![International Journal of Advances in Applied Sciences (IJAAS)
Vol. 9, No. 4, December 2020, pp. 261~264
ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp261-264 261
Journal homepage: http://ijaas.iaescore.com
Real power loss reduction by arctic char algorithm
Lenin Kanagasabai
Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India
Article Info ABSTRACT
Article history:
Received Jan 3, 2020
Revised Mar 4, 2020
Accepted Jun 8, 2020
This work presents Arctic Char Algorithm (ACA) for solving optimal
reactive power problem. In North America movement of Arctic char
phenomenon is one among the twelve-monthly innate actions. Deeds of
Arctic char have been imitated to design the algorithm. In stochastic mode
solutions are initialized with one segment on every side of to the route
ascendancy; particularly in between lower bound and upper bounds. Previous
to the movement, Arctic char come to a decision about the passageway based
on their perception. This implies stochastic mix up of control parameters to
push the Arctic char groups (preliminary solution) in mutual pathway
(evolutionary operators). Projected Arctic Char Algorithm (ACA) has been
tested in standard IEEE 14,300 bus test system and simulation results show
the projected algorithm reduced the real power loss extensively.
Keywords:
Arctic char
Optimal reactive power
Transmission loss
This is an open access article under the CC BY-SA license.
Corresponding Author:
Lenin Kanagasabai,
Department of EEE,
Prasad V. Potluri Siddhartha Institute of Technology,
Kanuru, Vijayawada, Andhra Pradesh, 520007, India.
Email: gklenin@gmail.com
1. INTRODUCTION
Reactive power problem plays a key role in secure and economic operations of power system.
Optimal reactive power problem has been solved by variety of types of methods [1-6]. Nevertheless,
numerous scientific difficulties are found while solving problem due to an assortment of constraints.
Evolutionary techniques [7-16] are applied to solve the reactive power problem, but the main problem is
many algorithms get stuck in local optimal solution & failed to balance the Exploration & Exploitation
during the search of global solution. This work presents Arctic Char Algorithm (ACA) for solving optimal
reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve-
monthly innate actions. Through mountain streams millions of Arctic chars will move for spawning. During
the movement starving bears, human fishers and waterfalls are generally significant threats they have to face.
In this work deed of Arctic char has been imitated to design the algorithm. In stochastic mode solutions are
initialized with one segment on every side of to the route ascendancy; particularly in between lower bound
and upper bounds. Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test
system and simulation results show the projected algorithm reduced the real power loss extensively.
2. PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss:
𝐅 = 𝐏𝐋 = ∑ 𝐠 𝐤𝐤∈𝐍𝐛𝐫 (𝐕𝐢
𝟐
+ 𝐕𝐣
𝟐
− 𝟐𝐕𝐢 𝐕𝐣 𝐜𝐨𝐬𝛉𝐢𝐣) (1)](https://image.slidesharecdn.com/02-20275-37711-1-ed-realpowerloss-editmukti-201021072835/85/Real-power-loss-reduction-by-arctic-char-algorithm-1-320.jpg)
![ ISSN: 2252-8814
Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 261 – 264
262
Voltage deviation given as follows:
𝐅 = 𝐏𝐋 + 𝛚 𝐯 × 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 (2)
Voltage deviation given by:
𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 = ∑ |𝐕𝐢 − 𝟏|
𝐍𝐩𝐪
𝐢=𝟏 (3)
Constraint (Equality)
𝐏 𝐆 = 𝐏 𝐃 + 𝐏𝐋 (4)
Constraints (Inequality)
𝐏𝐠𝐬𝐥𝐚𝐜𝐤
𝐦𝐢𝐧
≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤
𝐦𝐚𝐱
(5)
𝐐 𝐠𝐢
𝐦𝐢𝐧
≤ 𝐐 𝐠𝐢 ≤ 𝐐 𝐠𝐢
𝐦𝐚𝐱
, 𝐢 ∈ 𝐍 𝐠 (6)
𝐕𝐢
𝐦𝐢𝐧
≤ 𝐕𝐢 ≤ 𝐕𝐢
𝐦𝐚𝐱
, 𝐢 ∈ 𝐍 (7)
𝐓𝐢
𝐦𝐢𝐧
≤ 𝐓𝐢 ≤ 𝐓𝐢
𝐦𝐚𝐱
, 𝐢 ∈ 𝐍 𝐓 (8)
Qc
min
≤ Qc ≤ QC
max
, i ∈ NC (9)
3. ARCTIC CHAR ALGORITHM
In North America movement of Arctic char phenomenon is one among the twelve-monthly innate
actions. Through mountain streams millions of Arctic chars will move for spawning. During the movement
starving bears, human fishers and waterfalls are generally significant threats they have to face. In this work
deed of Arctic char has been imitated to design the algorithm.
