Quantum behaved artificial bee colony based conventional controller for optimum dispatch

International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 2, April 2023, pp. 1260~1271
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i2.pp1260-1271  1260
Journal homepage: http://ijece.iaescore.com
Quantum behaved artificial bee colony based conventional
controller for optimum dispatch
Himanshu Shekhar Maharana, Saroja Kumar Dash
Department of Electrical Engineering, GITA Autonomous College, Bhubaneswar, Biju Patnaik University of Technology,
Rourkela, India
Article Info ABSTRACT
Article history:
Received Apr 17, 2022
Revised Oct 8, 2022
Accepted Oct 28, 2022
Since a multi area system (MAS) is characterized by momentary overshoot,
undershoot and intolerable settling time so, neutral copper conductors are
replaced by multilayer zigzag graphene nano ribbon (MLGNR)
interconnects that are tremendously advantageous to copper interconnects
for the future transmission line conductors necessitated for economic and
emission dispatch (EED) of electric supply system giving rise to reduced
overshoots and settling time and greenhouse effect as well. The recent work
includes combinatorial algorithm involving proportional integral and
derivative controller and heuristic swarm optimization; we say it as Hybrid-
particle swarm optimization (PSO) controller. The modeling of two multi
area systems meant for EED is carried out by controlling the conventional
proportional integral and derivative (PID) controller regulated and monitored
by quantum behaved artificial bee colony (ABC) optimization based PID
(QABCOPID) controller in MATLAB/Simulink platform. After the
modelling and simulation of QABCOPID controller it is realized that
QABCOPID is better as compared to multi span double display (MM),
neural network based PID (NNPID), multi objective constriction PSO
(MOCPSO) and multi objective PSO (MOPSO). The real power generation
fixed by QABCOPID controller is used to estimate the combined cost and
emission objectives yielding optimal solution, minimum losses and
maximum efficiency of transmission line.
Keywords:
Chaotic self-adaptive search
Particle swarm optimization
Economic and emission
dispatching
Group search optimization
QABCOPID controller
This is an open access article under the CC BY-SA license.
Corresponding Author:
Saroja Kumar Dash
Department of Electrical Engineering, GITA Autonomous College, Bhubaneswar, Biju Patnaik University
of Technology
Rourkela, 769015, Odisha, India
Email: hodeegita@gmail.com
1. INTRODUCTION
Combined economic emission dispatch (EED) is a difficult problem to obtain most favorable result
for optimum dispatch of electric power with optimum emission level due to insecurity and rigorous time
consuming tests like Newton–Raphson method used by Chen and Chen [1], lambda-iteration method used by
Ramadan et al. [2], linear programming techniques and non-linear combined-integer quadratic rule method
practiced by Palanichamy and Babu [3], Kiani and Pourtakdoust [4], Papageorgiou and Fraga [5], and Abido
[6]. Improved heuristic methods mimicking natural selection processes applied by Bakirtzis [7], followed by
Daryani and Zare [8], in group search optimization (GSO) processes adopted by Bora et al. [9], chaotic
self-adaptive search (CHS) processes, and do not perform satisfactorily for bulky coal fired plants due to
presence of complex varying nonuniformities resulted out of multi-valving criteria of impulse reaction
turbines. Ghoshal [10] in the recent work put forth a modified technique by amalgamating limiting factors of

Int J Elec & Comp Eng ISSN: 2088-8708 
Quantum behaved artificial bee colony based conventional controller for … (Himanshu Shekhar Maharana)
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the quantum behaved artificial bee colony optimization technique and proportional, derivative and integral
controller giving rise to quantum behaved artificial bee colony optimization based proportional, derivative
and integral controller (QABCOPID) where a more balanced approach among regional and global search
capabilities can be used to examine the EED situation and swiftly arrive at the minimum location. More
specifically authors in this article present the utility of the quantum behaved artificial bee colony (ABC)
optimization technique to govern the real time proportional integral and derivative controller to ascertain tie
line response and incremental frequency deviations of two area systems and finally results real power
generations of the alternators. These real power generations solve the EED problem of huge thermal power
plants of western countries having bulky thermal generating units involving cubic cost, emission and
combined objective approach using different training methods for the 30-bus test case system depicted in
Figure 1 with six alternators and are utilized to estimate the limiting factors of transmission line and simulate
the power lines as well. Since the incremental real power generation depends upon the incremental frequency
deviation so by regulating the unbalanced power demand at the demand center and turbine intent and
governor setting at the place of generation and by touching the automatic load-frequency control (ALFC)
adopted by Prasanth and Kumar [11], Juang and Lu [12], automatic voltage regulation (AVR) parameters
through the quantum behaved artificial bee colony optimization proportional–integral–derivative
(QUABCOPID) controller model shown in Figure 2, the optimal values for cost of cost of coal and
greenhouse gas level rate are obtained for multi-area complex bulky thermal power plants connected through a
tie line by exploiting the waggle dance of artificial bees shown in Figure 3 to venture into across-the-board area.
