Archive for the ‘Electrical Thesis’ Category

What is Substation Automation?

Substation Automation may be best described by referring to Figure 3.1.


Figure: Functional Structure of Substation Automation

Substation Automation, by definition, consists of the following main components:

  • Electrical Protection
  • Control
  • Measurement
  • Monitoring
  • Data Communications

Substation Automation can be defined as a system for managing, controlling and protecting a power system. This is accomplished by obtaining real-time information from the system, having powerful local and remote control applications and advanced electrical protection. The core ingredients of a Substation Automation system are local intelligence, data communications and supervisory control and monitoring.

The term Substation Automation is actually too restrictive and may be misleading. It is too restrictive in the sense that it refers specifically to a substation only. However, the concepts encompassed in the definition have a much wider application than being limited only to substations. It is applicable to electrical power networks at large, from High Voltage transmission networks, to Medium Voltage distribution networks, to Low Voltage reticulation networks. The term may be misleading in that automation usually refers to some type of process automation, whereas the concepts involved in Substation Automation are quite unique and far removed from process automation, although there is some common ground in the underlying principles.

The term Substation Automation evolved due to the fact that most of the equipment that forms the core of such a system, is located in an electrical substation or switch-room, and these modern, intelligent devices ensure that the need for human presence or intervention in a substation is limited. The components of a Substation Automation system aim to protect monitor and control a typical electrical substation.

(Note: The term “substation” will be used throughout the text to describe mainly a building housing electrical switchgear, but it may also include switchgear housed in some sort of enclosure, for example a stand-alone Ring Main Unit, etc.)

Electrical Protection

Electrical Protection is still one of the most important components of any electrical switchgear panel, in order to protect the equipment and personnel, and to limit damage in case of an electrical fault.

Electrical protection is a local function, and should be able to function independently of the Substation Automation system if necessary, although it is an integral part of Substation Automation under normal conditions. The functions of electrical protection should never be compromised or restricted in any Substation Automation system.


Control includes local and remote control. Local control consists of actions the control device can logically take by itself, for example bay interlocking, switching sequences and synchronizing check. Human intervention is limited and the risk of human error is greatly reduced.

Local control should also continue to function even without the support of the rest of the Substation Automation system. Commands can be given directly to the remote controlled devices, for example open or close a circuit breaker. Relay settings can be changed via the system, and requests for certain information can be initiated form the SCADA station(s). This eliminates the need for personnel to go to the substation to perform switching operations, and switching actions can be performed much faster, which is a tremendous advantage in emergency situations.

A safer working environment is created for personnel, and huge production losses may be prevented. In addition, the operator or engineer at the SCADA terminal has a holistic overview of what is happening in the power network throughout the plant or factory, improving the quality of decision-making.

[For further description, please see the next parts of this series (Substation Automation)]


First of all let us introduce with some common elements of a circuit. It is a most common problem that an Electrical Engineer can understand these facts, but could not able to give proper definition of these. So, let see how to define them properly.

Figure 1: An electric network showing nodes, branches, elements and loops.

Electric Network A connection of various circuit elements can be termed as an electric network. The circuit diagram shown in Figure 1 is an electric network.

Electric Circuit A connection of various circuit elements of an electric network forming a closed path is called an electric circuit. The closed path is commonly termed as either loop or mesh. In Figure 1, meshes BDEB, ABCA and BCDB are electric circuits because they form a closed path. In general, all circuits are networks but not all networks are circuits.

Node A connection point of several circuit elements is termed as a node. For instance, A, B, C, D and E are five nodes in the electric network of Figure 1. Please note that there is no element connected between nodes A and C and therefore can be regarded as a single node.

Branch The path in an electric network between two nodes is called a branch. AB, BE, BD, BC, CD and DE are six branches in the network of Figure 1.

Now, come to the analytical part of circuit. There are three primary laws of solving a DC circuit: Ohm’s Law, Kirchoff’s Voltage Law (KVL) and Kirchoff’s Current Law (KCL). We all already have much better knowledge about Ohm’s Law, which is “V = IR”. It does not mean that we don’t know about KVL and KCL. Obviously we do, but I just want to re-install these in your mind with definitions and applications.

