Posts Tagged ‘Matlab’

Chapter-7: Conclusion and Future Scope

7.1. Conclusion and Discussion

This research work mainly represents the modeling and simulations of some Intelligent controllers for an AGC in single power systems. In this thesis, the computer simulations results based on system non-linear differential equations have been carried out for different load changes. The differential equations have been solved by using MATLAB programming environment. In this research we basically use fuzzy logic control. We have reduced the settling time and minimized the error (∆f). We have made the settling time 4.5 second which was above 6 second in other previous records.

Our research of frequency control is suitable for universal load system. By this method, the system will be stable within 4.5 seconds which is very effective.

 7.2. Scope of Future Work

In this research, all modeling and simulation of the proposed scheme was performed by MATLAB program. These will also be performed by using the latest simulation techniques MATLAB Fuzzylogy system.

In this research, some intelligent Controllers for an AGC in single power system have successfully achieved zero steady state error, but this research has some future scopes described as follows:

a)      The research work can be further extending considering boiler dynamics.

b)      Also deferent type of intelligent controller can be tested to get the better performance.

7.3. References

1)      M.G.Rabbani, J.B.X Devotta, S. E langovan, “A fuzzy set theory based control of superconductive magnetic energy storage unit improve power system dynamic performance” Electric power system Research 40 (1997), 107-114

2)      Y.L Karnavas, D.P Papadopoulos “AGC for autonomous power system using combined intelligent techniques” Electric Power System Research 62 (2002), 225-239.

3)      prof. Wah-Chun Chan, Yuan-Yih Hsu, “Automatic Generation Control of Interconnected Power system Using variable -structure Controllers”, IEEE Proceeding Vol. 128 , pt,  C,  No. 5 September 1981

4)      P.M Anderson and A.A Fuad, “Power system control and stability” Iowa State University press, Ames, lowa, 1977

5)      Hadi Saadat, “Power System Analysis”, Tata McGraw-hill Publishing Company Limited, New Delhi.

6)      O.I. Elgerd, “Electric Energy Systems Theory” McGraw-Hill Book company New York.

7)      S.c Tripathy, T.S Bhatti, C.S. Jha   O. P. Malik, G. S. Hope, “Smpled Data Automatic Generation Control Analysis with Reheat Steam Turbine and Governor Dead-Band Effects”, IEEE Transaction on Power Apparatus and System, Vol. PAS-103, No.4 May,1984

8)      Gilberto CD Sousa, Bose, “A Fuzzy Set Theory Based Control of a Phase-Controlled Converter DC Machine Drive”, IEEE Transactions on Industry Applications, Vol. 30, No.1, January 1994

9)      D.Driankov, et al, “An Introduction to Fuzzy Control” Springer-Verlag Berlin- Heidelberg, New York, 1993

10)  P.N. Paraskeveopoulos, “ Digital Control System” prentice Hall Europe 1996

11)  Nzsser Jaleeli, Louis  S. Vanslyck, Donald N. Ewart, Lester H. Fink, Arthur G. Hoffmann, “Understanding Automatic Generator Control” IEEE Transactions on power system, Vol.7 No. 3 August, 1992.

12)  W.C. Chan, Y.Y. Hsu, “Automatic Generation Control of Interconnected Systems Using variable structure controllers” IEE proceedings, vol. 128, Pt.C.No.pp. 269-279, September, 1981.

13)  C.T. pan, C.M. Lian, “An Adaptive Controller for Power System Load-Frequency Control”, IEEE Transactions on Power System, Vol. 4, No. 1 February, 1988

14)  M.H. Ali, “A Fuzzy  logic controlled Braking Resistor for Power System Trnsietn Enhancement”, in partial fulfillment of the Ph.D. degree in Electrical & Electronic Engineering, Kitami Institute of Technology, Japan.

15)  Zhog He Shaohua Tan and Chang- Chieh Hang, “Control of dynamical processes using an on –line rule adaptive fuzzy control system”, Elsevier science publishers B.V. All rights reserved fuzzy Sets and Systems 54(1993), 11-22

16)  Dr.S.P.Ghoshal , “Multi area frequency and tie- line p, December overflow     control with fuzzy logic based  integral gain scheduling” IE(I) jouranal-El, Vol 84 December 2003

17)  J.B.X. Devotta, M.G.  Rabbni S.Elagovan “Effects of SMES Unit on AGC dynamics” Internationl conference on Energy management and power Delivery 1998 EMPD, 98 Singapore.

