AUTOMATIC LOAD FREQUENCY CONTROL OF MULTI-SOURCE POWER SYSTEM USING FLOWER POLLINATION ALGORITHM

(1)

N. Ramya Sri and M.Jeyamurgan ijesird , Vol. II Issue XI May 2016/721

AUTOMATIC LOAD FREQUENCY CONTROL OF MULTI-SOURCE POWER SYSTEM USING

FLOWER POLLINATION ALGORITHM

N. Ramya Sri , M.Jeyamurgan.,B.E(EEE),M.E.(P.E&D)

Assistant professor (Sr.Gr.), Deparement of Electrical Engineering, K.L.N.College of Engineering, Sivagangai, India

ABSTRACT-

In this Power System, the frequency and tie line error for multi area multi source system should be within tolerable limit. For this analysis the system considered here is two Area with three different Sources such as Gas, Thermal and Hydro power plant.This paper is used to introduce the new flower pollination algorithm (FPA) for multimodal optimization and is used for automatic load frequency control (ALFC).This Proposed method is based on the flower pollination. The behavior of two types of pollination namely self-pollination and cross-pollination. Self- pollination occurs when pollen from one flower pollinates the same flower or different flowers of the same plant. On the other hand, cross-pollination means that the pollination can occur from a pollen from a flower of a different plant. Biotic, cross- pollination occurring at long distance may be called as the global pollination. This algorithm is applied in multi area and multi source realistic power system with and without DC link between two areas is used to tuning the P,PI,PID controller for automatic generation control (AGC).The dynamic characteristics of this system with a load disturbance of 1% is applied in the both area. Here the integral control is used to control the dynamic characteristic of the system .The integral square error(ISE) is used to control performance and provide better dynamic characteristic of the system.

Keywords: Automatic load frequency control, Automatic generation control, HVDC link, Flower pollination algorithm

I . INTRODUCTION

The main objective of power system control is to maintain the continuous balance between electrical generation and varying load demand and the associated system losses while system frequency and voltage level are maintained constant. The load Variations in the power system affect the quality of Power . If the power demand is more than the generated power system frequency will decrease and Power demand is lesser than the generated power system frequency will increase affecting the real power of the system [1]. load frequency control (LFC) is a very important issue in power system operation and control for supplying the sufficient

for both good quality and reliable power. Hence the balance of the power system gets disturbed power systems are interconnected and power is exchanged between the systems over the tie-lines by which they are connected.. The automatic load frequency control which is basically a part of automatic generation control plays an important role in power system by maintaining scheduled system frequency and scheduled tie-line power in normal operating condition during slow and Small perturbations. In the modern power system there is a multiple sources and multi area power system using with or without HVDC links connecting two areas of generation like hydro, thermal and gas power plants having many control areas. [2,3].The area control error is used to controlled the output of AGC is driven to zero in order to maintain the system frequency and tie line power deviations of control area is equal to zeros.the LFC of the power systems in system frequency and tie line power flow values during the normal operating condition.

The most applied controller is Conventional Proportional Integral (PI) [4]. One of the new algorithms is the flower pollination algorithm (FPA). The flower pollination algorithm which is developed by Y. Xin-SheYang is based upon the fly distance of bees and birds obey a Levy distribution characteristics of FPA [5]. In the LFC problem the

PI controller optimization

technique is used in flower algorithm. In order to m aintain the efficiency of the proposed optimization technique for the P,PI,PID controller, a comparative investigation is carried out.The performance of the P,PI,PID controller optimized based on FPA is compared with a conventional PID controller. The two areas interconnected reheat

(2)

thermal power system.

Flower pollination algorithm based controllers guar antee the good performance under various load condition

It is found in literature survey that most of the researchers adopt thermal,hydro and gas systems in AGC studies. that considers a single area or multi-area without or with HVDC link[6,7]

connecting two areas of realistic power system having generation from thermal, hydro and gas units[8,9]. Mohanty et al. have considered a two area of AC–DC system with parallel tie lines for frequency stabilization by using DE tuned PID controller that better performs[10] than an optimal output feedback controller for similar power system. [11] .Gozde et al. proposed the PSO algorithm for AGC system for an interconnected thermal power plants in the year 2011 . One year after,[12] Gozde et al.again proposed the artificial