Kirchoff’s Voltage Law (KVL):

“The sum of all the voltages (rises and drops) around a closed loop is equal to zero”

In other words, the algebraic sum of all voltage rises is equal to the algebraic sum of all the voltage drops around a closed loop. In figure 1, consider mesh BEDB, then according to KVL, V3 = V4 + V5

Example: In each of the circuit diagram in Figure 2, write the mesh equations using KVL.

Figure 2: Circuit diagrams to demonstrate the application of KVL in the above example.

Figure 2(a) contains a single loop hence a single current, is flowing around it. Therefore a single equation will result as given below,

Vs = IR1+IR2  ……………………………………………………………… (1.1)

If Vs, R1, R2 are known, then I can be found.

Figure 2(b) contains two meshes with currents I1 and I2 hence there will be two equations as shown below. Note that the branch containing R2 is common to both meshes with currents I1 and I2 flowing in opposite directions.

Left Loop:       Vs = I1R1+(I1I2)R2

Vs = (R1+R2)I1R2I2     ……………………………. (1.2)

Right Loop:     0 = (I2I1)R2+I2R3

0 = –R2I1+(R2+R3)I2         …………………………..…. (1.3)

Given Vs, R1, R2 and R3, equations 1.2 and 1.2 can be solved simultaneously to evaluate I1 and I2.

For the figure 2(c), three equations need to be written as follows. Also note that there is no circuit element shared between loops 2 & 3 hence I2 and I3 are independent of each other.

Left Bottom Loop:   Vs = (I1-I3)R1+(I1-I2)R2
Vs = (R1+R2)I1-R2I2-R1I3       
…………………. (1.4)

Right Bottom Loop:    0 = (I2-I1)R2+I2R3
0 = -R2I1+(R2+R3)I2           
………………….. (1.5)

Upper Loop:     0 = (I3-I1)R1+I3R4
0 = -R1I1+(R1+R4)I3                     ..
….……………… (1.6)

If Vs and resistors’ values are known, the mesh currents can be evaluated by solving equations 1.4, 1.5 and 1.6 simultaneously.

Resistors in Series: Consider figure 3 with one voltage source and two resistors connected in series to form a single mesh with current I.

According to KVL, Vs = V1+V2

Using Ohm’s Law (V = IR),

IReq = IR1+IR2

Req = R1+R2        ………………………… (1.7)

Where, Req = combined or equivalent resistance of the series network. In general, for n number of serial resistors, Req is given by,

Req = R1+R2+R3+….+Rn           ………………………………….. (1.8)

Voltage Divider Rule (VDR): VDR provides a useful formula to determine the voltage across any resistor when two or more resistors are connected in series with a voltage source. In figure 3, the voltage across the individual resistors can be given in terms of the supply voltage and the magnitude of individual resistances as follows,

…………………………….. (1.9)

………………………………… (1.10)

In general, for n number of resistors connected in series, the voltage across the i th resistor can be specified as,

…………………………….. (1.11)

This is everything till now. Next, I’ll discuss about KCL and more circuit analysis methods.

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.


6.1. Introduction

Fuzzy logic is a powerful problem-solving methodology with a myriad of application in embedded control and information processing. Fuzzy provides a remarkable a sense, fuzzy logic resembles human decision making with its ability to work from approximate data and find precise solution. Unlinked classical logic which requires a deep understanding of a system, exact equations, and precise numeric values, fuzzy logic incorporation an alternative way of thinking, which allows modeling complex systems using a higher level of abstraction organizing from our knowledge and experience. Fuzzy logic allows expressing this knowledge with subjective concepts such as very hot, medium cold, and a long time which are mapped into exact numeric ranges. Fuzzy logic is recently finding wide in various applications which cover a variety of practical systems, such as the control of cement kilns, train operation, parking control of car, heat exchangers, and in many other system, such as home appliances, video cameras, elevators, aero space, etc [10, 14]. Nearly every application can potentially realize some of the benefits of fuzzy logic, such as performance simplicity lower cost, and productivity [9, 13]. Fuzzy logic was first introduced by Zadeh in 1965, whereas the first fuzzy logic controller was implemented by Mamdani in 1974 [10, 14].

In this work, different types of fuzzy based controllers are designed for an automatic generation control in single power system.

