Improve the Electric Power Generation Issue by Cognitive Thinking
ASHISH DHAMANDA
Electrical Engineering Department,
Gurukula Kangri (Deemed to be University) Haridwar, Uttarakhand,
INDIA
Abstract: - The enormous increase in power demand due to the prevalent heat wave, the worst power shortage
in the last decades, and lack of sufficient fossil fuels like natural gas, coal, oil, etc. has attracted the attention of
researchers worldwide. One of the main reasons behind the problem is the mismanagement of proper regulation
of the electrical power generation unit. This paper looks at cognitive thinking to address and improve this issue
by noting that it is the process of gaining knowledge and understanding through thought, experience, and the
senses that have enabled researchers to infer cognitive processes. Highly controlled and rigorous methods of
study have always been employed to enable the work. Thermal energy power plants have been taken as the
source of power generation and genetic algorithm (GA), fuzzy, and PID (Proportional, Integral, Derivative)
controllers are used. All these controllers handle and control sudden changes in load frequency and power. For
better and more effective results of the system, combined feedback has been obtained with the help of
MATLAB Simulink software. The results obtained from the combined feedback are tabulated, which shows
that all the controllers improve the electrical power generation issue by modulating the changes in load
frequency and power, but the GA controller produces effective, efficient, and better results by adjusting to the
changes in less time. The use of this cognitive thinking of the controller helps in the proper management of
power demand which automatically improves and controls the power generation.
Key-Words: - Cognitive Thinking, Rampant Rise, Management, Genetic Algorithm, Fuzzy Controller, Power
Generation, Thermal Source.
Received: April 13, 2023. Revised: December 16, 2023. Accepted: December 27, 2023. Published: December 31, 2023.
1 Introduction
Amidst the exponential increase in electricity
demand worldwide, thermal power generation has a
central role to play in the effort to make electricity
supply and power generation technology even more
efficient. In most countries, these power plants are
used as base load power plants. The generated
electrical power can be input into the electricity grid
for use by society, this electricity must be managed
appropriately due to the constant variation of
demand by the consumer side. Cognitive thinking is
used for this purpose by implementing GA, fuzzy,
and PID controllers. Neisser, the father of the
cognitive approach, is the process that allows people
to focus on a specific stimulus in the environment,
pick up new things, synthesize information, and
integrate it with prior knowledge, which allows
people to take information through their senses.
Respond and interact with the world, involving
people in decision-making, problem-solving, and
higher reasoning. Cognitive science uses
experimental research methods to study mental
thinking and related processes such as decision-
making, amnesia, perception of attention, language,
and memory. This indicates that the human brain
works similarly to a computer and that the process
of acquiring knowledge and understanding through
thought, experience, and the senses has enabled
researchers to infer cognitive processes, [1], [2], [3],
[4], [5], [6], [7].
Thermal power plants, also called combustion
power plants, are powered by energy produced by
coal, natural gas, heating oil, as well as steam
boilers powered by biomass. The steam activates a
turbine which in turn drives an alternator to generate
electricity. A conceptual diagram of a thermal steam
power generation system is shown in Figure 1. This
shows the different parts of the steam power
generation process like; the steam boiler,
electrostatic, combustion turbine, alternator,
transformer, condenser, and cooling tower, [8], [9],
[10], [11], [12], [13].
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Ashish Dhamanda
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Fig. 1: Conceptual Diagram of Steam Power Generation
2 Issue Formulation
The constant fluctuation of load frequency and
associated power is the root cause of the electrical
power generation problem, this problem can be
formulated mathematically.
Equation of a sudden step deviation of power
demand ():
󰇛󰇜
(1)
The Load Power flow equation is;
Ptl =
 sin (δ1- δ2) (2)
In equation 2,  are magnitude of
voltage, δ1 ,and δ2 are the machine power angle, 
is the reactance of line. Incremental changes in
power angles (Δδ1 and Δδ2) when load demands
change.
The deviation in incremental power generation can
be expressed as here:
ΔPtl = 
 [cos (δ1- δ2) (Δδ1- Δδ2)] MW (3)
ΔPtl = T12 (δ1- δ2) (4)
Where T12 = 
 cos (δ1- δ2) MW/rad (5)
T12 is the stiffness coefficient or synchronizing
coefficient of the line.
The deviation in load power frequency is given
here:
󰇛󰇜|ΔPC(s)=0 = 

