New Control Strategy For Improvement Of Transient Behaviour In Multi Area Interconnected Power System With Emphasis On Robust Genetic Algorithm

New Control Strategy For Improvement Of Transient Behaviour In Multi Area Interconnected Power System With Emphasis On Robust Genetic Algorithm

Abstract- In this paper a new robust load frequencies controller (LFC) for two area interconnected power system is presented to quench the deviations in frequencies and tie line power due to different disturbances. The dynamic model of the interconnected power system is

developed with state variables with the integral and area

In this work, we investigate the optimum adjustment of the classical AGC using genetic algorithms [3] and

performance indices, namely the integral of time multiplied absolute value of the error (ITAE) [4], which is given by,

control error. The two area interconnected power system is subjected to a wide range of load disturbances. To

S t e(t) dt

0

(1.1)

validate the effectiveness of the proposed Genetic Algorithm controller over the PI controller, PID Controller, Fuzzy Logic Controller is compared. The results prove that the transient performance with the proposed Genetic Algorithm controller placed in both the areas is better than these obtained by the other controllers.

Keywords: Interconnected power system, Load Frequency Control (LFC), Fuzzy Logic Controller (FLC), Genetic Algorithm (GA).

1. Introduction

   Power engineers have the responsibility to deliver adequate and quality power to consumers. In order to achieve this, the power system must be maintained at the desired operating level by suitable modern control strategies. The modern power systems with industrial and commercial loads need to operate at constant frequency with reliable power. The load frequency control of an interconnected power system is being improved over the last few years. The goals of the LFC are to maintain zero

   steady state errors in a multi area interconnected power system [1-2]. Studies on two area interconnected power system networks were presented based on conventional

   and modern optimal control techniques. Recently many researchers have applied genetic algorithm controllers to improve the dynamic performance of the system. Genetic Algorithm, Fuzzy logic, PI, PID controllers were used to

   damp oscillations resulted from load perturbations [3-6].

   Subsequently load frequency control of two area interconnected power systems using fuzzy control

   approach to quench the transients in frequency deviations and tie line power deviations is presented [3,8]. In all these works the basic dynamic model representation of a two

   area power system given in the reference [2] is considered and the responses of two area power systems are evaluated using proportional plus integral and derivative control

   with the help of fuzzy control methods. These results show that the frequency deviations are oscillatory.

   A digital simulation is used in conjunction with the genetic algorithms optimization process to determine the optimum values of the AGC for the performance indices considered. Genetic algorithms are used as parameters search

   techniques which utilize the genetic operators to find near optimal solutions [5].

   The work reported in this paper deals with the dynamic model of the power system with integral action and area control error. So Load Frequency Control Problem is restructured as a state transfer problem. The ultimate aim of this work is that, by using a suitable control strategy the system should be transferred from an initial state to the final state without any oscillations in frequency deviations and tie line power deviations so that the time required to reach the final steady state can be reduced greatly. The transient behaviour of the system for different load changes for different controllers are obtained and they are compared based on PI controller, PID Controller, Fuzzy Logic controller and proposed Genetic Algorithm (GA) controller for a two area interconnected system. The robustness of proposed genetic algorithm was applied and observed that the frequency transients are quenched at much faster rates without any oscillations, when compared with other control methods.

2. Modelling of twoarea interconnected power system

   A two area interconnected power system is shown in fig.1. Here k1 and k2 are integral control gains and u1 and u2 are stabilizing signals. The stabilizing signals are generated by the proposed genetic algorithm controller. Each power area has a number of generators which are closely coupled together so as to form a coherent group, i.e. all the generators respond in unison to changes in the load. Such a coherent area is called a control area. Each control area can be represented by an equivalent generator, governor and turbine system. The conventional LFC shown in fig.1 is based on tie-line bias control, where each control area tends to reduce the Area Control Error (ACE) to zero.

   Fig.1: Two Area Interconnected Power System

3. New genetic algorithm controller for the interconnected power system

Genetic algorithms (GA) are global search techniques, based on the operations observed in natural selection and genetics [6]. They operate on a population of current

approximations. The individuals initially drawn at random, from which improvement is sought. Individuals are encoded as strings (chromosomes) constructed over some particular alphabet, e.g., the binary alphabet {0, 1}, so that chromosomes values are uniquely mapped onto the decision variable domain. Once the decision variable domain representation of the current population is calculated, individual performance is assumed according to the objective function which characterizes the problem to be solved. It is also possible to use the variable parameters directly to represent the chromosomes in the GA solution.