In stochastic mode solutions are initialized with one segment on every side of to the route
ascendancy; particularly in between lower bound and upper bounds. Solutions are initialized arbitrary mode
with reverence to the solution space.
𝑃𝑟𝑒𝑙𝑖𝑚𝑖𝑛𝑎𝑟𝑦 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 = 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 + 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (𝑢𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑) (10)
Previous to the movement, Arctic char come to a decision about the passageway based on their
perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary
solution) in mutual pathway (evolutionary operators).
𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 ∶ {
𝑁𝐴1
= [𝜇 ∗ 𝐴𝐶𝑠]
𝑁𝐴2
= 𝐴𝐶𝑠 − 𝑁𝐴1
(11)
Where 𝑁𝐴1
is the total number of Arctic char groups which pass through ocean and ponds, 𝑁𝐴2
is
the total number of Arctic char group transit through forest area and mountain valley, 𝐴𝐶𝑠 is the total number
of Arctic char groups which partake in the relocation and 𝜇 is Distribution factor is symbolizes the Arctic
char intuition.
Crossing of ocean, lakes and ponds has been scientifically modeled as,
{
𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑢𝑏 − 𝑌𝐹))
𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑦 𝐹 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑))
(12)
Where t symbolizes the present iteration number, 𝑌𝑁 symbolize a new-fangled perceive region (fresh
solution) and 𝑌𝐹 shows the previous area of the explore convey (preceding solution). 𝛿(𝑦, 𝑧) is computed by,
𝛿(𝑦, 𝑧) = 𝑧 ∗ 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (1 −
𝑦
𝑇
)
𝑏
(13)](https://image.slidesharecdn.com/02-20275-37711-1-ed-realpowerloss-editmukti-201021072835/85/Real-power-loss-reduction-by-arctic-char-algorithm-2-320.jpg)
![Int J Adv Appl Sci ISSN: 2252-8814
Real power loss reduction by arctic char algorithm (Lenin Kanagasabai)
263
Where T indicates maximum number of iterations, b is an arbitrary number normally larger than
value 1
Chief hunters discover the regions with a satisfactory Arctic char concentration (solution fitness).
Subsequent to that, they notify the engaged agent to make use of close by area to discover additional
concentrated regions (solution with elevated fitness). This operation has been precisely defined by,
𝑌𝑅 = 𝛽 ∗ (𝑌𝑀1 − 𝑌𝑀2) + 𝑌𝑀1 (14)
Where β is a arbitrary number which span from 0 and 1 with consistent distribution, YR stand for
the freshly spotted solution by the engaged agent, YM1 is the solution acquired by the primary key hunter and
YM2 is the solution acquired by the subsequent one.
Second operator simulates the bears hunt line of assault. Naturally they notify each other when they
find an appropriate region. When they discover a region with superior Arctic char concentration, they notify
to other bears. It expressed as,
𝑌𝐵 = 𝑐𝑜𝑠(𝜑) ∗ (𝐵𝑇𝑅 − 𝐶𝐿 𝑅) + 𝐵𝑇𝑅 (15)
Where YB symbolizes the new-fangled spotted area, BTR is the most excellent accounted area by
the hunting squad, CLR is the present area for which the bears have determined to execute the local
exploitation and φ is a capricious angle with a segment on every side of 0 to 360 degrees. cos (φ) articulate
the bears to their objective.
End of the repositioning, stay alive Arctic char will get together in their mark for spawning.