The authors of this work attempted to improve the convergence and across-the-board optimization capabilities of
the QABCOPID controller in order to increase its relevance as a more effective approach for assessing various
EED problems. The advantage of this strategy is that it allows for instantaneous tuning of proportional–integral–
derivative (PID) controllers, as demonstrated by Sahu et al. [13], Sharifi et al. [14], Dhawane and Bichkar [15],
Pradhan and Majhi [16]. The following are the characteristics discussed in this dissertation.
Figure 1. IEEE 30 bus 6-unit test case system Figure 2. Model for the proposed method
Figure 3. Waggle dance of honey bee

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Int J Elec & Comp Eng, Vol. 13, No. 2, April 2023: 1260-1271
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The enhanced particle swarm optimization (PSO) algorithm, namely the QABCOPID technique
established by Xu and Sun [17], is implemented to obtain the most favorable result for EED and to perform
better analyses on the results obtained with the proposed QABCOPID algorithm, which includes a
formulation of the EED problem with losses. The outcomes of EED solutions achieved using QUABCOPID
were compared to those acquired using other heuristic optimization algorithms. The obtained real optimal
powers are used to access the stability of transmission lines using the transmission characteristics determined
using a bench power transmission simulator based on HONALEC.
The following is the structure of this article: The formulation of the combined dispatch problem was
described in section 2. Section 3 demonstrated the conventional PSO technique and the new QUABCOPID
technique, respectively. Section 4 details the aforementioned approach's flow chart and implementation.
Section 5 showed the optimization findings obtained using the suggested technique, including a case study,
and compared them to previously published researches, as well as plotted the results. Section 6 discusses the
methods and results of a simulation of a 252-kilovolt transmission line. Finally, sections 7 and 8 give
findings and future prospects.
2. FORMULATION OF EED PROBLEM
The QUABCOPID optimization approach is introduced in this study to optimize fuel cost and
emission level as objective functions. The alternator units are all thermal and operate continuously on-line,
posing many limitations such as equality, inequality, and including transmission losses to ensure the novel
project's completion. A combinatorial approach based on quantum behaved artificial bee colony optimization
and a conventional PID controller were used by Rahimian and Raahemifar [18], Shankar and Mukherjee
[19], Soni and Bhat [20], Huang et al. [21], Lu et al. [22], to optimize a cubic cost, emission, and combined
objective function for a six-unit thermal power plant.
2.1. Formalization of the fundamental objective function
Taking, 𝑎𝑖, 𝑏𝑖, 𝑐𝑖, 𝑑𝑖, 𝑒𝑖𝑎𝑛𝑑 𝑓𝑖as the price Table 1 of fuel price function 𝑓𝑙𝑖
𝑓1𝑖 = ∑ (𝑎𝑖
𝑛
𝑖=1 + 𝑏𝑖𝑃𝑖 + 𝑐𝑖𝑃𝑖
2
+ |𝑑𝑖𝑠𝑖𝑛{𝑒𝑖(𝑃𝑖
𝑚𝑖𝑛
− 𝑃𝑖)}| + 𝑓𝑖𝑃𝑖
3
Rs/hr (1)
𝑓1𝑖 is fuel price function of thermal units, Pgi is real unit outage of the ith
unit, n is total number of
alternators, 𝑃𝑔𝑖
𝑚𝑖𝑛
is minimum power of 𝑖𝑡ℎ
unit.
𝑓2𝑖 = ∑ (αi + βiPi + γiPi
2
+ i
η exp(δiPi))
n
i=1 + ΓiPi
3
kg/hr (2)
Taking, 𝛼𝑖, 𝛽𝑖, 𝛾𝑖, 𝜂𝑖, 𝛿𝑖𝑎𝑛𝑑 Γ𝑖 as emission coefficients of emission function 𝑓2𝑖. 𝑓2𝑖 is emission level
function in kg/hr. Constraints on equality: In equation, the overall output power outage of the units must
equal the output at the demand center plus transmission line losses in the power lines (3):
∑ 𝑃𝑖 − (𝑃𝐷 + 𝑃𝐿)
𝑁
𝑖=1 = 0 (3)
wherein, 𝑃𝐷 is actual energy demand.
PL denotes power dispatch losses, which are modelled using loss factors (4):
𝑃𝐿 = ∑ ∑ 𝑃𝑖𝐵𝑖𝑗
𝑃𝑗
𝑁
𝑗=1
𝑁
𝑖=1 𝑀𝑊 (4)
where B-coefficients for six-generating units are calculated and denoted (5).