bee colony based PI and PID

controller parameters are tuned toperformance com pared to PSO with transient response analysis method in 2012 [13]. However, the proposed FPA a parameter free algorithm is very simple in concept and easy implementation to tune the controller of AGC for the realistic power system. In the present work, a brief overview of FPA technique with a flow chart for the sake of completeness and better reading of the paper and also request the readers to refer to for more details of FPA and its application[14].A large frequency deviation can damage the equipment, degrade load performance, cause the transmission lines to be overloaded and can interfere with system protection schemes, ultimately leading to an unstable condition for the power system [15]. Maintaining frequency and tie- line power interchanges with neighboring control areas at the scheduled values are the two main primary objectives of a power system LFC. These objectives are met by measuring a control error signal, called the area control error (ACE), which represents the real power imbalance between generation and load, and is a linear combination of net interchange and frequency deviations. After filtering, the ACE is used to perform an input control signal for a usually proportional integral (PI) controller. Depending on the control area

characteristics, the resulting output control signal is conditioned by limiters, delays and gain constants.

A. THE PRESENT WORK:

 To propose a new inspired algorithm as flower pollination algorithm (FPA) which is used in the load frequency control of the power system.

 To solve the optimization problem of minimizing the Integral square error with the optimal selection of Gain values for I, PI and PID controllers

 To compare the dynamic performance of FPA based PI controller to that of optimal controller and DE tuned to control the single area power system.

 To compare the dynamic performance of FPA based PID controller to that of optimal controller and DE tuned controller for multi- source and multi area power system using without and with HVDC link.

 To proposed the FPA-PID controller with its optimum parameters and its stable to wide changes in loading pattern an d several system parameters and their nominal values and also changes in size and locations of load and different cost functions.

B. SYSTEM INVESTIGATED

Multi source multi area realistic power system with HVDC link

The two area power system interconnected by parallel AC DC tie lines which combine more practical combination of generating units such as reheat thermal, hydro and gas units in each area is simulated by using MATLAB Simulink . Furthermore, the generators in each area may or may not participate in the LFC task and the participation rates are not same for all participating generators. The summation of

participation factors of all participating generators is equal to unity in a control area. Transfer function model of multi-source multi area with HVDC link with the integral controllers is depicted

II. METHODOLOGY OF FLOWER POLLINATION ALGORITHM

(3)

N. Ramya Sri and M.Jeyamurgan ijesird , Vol. II Issue XI May 2016/723 FPA based stochastic algorithm is used to

solve the optimization problem of minimizing the Integral square error with the optimal selection of Gain values for I, PI and PID controllers. Nature based algorithms are highly effective and efficient to solve difficult optimization problems. FPA is nature inspired algorithm and gaining popularity recently for solving nonlinear optimization problems technique.

Flower pollination algorithm is proposed by Xin- She Yang in 2012, is based on flower pollination behavior. Two types of pollination occur namely self-pollination and cross-pollination. Self- pollination

occurs when pollen from one flower pollinates the same flower or other flowers of the same plant. On the other hand, cross pollination me ans pollination can occur from pollen of a flower of a different plant. Biotic, cross-pollination occurring at long distance may be called as the global pollination initiated by the pollinating agents such as bees, bats, birds and flies which could fly a long distance. Behaviors such as jump or fly distance of bees and birds obey a Levy distribution.

III. CHARACTERISTICS OF FLOWER POLLINATION.

1. Global pollination is assumed for biotic and cross-pollination process with pollen carrying agents such as bees and birds performing the Levy flights.

2. Local pollination is assumed for abiotic and self-pollination.

3. Reproduction probability is assigned to flower constancy and is proportional to si milarity of two flowers involvd

4. Switch probability p ∈ [0, 1] controls the local pollination and global pollination.T he pollination and reproduction of the fittest is given as below



S  S₀ 0



(2)

Where v^t_i is the pollen i or solution vector v_i at iteration t, and d* is the current best solution found among all solutions at the current generation/iteration. L is the strength of the pollination, which is used to step size. Pollinators can move over a long distance with variousdistance steps, Levy flight distribution is used to minimize the characteristic efficiently. Γ(

µ) is the standard gamma function, and this distribution function is valid for large steps s > 0. In all our simulations, we have used µ = 1.5 and s [0, 10]. L > 0 is assumed for Levy distribution. The local pollination and flower constancy can be represented as

Where, v_j^t and v_k^t are pollens from the different flowers of the same plant species.