In Arctic char this normal event is replicated through a compilation stem. After Arctic char surpass through
their passageway (operator’s performance), the Arctic char subgroups (solutions) are composed in
a exclusive stem. Solutions are extorted from mutual operators and make an exclusive population. At this
condition, the proposed algorithm has reached the ending stage of the primary iteration.
Step1. Determine the population size, solution space, total number of variables, iterations
Step2. Regulate the Arctic char subgroups chaotically
Step3. Depending on migration 𝜇 passageway is chosen
Step4.Compute the fitness of hunted Arctic char
Step5. Arctic char used for spawn has been rumple together
Step6. If yes obtain the global solution or else go to step number 3.
4. SIMULATION RESULTS
At first in standard IEEE 14 bus system the validity of the proposed Arctic Char Algorithm (ACA)
has been tested & comparison results are presented in Table 1.
Table 1. The validity of the proposed Arctic Char Algorithm (ACA)
Control variables ABCO [17] IABCO [17] ACA
V1 1.06 1.05 1.00
V2 1.03 1.05 1.05
V3 0.98 1.03 1.03
V6 1.05 1.05 1.01
V8 1.00 1.04 0.90
Q9 0.139 0.132 0.100
T56 0.979 0.960 0.900
T47 0.950 0.950 0.900
T49 1.014 1.007 1.000
Ploss (MW) 5.92892 5.50031 4.1652
Then IEEE 300 bus system [18] is used as test system to validate the performance of the Arctic Char
Algorithm (ACA). Table 2 shows the comparison of real power loss obtained after optimization.
Table 2. Comparison of real power loss
Parameter Method EGA [19] Method EEA [19] Method CSA [20] ACA
PLOSS (MW) 646.2998 650.6027 635.8942 616.2596](https://image.slidesharecdn.com/02-20275-37711-1-ed-realpowerloss-editmukti-201021072835/85/Real-power-loss-reduction-by-arctic-char-algorithm-3-320.jpg)
![ ISSN: 2252-8814
Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 261 – 264
264
5. CONCLUSION
In this work Arctic Char Algorithm (ACA) successfully solved the optimal reactive power problem.
Deeds of Arctic char have been imitated to design the algorithm. End of the repositioning, stay alive Arctic
char will get together in their mark for spawning. In Arctic char this normal event is replicated through
a compilation stem. After Arctic char surpass through their passageway (operator’s performance), the Arctic
char subgroups (solutions) are composed in a exclusive stem. Solutions are extorted from mutual operators
and make an exclusive population. Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE
14,300 bus test system and simulation results show the projected algorithm reduced the real power
loss extensively.
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[20] S. Surender Reddy, “Optimal reactive power scheduling using cuckoo search algorithm,” International
Journal of Electrical and Computer Engineering, vol. 7, no. 5, pp. 2349-2356, 2017.](https://image.slidesharecdn.com/02-20275-37711-1-ed-realpowerloss-editmukti-201021072835/85/Real-power-loss-reduction-by-arctic-char-algorithm-4-320.jpg)
Real power loss reduction by arctic char algorithm
- 1. International Journal of Advances in Applied Sciences (IJAAS) Vol. 9, No. 4, December 2020, pp. 261~264 ISSN: 2252-8814, DOI: 10.11591/ijaas.v9.i4.pp261-264 261 Journal homepage: http://ijaas.iaescore.com Real power loss reduction by arctic char algorithm Lenin Kanagasabai Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, India Article Info ABSTRACT Article history: Received Jan 3, 2020 Revised Mar 4, 2020 Accepted Jun 8, 2020 This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Deeds of Arctic char have been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively. Keywords: Arctic char Optimal reactive power Transmission