𝐵𝑖𝑗 =
(
0.00010.000010.000010.000010.000020.00002
0.000010.000050.000010.000010.000010.00001
0.000010.000010.000060.000010.000020.00001
0.000010.000010.000010.000060.000020.00002
0.000020.000010.000020.000020.000060.00003
0.000020.000010.000010.000020.000080.00003)
(5)
The inequality constraints imposed on generator real power output are
𝑃𝑖
𝑚𝑖𝑛
≤ 𝑃𝑖 ≤ 𝑃𝑖
𝑚𝑎𝑥
(6)

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where, 𝑃1
𝑚𝑖𝑛
is the lower limit and 𝑃1
𝑚𝑎𝑥
is the top limit of generator output [18], [19]
⇒ 𝑃𝐿 = (
𝐵11𝑃1
2
+ (2 ∑ 𝐵1𝑖𝑃𝑖 + 𝐵01)
𝑛
𝑖=2 𝑃1
+ ∑ ∑ 𝑃𝑖𝐵𝑖𝑗𝑃𝑗 + ∑ 𝐵0𝑖
𝑛
𝑖=2
𝑛
𝑗=2
𝑛
𝑖=2 𝑃𝑖
+𝐵00
) (7)
where, 𝐵00, 𝐵01, 𝐵𝑙𝑖, 𝐵0𝑖, 𝑎𝑛𝑑 𝐵𝑖𝑗 are B (transmission loss) coefficients.
Table 1. Cost and emission co-efficient with real power generation bounds for QUABCOPID controller
Units 𝑃𝑖
𝑚
𝑃1 𝑃𝑖
𝑚𝑎𝑥
𝑎𝑖 𝑏𝑖 𝑐𝑖 𝑑𝑖 𝑒𝑖 𝑓𝑖 𝑎𝑖 𝛽𝑖 𝛾𝑖 𝜂𝑖 𝛿𝑖 Γ𝑖
1 10 32.6 40.6 1000 40.54 12 33 0.01 0.001 360 3.98 0.04 0.25 0.01 0.022
2 10 25.5 20.5 950.6 39.58 10 25 0.01 0.002 350 3.95 0.04 0.25 0.01 0.028
3 47 150 158 900.7 36.51 12 32 0.01 0.008 330 3.9 0.04 0.25 0.01 0.022
4 50 147 155 800.7 39.51 12 30 0.01 0.002 330 3.9 0.04 0.25 0.01 0.011
5 50 285 293 756.8 38.53 15 30 0.01 0.017 13.8 0.32 0 0.24 0.01 0.03
6 70 282 290 451.3 46.15 10 20 0.01 0.005 13.8 0.32 0 0.24 0.01 0.02
3. UTILITY OF QUABCOPID OPTIMIZATION TECHNIQUE
To overcome the slow convergence of Newton Raphson method a quantum behaved artificial bee
colony based controller involving ABC technique is implemented vide a Simulink model to control the up
Q-bit and down Q-bit of quantum behaved algorithm which in turn regulates the conventional PID
parameters namely Kp, Ki and Kd to yield optimal values of real power that normally goes erroneous
following mismatch in power demand, equality, inequality constraints, congestion constraints security
constraints, ramp rate constraints involving prohibited operating zones. This optimized real power reduces
frequency mismatches to great extent in timeline power control unit. In the proposed work bio-inspired
algorithms (BIAs) applied to unravel various economic and emission objective issues have shown promising
outcomes that are vital in this severely complex real-world system. QUABCOPID controller algorithm, a
type of BIA that produces enormous results compared to certain other soft computing methods. The purpose
of this study is to offer a replacement-modified algorithm based with the goal of increasing convergence
speed and avoiding convergence rate. To demonstrate the suggested algorithm's robust application, it has
been used to solve the active power optimization problem. The proposed individual's outputs outperform other
ABC variations in terms of fast convergence. Additionally, the suggested method has demonstrated superior
performance and solution of economic-emission objective functions.
4. PROPOSED QUABCOPID TECHNIQUE FOR EED
The proposed method, which utilizes a QUABCO and PID controller, is discussed in this section. It
is used for multi-objective generation dispatching when dealing with severe non-linear load behavior of large
thermal power stations subjected to multiple constraints. It utilizes a cubic cost and emission threshold
objective function. The ABC approach along with incremental fuel costs regulates q-bits of quantum behaved
algorithm and the combinatorial approach of ABC and quantum behaved algorithm regulates the PID
parameters bringing thereby an optimal value of real power that becomes suitable to meet out the cost and
emission objectives of economic load dispatch.