At the reproduction stage, a fitness value is derived from the raw individual performance measure given by the objective function, and used to bias the selection process. Highly fit individuals will have increasing opportunities to pass on genetically important material to successive generations. In this way, the genetic algorithms search from many points in the search space at once and yet continually narrow the focus of the search to the areas of the observed best performance. The selection individuals are then modified through the application of genetic operators, in order to obtain the next generation. Genetic operators manipulate the characters (genes) that constitute the chromosomes directly, following the assumption that certain genes code, on average, for fitter individuals than other genes. Genetic operators can be divided into three

main categories [5], reproduction, cross over and mutation.

1. Reproduction: Selects the fittest individuals in the current population to be used in generating the next population.

2. Cross over: Causes pairs, or larger groups of individuals to exchange genetic information with one another.

3. Mutation: Causes individual genetic representations to be changed according to some probabilistic rule.

Genetic algorithms are more likely to converge to global otimal than conventional optimization techniques, since they search from a population of points, and are based on probabilistic transition rules. Conventional optimization techniques are ordinarily based on deterministic hill climbing methods, which, by definition, will only find local optima. Genetic algorithms can also tolerate discontinuities and noisy function evaluations.

In this study, the optimal values of the parameters frequency deviations and change in frequency deviations which minimize an array of different performance indices are easily and accurately computed using a genetic algorithm. In a typical run of the GA, an initial population

is randomly generated. This initial population is referred to as the 0th generation. Each individual in the initial

population has an associated performance index value. Using the performance index information, the GA then produces a new population. The application of a genetic algorithm involves repetitively performing two steps.

1. The calculation of the performance index for each of the individuals in the current population. To do this, the system must be simulated to obtain the value of the performance index.

2. The genetic algorithm then produces the next generation of individuals using the reproduction, cross over and mutation operators.

These two steps are repeated from generation to generation until the population has converged, producing the optimum parameters. A flow chart of the genetic algorithm optimization procedure is given in fig.2.

Fig.2: Genetic Algorithm Controller Flow Chart

1. Simulation study

   In this paper the controllers have been applied to solve the load frequency control of a two area power system having the numerical data shown in Table1.

   Table 1: Two area power system parameters

   Parameters	Area1	Area2
   Tp	20	22
   Tg	0.2	0.3
   Tt	0.4	0.5
   R	2.5	3
   Kp	120	100
   Ttie (or) T12	0.08
   a12	1

   Load frequency control of a two area interconnected power system to quench the deviations in frequency and tie line power due to different load disturbances in any one area using different control strategies is attempted. The major objective of the work presented here is to obtain a suitable controller to improve the transient performance of the two-area power system without oscillations in less time of state transfer. The responses of the two area interconnected power system are evaluated with i) PI controller, ii) PID controller iii) Fuzzy PI controller and

   iv) Genetic algorithm controller. The two area inter connected power system is subjected to a wide range of load disturbances and the controllers are switched at t = 0 seconds, simulated results are obtained with different configurations by placing the controller in both the areas with load changes are assumed to be in both the areas.

   The corresponding responses are shown in Figures 3 to 11.

   Figure 3: Change in frequency deviation of area1 for a load change of Pd1= Pd2= 0.1 with controllers in both areas

   Figure 4: Change in frequency deviation of area2 for a load change of Pd1= Pd2= 0.1 with controllers in both areas

   Figure 5: Change in tie line power deviation for a load Change of Pd1= Pd2= 0.1 with controllers in both areas

   Figure 6: Change in frequency deviation of area1 for a load change of Pd1= Pd2= 0.2 with controllers in both areas

   Figure 7: Change in frequency deviation of area2 for a load change of Pd1= Pd2= 0.2 with controllers in both areas

   Figure 8: Change in tie line power deviation for a load change of Pd1= Pd2= 0.2 with controllers in both areas

   Figure 9: Change in frequency deviation of area1 for a load change of Pd1= 0.1 & Pd2= 0.2 with controllers in both areas

   Vol. 2 Issue 10, October – 2013

   Figure 10: Change in frequency deviation of area2 for a load change of Pd1= 0.1 & Pd2= 0.2 with controllers in both areas

   Figure 11: Change in tie line power deviation for a load change of Pd1= 0.1 & Pd2= 0.2 with controllers in both areas

2. Conclusion

The load frequency control problem of two tie line interconnected power system is studied with various controllers for different configurations of the power system using dynamic model. The uncontrolled system responses reveal that the static errors are increasing with increase of load changes and regulation constants. The control system responses indicate that the deviations of frequency in each area and tie line power deviations are increasing with increase in load. All the deviations are oscillatory for all the controllers placed in area1 with load changes in area1 and area2. However it is observed that the responses are non-oscillatory only in the case of new genetic algorithm controllers placed in both the areas with load changes in area1 and area2. The deviations of frequency in each area and tie line powers are found to be the lowest with less times of state transfer for this case. Similar trend in the behaviour of the system with increase

in load changes is noticed except that the settling times and negative over shoots is more. Even with the wide variations of the load and switching times the system is reaching steady state without any static error with least deviations the scheme of genetic algorithm controllers placed in both the areas is considered to be more robust in nature.

