1. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
6545(Print), ISSN 0976 – 6553(Online) Volume 4, Issue 4, July-August (2013), © IAEME
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APPLICATION OF GENETIC ALGORITHM AND NEURO FUZZY
CONTROL TECHNIQUES FOR AUTOMATIC GENERATION CONTROL
OF INTERCONNECTED POWER SYSTEMS AND TO STUDY THE
DEVELOPMENT OF A HYBRID NEURO FUZZY CONTROL APPROACH
J.Srinu Naick Dr K. Chandra sekar
H.O.D/E.E.E H.O.D/E.E.E
PNC and Vijay Institute of Engg. & Tech RVR & JC Engg. College
Guntur. A.P- India. Guntur. A.P-India.
ABSTRACT
Extensive work has been reported in literature on automatic generation and control (AGC) of
power systems. Frequency changes are recognized as a direct consequence of imbalance between
load and power generation. The main function of AGC is to shift the operating point in order that an
equilibrium is reestablished, whenever an imbalance occurs between generation and load. AGC
consists of secondary frequency controls and maintains the scheduled frequency during abnormal
operating conditions. Several control techniques have been reported to achieve improved
performance of interconnected power systems. Application of Generic algorithms (GA) is a very
useful tool for tuning the control parameters of AGC systems. The genetic algorithm method is
overviewed. GA is a numerical optimization algorithm capable of being applied to wide range of
optimization problems that guarantees the survival of the fittest. Literature reported application of
simplified models for interconnected power systems using GA. Too much of simplification in
frequency response models lead to wide range of optimal solutions, which cannot be used in practice.
To address this issue literature reported application of fuzzy logic control for AGC which is a
satisfactory alternative to above conventional control methodology. The fuzzy logic approach can be
effectively used for complex processes to solve wide range of control problems in power systems.
This system basically uses a learning algorithm derived from neural networks theory. However this
method cannot handle the system non-linearities and is a slow processing technique.
An attempt is made in this paper to design and develop a hybrid neuro fuzzy approach which
is a fusion of neural network and fuzzy logic. This approach can handle systems with non-linearities
and at the same time the proposed approach is faster than the conventional controllers.
INTERNATIONAL JOURNAL OF ELECTRICAL ENGINEERING &
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ISSN 0976 – 6545(Print)
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Volume 4, Issue 4, July-August (2013), pp. 62-66
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2. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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The major contribution of the present work is to make a comparison on the application,
merits and demerits of the conventional GA and fuzzy logic approaches and to study the
development of a hybrid neuro fuzzy control approach addressing the limitations present in the
conventional approaches.
It has been concluded that the hybrid neuro fuzzy control approach can effectively handle
systems with non-linearities and the processing speed is higher than conventional approaches.
Keywords: AGC, imbalance, Genetic Algorithms, nonlinearity, Fuzzy logic.
1.0 INTRODUCTION
In general the effective functioning of any power system is affected by the imbalance
between generation and load. This imbalance often results frequency shifting from rated value and
has to be reset using various control approaches[1]. The process of identifying the imbalance and
shifting the operating point is called as automatic generation control (AGC). Normally the AGC
systems operate on secondary frequency controls [2]. The primary frequency controls are achieved
through the system governor mechanism. The AGC systems maintain secondary frequency controls
during abnormal operating conditions [3]. The primary function of AGC is to adjust the generator set
point automatically so that the mismatch between load and generation is taken care of. Hence a
strategic design of AGC system assumes importance in power generation systems and needs
extensive research work to be undertaken [4]. The area control error (ACE), namely the quantum of
mismatch between generation and load is obtained through the AGC system, which dynamically
controls and adjusts the operating point. Several control strategies using genetic algorithms (GA) and
neuro fuzzy algorithms are developed to achieve the control. All these strategies aim at zero ACE
signals. A detailed analysis of the basic constraints present in the physical system dynamics is to be
made, before attempting to develop AGC [5]. An important physical constraint, to be considered in
thermal and mechanical factors is the generation unit [6]. Another important issue to be addressed in
the design of AGC is to consider the time lag between detection and response action of the control
system [7,8].
Genetic algorithms (GA) are a numerical optimization algorithm which can be applied to
wide range of optimization problems that guarantee the survival of the fittest [9]. Literature indicated
that though the approach is effective, it has inherent limitations in giving wide range of optimal
solutions, which cannot be used in practice. To address this issue literature reported application of
fuzzy logic and neuro fuzzy logic approach which offered a satisfactory solution. However this
approach cannot handle system non-linearities and is a slow processing technique especially in
deregulated power systems.
The work proposed in this paper attempts to identified the limitations in conventional
methods and to propose a hybrid neuro fuzzy approach, which combines neural network and fuzzy
logic.