5. ABC TECHNIQUE
The fast convergence process of ABC technique is discussed vividly in the following steps involved
in the algorithm. Typically, spectator bees wait inside the hive and collect food sources by exchanging
information with the hired foragers, and there is a far greater likelihood of watchers identifying more
profitable sources. As follows is a description of the ABC algorithm:
Step 1: The first step is to provide food sources for any and all engaged bees.
Step 2: An engaged bee explores a food source, striking her memory and establishing a familiar source, then
judges the amount of honey produced by conducting a flutter kick within the hive shown in Figure 1.
Step 3: An observer sees engaged bees prance and then approaches one of their sources based on the waggle
prances. She estimates the amount of honey produced by the source after choosing an acquaintance in
the vicinity.
Step 4: Unwanted food habitats are identified and swapped for newly discovered food habitats by vanguards.
Step 5: Till all requirements are met, the smallest food habitat discovered during this procedure is noted.

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6. PID TECHNIQUE
Conventional PID controller using Zeigler Nichole’s tuning method involving S shaped curves finds
suitable to some extent for controlling system stability except for multi-constraint and contradictory multi-
objective electrical power system dispatch problem. The traditional controller must be updated with the
assistance of the heuristic controller (QUABCOPID) for meeting out the contradictory fuel cost and emission
objectives for economic dispatch. Simulation studies were performed for a system comprising a 2-area
system associated by a tie line whose real power is updated to obtain the optimal power for meeting out the
cost and emission objective for economic dispatch. Each area of electrical power grid comprises 6 generating
units contributing to 30 busses, is modelled by a group of non-linear differential equations supported Park’s
equations. The prototype comprises typical flow restriction and gain impregnation in power plants. Using the
PID parameters developed by Dash et al. [23] gain values are tabulated in Table 2, the traditional PID
controller metamorphoses to a QUABCOPID controller which is regulated by the hybrid action of the
quantum behaved algorithm and ABC technology to regulate the up Q-bit and down Q-bit states of tie line
power and governor output and regulate the nectar level by appointing more scout bees to interact with
employed bees within the neighborhood to perform the sort of waggle dance inside the hive. These vanguard
bees plan for help in achieving the worldwide optimum solution in economic and emission dispatch
problems. The results shown in Figures 4 to 6 clearly demonstrate that the QUABCOPID controller possesses
much slighter undershoot and quicker settling time to understand the steadiness of a feedback system for all
frequency range. The open-loop frequency response curve must get on the right-hand side and away from the
juncture (-1, jo). This vital objective is fulfilled by using QUABCOPID controller that constantly monitors
Kp, Ki and Kd values following the mismatch in power demand, equality, inequality constraints, congestion
constraints security constraints, ramp rate constraints involving prohibited operating zones.
Table 2. Gain values and time constants of fast acting ALFC loop with QUABCOPID controller
S.L No Component Gain Time constant in Second
1 Governor Gg=1 Tg=0.4
2 Turbine Gt=1 Tt=0.47-0.44
3 Amplifier Ga=0.9 Ta=0.19-0.14
4 Exciter Ke=1 Te=1.5
5 Generator Gp=16.94 Tp=1.6
6 Sensor Kr =1 Tr=0.06
7 Inertia constant H=5 sec
8 Regulation R=0.04 Hz PU MW
9 Proportion controller (QUABCOPID) Kp=0.0041
10 Integral controller (QUABCOPID) Ki =0.25
11 Derivative controller (QUABCOPID) Kd=0.26
12 Step load change for area1 0.03 PU
13 Step load change for area2 0.06 PU
Figure 4. Simulink model for incremental frequency Δ𝐹1 Figure 5. Simulink model for incremental frequency Δ𝐹2
Figure 6. Simulink model for incremental time line power 𝛥𝑃12
Area
1
incremental
frequency
Δ𝐹
1
frequency
in
PU
Area
2
incremental
frequency
Δ𝐹
2
frequency
in
PU
Time T in Sec
Δ𝐹1 vs T
Time
line
power
ΔP
12
for
2
area
system
in
PU
Time T in Sec
Δ𝐹2 vs T
ΔP12 vs T
Time T in Sec

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7. QPSO ALGORITHM FOR OPTIMAL EED SOLUTION
Quantum behaved particle swarm optimization (QPSO) intends to enhance traditional PSO making
use of alternate particle creation formulae. Quantum behaved PSO supported trajectory analysis being a
worldwide convergence technique updates the particle position (8) and (9):
𝑋𝑖
𝑡+1
= 𝑃𝑖
𝑡
+ 𝛼|𝑥𝑖
𝑡
− 𝑚𝑏𝑒𝑠𝑡𝑡|𝑙𝑛
1
𝑢𝑖
𝑡 , 𝑖𝑓𝑟𝑎𝑛𝑑𝑣 ≤ 0.5 (8)
𝑋𝑖
𝑡+1
= 𝑃𝑖
𝑡
− 𝛼|𝑥𝑖
𝑡
− 𝑚𝑏𝑒𝑠𝑡𝑡|𝑙𝑛
1
𝑢𝑖
𝑡 , 𝑖𝑓𝑟𝑎𝑛𝑑𝑣 < 0.5 (9)
where, 𝑃𝑖
𝑡
is local attractor of particle 𝑖 = iteration number, mbestt
denotes magnificent mean positions in the
ith
iteration and 𝛼 is the contraction expansion coefficient for regulating the concurrence rate of the QPSO
adopted at par with Tahyudin and Nambo [24], Giri et al. [25], Sui et al. [26]. Identities 𝑢𝑖
𝑡
and rand v are
functions of uniform probability distribution in the range [0, 1]. The local point is processed (10):
𝑃𝑖
𝑡
= 𝑓𝑡
× 𝑃𝑏𝑒𝑠𝑡𝑡
+ (1 − 𝑓𝑡)𝑔𝑏𝑒𝑠𝑡𝑡
(10)
where, 𝜙𝑡
is a random parameter with a uniform distribution in the interval [0, 1], and are personal best of the
particle and universal best of swarm respectively.
