REAL POWER LOSS REDUCTION BY MAINE COON AND PEROGNATHINAE BASED OPTIMIZATION ALGORITHM Текст научной статьи по специальности «Электротехника, электронная техника, информационные технологии»

UDC 519.6

DOI: 10.18698/1812-3368-2023-3-61-84

REAL POWER LOSS REDUCTION BY MAINE COON

AND PEROGNATHINAE BASED OPTIMIZATION ALGORITHM

L. Kanagasabai gklenin@gmail.com

Prasad V. Potluri Siddhartha Institute of Technology, Kanuru, Vijayawada, India Abstract

This paper proposes Maine Coon and Perognathinae based optimization (MPO) algorithm for solving the power loss lessening problem. Usual behaviour between Maine Coon and Perognathinae is imitated to formulate the MPO algorithm. In the proposed MPO, the crusade of Maine Coon towards Perognathinae as well as the spurt of Perognathinae in the direction of anchorages is replicated. Proposed MPO is population-based procedure which is premeditated by imitating the natural actions of a Maine Coon assaults on Perognathi-nae and absconding of Perognathinae to the anchorage. The exploration agents in the projected MPO algorithm are alienated into two clusters of Maine Coon's and Perognathinae that examine the problem exploration space with arbitrary activities. The projected MPO algorithm apprises population associates in two segments. In the principal segment, the crusade of Maine Coon's in the direction of Perognathinae is modelled, and in the subsequent segment, the absconding behaviour of Pe-rognathinae to anchorages to protect its life is designed. From a scientific fact of opinion, every associate of the populace is a recommended solution to the problem. In detail, an associate of the population postulates standards for the problem parameters rendering to its location in the exploration space. Proposed MPO algorithm is appraised in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained

Keywords

Optimal reactive power, transmission loss, Maine Coon, Perognathinae

Received 18.08.2021 Accepted 24.09.2021 © Author(s), 2023

Introduction. Reactive power problem plays an important role in secure and economic operations of power system. In power system lessening of factual power loss is a noteworthy aspect. Lee et al [1] has done fuel-cost minimization

for power problem. Deeb et al [2] found a well-organized method for solving the loss problem by using a reviewed linear programming method. Bjelogrlic et al [3] had done use of Newton's optimal power flow in reactive power control. Granville [4] solved the problem by interior point methods. Grudinin [5] had solved the problem by means of successive quadratic programming technique. The works should be noted [6-9]. Mouassa et al [10] applied Ant lion algorithm for solving the problem. Mandal et al [11] solved the problem by using quasi-oppositional teaching. Khazali et al [12] solved the problem by harmony search procedure. Tran et al [13] solved problem by innovative enhanced stochastic fractal search procedure. Polprasert et al [14] solved the problem by using enhanced pseudo-gradient pursuit particle swarm optimization. Duong et al [15] proposed to use an effictive metaheuristic algorithm to calculate the optimal reactive power flow for large-scale power systems. Bhattacharya et al [16] solved reactive power flow using biogeography-based optimization. Duman et al [17] soled optimal reactive power dispatch using a gravitational search algorithm. Li Wu [18] solved optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and levy walk. MATPOWER is available for solving the problems of modelling and optimizing stationary power systems [19]. Dai et al [20] used seeker optimization procedure for solving the problem. Subbaraj et al [21] used self-adaptive real coded genetic procedure to solve the problem. Pandya et al [22] applied Particle swarm optimization to solve the problem. Hussain et al [23] applied amended particle swarm optimization to solve the problem. Vishnu et al [24] applied an enhanced particle swarm optimization to solve the problem. Basu et al [25] applied improved particle swarm optimization for global optimization of unimodal and multimodal functions. Arya et al [26] did active power rescheduling for avoiding voltage collapse using modified bare bones particle swarm optimization. Jain et al [27] had done a review of particle swarm optimization. Kela et al [28] did optimization of radial distribution systems employing differential evolution and bare bones particle swarm optimization. Jain et al [29] had done economic load dispatch using adaptive social acceleration constant based particle swarm optimization. Verma et al [30] applied modified sigmoid function based gray scale image contrast enhancement using particle swarm optimization. Chiu et al [31] have done image reconstruction of a buried conductor by modified particle swarm optimization. Dash et al [32] applied hybrid particle swarm optimization and unscented filtering technique for estimation of non-stationary signal parameters. Fahimeh et al [33] did optimizing radio frequency identification networks planning by using particle swarm optimization algorithm with fuzzy logic controller and mutation. Biswal et al [34] did time frequency analysis and