6.2. Basic Control of Fuzzy Logic

A fuzzy logic, unlike the crispy logic in Boolean theory that uses two logic (0 to 1) is a branch of logic that admits infinity logic level (from 0 to 1) to solve a problem that has uncertainties or imprecise situations. A variable in fuzzy logic has sets of values which are characterized by linguistic expressing, such a SMALL, MEDIUM, LARGE, etc. linguistic expressions are represented numerically by fuzzy sets. Even Fuzzy set is characterized by a membership function, which varies from 0 to1 .Although fuzzy theory deals with imprecise information; it is based on sound quantitative mathematical theory [8, 14].

Again fuzzy control is a process that is based on fuzzy logic and is normally characterized by if-THEN rules. A fuzzy control algorithm for a process control system embeds the intuition and experience of operator, designer and researcher. The control does not need accurate mathematical model of a plant, and therefore, it suits well to a process where the model is unknown or ill defined. The fuzzy control also works well for complex nonlinear multi dimensional system, system with parameter variation problem, or where the sensor signals are not precise. The fuzzy control is basically nonlinear and adaptive in nature, giving robust performance under parameter variation and load disturbance effect [8, 14].

To gain an in depth on fuzzy logic, the following terms the need to be studied.

  1. Degree of Membership (µ): It is a number between (0 to 1) that expresses the confidence that a given element belongs to a fuzzy set.
  2. Fuzzy Set (fuzzy subset): This is defined as a set consisting of elements having degree of membership varying between 0 (member) to 1 (full member). It is usually characterized by a membership function, and associated with linguistic valuably characterized by a membership function, and associated with linguistic values, such as SMALL, MEDIUM, and LARGE etc.
  3. Membership Function: It is a function that defines a fuzzy subset, by associating every element in the set with a number between 0 and 1.
  4. Linguistic Variables: Any variable (such as temperature, speed, etc) whose values are defined by language, such as LAGE, SMALL, etc. is called a linguistic variable or fuzzy variable.
  5. Universe of Discourse: It is the range of values associated with a fuzzy variable.

6.3. Advantages of Fuzzy Logic Controller (FLC)

When compared to classical control theory a fuzzy logic approach to control offers the following advantages [14].

  1. It can be used in systems which cannot be easily modeled mathematically in particular, systems with non linear responses are difficult to analyze may respond to a fuzzy control approach.
  2. It is inherently robust since it does not require precise, noise free inputs. The output control is a smooth control function despite a wide range of input variations.
  3. Since the fuzzy logic controller (FLC) processes user defines rules governing the target control system, it can be modified easily to improve or drastically alter system performance.
  4. Continuous variable may be represented by linguistic terms that are easier to understand, making the controller easier to implement and modify. For example, instead of using numeric values, temperature may be represented as “cold, cool warm, or hot”.
  5. Complex processes can often be controlled by relatively few logic rules, allowing a more understandable controller design and faster computation for real time applications.


6.4. Design of Fuzzy Logic Controller (FLC)

Fuzzy control is special form of knowledge-based control. In designing a fuzzy control system, the precise mathematical model of target plant is not needed. Only the relevant experiences and heuristics concerning the pant are utilized to from a set of fuzzy control rules. These are linguistic in nature and often use the simple cause- effect relationship to link a fuzzy partitioning of certain state-space of the plant with a fuzzy petitioning of the control action. The final control signal is generated by an appropriate defuzzifying process [15].

The reference signal and plant output, which are crisp values but non-fuzzy variables, must be fuzzified by a fuzzification procedure. Similarly, the fact that the controlled plant can not directly respond to FL controls accounts for the reason why the FL control signal generated by the fuzzy algorithm must be defuzzification before applied to control the actual plant. A rule base consists of a set of fuzzy rules. The data base contains the membership functions of the fuzzy subsets. A fuzzy rule may contain fuzzy variables and fuzzy subsets characterized by membership functions and conditional statement. The fuzzy control algorithm

Fig: 6.1 Block diagram of a typical close-loop fuzzy control system

6.4.1. Fuzzification:

The fuzzification procedure consists of finding appropriate membership function to describe crisp data. The membership functions for the fuzzy variables may have several shapes. The most popular choices for the shape of the membership function include, triangular trapezoidal, and bell-shaped functions. Among them, triangular membership function is the most economic one because of its minimal use of memory & efficiency, in terms of real time requirements, by the inference engine. To design the fuzzy controller in the work, the triangular membership functions for an input & output variables. The memberships functions are system dependent. The membership functions used for the fuzzy logic controller (FLC) design in this work. Usually, membership functions are determined by trail & error. In this work, the membership functions have been determined by trail & error method. The precise numerical values are obtained by measurements that are converted to membership values of the various linguistic variables. For the FLC controller the inputs are defined as:

Input 1: error =∆f=fnom– ft=et

Input 2: change in error=∆f2-∆f1=cet.