𝑚𝑏𝑒𝑠𝑡𝑡
=
1
𝑁
∑ 𝑃𝑏𝑒𝑠𝑡𝑖
𝑡
𝑁
𝑖=1 (11)
QPSO does not alone become useful for complex engineering problems on EED following more and more
numbers of search iterations. It works together with ABC technique for obtaining fast convergence that is
required solving complex multi-objective nonlinear problems.
8. IMPLEMENTATION OF QABCOPID METHOD
This part of the article describes an improved hybrid algorithm for solving the ED problem. This
hybrid technique recognized by Hullender et al. [27], is a combination of two strategies described through
steps 1-13 that finds use in solving complex multi-objective problems subjected to complex nonlinearities
necessitating fast convergence. First strategy uses incremental fuel cost to decide the real power generations
of the generating units involving the quantum behaved algorithm and the second strategy acknowledges these
real generations as initial values for training the artificial bee colony optimization to ascertain the favorable
value of the initial neighborhood. This hybrid strategy is named as QUABCO. The constraints of ED
experimented by Semlyen and Deri [28] and Deb [29] are transmission losses, power demands and the
practical limits associated with alternators to demonstrate the effectiveness of the QUABCO algorithm as presented.
we take two test cases one 6unit 30 bus system subjected to QUABCO approach and other one a 3-unit 15 bus system
subjected to quantum behaved evolutionary algorithm based PID controller involving MATLAB/Simulation with and
without transmission losses.
The QUABCO algorithm to train the PID controller is summarized as follows:
Step 1: Assume initial values for the key elements of QUABCO as referred in Table 1.
Step 2: Compute the incremental fuel costs (𝜆) differentiating (1) using the Pd value and making use of
mbestt
value from the QPSO algorithm.
Step 3: Compute the real power of the ith
alternator (Pi) using 𝜆 equation as under:
𝜆 − 𝑏𝑖 − 2𝑐𝑖𝑃𝑖 − 3𝑓𝑖𝑃𝑖
2
+ |𝑑𝑖𝑐𝑜𝑠𝑒𝑖(𝑃𝑖
𝑚𝑖𝑛
− 𝑃𝑖)| = 0
Step 4: Pi, lower= Pi (1-Rank) and Pi, upper =Pi (1+Rank)
where, Pi, lower and Pi,upper are the minimum and maximum power output of the ith
generating unit and
rank is the rank ofreal power generation output.
Step 5: Create the population (N) of the real generations satisfying the power balance constraints and express it as;
Pi=Pi, lower+ ((Pi, upper- P i, lower). rand (0, 1))
Step 6: Obtain the population suit abilities in increasing order.
Step 7: Chose best solution groups for the neighborhood search and isolate them into two groups.
Step 8: Obtain the neighborhood size for each feasible solution. Mark neighborhood size equal to NQ for
solution group (Q) and NP for solution group (S-Q)
Step 9: Create solutions within the selected solutions in the neighborhood sizes (NP, NQ) and obtain the
suitability value from individual group. Then, choose the most feasible solution from individual
group.
Step 10: Validate the stopping criteria if no, update the iteration count.

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Step 11: Assign the new population (N-Q) to create new real generation of the ith
alternator unit.
Step 12: If the stopping rule is satisfied for the quantum behaved ABC optimization, then obtain the suitable
solution and utilizing the same create a proportional, derivative and integral signal to control Kp, Kd
and Ki of the proportional integral and derivative controller involving automatic generation controller
and MATLAB/Simulink model.