non-stationary signal classification using PSO based fuzzy C-means algorithm. Chaitanya et al [35] had done antenna pattern synthesis using the quasi-Newton method, firefly and particle swarm optimization techniques. Pranav et al [36] applied a hybrid PSO-ANN-based fault classification system for EHV transmission lines. Kumar et al [37] did enhancing the performance of healthcare service in IoT and cloud using optimized techniques. Sahu Barnali et al [38] applied adaptive improved binary PSO based learnable Bayesian classifier for dimensionality reduced microarray data. Ayoubi et al [39] did synchronization of SA and AV node oscillators using PSO optimized RBF-based controllers and comparison with adaptive control. Mishra et al [40] had analysed the performance optimization of PV powered SRM driven water pump using modified Cuk converter. Singh et al [41] did unity power factor operated PFC converter-based power supply for computers. Singh et al [42] had done multiobjective economic load dispatch problem solved by new PSO. Gupta et al [43] did performance analysis of radial distribution systems with UPQC and D-STATCOM. Hazarika et al [44] had done a voltage stability index for an interconnected power system based on network partitioning technique. Teeparthi et al [45] applied an improved artificial physics optimization algorithm approach for static power system security analysis. Kumar Sharma et al [46] did an analysis of mesh distribution systems considering load models and load growth impact with loops on system performance. Chejarla et al [47] found multiple solutions for optimal PMU placement using a topology-based method. Kumar et al [48] had done about FACTS devices impact on congestion mitigation of power system. Gupta et al [49] did comparison of deterministic and probabilistic radial distribution systems load flow. Kanagasabai [50-52] solved real power loss reduction by North American sapsucker algorithm, did real power loss reduction by Duponchelia fovealis optimization and enriched squirrel search optimization algorithms, solved optimal reactive power problem by Alaskan Moose Hunting, Larus Livens and Green Lourie swarm optimization algorithms. Omelchenko et al [53-55] did development of a design algorithm for the logistics system of product distribution of the mechanical engineering enterprise, the work on organization of logistic systems of scientific productions, and solved the problems and organizational and technical solutions of processing management problems of material and technical resources in a design-oriented organization. Yet many approaches failed to reach the global optimal solution. In this paper Maine Coon and Perognathinae based optimization (MPO) algorithm is proposed to solve the power loss lessening problem. Usual behaviour between Maine Coon and Perognathinae is imitated to formulate the MPO algorithm. In the proposed MPO algorithm, the crusade of Maine Coon towards Perognathinae as well as the spurt of Perognathinae in the

direction of anchorages is replicated. Proposed MPO algorithm is population-based procedure which is premeditated by imitating the natural actions of a Maine Coon assaults on Perognathinae and absconding of Perognathinae to the anchorage. The exploration agents in the projected MPO algorithm are alienated into two clusters of Maine Coon's and Perognathinae that examine the problem exploration space with arbitrary activities. The projected MPO algorithm apprises population associates in two segments. In the principal segment, the crusade of Maine Coon's in the direction of Perognathinae is modelled, and in the subsequent segment, the absconding behaviour of Perognathinae to anchorages to protect its life is designed. From a scientific fact of opinion, every associate of the populace is a recommended solution to the problem. In detail, an associate of the population postulates standards for the problem parameters rendering to its location in the exploration space. Consequently, every associate of the population is a vector whose standards regulate the parameters of the problem. Every associate of the populace controls the projected values for the parameters of the problem. Consequently, for every associate of the populace, a rate is quantified for objective function. Grounded on the rates attained for the objective functions, the associates of the populace are categorized from the finest associate with the lowermost rate and sequentially the poorest associate of the populace with the uppermost rate of the objective function. In the proposed MPO algorithm the population matrix possesses two clusters of Maine Coon and Perognathinae. It is presumed that partial population associates and providing improved standards for the objective function establish the populace of Perognathinae and remaining population associates with inferior rates for the objective function establish as Maine Coon population. In the subsequent segment of the proposed MPO algorithm, the absconding of Perognathinae to anchorages is designed. In MPO algorithm, it is presumed that there is an arbitrary anchorage for every Perognathinae, and in these anchorages Perognathinae will hide. The location of the anchorages in the exploration space is arbitrarily generated grounded on modelling the locations of dissimilar associates of the procedure. Proposed MPO algorithm is appraised in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation has been attained.

