

and area 2, respectively, = 0.5 p.u.

Tie-line [1–4]:

A12 = synchronizing power coefficient = –1T = synchronizing coefficient = 10% of area capacity =

0.1Cos δ12 = 0.0707

SMES [14, 17, 18]:

Tsmes = SMES time constant = 0.03 secKsmes = SMES gain constant = 0.297T11, T21, T3, T4 = second-order frequency stabilizer constants

= 0.121, 0.800, 0.011, and 0.148 sec, respectively

SSSC [19–21]:

Tsssc = SSSC time constant = 0.03 secKsssc = SSSC gain constant = 0.001Tw = wash-out time constant = 10 sec

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934 Electric Power Components and Systems, Vol. 42 (2014), No. 9

T5, T6, T7, T8 = second-order frequency stabilizer constants =0.2651, 0.2011, 0.6851, and 0.2258 sec, respectively

BIOGRAPHIES

Vijaya Chandrakala obtained her B.Tech in electrical andelectronics engineering from NSS College of Engineering,Palakkad, India, and her M.Tech in power systems from Thris-sur College of Engineering, India. She has obtained her doc-toral degree in the area of power system control from AnnaUniversity, Chennai, India. Currently she is an assistant profes-sor (selection grade) in the EEE Department of Amrita VishwaVidyapeetham University, Coimbatore. Her areas of interestsare load frequency control, soft computing techniques, and thesmart grid.

Balamurugan Sukumar completed his B.E. in electrical andelectronics engineering from Annamalai University, India, in

2001, and his M.Tech in power systems from the same uni-versity in 2001. He obtained his Ph.D. from Anna University,Chennai, India, in 2010. Currently he is working as an associateprofessor in the EEE Department of Amrita School of Engi-neering, Coimbatore. His research interest areas are powersystem control, soft computing techniques, energy manage-ment, deregulation, and the smart grid.

Krishnamoorthy Sankaranarayanan completed his B.E. inelectronics and communication engineering in 1975, and hisM.E. in applied electronics in 1978 from P.S.G.College ofTechnology, Coimbatore at the University of Madras. He re-ceived his Ph.D. in biomedical digital signal processing andmedical expert system in 1996 from P.S.G. College of Technol-ogy, Coimbatore, Bharathiar University. His areas of interestinclude digital signal processing, biomedical electronics, neu-ral networks and their applications, and opto electronics.

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