 Open Access
 Total Downloads : 23
 Authors : Gursharan Kaur, Amarjeet Kaur
 Paper ID : IJERTCONV4IS15012
 Volume & Issue : ACMEE – 2016 (Volume 4 – Issue 15)
 Published (First Online): 24042018
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Fuzzy Logic based Automatic Generation Control of Interconnected Power System
Gursharan Kaur,
Student, M tech, Power Engineering, BBSBEC
Amarjeet Kaur, Assistant professor,
Electrical Engineering Department, BBSBEC
Abstract – The main role of automatic generation control (AGC) is to maintain system frequency and tie line flow at their scheduled values during normal period in an interconnected system. This paper presents the automatic generation control of an interconnected two area thermal reheat system using conventional and fuzzy logic controller. Change in generation in both areas and change in overall frequency of system is examined using MATLAB SIMULINK. Area control error (ACE) is measured and results of conventional methods and fuzzy logic are compared. The aim of proposed controller is to restore the system frequency to its nominal value in smallest possible time whenever there is any change in load demand.
Keywords – Automatic generation control, Area control error, Fuzzy logic controller, Load frequency control. Generation Rate Constraints

INTRODUCTION
Power systems are used to convert natural energy into electric power and are interconnected to provide secure and economical operations. They transmit electricity to factories and houses to satisfy all kinds of power needs. To optimize the performance of electrical equipment, it is important to ensure the quality of the electric power. During the transmission, both the active power balance and the reactive power balance must be maintained between generating and utilizing the AC power. Those two balances correspond to two equilibrium points: frequency and voltage. A good quality of the electric power system requires both the frequency and voltage to remain at standard values during operation. It will be impossible to maintain the balances of both the active and reactive powers without control. As a result of imbalance, the frequency and voltage levels vary with the changing load. Thus a control system is essential to cancel the effects of the random load changes and to keep the frequency and voltage at the standard values. The control problem of the frequency and voltage can be decoupled. The frequency is dependent on the active power while the voltage is dependent on the reactive power and the active power and frequency control is referred to as load frequency control (LFC). [1]
When area load changes and abnormal fault conditions occurs the system frequency and scheduled power interchange between them which is to required to be controlled in order to normalize the system frequency and tie line power interchange thus meeting the power demands. For this the Automatic Generation Control (AGC) is one of the essential control that considers the sudden and small load perturbations. The analysis and design of Automatic Generation Control (AGC) system of individual generator eventually controlling large interconnection between different control areas plays a vital
role in automation of power system. The purpose of AGC is to maintain system frequency very close to a specified nominal value to maintain generation of individual units at the most economical value. Many investigations in the area of Load Frequency Control (LFC) problem of interconnected power system using different controllers have been carried out successfully among which the most widely employed are the conventional proportional integral (PI) and proportional integral derivative (PID) controllers. [2]

PROBLEM FORMULATION
An interconnected power system is considered in the present study to design the fuzzy controller. The system comprises of twoarea thermal system provided with supplementary controllers. A step load perturbation of nominal loading has been considered in area1. Small perturbation transfer function block diagram of a twoarea reheat thermal system is shown in Fig. 1 [2]. Here, the tieline power deviations can be assumed as an additional power disturbance to any area k. For the load frequency control, the proportional integral controller is implemented.
Fig.1: Transfer function model of twoarea reheat thermal system
Load Frequency Control with GRC: Load frequency control problem discussed so far does not consider the effect of
the restrictions on the rate of change of power generation. In power systems having steam plants, power generation can change only at a specified maximum rate. The generation rate (from safety considerations of the equipment) for reheat units is quite low. Most of the reheat units have a generation rate around 3% / minute. Some have a generation rate between 5 to 10% / minute. If these constraints are not considered, system is likely to chase large momentary disturbances. This results in undue wear and tear of the controller. Several methods have been proposed to consider the effect of GRC for the design of automatic generation controllers. When GRC is considered, the system dynamic model becomes nonlinear, and linear control techniques cannot be applied for the optimization of the controller setting [7,8].
If the generation rates are included in the state vector, the system order will be altered. Instead of augmenting them, while solving the state equations, it may be verified at each step, if GRCs are violated. Another way of considering GRCs for both areas is to add limiters to the governors as shown in Fig.2.
1/ R1
1/ R1
_
_
1/
1/
+
_
Fig.2: Governor Model with GRC

OBJECTIVES
In this work, a control strategy has been used to remove ar (

Identify the variables (inputs, states, and outputs) of the plant.

Partition the universe of discourse or the interval spanned by each variable into a number of fuzzy subsets, assigning each a linguistic label (subsets include all the elements in the universe).

Assign or determine a membership function for each fuzzy subset.

Assign the fuzzy relationships between the inputs or states fuzzy subsets on the one hand and the outputs fuzzy subsets on the other hand, thus forming the rulebase.

Choose appropriate scaling factors for the input and output variables in order to normalize the variables to the [0, 1] or the [1, 1] interval.

Fuzzify the inputs to the controller.