Step 13: Obtain Δ𝐹1 𝑣𝑠. 𝑇, Δ𝐹2 𝑣𝑠. 𝑇 and Δ𝑃12 𝑣𝑠. 𝑇 characteristics shown in Figure 4 to 6 using
QUABCOPID controller Simulink model shown in Figure 2 exploiting the flow chart of
QUABCOPID controller shown in Figure 7. Finally obtain the variation of tie line power with time as
shown in Figure 8 for the test case 2 comprising 3-unit 15 bus system using quantum behaved
evolutionary algorithm hybridized with artificial bee colony optimization and PID controller to
analyze the EED problem.
Figure 7. Flow chart for QUABCOPID optimization

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Figure 8. Simulation output for 3-unit 15 bus system
9. RESULTS ANALYSIS AND PERFORMANCE CHARACTERISTICS OF PROPOSED
QUABCOPID CONTROLLER
To determine the viability and efficacy of the suggested hybrid quantum-behaved PSO, as well as
the ABC method with a PID controller QUABCOPID, a series of experimental simulation studies, were
conducted on a conflicting multi-objective EED optimization process for two different test cases, one using
the QUABCOPID approach with a 6-unit 30 bus test case system and the other using a quantum behaved
evolutionary computing approach with a PID controller to find the optimal value of the fuel. The second
experiment incorporates a two-crossover method into the standard QEA-based ABC optimization process.
The t characteristic of experiment 2 is illustrated in Figure 8. It is quite obvious that the experimental results
of the QUABCOPID controller showing the variation of fuel cost and emission rate with respect to the real
energy production with and without line loss, as seen in Figures 9 to 12 and tabulated in Table 3, outperform
the QEA-based optimal solution results in Table 4. More precisely, the second experiment exhibits a higher
frequency of transient excursion than the first. QUABCOPID controller, on the other hand, has significantly
superior steady-state performance, which results in the ideal fuel economy and emission level again for
economic-emission dispatch problems. When compared to the results produced using the other heuristic
approaches listed in Table 3, the QUABCOPID controller's outcome is determined to be more promising. Both
experimental approaches were written in MATLAB and run on a 512 MB device running on Windows 8.
Figure 9. Operating cost function versus output
power without transmission loss
Figure 10. Emission level function versus output
power without transmission loss
Figure 11. Operating cost function versus output
with transmission loss
Figure 12. Emission level function versus output
power with transmission loss
Time
line
power
ΔP
12
for
2
area
system
in
PU
for
test
case
2
involving
3-unit
15
bus
system
Time T in Sec
Operating
cost
function
𝑓1𝑖
in
S/hr
Output power in (MW)
Emission
level
function
𝑓1𝑖
in
S/hr
Output power in (MW)
0 50 100 150 200 250 300
Operating
cost
function
𝑓1𝑖
in
S/hr
Emission
level
function
𝑓1𝑖
in
S/hr
Output power in (MW) Output power in (MW)

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Table 3. Numerical and comparative results for QUABCOPID controller (30-bus system)
Time 1 2 3 4 5 6 7 8 9 10
Max 19.7648 19.7613 19.7648 19.7648 19.7649 19.7649 19.7649 19.7680 19.7504 19.7449
Average 19.6740 19.6824 19.6999 19.7048 19.7177 19.7249 19.7249 18.4671 19.4816 19.7849
Time 11 12 13 14 15 16 17 18 19 20
Max 19.6614 19.7794 19.7849 19.7949 19.7949 19.6737 19.6949 19.7362 19.7804 19.8823
Average 19.7432 19.7953 19.8049 19.8049 19.8048 19.6937 19.7947 19.6853 19.7902 19.7926
Average (max)=19.7583
Table 4. Test results of experiment 2