This essentially mimics the flower constancy in a li mited neighborhood. Mathematicallyif v_j^t and v_k^t comes from the same species or selected from the same population and this become a local random if we draw ε from a uniform distribution in [0, 1].

Pollination activities in FPA algorithm can occur at both local and global scale. Flowers at closer proximity are more likely to pollinate compared to flowers at distance so a switch probability or proximity probability p to vary between p = 0.5(initially) to 0.8.The value of p = 0.8 works better for most applications.

IV. STRUCTURE OF FLOWER POLLINATION ALGROITHM

1. Initialize the objective function f1 as given in the equation

2. Initialize a population of X ( , ) flowers/pollen gametes with the population size of NF x N. Where NF is the Number of flowers as 30 and N is the Dimension size depends on the number of Controller Gain values for each area in the two area system. In this paper, two



^v ^d



⁽¹⁾

L v

v^t_i^¹  ^t_i  ^t_i  _*



^v ^v



⁽³⁾

ε v

v^t_i^¹ ^t_i  ^t_j  ^t_k

(4)

N. Ramya Sri and M.Jeyamurgan ijesird , Vol. II Issue XI May 2016/724 area systems, three different controllers such as

P,PI,PID.So N must be the two for I controller, four for PI controller, six for PID controller location. Calculate the Fitness for each solutions.

3. Find the best solution to the initial population by subs

Define a switch probability p ∈ [0, 1]

while (t <Maximum Generation) for i = 1 : n

(all n flowers in the population)

if rand < p,Global pollination has been done using equation (1).

Else Draw ε from a uniform distribution in [0,1] Randomly choose j^th and k^th flower among all the solutions

Do local pollination via equation (3).

end if

4. Evaluate new solutions using the objective function.

If new solutions are better, update them in the population

end for

5. Find the current best solution d based on the objective fitness value

end while

Fig.1(a) Transfer Function Model of Multi-Source Multi area WITH DC Link

Fig.1(b) Transfer Function Model of Multi-Source Multi area WITH

OUT DC Link

V .PROBLEM FORMULATION

1,2,3 j and I P,

max K

min _i





 i

K

K_ij _ij (2)

2 2 2

1 1 1

1 3 1 3 1 3

1 2 1 2 1 2

1 1 1 1 1 1

Tie Tie

D I

P G

D I

P H

D I

P T

P F B ACE

dt K dACE ACE

K ACE K U

dt K dACE ACE K ACE K U

dt K dACE ACE

K ACE K U































₍₃₎

) 4 (

) 5 (

) 6 (

) 7 (

 

simulation of

range time the t

power line tie the in change l incrementa the

P

deviations frequency

the F and F

dt t P F F

sim Tie

Tie

























2 1

2 t

0

1 (1) ISE

J

sim

(5)

N. Ramya Sri and M.Jeyamurgan ijesird , Vol. II Issue XI May 2016/725 Minimize J subject to PI Controller gains su

ch as the differential equation of I,PI,PID controller of each unit of power system that are thermal, hydro and gas with respect to the input controller as UT,UH,UG may be written as time range of simulation. The ACE signal is the area control error which includes the data about the frequency error and the tie line power error for the related control area. it may be represented in the equation as (6) and (7) for area-1 and area-2, respectively.

Proportional Integral (PI) controllers are the most often type used today in industry. A control without Derivative (D) mode is used when the fast response of the system is not required. the many researchers

are used to

analyzd the algorithm to inspired by the behaviors o f natural phenomena. The intelligent algorithms are well suited to solve the many computational problems and the traditional optimiz

ation techniques has been used. This algorithm is us ed to increase the speed of convergence and decrease the rate of the convergence. to increases the probability of finding the global optimality, while strong investigation tends to make the algorithm being trapped in a local optimum.

Therefore, there is a fine balance between the right amount of investigation and the ri ght degree o exploitation. it is importance, there is no practical guideline for this balance that are population of solutions, not a single point solution no limitation of size of the problem, independent of initial calculation of the variables, simple concept and easy implementation, very close to hundred percent success rate, significantly fast and stable due

to competition and selection and also incredibly wel l solving the realistic AGC problem.

Table1. Tuneduned controller parameter P,PI,PID of multi-source multi area system without and with HVDC link.