Use fuzzy approximate reasoning to infer the output contributed from each rule.

Aggregate the fuzzy outputs recommended by each rule.

Apply defuzzification to form a crisp output.
Fig.3: A simple fuzzy logic control system block diagram
The functions of the above modules are described below.

The Fuzzification:
ea control error ACE) and to maintain the tieline power
flow at their scheduled values during normal period in an interconnected power system.
This paper presents the automatic generation control (AGC) of an interconnected two area system. The inputs of the proposed Fuzzy controllers are area control error (ACE), and change of frequency (F).


METHODOLOGY

Fuzzy Logic Controller
Firstgeneration (nonadaptive) simple fuzzy controllers can generally be depicted by a block diagram as shown in Fig.

The knowledgebase module in Fig. 3 contains knowledge about all the input and output fuzzy partitions. It will include the term set and the corresponding membership functions defining the input variables to the fuzzy rulebase system and the output variables, or cntrol actions, to the plant under control[5,11]. The steps in designing a simple fuzzy control system are as follows:

Measure the values of input variables

Performs a scale mapping that transforms the range of values of input variables into corresponding universe of discourse.

Performs the function of Fuzzification that converts input into suitable linguistic values, which may be, viewed labels of fuzzy sets.



The Knowledge Base:
It consists of data base and linguistic control rule base.

The database provides necessary definitions, which are used to define linguistic control rules and fuzzy data, manipulation in an, FLC.

The rule base characterizes the control goals and control policy of the domain experts by means of set of linguistic control rules.


The Decision Making Logic:
It is the kernel of an FLC; it has the capability of simulating human decision making based on fuzzy concepts and of inferring fuzzy control actions employing fuzzy implication and the rules of inference in fuzzy logic.

The Defuzzification:

A scale mapping which converts the range of values of input variables into corresponding universe of discourse.

Defuzzification, which yields a nonfuzzy, control action from an inferred fuzzy control action.
B. System Model
A twoarea reheat type thermal system consists of two single areas connected through a power line called the tieline as shown in Fig.1. The simulink model of the system considered is depicted in Fig.4 below. Each area feeds its user pool and tieline allows the electric power to flow between areas [2]
Since both areas are tied together, a load agitation in one area affects the output frequencies of both areas as well as the power flow on the tieline. In the above Fig., reheat and generation rate constraints (GRC) are considered. GRC inclusion increases the system nonlinearity much more and if in that condition, the controller used responds more precisely to have more stable response, then linearization is achieved in a nonlinear environment.
Investigations have been carried out on an interconnected two area thermal system provided with reheat type of turbine with GRC. For carrying out AGC for the two area system conventional and fuzzy logic controller is used in and change in generation in both the areas and change in overall error of the system is examined using MATLAB SIMULINK. The responses have been studied for f1, f2, Ptie. Area Control Error (ACE) is measured and results of the conventional method and the fuzzy logic are compared. The aim of the proposed controller is to restore the change in error to its nominal value in the smallest possible time whenever there is any change in the load demand etc.
deviation and the magnitude of the appropriate control action. The membership functions used are as:
NL = negative large NS = negative small ZE = zero
PS = positive small PL = positive large
The rule table thus formed for the above membership functions using the inputs is drawn out to be as follows;
Table 1: Fuzzy Rule Table
ACE
d/dt(F)
NL
NS
ZE
PS
PL
NL
NL
NL
NS
NS
ZE
NS
NL
NL
NS
ZE
ZE
ZE
NS
NS
ZE
PS
PS
PS
ZE
PS
PS
PL
PL
PL
ZE
ZE
PS
PL
PL


RESULTS
The simulink model shown in Fig.4 using the fuzzy logic controller at the input to study out the system response gives the outputs of various areas as shown in figures below.
Mw
Time(sec)
Fig.5: Tieline power deviations with Fuzzy Logic controller
Fig. 4. Simulink model with Fuzzy Logic controller
The control rules are formulated in linguistic terms using fuzzy sets to describe the magnitude of error, the frequency
The power deviations for tie line are shown in Fig.5. The response shows that with the power interchange between the systems the tie line power deviating achieves a steady state at about 35 seconds.
The output response of the system when fuzzy controller is used is shown in figure above. For the power interchange occurring the output of the system under the act of GRC settles
down at about 35 seconds with the first overshoot at 0.01Hz and undershoot occurring at 0.075Hz.
Hz
Time(sec)
Fig. 6: Frequency deviations in Area1 with Fuzzy Logic controller
Hz
Time(sec)
Fig. 7: Frequency deviations in Area2 with Fuzzy Logic controller
The output response of the system for area 2 when fuzzy controller is used is shown in figure above. For the power interchange occurring the output of the system under the act of GRC settles down at about 35 seconds with the first overshoot at 0.0012Hz and undershoot occurring at 0.1059Hz.
DISCUSSION ON RESULTS
In conventional controllers I(Integral), and PID(Proportional Integral Derivative), PID(Proportional Integral Derivative) controller is better than I(Integral) controller. Because PID controllers over shoot and settling time is much smaller than Integral and Proportional Integral controller. It is observed that the PID controller gives the better results among all the conventional control techniques but when compared with results of Fuzzy controller, it is observed that the Fuzzy Control gives better results than conventional control techniques .The advantage of Fuzzy controller is that it can handle the system non linearity and at the same time the Fuzzy controller is faster than conventional controllers and gives reduced oscillations and settling time.