loss This is an open access article under the CC BY-SA license. Corresponding Author: Lenin Kanagasabai, Department of EEE, Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, Andhra Pradesh, 520007, India. Email: gklenin@gmail.com 1. INTRODUCTION Reactive power problem plays a key role in secure and economic operations of power system. Optimal reactive power problem has been solved by variety of types of methods [1-6]. Nevertheless, numerous scientific difficulties are found while solving problem due to an assortment of constraints. Evolutionary techniques [7-16] are applied to solve the reactive power problem, but the main problem is many algorithms get stuck in local optimal solution & failed to balance the Exploration & Exploitation during the search of global solution. This work presents Arctic Char Algorithm (ACA) for solving optimal reactive power problem. In North America movement of Arctic char phenomenon is one among the twelve- monthly innate actions. Through mountain streams millions of Arctic chars will move for spawning. During the movement starving bears, human fishers and waterfalls are generally significant threats they have to face. In this work deed of Arctic char has been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Projected Arctic Char Algorithm (ACA) has been tested in standard IEEE 14,300 bus test system and simulation results show the projected algorithm reduced the real power loss extensively. 2. PROBLEM FORMULATION Objective of the problem is to reduce the true power loss: 𝐅 = 𝐏𝐋 = ∑ 𝐠 𝐤𝐤∈𝐍𝐛𝐫 (𝐕𝐢 𝟐 + 𝐕𝐣 𝟐 − 𝟐𝐕𝐢 𝐕𝐣 𝐜𝐨𝐬𝛉𝐢𝐣) (1)
- 2. ISSN: 2252-8814 Int J Adv Appl Sci, Vol. 9, No. 4, December 2020: 261 – 264 262 Voltage deviation given as follows: 𝐅 = 𝐏𝐋 + 𝛚 𝐯 × 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 (2) Voltage deviation given by: 𝐕𝐨𝐥𝐭𝐚𝐠𝐞 𝐃𝐞𝐯𝐢𝐚𝐭𝐢𝐨𝐧 = ∑ |𝐕𝐢 − 𝟏| 𝐍𝐩𝐪 𝐢=𝟏 (3) Constraint (Equality) 𝐏 𝐆 = 𝐏 𝐃 + 𝐏𝐋 (4) Constraints (Inequality) 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 𝐦𝐢𝐧 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 ≤ 𝐏𝐠𝐬𝐥𝐚𝐜𝐤 𝐦𝐚𝐱 (5) 𝐐 𝐠𝐢 𝐦𝐢𝐧 ≤ 𝐐 𝐠𝐢 ≤ 𝐐 𝐠𝐢 𝐦𝐚𝐱 , 𝐢 ∈ 𝐍 𝐠 (6) 𝐕𝐢 𝐦𝐢𝐧 ≤ 𝐕𝐢 ≤ 𝐕𝐢 𝐦𝐚𝐱 , 𝐢 ∈ 𝐍 (7) 𝐓𝐢 𝐦𝐢𝐧 ≤ 𝐓𝐢 ≤ 𝐓𝐢 𝐦𝐚𝐱 , 𝐢 ∈ 𝐍 𝐓 (8) Qc min ≤ Qc ≤ QC max , i ∈ NC (9) 3. ARCTIC CHAR ALGORITHM In North America movement of Arctic char phenomenon is one among the twelve-monthly innate actions. Through mountain streams millions of Arctic chars will move for spawning. During the movement starving bears, human fishers and waterfalls are generally significant threats they have to face. In this work deed of Arctic char has been imitated to design the algorithm. In stochastic mode solutions are initialized with one segment on every side of to the route ascendancy; particularly in between lower bound and upper bounds. Solutions are initialized arbitrary mode with reverence to the solution space. 𝑃𝑟𝑒𝑙𝑖𝑚𝑖𝑛𝑎𝑟𝑦 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 = 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 + 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (𝑢𝑝𝑝𝑒𝑟 𝑏𝑜𝑢𝑛𝑑 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑) (10) Previous to the movement, Arctic char come to a decision about the passageway based on their perception. This implies stochastic mix up of control parameters to push the Arctic char groups (preliminary solution) in mutual pathway (evolutionary operators). 𝐷𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑖𝑜𝑛 𝑜𝑓 𝑠𝑜𝑙𝑢𝑡𝑖𝑜𝑛 ∶ { 𝑁𝐴1 = [𝜇 ∗ 𝐴𝐶𝑠] 𝑁𝐴2 = 𝐴𝐶𝑠 − 𝑁𝐴1 (11) Where 𝑁𝐴1 is the total number of Arctic char groups which pass through ocean and ponds, 𝑁𝐴2 is the total number of Arctic char group transit through forest area and mountain valley, 𝐴𝐶𝑠 is the total number of Arctic char groups which partake in the relocation and 𝜇 is Distribution factor is symbolizes the Arctic char intuition. Crossing of ocean, lakes and ponds has been scientifically modeled as, { 𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑢𝑏 − 𝑌𝐹)) 𝑌𝑁 = 𝑌𝐹 + 𝛿(𝑡, (𝑦 𝐹 − 𝑙𝑜𝑤𝑒𝑟 𝑏𝑜𝑢𝑛𝑑)) (12) Where t symbolizes the present iteration number, 𝑌𝑁 symbolize a new-fangled perceive region (fresh solution) and 𝑌𝐹 shows the previous area of the explore convey (preceding solution). 𝛿(𝑦, 𝑧) is computed by, 𝛿(𝑦, 𝑧) = 𝑧 ∗ 𝑟𝑎𝑛𝑑𝑜𝑚 ∗ (1 − 𝑦 𝑇 ) 𝑏 (13)