Problem formulation. Power loss minimization is defined by

min F ( d, e"),

where min is minimization of power loss. Subject to the constraints

A (d, e ) = 0, B (d, e ) = 0.

Real Power Loss Reduction by Maine Coon and Perognathinae Based Optimization Algorithm

Here d, ~e are control and dependent variables,

d = [VLG 1,..., VLGNg; QCi,..., QCnc ; Ti,..., Tnt ],

e = [ PGsiack; VLi,..., VLnl ; QGi,..., QG^g; SLi,..., SLnt ],

where VLG is level of the voltage; QC reactive power compensators; T is tap setting of transformers; PGsiack is slack generator; VL is voltage on transmission lines; QG is generation unit's reactive power; SL is apparent power.

The fitness function (Fi, F2, F3) is designed for power loss (MW) lessening, voltage deviancy, voltage constancy index (L-index) is defined by:

F = Pmin = min

NTL

F2 = min

X Gm [V2 + Vj - IViVj cos 0,

m

2 Ng

X |VLfc -VLfsired\ +X|QGk -QGK

NLB

X

i=l

lim

i=i

F3 = min Lmax,

where NTL is number of transmission line; VLk is load voltage in k-th load bus; VLdkestred is voltage desired at the k-th load bus; QGK is reactive power generated at k-th load bus generators; QGKlim is reactive power limitation; NLB, Ng are number load and generating units,

Lmax = max [ Lj ], j = i, „., NLB,

if

then

NPV

Lj = l- E Fji (Vi / Vj), i=i

Fji = ~[Yi 31 [ Y2 ], 1 "tY1 ]_1 [ Y2 ](V / Vj У

Lmax — max

Parity constraints

0 = PGi - PDi - Vi X Vj [ Gij cos [ 0i - 0j ] + Bj sin [ 0> - 0j ] ],

j e NB

0 = QGi - QDi - Vi X Vj [ Gij sin [ 0> - 0j ] + Bj cos [ 0, - 0j ] ].

j e NB

Disparity constraints

PGmn * PGslack < PGsak, QGjmin < QGi < QGimax, i e Ng, VLmin < VLi < VLmax, i e NL, Tmin < I- < Tmax, i e NT, QCmin < QC < QCmax, i e NC, |SLi| < SLmax, i e NTL, VGmin < VGi < VGmax, i e Ng. Multi objective fitness

MOF = F + riF2 + uF, =

= F +

NL 2 Ng 2

X Xv [ VLi - VLmin ] + X rg [ QGi - QG™" П i = 1 i = 1

+ rfF,

u is dependent variables;

(VLrnax, VL > VLf1ax, . [ QGmax, QGi > QGmax,

VLmm I i ' ^ i QG.mln =JV ^ V i

i [ VLmin, VLi < VLmin; i [ QGmin, QG{ < QGmin.

Maine Coon and Perognathinae based optimization algorithm. Usual behaviour between Maine Coon and Perognathinae is imitated to formulate the MPO algorithm. In the proposed MPO algorithm, the crusade of Maine Coon towards Perognathinae as well as the spurt of Perognathinae in the direction of anchorages is replicated.

Proposed MPO is population-based procedure which is premeditated by imitating the natural actions of a Maine Coon assaults on Perognathinae and absconding of Perognathinae to the anchorage. The exploration agents in the projected MPO algorithm are alienated into two clusters of Maine Coon's and Perognathinae that examine the problem exploration space with arbitrary activities. The projected MPO algorithm apprises population associates in two segments. In the principal segment, the crusade of Maine Coon's in the direction of Perognathinae is modelled, and in the subsequent segment, the absconding behaviour of Perognathinae to anchorages to protect its life is designed. From a scientific fact of opinion, every associate of the populace is a recommended solution to the problem. In detail, an associate of the population postulates standards for the problem parameters rendering to its location in the exploration space. Consequently, every associate of the population is a vector whose standards regulate the parameters of the problem. The population of the procedure is defined by population matrix as follows:

z =

Zn

Zl,1

ZN ,1

Zl,;

ZN,.