Usually, two input variables (error of the variable of interest for the control and change of error) are used fore fuzzy logic controller (FLC) design [18]. However, in this work, two input and single output are used for FLC design. The use of two input and single output variable makes the design of the controller very straightforward [9-13].

6.4.2. Fuzzy Rule Base:

The rule base is the heart of a fuzzy controller, since the control strategy used to control the closed-loop system is stored as a collection of control rules. The heuristic rules of the knowledge base are used to determine the fuzzy controller action.

Controller has 2 inputs et, and cet,and one output ut. Then a typical control rule has the form.

If et, is A and cet, is B then and uis C. …………………………(6.1)

Where A, B and C are linguistic terms, such as very low, very high and medium, etc. the control rule (6.1) is composed of two parts: ‘if’ part and the ‘then’ part. The ‘if’ part is the input to the controller and the ‘then’ part is the output of the controller. The ‘if ’part is called the premise (or antecedent or condition) and the ‘then’ part is called the consequence (or action).

Though it is possible to derive a membership value for this variable in many possible ways, one of the rules that has been chosen is,

µ(et,cet.)=min[µ(et.),µ(cet.)] ……………………………(6.2)

The fuzzy rules are system dependent. The most usual source fore constructing linguistic control rules is human experts. However, it is often the case that no expert is available.

Therefore trial and error method is usually used to find fuzzy control rules.

6.4.3. Fuzzy Inference

The basic operation of the inference engine is that it infers i.e. it is deduces (from evidence or data) a logical conclusion. Let us consider the following example describe by the logical rule known as modus ponens:

Premise 1: If and animal is a cat, then it has four legs.

Premise 2: My pet is a cat.

Conclusion: My pet has four legs.

Here, premise 1 is the rule base, 2 are the fact (or the data) and the conclusion is the consequence. Actually, the inference engine is a program which uses the rule base and the input data of the controller to draw the conclusion, very much in the manner shown by the above the modus ponens rule [9]. The conclusion of the inference engine is the fuzzy output of the controller, which subsequently becomes the input to the defuzzification interface.

Usually, two types of fuzzy inference are available in the literature. One is Takagi-Sugeno-Kang (TSK) fuzzy inference and other is Mamdani type fuzzy inference. For the inference mechanism of the fuzzy logic controller in this work, Mamdanis method [8] has been utilized. Compared to other methods, the advantages of Mamdant’s are:

  1. Calculation time is very short;
  2. Inference mechanism is very simple;
  3. Parameters can be changed easily.

6.4.4. Defuzzification

In this last operation, the fuzzy conclusion of the inference engine is defuzzified, i.e. it is converted into a crisp signal .this last signal is the final product of the FLC which is of course, the crisp control single to the process. There are several methods for defuzzification  available in the fuzzy interaction, such as Center -of -Area  or  Center -of –Gravity defuzzification Center -of –Sums defuzzification Center -of –Largest-Area, defuzzification  first- of- Maxima defuzzification Middle-of- Maxim defuzzification, Height defuzzification etc. the Center -of -Area  or  Center -of –Gravity method which is implemented in this work to determine the output crisp value. The well-known center of gravity defuzzification method is given by the following expression:

……………………………………….. (6.3)

Where, Z is the crispy output function and symbols have already been defined in the previous section. The membership function, knowledge base and method of defuzzification essentially determine the controller performance [1].

6.5. The Proposed Fuzzy Logic Control

Fig (6.2): fuzzy controller for the SMES unit

The proposed controller along with SMES unite is shown in fig (6.2), the ∆f are the input to the corresponding fuzzy controllers. The output of the Fuzzy Frequency Controller (FFC) is KI. ∆f is changing with changing KI.