- 3. Int J Adv Appl Sci ISSN: 2252-8814 Real power loss reduction by arctic char algorithm (Lenin Kanagasabai) 263 Where T indicates maximum number of iterations, b is an arbitrary number normally larger than value 1 Chief hunters discover the regions with a satisfactory Arctic char concentration (solution fitness). Subsequent to that, they notify the engaged agent to make use of close by area to discover additional concentrated regions (solution with elevated fitness). This operation has been precisely defined by, 𝑌𝑅 = 𝛽 ∗ (𝑌𝑀1 − 𝑌𝑀2) + 𝑌𝑀1 (14) Where β is a arbitrary number which span from 0 and 1 with consistent distribution, YR stand for the freshly spotted solution by the engaged agent, YM1 is the solution acquired by the primary key hunter and YM2 is the solution acquired by the subsequent one. Second operator simulates the bears hunt line of assault. Naturally they notify each other when they find an appropriate region. When they discover a region with superior Arctic char concentration, they notify to other bears. It expressed as, 𝑌𝐵 = 𝑐𝑜𝑠(𝜑) ∗ (𝐵𝑇𝑅 − 𝐶𝐿 𝑅) + 𝐵𝑇𝑅 (15) Where YB symbolizes the new-fangled spotted area, BTR is the most excellent accounted area by the hunting squad, CLR is the present area for which the bears have determined to execute the local exploitation and φ is a capricious angle with a segment on every side of 0 to 360 degrees. cos (φ) articulate the bears to their objective. End of the repositioning, stay alive Arctic char will get together in their mark for spawning. In Arctic char this normal event is replicated through a compilation stem. After Arctic char surpass through their passageway (operator’s performance), the Arctic char subgroups (solutions) are composed in a exclusive stem. Solutions are extorted from mutual operators and make an exclusive population. At this condition, the proposed algorithm has reached the ending stage of the primary iteration. Step1. Determine the population size, solution space, total number of variables, iterations Step2. Regulate the Arctic char subgroups chaotically Step3. Depending on migration 𝜇 passageway is chosen Step4.Compute the fitness of hunted Arctic char Step5. Arctic char used for spawn has been rumple together Step6. If yes obtain the global solution or else go to step number 3. 4. SIMULATION RESULTS At first in standard IEEE 14 bus system the validity of the proposed Arctic Char Algorithm (ACA) has been tested & comparison results are presented in Table 1. Table 1. The validity of the proposed Arctic Char Algorithm (ACA) Control variables ABCO [17] IABCO [17] ACA V1 1.06 1.05 1.00 V2 1.03 1.05 1.05 V3 0.98 1.03 1.03 V6 1.05 1.05 1.01 V8 1.00 1.04 0.90 Q9 0.139 0.132 0.100 T56 0.979 0.960 0.900 T47 0.950 0.950 0.900 T49 1.014 1.007 1.000 Ploss (MW) 5.92892 5.50031 4.1652 Then IEEE 300 bus system [18] is used as test system to validate the performance of the Arctic Char Algorithm (ACA). Table 2 shows the comparison of real power loss obtained after optimization. Table 2. Comparison of real power loss Parameter Method EGA [19] Method EEA [19] Method CSA [20] ACA PLOSS (MW) 646.2998 650.6027 635.8942 616.2596
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