N x m

N x m

where Zi is is the explore agent; N is population number; m is number of parameters in the problem.

Every associate of the populace controls the projected values for the parameters of the problem. Consequently, for every associate of the populace, a rate is quantified for objective function. The rate attained for the objective function is symbolized by a vector as follows:

OFR1

OFR =

OFRi

OFRn

JN x 1

where OFRi functional rate of i-th explore agent.

Grounded on the rates attained for the objective functions, the associates of the populace are categorized from the finest associate with the lowermost rate and sequentially the poorest associate of the populace with the uppermost rate of the objective function. The organized population matrix as well as the categorized objective function is defined as

zf

zc =

Zi

zc ZN

61,1

"N ,1

"1,m

"N ,m

(1)

N x m

N x m

OFR =

" OFRc ' minOFR

_OFRN_ N х 1 maxOFR N x1

(2)

Here Zc is classified population rate; Zf is the i-th associate of the classified population.

In the proposed MPO algorithm the population matrix possesses two clusters of Maine Coon and Perognathinae. It is presumed that partial population associates and providing improved standards for the objective function establish the populace of Perognathinae and remaining population associates with inferior rates for the objective function establish as Maine Coon population. Grounded on this notion, the populaces of Maine Coon and Perognathinae are defined as

MC =

MC1 = ZNp +1

MCj = ZNp + j

MCnmc = Zc

"NP +1,1

_ Np +NMC ,1

"NP +1, P

"Np + NMC, P J

(3)

NP x P

P =

Np +NMC _

P1 = zc

Pi = zc

PNP = zNP j

^1,1

_ NP ,1

'1, P

"NP, P _

(4)

NP x P

where MC is the Maine Coon population; P is Perognathinae's population; NP is number of Perognathinae; NMC is the number of Maine Coon.

To modernize the exploration elements, in the principal segment, the alteration of location of Maine Coon is modelled grounded on the normal behaviour of Maine Coon and crusade in the direction of Perognathinae. This segment of the modernization of the projected MPO algorithm is scientifically defined as

MCnew

:MCnJ = MCjd + R ( pk,d - QMCjd ) and j = 1, Nmc,d = 1, —, MC, k e 1, .„, Nmc , Q = circle + (1 + Rand),

MCj =

MCnew,

OFRMCWj < OFRmc , j,

MCj, otherwise,

(5)

(6)

(7)

(8)

is the new position of Maine Coon; R e[ 0,1]; pk,d is the k-th Perognathinae dimension.

MCnew

In the subsequent segment of the proposed MPO algorithm, the absconding of Perognathinae to anchorages is designed. In MPO algorithm, it is presumed that there is an arbitrary anchorage for every Perognathinae, and in these anchorages Perognathinae will hide. The location of the anchorages in the exploration space is arbitrarily generated grounded on modelling the locations of dissimilar associates of the procedure. This segment of modernizing the location of Perognathinae is scientifically defined as

Ai :ai,d = zi,d, (9)

Np,d = 1,...,P, l el, ...,N, (10)

pnew : pnew = p.,d + r (ui4 _ Qp,d ) sign(OFRf - OFRf ) and i = 1, (11)

i = 1,...,Np, d = 1,..., m, (12)

pnew

Pi =

OFRp,new < OFRf,

i i (13)

Pi, otherwise.

Here Ai is the anchorage of the Perognathinae; OFRa is the rate of the objective function; pnew is the new position of the Perognathinae.