Power generation for a power demand of (492MW) and a transmission loss (TL) of 12.30 MW Fuel
Cost
Emission
rate
Simulation
time
Method P1
(in MW)
P2
(in MW)
P3
(in MW)
P4
(in MW)
P5
(in MW)
P6
(in MW)
FC
(in $/h)
PE (in T/h) T in (sec)
QABCOPID without
transmission loss (TL)
32.6 12.5 1.50 1.47 285 282 6865.07 194.45 5.0
QABCOPID with TL 40.6 20.5 1.58 1.55 293 290 6883.08 194.47 5.1
MM 33.77 12.65 150.56 148.50 296.29 293.63 6900.05 196.98 8.2
NN based PID 32.66 12.7 152 149 287 285.3 6897.07 196.23 7.3
MOCPSO 33.71 12.65 150.56 148.50 296.30 293.72 6890.35 195.12 6.2
10. SIMULATION OF TRANSMISSION LINE
The bench line device analyses single-phase as well as 3 transmission lines operating at varied loads
and subjected to fluctuating power and inserted fault circumstances. A single-phase line is composed of a
series of inductive impedances that are connected asynchronously. The tapings thus provide the user with the
ability to adjust the duration of the replicated line, develop nominal 'Pi' or 'Tee' techniques for identifying
losses by utilizing differing capacitance values, and monitor voltage, current, and output at any point on the
road. Six portions of the three-phase line are depicted diagrammatically in 'per-unit' values. It has provisions
for controlling below-variable balanced or imbalanced resistive, inductive, and capacitive (RLC) masses, as
well as selectable neutral provision for adjusting the length parameters. The electrical or electronic circuit
and tangency variables of a 252 kilometers long unit line with such a litigating electrical circuit wattage of
275 V and a point of intersection voltage of 60 V were determined during this experiment, as well as the
associated voltages of area units. Using those specifications, as well as additional information quantified in
Table 5, the propagation variables of the said predicted line systems are currently are calculated as:
𝐶𝑜𝑝𝑝𝑒𝑟 𝑙𝑜𝑠𝑠 = 𝐼𝑜𝑠
2
𝑅 = (
𝑃𝑠
𝑝ℎ𝑎𝑠𝑒
−
𝑃𝑟
𝑃ℎ𝑎𝑠𝑒
)
𝐼𝑜𝑠
2
=
(
𝑃𝑠
𝑝ℎ𝑎𝑠𝑒
−
𝑃𝑟
𝑃ℎ𝑎𝑠𝑒
)
𝑅
𝑅/𝑘𝑚/𝑃ℎ𝑎𝑠𝑒 = 𝑅/252 = (𝑓𝑜𝑟 7 𝑠𝑒𝑐𝑡𝑖𝑜𝑛𝑠 𝑜𝑣𝑒𝑟 𝑎 𝑠𝑝𝑎𝑛 𝑜𝑓 180 𝑘𝑚).
From Short circuit test, 𝑃𝑠 = 47.49 𝑤, 𝑃𝑟 = 𝑉𝑟 𝑥 𝐼𝑜𝑠 𝑥 𝑐𝑜𝑠Ф𝑟 = 117.2𝑥0.45𝑥0.917 = 45.12 𝑤𝑠 𝐼𝑜𝑠 =
0.456 𝐴, 𝑅 = 12.69 Ω, 𝑅/𝑝ℎ𝑎𝑠𝑒/𝑘𝑖𝑙𝑜𝑚𝑒𝑡𝑒𝑟 = 12.69/3𝑥252 = 0.0167. Calculation of the line's
impedance per km section (z). 2𝜋𝑓𝐿 = 0.0569 𝐿, so 𝐿 = 0.00016 𝐻 from data on open circuit. 𝐼0𝑆/𝑉0𝑆 =
2 𝑓𝐶 𝐶 = 7.66 𝑓 (this value should be for all seven sections). 𝐶 = 7.66 𝑓/252 𝐶 = 0.03 𝑓 is the
capacitance/part per kilo meter. As with medium conductors, the long line can be split into similar segments
within the shielded conductor. The series electrical phenomenon is denoted by Z′, and the shunt admittance is
denoted by Y′. As a consequence, the ABCD components of this huge line will be specified analogous to that
of a medium line, i.e. in terms of transport protocol, and will be arranged as:
𝑦 = 2 × 𝜋 × 𝑓 × 𝐶 = 2 × 𝜋 × 49.82 × 0.030 × 10−6
= 9.39 × 10−6
where, y is the capacitive susceptance in Mho/Phase/km
As with the intermediate power line, an analogous representation can be used to approximate the
long string. The series impedance is denoted by Z′ in the long power line's-equivalent, but the shunt
admittance is denoted by Y′.

1269
where, 𝛾 = √𝑧𝑦, 𝑍𝑐 = √
𝑧
𝑦
, 𝑍 = 𝑧 × 1, 𝑎𝑛𝑑 𝑌 = 𝑦 × 1
As a consequence, the ABCD components of this massive line can be stated in the same way as that of a
medium transmission line:
Z’ = Z_c sinhγl = √(z/y) × sinh√yz × l = zlsinhγl/(l√yz) = (Zsinhγl )/γl (12)
coshγl = 1 + (Z^′ Y^′)/2 = Y′/2 × Z_c sinhγl + 1 (13)
We obtain by rearranging the elements in the preceding (14) to (17).