Unlikely, the conventional controller, in the proposed method  the change in  ∆f the signal are also consider as explained below. In general, the input variables considered in the fuzzy rule base are:



Where E(k) is the loop error (present deviation), CE(k) is the change in loop error, R(k) is the reference signal, C(k) is the present signal, and k is the sampling interval.

The structure of a general rule can be given as:

IF E(K) is X AND CE(K) is Y THEN U(K) is Z

The variable can be expressed as per unit quantities as follows:

e(p.u) = E(k)/GE

ce(p.u) = E(k)/GCE

Where, GE and GCE are the respective gain factor of the controllers. Fig (6.4) shows the membership function of e(p.u), ce(p.u) and their respective output variable.

Note that the fuzzy the subsets for output variable have an asymmetrical shape causing more crowding near the origin. This allows precision control near the steady state operating point. Also large number of subsets is selected to obtain accurate control.

Table 1 gives the rule base matrix for frequency control can be summarized as follows:

  1. Sample the reference frequency f* and the actual frequency fact
  2. Compute error (e) and change of error (ce) in their respective p.u. values are as follows:



  1. Identify the interval indices for e (p.u) and ce(p.u) respectably, by the comparison method.
  2. Compute the degree of membership of e (p.u) and ce (p.u) for the relevant fuzzy subsets.
  3. Identify the four valid rules in table 1 and calculate the degree of membership µRi MIN operation.

Fig (6.4): Membership function of fuzzy frequency control (FFC)

            6. Integrating gain KI for each rule from table

           7. Calculate the crisp value of KI higher defuzzification  method as follow:

………………………. (6.4)

Table 1: Rule base of frequency control

The fuzzy sets of the each linguistic variable adopted in this work are: NVB: Negative very Big, NB: Negative Big; NM: Negative Medium NS: Negative small; NVS: Negative Very small Z: Zero; PVS: Positive Very small; PS: Positive small; PM: Positive Medium PB: Positive Big. PVB: Positive Very Big. Each fuzzy set has a triangular shape and is determined by three parameters consequently, if a linguistic variable is supported by N fuzzy sets, then the total number of parameters needed is N+1, considering that the zero.

6.6. Simulation Results and Discussion:

In Order to Demonstrate the beneficial damping effect of the proposed fuzzy set theory based-gain scheduling of automatic generation controller in a single area power system, computer simulations results based on system non-liner differential equations have been carried out for different load changes. The differential equations have been solved by using MATLAB environment. Figure 6.5, 6.6 and Fig 6.7 depict the simulation results with & without considering the load changes of ∆PL =0.01, 0.015, and 0.02 P.U M W respectively.

The system frequency is not found satisfactory with fixed integral gain, KI. With the addition of proposed schemes, the damping is improved significantly. The generator frequency is almost diminished with the proposed mode of control. It is clearly shown that considering governor is greatly minimized error. Moreover, this eventually reduces the settling time of the speed for both cases, which in turn brings the FGPI controller in more advantageous position for subsequent use. The frequency deviation is diminished with the Fuzzy controller. The system frequency deviation is almost minimized. The settling time becomes smaller.

Fig: 6.5 Frequency deviation and variation of KI of a typical single area power system with the step load ΔPL =0.01(p.u)

Fig: 6.6 Frequency deviation and variation of KI of a typical single area power system with the step load ΔPL =0.015(p.u)

Fig: 6.7 Frequency deviation and variation of KI of a typical single area power system with the step load ΔPL =0.02(p.u)

6.7. Effect of Variable Load

Fig: 6.8. Frequency deviation for single area power system with variable load change

Table: 2 Table for frequency deviation for single area power system with variable load change. In the table 2 that get from the fig 6.8., Load change from .005(pu) to 0.25(pu), then maximum deviation, settling time, overshoot change. Above table settling time is nearly 4.5(s).

6.8. Conclusion

Fuzzy logic is getting increasing acceptance in control application over the past few years. In this work the fuzzy logic control (FLC) is used for the designed of some controllers for an Automatic Generator Control (AGC) of single and single-area power system. Again in order to see how well, robust and effect designed of the fuzzy controllers are and its performance compare to that of the control. This chapter presents overview of fuzzy controller, design of fuzzy logic control.

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):