Integral ISE

Proportional Gain Value

in Area1

Proportional Gain Value

in Area2

Integral Gain Value

in Area1

Integral Gain Value

in Area2

Differential Gain Value in Area1

Differential Gain Value in Area2 Without Controller

AC only 11.5193 - - - - - -

Without Controller

AC & DC Line 0.8912 - - - - - -

With FPA I

Controller AC only 2.650689233 - - 4.43044228 0.28292938 - - With FPA PI

Controller AC only 0.680660333 6.310054687 2.264462099 1.47649836 0.3234975 - - With FPA PID

Controller AC only 0.137912912 1.417554293 4.956104535 8.30050514 3.962022391 1.76287199 2.82038061 With FPA I

Controller AC &DC

Line 0.059289113 - - 4.86192928 9.999015213 - -

With FPA PI Controller AC &DC

Line 0.040245254 3.82785578 2.5273611 5.55164769 7.400108186 - - With FPA PID

Controller AC &DC

Line 0.033761911 2.993049326 1.859600221 8.79397176 9.999736406 0.97455825 0.21964375

(6)

Fig.1.Frequency deviation of the control area-1 By using P,PI PID controller for 1% change in area-1 with and without AC-DC line.

Fig.2.Frequency deviation of the control area-2 By using P,PI PID controller for 1% change in area-1 with and without AC-DC line

DEL F1 DEL F 2

Settling time in Secs Peak overshoot

Settling time in

Secs Peak overshoot Without Controller

AC only NA NA NA NA

Without Controller

AC & DC Line NA NA NA NA

With FPA I

Controller AC only 18 -0.009047 19 -0.096

With FPA PI

Controller AC only 16 -0.008216 14.6 -0.06

With FPA PID

Controller AC only 11.2 -0.006895 13 -0.04415

With FPA I Controller AC &DC

Line 14 -0.006171 9.4 -0.04415

With FPA PI Controller AC &DC

Line 5.4 -0.006047 10.2 -0.02

With FPA PID Controller AC &DC

Line 4.2 -0.006084 7.3 -0.02

Table 2. Tuned control parameter ,settling time, maximum overshoot and ISE objective function of multi source multi source multi area system.

(7)

N. Ramya Sri and M.Jeyamurgan ijesird , Vol. II Issue XI May 2016/727 VI. RESULT AND DISCUSSION

The present work has to been implemented in Matlab-7.10.0.499 (R2010a) environment on a 3.06 GHz, Pentium-IV; with 1 GB RAM PC for the controller parameter tuning in the automatic load frequency control. The model of the system under study has been developed in MATLAB/SIMULINK environment and FPA program has been written (in .m file). The developed model is used to simulated in a separate .m file using initial gain scheduling parameters considering a 1% step load perturbation in area-1 at time, t = 0 s. The objective function is calculated in .m file and used in optimization algorithm for tuning the gains of PI controller for power system.

Multi area multi-source power system without and with HVDC

This power system comprises two control areas, each area having three PI controllers designed for investigation of load frequency control. A step load is change of 1% in the area 1 is considered at t = 0 s. The optimal controller parameters obtained by FPA algorithm for the system with AC lines and with AC–DC parallel tie lines are reported.

VII CONCLUSION

FPA algorithm is proposed in this paper to tune the parameters of I, PI, PID controller. A multi-source single area and the multi-source multi

area without and with HVDC link is considered to demonstrate the proposed method is used as an integral time absolute error of the frequency deviation of both areas and the tie line power is taken as the objective function to improve the system reacti in terms of the settling time and overshoots. Simulation results emphasis that the proposed FPA tuned PID controller is stable in its operation and gives a superb damping performance for

frequency and the tie line power deviation followin g a step load perturbation (SLP) compared to the optimal output feedback controller and DE tuned PID controller gives the best solution. Furthermore, it is found that the proposed system is more robust

and stable to

wide changes in the system loading, parameters, siz e and the location of step load perturbation and various cost functions. Besides, the simple concept and the architecture of the proposed controller it can be implemented for real time application.