Subsequently all associates of the procedure's populace have been rationalised, the procedure pass in to the subsequent iteration and, grounded on equations the iterations of the procedure endure up until the end situation is grasped. The condition for ending the optimization procedures can be a definite number of iterations, or by outlining an adequate error amongst attained elucidations in successive recapitulations. Additionally, the situation for ending the procedure may be within assured stage of time. Figure 1 shows the procedure flow diagram of MPO algorithm:

a. Start

b. Parameters are fixed

c. Fix the number of explore agents

d. Set the number of iterations

e. Arbitrarily engender the preliminary population matrix

f. Calculate the objective functional value

g. For t = 1, ..., T

h. Based on objective function categorize the population matrix

i. (1)

j. (2)

k. Select the Perognathinae population l. (4)

m. Select the Maine Coon population n. (3)

o. First segment: modernize the position of Maine Coon p. For j = 1,..., Nmc

q. Modernize the position of the j-th Maine Coon

r. (5)

s. (6)

t. (7)

u. (8)

v. End

w. Second segment: modernize the position of Perognathinae x. For i = 1,..., NP

y. Construct anchorage for the i-th Perognathinae z. (9) aa. (10)

bb. Modernize the position of the i-th Perognathinae

cc. (11)

dd. (12)

ee. (13)

ff. End

gg. End

hh. Output the most excellent optimal solution ii. End

Simulation results. Projected MPO algorithm is corroborated in IEEE 30 bus system [20]. In Table 1 is shown the loss appraisal, Table 2 shows the voltage aberration evaluation and Table 3 gives the L-index assessment. Figures 2 to 4 give the graphical appraisal between the methods. MSO and EMSO abridged the power loss efficiently. Appraisal of loss has been done with PSO, adapted PSO, enhanced PSO, comprehensive learning PSO, Adaptive genetic algorithm, Canonical genetic algorithm, enhanced genetic algorithm, Hybrid PSO-Tabu search (PSO-TS), Ant lion (ALO), quasi-oppositional teaching learning based (QO-TBO), enhanced stochastic fractal search optimization algorithm (ISFS), harmony search (HS), upgraded pseudo-gradient search particle swarm optimization and cuckoo search algorithm. Power loss abridged competently and proportion of the power loss lessening has been enhanced. Predominantly voltage constancy augmentation attained with minimized voltage deviancy.

Fig. 1. Procedure flow diagram of MPO algorithm

The Table 1 and Fig. 2 show the appraisal of power loss and assessment done with Basic PSO-TS [10], Standard TS [10], Basic PSO [10], ALO [11], Basic QO-TLBO [12], Standard TLBO [12], Standard GA [13], Basic PSO [13], HAS [13], Standard FS [14], IS-FS [14] and Standard FS [15].

Table 1

Assessment of entire power loss

Algorithm Power loss, MW Algorithm Power loss, MW

Basic PSO-TS [10] 4.5213 Basic PSO [13] 4.9239

Standard TS [10] 4.6862 HAS [13] 4.9059

Basic PSO [10] 4.6862 Standard FS [14] 4.5777

End of the Table 1

Algorithm Power loss, MW Algorithm Power loss, MW

ALO [11] 4.5900 IS-FS [14] 4.5142

Basic QO-TLBO [12] 4.5594 Standard FS [16] 4.5275

Standard TLBO [12] 4.5629 MPO 4.4555

Standard GA [13] 4.9408

£ S

u

о &

's

У ai

5.0 4.9 4.8 4.7 4.6 4.5 4.4 4.3 4.2

¿¡¡F

cf

¿fef

cf

Fig. 2. Assessment of real power loss, MW

Table 2 and Fig. 3 show the evaluation of voltage deviancy and assessment done with Basic PSO-TVIW [15], Basic PSO-TVAC [15], Standard PSO-TVAC [15], Basic PSO-CF [15], PG-PSO [15], SWT-PSO [15], PGSWT-PSO [15], MPG-PSO [15], QO-TLBO [12], TLBO [12], Standard FS [14], ISFS [14] and Standard FS [16].