18)  M.H.Ali, et al,” Braking Resistor Switching By Genetic algoritam optimized Fuzzy logic controller in Muli-machine power system” Transation of IEE,Japan, Vol 123 –B No.315-323, 2003.


Chapter- 5: Integral Gain Control of Automatic Generation Control (AGC)

5.1. Introduction

The first and overriding requirement is the selection of parameter which will result in a power stable system .Having secured a stable system; our next objective is to adjust the parameter until we have a best optimum response [6]. One way to improve the stability of power system optimizes the basic factor controller parameter with change of frequency. This factor is integrating gain [7].

5.2. Automatic Generation Control (AGC)

If the load on the system is increased, the turbine speed drops before the governor can adjust the input of the stream to the new load .As the change in the value of speed diminishes, the error signal becomes smaller and the position of the governor fly balls gets closer to the point required to maintain a constant speed. However, the constant speed will not be the point, and there will be an offset. One way to restore the speed or frequency to its nominal value is add an integrator. The integral unit monitors the average error over a period of time and will overcome the offset. Because of its ability to return a system to its point, integral action is also known as the reset action. Thus as the system load changes continuously, the generation is adjusted automatically to restore the frequency to the nominal value. This scheme is known an the automatic generation control (AGC) [13].

5.3. Integral Control

By using the control strategy shown in figure 5.1 maintains an overall system that will meet performance. The speed change is commanded by a signal stained by first amplifying and then integrating the frequency error [2].


Note the negative polarity of the integral controller. This polarity must be chosen so as to cause a positive frequency error to given rise to a negative, or decrease command. The signal fed into the integrator is referred to as area control error (ACE).

ACE = ∆f ………..……………………………………………… (5-2)

The integral control will give rise to zero static frequency error following a step load change for the physical reason. As long an error remains the integrator output will increase, causing the speed changer to move. The integrator output, and thus the speed changer position, attains a constant value only when the frequency error has been reduced to zero [5].


5.3.1. Integral Gain Value KI of Automatic Generation Control

The gain constant KI control the rate of integration, and thus the speed of response of the loop. The integration is actually performed in electronic integrators of the same type as found in analog computers. Here follows an analysis of the proposed system, subject to a step load change. To avoid cumbersome numerical analysis, we shall as before neglect the time constants TT and TG .In addition we also make the assumption that the speed changer action is instantaneous. This is not perfect correct, since the device is electromechanical and will therefore have a nonzero response time. These approximations will make possible relatively simple analysis without distorting essential features of the response. It is also worth mentioning that the errors we thus introduce our analysis affect only the transient, not the static, response.

By derivations equation (5-1), we get equation,

…………………………..…………………….. (5-3)

5.3.2. Effect of Constant Gain KI

The value of gain KI is constant then ∆f create more overshot from the x –axis

And get more time to create ∆f =0. When ∆f is allow age zero then curve flow through the x –axis. We see that settling time is more than 6s.

When KI=1, 0.7, 0.5 effect of the ∆f curve see in figure below:

Fig: 5.2 frequency deviation for single area power system with step load change ∆PL=0.01(p.u) MW

In the above picture we see that when KI =1 then overshoot 0.02 (pu) and settling time is more than 10s. When KI =0.7 then overshoot 0.012 (pu) and setting time is more than 9s. And when KI =0.5 then overshoot 0.005 (pu) and setting time is more than 7s.

5.3.3. Effect of Variable Gain KI

Frequency deviation for single area power system with step load change ∆PL=0.01(p.u) MW:

Fig: 5.3 frequency deviations for single area power system with variable gain controlled

5.3.4. Compare Between Constant and Variable Gain

When KI=1, 0.5 and vary (1-0.25) then effect of the ∆f curve see in figure below:

Fig: 5.4 frequency deviation for single area power system with step load change ∆PL=0.01(p.u) MW

In the above picture we see that when KI =1 and 0.5, then overshoot is more than 0.009 (p.u) and setting time is more than 7s. We also see when KI is variable gain vary (1-.25) then overshoot is more than 0.002 (pu) and setting time is more than 4.5s.

5.4. Matlab Program:

Variable Gain Control of Automatic Generation Control (AGC):