APPENDIX A

The typical values of system under study as give below:

f = 60 Hz; B1= B2= 0.4312 pu MW/Hz; PR= 2000 MW (rating), PL= 1840 MW (nominal loading); R1= R2= R3=

2.4 Hz/pu MW; Tsg= 0.08 s; Tr= 10 s; Kr= 0.3; Tt= 0.3 s;

KT= 0.543478; KH= 0.326084; KG= 0.130438; Tgh= 0.2 s;

Trh= 28.75 s;Trs= 5 s; TW= 1 s; bg = 0.5; cg = 1; Xc= 0.6 s;

Yc= 1 s; Tcr= 0.01 s; Tfc= 0.23s; Tcd= 0.2 s; Tps= 11.49 s; Kps= 68.9566 Hz/pu MW; Tdc= 0.2 s; Kdc= 1; T12=

0.0433 pu; a12= 1 .

REFERENCE

1. M.Peer Mohamed, E.A.Mohamed Ali, I.Bala Kumar

“BFOA based tuning of PID controller for a load frequency control in four area power system” 2012 International Journal of Com

2. Jawat, T.and Fadel, A, B. “Adaptive Fuzzy Gain Scheduling for Load frequency control”, IEEE Trans. on PAS, vol. 14, No 1, February 1999

3. Parmar KPS,Majhi S.Kothari DP.“Improvement of dynamic performance of LFC of the two area power system. An analysis using matlab”.Int. journal comp. appl.

2012;40:28-32.

4. Parmar KPS,Majhi S.Kothari DP. “Load frequency control of a realistic power system with multi-source power generation”.Int. journal electrical. power energy system 2012:42:426-33.

5. Khutia SR, Panda S. “Simulation study for automatic generation control of a multi-area power system by ANFIS approach”.Applied soft computation2012;12:333-41.

6. Rout UK,Sahu RK,Panda S. “Design and analysis of differential evolution algorithm based automatic generation control for interconnected power system”,ain shams eng j 2012

7. Parmar KPS,Majhi S.Kothari DP. “ Load frequency control of a realistic power system with multi-source power generation”.Int. journal electrical. power energy system 2012:42:426-33.

8. Saikia LC,Nanda J,mishra S. “Performance comparison of several classical controllers in AGC for multi area interconnected thermal system”. Int. journal electrical.

power energy system 2011;33:394-401

9. Parmar KPS,Majhi S.Kothari DP. “Improvement of dynamic performance of LFC of the two area power system. An analysis using matlab”.Int. journal comp. appl.

2012;40:28-32.

(8)

10. Mohanty B, Panda S,Hota PK. “Controller parameter tuning of differential evolution algorithm and its application to load frequency control of multi source power system” .Int. journal electrical energy system.

2014;54:77-85.

11. Gozde H,Taplamacioglu MC,“Automatic generation control application with craziness based particle swarm optimization in a thermal power system” Int. journal electrical power energy syst. 2011;33(1):8-16.

12. A.K.Barisal“Comparative performance of teaching learing based optimization for automatic load frequency control of multi-source power system”.Int.journal electrical power and energy systems.66 (2015) 67-77.

13. Gozde H,Taplamacioglu MC. “Comparative performance analysis of artificial bee colony algorithm in automatic generation control for interconnected reheat thermal power s ystem”.Int. journal electr. power syst. 2012;42:162-78.

14. Rao RV,Savsani VJ,Vakharia DP. “Teaching learning based optimization: an optimization method for continuous non linear large scale problems”.Inform.Sci.2012;183(1):8 16.

15. Roy PK. “Teacher learning based optimization for short- term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint”. Int.

journal electrical. power energy syst. 2013;53(1):10-9..

16 K. Lenin, Dr.B.Ravindhranath Reddy Flower pollination algorithm for solving optimal reactive power dispatch problem International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS), ISSN 2350-1049 Vol.

1, Issue 2, pp: (7-16), Month: July 2014 - September 2014.

17 Puja Dash a, Lalit Chandra Saikia b, Nidul Sinha b Flower Pollination Algorithm Optimized PI-PD Cascade Controller in Automatic Generation Control of a Multi-area Power System Electrical Power and Energy Systems 82 (2016) 19–28

18 Xin She Yanga,1,∗, Mehmet Karamanoglua, Xingshi Heb, Multi- objective FlowerAlgorithm for Optimization International Conference on Computational Science, ICCS 2013.

19 Kamalam Balasubramani, Karnan Marcus, A Study on Flower Pollination algorithm and Its Applications, Interna tional Journal of Application or Innovation in Engineering

& Management (IJAIEM), Volume 3, Issue 11, November 2014.