Nomenclature

Pg : Generated power derivation Pd : Change in power demand

Pc : Change in speed changer position Ptie : Incremental tie line power

Kp : Static gain power system inertia dynamic block Tp : Time constant of power system inertia dynamic

block

Tg : Governor time constant

Tt : Turbine (non reheat type) time constant R : Speed regulation parameter

B : Frequency-biasing factor

References

1. Elgerd, O.I, Electric Energy system theory: An Introduction McGraw-Hill, TMH edition, 1971.

2. Haadi Sadat, Power Systems Analysis McGraw-Hill companies Inc. 1999.

3. Prasanth, B.V et al. New control strategy for load frequency problem of a single area power system using fuzzy logic control, Journal of Theoretical and Applied Information Technology, Vol .4, No. 4, April 2008, pp. 253 260.

4. Prasanth, B.V. et al. New control strategy for load frequency problem using fuzzy control, I managers journal of electrical engineering, Vol .1, No. 3, January march 2008, pp. 53 57.

5. Prasanth, B.V. et al. Load frequency control for a two area interconnected power system using robust genetic algorithm controller, Journal of Theoretical and Applied Information Technology, Vol.5, No.2, Dec 2008, pp. 1204-1212.

6. Prasanth, B.V. et al. Comparative study of different control strategies for a load frequency problem with emphasis on new fuzzy logic controller, International journal of electronics and electrical engineering, Vol.1, No.1, Nov 08-Jan 09 spring edition 2009, pp.25-33.

7. Prasanth, B.V. et al. New robust fuzzy load frequency controller for a two area interconnected power system, Journal of Theoretical and Applied Information Technology, Vol.5, No.2, Feb 2009, pp. 242-252.

8. Prasanth, B.V. et al. Control of load frequency of a single area power system for different control strategies comparison with fuzzy control, The ICFAI university journal of electrical and electronics engineering, Vol.1, No. 2, April 2008, pp. 26 38.

9. Prasanth, B.V. et al. The effect of switching ime of different control strategies for a load frequency problem i-managers Journal on Electrical Engineering, Vol. 5, No. 2, Oct – Dec 2011, pp.14 19.

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

11. Nilay.N.Shah, Dr.C.D.Kotwal The State Space Modelling of Single, Two and Three ALFC of Power System Using Integral Control and Optimal LQR Control Method in IOSR Journal of Engineering March-2012Vol2 (3) pp: 501-510.

12. Anand, B. And Ebenezer, A.J., Load Frequency control with fuzzy logic controller considering Nonlinearities and Boiler Dynamic ICGST-ACSE Journal vol. 8, issue 111, Jan. 2009.

Vol. 2 Issue 10, October – 2013 Ms. M. Swathi Nair received the B.Tech. degree in electrical & electronics engineering

from Jawaharlal Nehru Technological University, Kakinada , India , in

2011. She is a M. Tech. scholar of Jawaharlal Nehru Technological University, Kakinada,

India. Her interests are in power system control design and intelligent techniques.

Sri. R. Sathish Kumar received the B.Tech. degree in Electrical & Electronics Engineering from Jawaharlal Nehru Technological University, Hyderabad, India, 2005 & M.Tech. degree in electrical & electronics engineering from Jawaharlal Nehru Technological University, Hyderabad, India, in 2010.

Currently, he is an Associate Professor in QIS College of Engineering and Technology, Ongole, India. He has published a number of papers in various national & international journals & conferences. His research areas are power system operation & control and economic load dispatch.

Dr. B. Venkata Prasanth received the B.Tech. degree in Electrical & Electronics Engineering from Sri Krishnadevaraya University & M.Tech. degree in Electrical Power Systems from Jawaharlal Nehru Technological University, Ananthapur, India. He received his Ph.D. degree in Electrical & Electronics

Engineering from Jawaharlal Nehru Technological University, Hyderabad, India. He has got a teaching experience of more than 14 years. Currently, he is working as Professor & Head in QIS College of Engineering and Technology, Ongole, India in the Dept. of Electrical & Electronics Engineering. He has published a number of papers in various national & international journals & conferences. He is also guiding a number of research scholars in various topics of electrical engineering. His research interests include application of intelligent controllers to power system control design, power system restructuring, power system economics & optimization.