2.0 LITERATURE REVIEW
Work related to AGC of power systems is reviewed from literature. Bevrani etal [10] made
extensive research work on feasibility of regional frequency based emergency control plans. Kumar
etal [11]. Published their work on recent philosophies AGC strategies in power systems. Jalecli etal
[12] made pioneering work on AGC of hydrothermal systems in a deregulated environment. Stagetal
[14] applied computer capabilities for AGC issues. Donde etal [15] worked on tuning of PID
controllers with fuzzy logic. Venkateswarn etal [17]. Worked on load frequency control using output
feed backs. Misbra etal [18] published their work on development and implementation of a fuzzy

3. International Journal of Electrical Engineering and Technology (IJEET), ISSN 0976 –
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logic based constant speed DC drives. Chown etal [19] made pioneering work on design of fuzzy
logic control tools for AGC. Jaleeli etal [20] worked on understanding automatic generation of
power.
3.0 PRESENT WORK
Application G.A approach of AGC is reviewed and its limitations are noted. Similarly the
merit of neural fuzzy logic approach of GA approach is reviewed. The limitation of neuro fuzzy
logic approach is also reviewed.
Discrete references are seen in literature on the application of hybrid neuro –fuzzy logic
approach (HNF). In recent times this method gained considerable importance inview of their user
friendly character in the areas of power generation control, pattern recognition, image processing,
image denoising, image mining etc. Based on the expected outcome, the hybrid neuro fuzzy logic
approaches are proposed. These hybrid neuro logic results from a fusion of neural networks and
fuzzy logic.
The main elements of a hybrid neuro fuzzy logic controller are i) fuzzifier, ii) rules consisting
of “if” and “then”, and iii) defuzzifier.
3.1 Fuzzifier
To start with the variables governing the dynamic performance of the system are considered
as inputs to the proposed controller. Numerical values are replaced by linguistic variables. This
process is nothing but fuzzification. The input variables include state errors, state error derivatives,
integral etc. In the particular application of AGC of power systems, the area control error (ACE) and
its time variant derivative d (ACE) / dt are chosen as the input parameters. At this stage a
membership function is defined as a graphical representation of magnitude of participation (its
effect) of each input. A trapezoidal membership function is proposed in the current work. If required
the number of memberships can be more than unity. In fact the larger the membership units, the
better will be the quality of control. Equality the more the membership, the more will be time of
processing. Hence a compromise is made. The fuzzified linguistics are chosen as negative big (NB),
negative small (NS), zero (NL), positive big(PB) and positive small (PS).
3.2 “if” and “then” rules
The above rule statements can be as described. If error is Ei and change in error is E then
output is 0. Hence “if”, part is concerned with process state interms of fuzzy proportions.
“Then” part of the rule describes the control output interms of logical combination of fuzzy
propositions. According to above methodology a rule table for fuzzy controller can be drafted.
3.3 Defuzzification
Defuzzification is the reverse process of fuzzification. While the controllers output is interms
of linguistic variables. These variables are converted into crisp outputs using centre of gravity
method. It obtains the centre of area occupied by the fuzzy sets and is grow by
X=EU(x) xdx/U(x)dx.
As a case study the AGC of an interconnected system in a deregulated environment is
considered. The three steps namely fuzzition “if and then rules” and defuzzification are considered
and the output is analyzed. The result of current study on the face of it, indicated that there is slight
improvement in performance of the system.

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4.0 RESULTS AND DISCUSSIONS
1. The application of GA for AGC consisted of defining an objective function J which attempts to
bring frequency to the nominal value for each of the area in an independent manner.
2. Simplified models are used in literature for interconnected power systems without any constraints
which resulted in a wide range of optimal solutions.
3. Neuro fuzzy logic control applies fuzzy logic theory, and is an excellent alternative for
conventional control routes. This approach can handle processes which are too complex for analysis
using conventional methods.
4. The reliability of fuzzy logic make fuzzy logic controllers useful for solving a wide range of
control problems in power systems.
5. The important constituents of a hybrid fuzzy logic controller are fuzzifier, defuzzifier and
inference engine.
6. The input signals for any AGC device are the variables connected with the system dynamic
performance.
7. MATLAB based adaptive net works which are functionally equivalent to fuzzy inference systems
are reviewed.
8. A companion of the developed hybrid system with conventional system is made.
5.0 CONCLUSIONS
1. The proposed hybrid neuro fuzzy approach has improved dynamic response and works faster than
conventional systems.
2. There is a slight improvement in the performance of the controller device compared to
conventional systems.
3. In case of GA approach the undershot or overshot of system governor delays, the control system
and cannot regain the match between frequency and load. In such cases the system loses its
credibility.
4. Further there is a slight improvement in the performance of the system using hybrid neuro fuzzic
approach.
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