Table 2

Comparison of voltage deviancy

Algorithm Voltage deviancy, PU Algorithm Voltage deviancy, PU

Basic PSO-TVIW [15] 0.1038 MPG-PSO [15] 0.0892

Basic PSO-TVAC [15] 0.2064 QO-TLBO [12] 0.0856

Standard PSO-TVAC [15] 0.1354 TLBO [12] 0.0913

Basic PSO-CF [15] 0.1287 Standard FS [14] 0.1220

PG-PSO [15] 0.1202 ISFS [14] 0.0890

SWT-PSO [15] 0.1614 Standard FS [16] 0.0877

PGSWT-PSO [15] 0.1539 MPO 0.0819

Fig. 3. Appraisal of voltage deviancy, PU

Table 3 and Fig. 4 shows the voltage constancy and assessment done with Basic PSO-TVIW [15], Basic PSO-TVAC [15], Standard PSO-TVAC [15], Basic PSO-CF [15], PG-PSO [15], SWT-PSO [15], PGSWT-PSO [15], MPG-PSO [15], QO-TLBO [12], Standard TLBO [12], ALO [11], ABC [11], Standard GWO [11], Basic BA [11], Standard FS [14], IS-FS [14] and Standard FS [15].

Table 3

Appraisal of voltage constancy

Algorithm Voltage constancy (L-index), PU Algorithm Voltage constancy (L-index), PU

Basic PSO-TVIW [15] 0.1258 ALO [11] 0.1161

Basic PSO-TVAC [15] 0.1499 ABC [11] 0.1161

Standard PSO-TVAC [15] 0.1271 Standard GWO [11] 0.1242

Basic PSO-CF [15] 0.1261 Basic BA [11] 0.1252

PG-PSO [15] 0.1264 Standard FS [14] 0.1252

SWT-PSO [15] 0.1488 IS-FS [14] 0.1245

PGSWT-PSO [15] 0.1394 Standard FS [16] 0.1007

MPG-PSO [15] 0.1241 MPO 0.1001

QO-TLBO [12] 0.1191 ALO [11] 0.1161

Standard TLBO [12] 0.1180 ABC [11] 0.1161

MPO

Standard PSO-

JVIW Standard PSO-TSV

Standard FS IS-FS

Basic FS

Standard PSO-TVAC Standard PSO-CF PG-PSO

Standard WTPSO PGSWT-PSO

ALO

MPG-PSO QO-TLBO

TLBO

Fig. 4. Assessment of voltage constancy, PU

Then projected MPO algorithm is substantiated in IEEE 14, 30, 57, 118 and 300 bus test systems deprived of L-index. Loss appraisal is shown in Tables 4 to 8. Figure 5 to 9 gives graphical comparison between the approaches with orientation to power loss. Proposed algorithms are compared with Adapted PSO, PSO, EP, SARGA, CGA, AGA, EPSO, CLPSO, AGA, FEA and CSO.

Table 4 and Fig. 5 shows the actual power loss appraisal for IEEE 14 bus system without considering voltage constancy and assessment done with Base case [23], Adapted PSO [23], PSO [23], EP [22] and SARGA [21].

Table 4

Assessment of results (IEEE 14 bus)

Algorithm True loss, MW Ratio of loss diminution

Base case [23] 13.550 0

Adapted PSO [23] 12.293 9.2

PSO [22] 12.315 9.1

EP [22] 13.346 1.5

SARGA [21] 13.216 2.5

MPO 9.9999 26.2

Base case

Fig. 5. Power loss appraisal (IEEE 14 bus system)

Table 5 and Fig. 6 shows the actual power loss appraisal for IEEE 30 bus system without considering voltage constancy and assessment done with Base case value [23], M-PSO[23], Basic-PSO [22], EP [20], S-GA [21], PSO [24], DEPSO [24] and JAYA [24].

Table 5

Appraisal of loss (IEEE 30 bus system)

Algorithm Actual power loss, MW Proportion of lessening in power loss

Base case value [23] 17.5500 0

M-PSO [23] 16.0700 8.40000

Basic PSO [22] 16.2500 7.40000

EP [20] 16.3800 6.60000

S-GA [21] 16.0900 8.30000

PSO [24] 17.5246 0.14472

DEPSO [24] 17.5200 0.17094

JAYA [24] 17.5360 0.07977

MPO 13.1900 24.8433

The Table 6 and Fig. 7 show the actual power loss appraisal for IEEE 57 bus system without considering voltage constancy and assessment done with Base case [23], Adapted PSO [23], PSO [22], CGA [21] and AGA [21].