CONCLUSION
The different conventional controllers and Fuzzy controller have been implemented for the AGC of twoarea power system. It is clear from the results that the performance of PID controller is better than an Integral controller .In case of the PID controller over shoot and settling time is much smaller as compared to Integral controller .The performance of Fuzzy controller has been compared with that of conventional Integral, proportional integral(PI) controller as well as Proportional Integral Derivatives (PID).The Fuzzy controller is faster than conventional controllers and gives reduced oscillations and settling time. This dissertation concludes that the Fuzzy controller is the best out of all the controllers implemented and gives good dynamic performance.

FUTURE SCOPE
The combined use of Fuzzy logic and other intelligent techniques may prove to be better in AGC problems.

APPENDIX
The nominal system parameters are: f = 50 Hz, Rk = 2.4 Hz / Unit, Tg = 0.08 Sec, Tr = 10.0 Sec, Hk = 5 .0 Sec, Kr= 0.5, Tf = 0.3 Sec, 2Tki = 0.05 Mw, Dk= 0.00833 pu Mw/Hz
REFERENCES

P. Kundur, Power System Stability and Control, New York, McGrawHill, 1994.

Gayadhar Panda, Sidharth Panda and Cemal Ardil, Automatic Generation Control of Interconnected Power System with Generation Rate Constraints by Hybrid Neuro Fuzzy Approach, World Academy of Science, Engineering and Technology, vol.5, no.1,pp. 543548, December 2009.

O.I. Elgerd, Electric Energy Systems Theory An Introduction, McGrawHill, 1982.

B.R. Gupta, Generation of Electric Energy, Eurasia publishing house LTD, 2002.

G.A. Chown and R. C. Hartman, Design and Experience with a Fuzzy Logic Controller for Automatic GenerationControl (AGC), IEEE Transactions on Power System, vol.13, no.3, pp. 965970, August 1998.

Bjorn H. Bakken and Ove S. Grande, Automatic Generation Control in a Deregulated Power System, IEEE Transactions on Power Systems, vol.13, no.4, pp. 1401 1406, November 1998.

Ignacio Egido, Fidel FernandezBernal, Luis Rouco, Eloisa Porras, and Angel Saiz Chicharro, Modeling of Thermal Generating Units for Automatic Generation Control Purposes, IEEE Transactions on Control Systems Technology, vol.12, no.1, pp. 205210, January 2004.

Barjeev Tyagi and S. C. Srivastava, A Decentralized Automatic Generation Control Scheme for Competitive Electricity Markets, IEEE Transactions on Power Systems, vol. 21, no.1, pp. 312320, February 2006.

Janardan Nanda, Ashish Mangla, and Sanjay Suri, Some New Findings on Automatic Generation Control of an Interconnected Hydrothermal System With Conventional Controllers, IEEE Transactions on Energy Conversion, vol.21, no.1, pp. 187194, March 2006.

George Gross, Fellow, IEEE and Jeong Woo Lee, Analysis of Load Frequency Control Performance Assessment Criteria, IEEE Transactions on Power Systems, vol. 16, no. 3, pp. 520525, August 2001.

A. Mangla and J. Nanda, Automatic Generation Control of an Interconnected Hydro Thermal System Using Conventional Integral and Fuzzy Logic Controller, International Conference on Electrical Utility, Deregulation, Restructuring, and Power Technologies, vol.15, no.4, pp. 372377, April 2004.

Li Pingkang Beijing and Ma Yongzhen, Some New Concepts in Modem Automatic Generation Control Realization , IEEE Transactions on Power Systems, vol.10, no.3, pp. 12321236, December 1998.

J. Nanda and Manoranjan Parida, Automatic Generation Control of a Hydro Thermal System in Deregulated Environment, IEEE Transactions on Energy Conversion, vol.28, no.3, pp. 942947, July 2001.

Noureddine Bekhouche and Ali Feliachi, Decenteralized estimation for the automatic generation control problem in power systems, IEEE Transactions on Power Systems, vol.20, no.5, pp. 632621, June 1992.

M. G. Rabbani, M. F. Hossain, M. R. I. Sheikh and M. S. Anower
,Application of fuzzy controlled SMES unit in Automatic Generation Control, 3rd ICECE 2004, pp. 2830, December 2004.

J. Nanda, M. Parida, A. Kalam, Automatic generation control of a multiarea power system with conventional integral controllers, IEEE Transactions on Power System, vol.16, no.4, pp. 10101018, December 2002.

N. Jaleeli, L. VanSlyck, D. Ewart, L.Fink and A. Hoffmann, Understanding Automatic Generation Control, IEEE Transactions on Power Systems, vol.7, no.6, pp. 11061122, August 1992.