⇒ Y′/2 = 1/Z_c (coshγl − 1)/sinhγl = √(y/z) tanh yl/2 = yl/2 × (tanh γ_l/2)/
(l/2 √yz) = Y/2 × (tanh γ_l/2)/(l/2 √yz) ÞY′ = Y × (tanh γ_l/2)/(γ_l/2) (14)
⇒ A = D = 1 +
Y′Z′
2
per unit (15)
𝐵 = 𝑍′
𝛺 (16)
𝐶 = 𝑌 (1 +
𝑍′𝑌′
4
) 𝐽 (17)
⇒ A = 1.0160, B = 13.5793 Ω, C = 0.0024 ℧ and D = 1.0160
Table 5. Transmission line test case data and simulation result for simulating a 252 km
HVAC transmission line
Sending
end
voltage on
open
circuit
(V0s) in
Volts
Sending
end
current on
open
circuit
(I0s) in
Ampere
Sending
end
voltage on
short
circuit
(Vsc) in
Volts
Receiving
end
current on
short
circuit
(Isc) in
Ampere
Line
Length
in km
Analys
is
method
Number
of π
sections
utilized
Sending
end
Power Ps
In Watts
per phase
Receiving
end Power
Pr in Watts
per phase
A=D B In
Ω
C In
Mho
175 0.456 60 4.44 252 Nominal π 7 47.49 45.12 1.0160 13.5793 1.0160
11. SCOPE FOR FUTURE WORK
The QUABCOPID optimization algorithm will be applied to realistic systems with many objectives
and constraints. The proposed technique will be hybridized with completely different techniques such as
symbolic logic, DE, EP, PSO, artificial neural network (ANN), and good grid observance to address the
inadequacy of the MOELD solution discovered by Ghasemi et al. [30]. This technology can be implemented
to ship climate routing systems with acceptable initial conditions, as demonstrated by Vettor and Soares [31]
as well as smart grids incorporating distributed generations (DGs) [32].
12. CONCLUSION
A proportional integral and derivative (PID) controller tuned through QUABCO has been hotly
proposed as a solution to the ALFC's multi-area disadvantage. The results revealed that a PID based on
QUABCO is capable of ensuring high stability and performance under a variety of load conditions and
parameter changes for a variety of pricing functions. The proposed controller was successful in dampening
all oscillations, limiting subsidence time, and eliminating overshoot, all of which resulted in decreased wear
on high-speed valves and gates. We will extend the QUABCO rule to renewable energy power plants such as
wind turbines in the future. Additionally, we frequently employ it to set the upper limit on load disruptions
that can result in the ability system's instability downside. The transmitting parameters obtained through
QUABCOPID advancement during this proposed work by combining real power generation, real power
demand, and power loss area unit are really quite adequate for meeting the erection demand for power lines
inside the selected as well as proclaimed great areas and alternate solution busy cities. Thus, these optimized
values of real power generations fulfil the fundamental objective of the simulation of conductor demand for
meeting out the transmission and distribution necessities of varied recent and good cities.

 ISSN: 2088-8708
1270
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BIOGRAPHIES OF AUTHORS
Himanshu Shekhar Maharana completed his B. Tech degree in EEE from
JITM, Paralakhemundi under BPUT university, Rourkela, Odisha in 2010 and Completed M.
Tech degree in Power System Engineering from GITA, Bhubaneswar under BPUT university,
Rourkela, Odisha in the year 2014. Prior to it he worked on Java Full Stack Development
Software Projects through online training-based mode while doing PhD research work. At
present he is continuing full time PhD in the Department of Electrical Engineering at GITA,
Bhubaneswar under Biju Patnaik University of Technology (BPUT), Rourkela, Odisha, India
vides the guidance of professor Dr. Saroja Kumar Dash. His research areas are power system
engineering, applications of power system optimization techniques relating to economic load
dispatch of electric power system. He can be contacted at: hsmaharana@gmail.com.
Saroja Kumar Dash received the UG degree in Electrical Engineering from I.E,
India in 1991 and accomplished Master’s Program in Electrical Engineering from VSSUT,
Burla (Sambalpur University), India, in 1998 and the PhD degree from Utkal University,
Odisha, India in the year 2006. Dr. S.K. Dash is working at present as a Professor and Head in
the department of Electrical Engineering at GITA, Autonomous College, Bhubaneswar under
Biju Patnaik University of Technology (BPUT, Rourkela, Odisha, India). Dr. S. K. Dash
received Pundit Madan Mohan Malviya award, Union Ministry of Power Prize and gold
medals thereof for his research papers entitled “Economic load dispatching of generating units
with multi-fuel options” and “Short term generation scheduling with take or pay fuel contract
using evolutionary programming technique” on Multi Objective Generation Dispatch. His
research areas include power system planning, operation, and optimization techniques relating
to economic dispatch of electric power system. He can be contacted at: hodeegita@gmail.com.

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