 
󰥟
-˟ 
(6)
󰇛󰇜|ΔPC(s)=0=-󰥟
 󰥟

󰇛
 󰇜 (7)
󰇛󰇜=  (s)
󰇛󰇜= - 
 󰇩

󰇪  (8)
Equation (8) indicates the load frequency
deviation (󰇛󰇜) for sudden step load ()
deviation, [14], [15], [16].
3 Solution with Cognitive Thinking
When load frequency and power are continuously
changing, PID, fuzzy controllers, and genetic
algorithms are employed as cognitive thinking
techniques to solve the problem of electrical power
generation. Here are the details of the implemented
controllers;
3.1 Genetic Algorithm
Natural selection, the mechanism that propels
biological evolution, provides the basis for both
limited and unconstrained optimization problems,
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DOI: 10.37394/23203.2023.18.64
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which can be solved using genetic algorithms. GA
continuously improves individual solutions for
many populations. GA controller flow chart shown
in Figure 2.
Fig. 2: Flow Chart of GA for Power Generation
Issue
GA parameters for the thermal system are given
below in Table 1, [17], [18]:
Table 1. Parameters of GA for System
GA Parameters
Thermal Power System
Fitness Function
@agma_asd
Variables
22
Population Size
32
Selection
Stochastic Uniform
Mutation
Constraint Dependent
Cross Over
Scattered
Bound Limit
Upper [0] and Lower [-5]
3.2 Fuzzy Controller
A mathematical framework created to let computers
distinguish between data that isn't true or false.
Something like the process of human reasoning. The
fuzzy controller consists of four main components,
the fuzzification interface, the knowledge base, the
inference mechanism, and the defuzzification
interface. The fuzzy controller's job is to determine
the action variable values based on observations of
the state variables of the controlled process shown
in Figure 3, [19], [20].
Fig. 3: Fuzzy Controller for Power Generation Issue
3.3 PID Controller
Proportional, Integral, and Derivative controllers
what are known as PID controllers. Its job is
essentially to take this error signal and perform three
different mathematical operations on it. The
controller can be shown mathematically in equation
9. The transfer function of the PID controller is:
󰇛󰇜
  (9)
Kp is proportional gain, Ki is an integral gain
and Kd is derivative gain, PID controller shown in
Figure 4, [21], [22], [23].
Fig. 4: PID Controller for Power Generation Issue
4 Result
In this paper cognitive thinking based GA, fuzzy
and PID controllers have been implemented to
improve the power generation issues such as
streamlining frequency and power deviations, the
result of frequency and power deviation from GA,
Fuzzy, and PID controllers is shown in Figure 5,
Figure 6, Figure 7 and Figure 8.
Fig. 5: Result of load frequency changes in system 1
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Fig. 6: Result of power changes in system 1
Fig. 7: Result of load frequency changes in system 2
Fig. 8: Result of power changes in system 2
The comparative result of settling time of load
frequency and power deviation are tabulated in
Table 2.
Table 2. Result of Solution of Electricity Problem
Controller
Power Changes
Settling Time (Sec)
System 1 System 2
GA
16 18
Fuzzy
22 27
PID
26 42
Table 2 demonstrates that, in comparison to
other controllers, the GA controller settles the
deviation in fewer seconds, providing a better
solution to the electrical power generation problem.
5 Conclusion
This paper shows improvements in the issue of
electrical energy generation using cognitive
thinking, which involves the ability to obtain
factual information that can be easily separated
from the social, emotional, and creative
development that underpins human perception,
learning, cognition, and experience and is related
to it. The senses enable researchers to study
cognitive processes and apply rigorous methods to
research work. To improve the power generation
problem, GA, fuzzy, and PID controllers were
applied to the thermal power system and the
combined results are tabulated in Table 2. This
shows that the GA controller is efficient, effective
and gives better results than the other controller by
adjusting continuous variation in load frequency and
power in less time. Therefore, it can be concluded
that using appropriate techniques and management
will automatically improve power generation and
help to maintain and meet the world's major
consumers' demand for electricity.
Acknowledgement:
This research received no specific grant from any
funding agency in the public, commercial, or not-
for-profit sectors. I thank the anonymous reviewers
for carefully reading this manuscript and their many
insightful comments and suggestions.
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DOI: 10.37394/23203.2023.18.64
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