Base case value

MPO

Adapted PSO

JAYA

DEPSO

Power loss, MW

Proportion of lessening

Standard EP i*P°werloss

Basic PSO

Standard GA

Fig. 6. Appraisal of power loss (IEEE 30 bus system)

Table 6

Assessment of parameters

Algorithm True loss, MW Ratio of loss diminution

Base case [23] IEEE 57 bus sys 27.800 tem 0

Adapted PSO [23] 23.510 15.400

PSO [22] 23.860 14.100

CGA [21] 25.240 9.2000

AGA [21] 24.560 11.600

MPO 20.009 28.0251

Base case [23] IEEE 118 bus sys 132.8 tem 0.00

Adapted PSO [23] 117.19 11.700

PSO [22] 119.34 10.100

EPSO [20] 131.99 0.600

CLPSO [20] 130.96 1.300

MPO 110.90 16.4909

Table 6 and Fig. 8 shows the real power loss appraisal for IEEE 118 bus system without considering voltage constancy and assessment done with Base case [23], Adapted PSO [23], PSO [22], EPSO [20] and CLPSO [20].

Base case 30/

MPO

SARGA

Adapted PSO

True loss, MW

Ratio of loss diminution

PSO

Fig. 7. Power loss appraisal (IEEE 57 bus system)

Base case 140/

MPO

CLPSO

Adapted PSO

True loss, MW

Ratio of loss diminution

PSO

EPSO

Fig. 8. Power loss appraisal (IEEE 118 bus system)

The Table 7 and Fig. 9 show the real power loss appraisal for IEEE 300 bus system without considering voltage constancy and assessment done with AGA [34], FEA [34] and CSO [33].

Table 7

Power loss appraisal (IEEE 300 bus system)

Algorithm True loss, MW

AGA [34] 646.299S00

FEA [35] 650.602700

CSO [34] 635.S94200

MPO 624.11234S

AGA

Fig. 9. Power loss appraisal (IEEE 300 bus system)

Conclusion. MPO algorithm successfully solved the power loss lessening problem. In the proposed MPO algorithm, the crusade of Maine Coon towards Perognathinae as well as the spurt of Perognathinae in the direction of anchorages is replicated. Proposed MPO algorithm is population-based procedure which is premeditated by imitating the natural actions of a Maine Coon assaults on Perognathinae and absconding of Perognathinae to the anchorage. The exploration agents in the projected MPO algorithm are alienated into two clusters of Maine Coon's and Perognathinae that examine the problem exploration space with arbitrary activities. The projected MPO algorithm apprises population associates in two segments. In the principal segment, the crusade of Maine Coon's in the direction of Perognathinae is modelled, and in the subsequent segment, the absconding behaviour of Perognathinae to anchorages to protect its life is designed. From a scientific fact of opinion, every associate of the populace is a recommended solution to the problem. In detail, an associate of the population postulates standards for the problem parameters rendering to its location in the exploration space. Consequently, every associate of the population is a vector whose standards regulate the parameters of the problem. Every associate of the populace controls the projected values for the parameters of the problem. Consequently, for every associate of the populace, a rate is quantified for objective function. Grounded on the rates attained for the objective functions, the associates of the populace are categorized from the finest associate with the lowermost rate and sequentially the poorest associate of the populace with the uppermost rate of the objective function. In the proposed MPO algorithm the population matrix possesses two clusters of Maine Coon and Perognathinae. It is presumed that partial population associates and providing improved standards for the objective function establish the populace of Perognathinae and remaining population

associates with inferior rates for the objective function establish as Maine Coon population. In the subsequent segment of the proposed MPO algorithm, the absconding of Perognathinae to anchorages is designed. In MPO, it is presumed that there is an arbitrary anchorage for every Perognathinae, and in these anchorages Perognathinae will hide. The location of the anchorages in the exploration space is arbitrarily generated grounded on modelling the locations of dissimilar associates of the procedure. Subsequently all associates of the procedure's populace have been rationalised, the procedure pass in to the subsequent iteration and, grounded on Equations the iterations of the procedure endure up until the end situation is grasped. The condition for ending the optimization procedures can be a definite number of iterations, or by outlining an adequate error amongst attained elucidations in successive recapitulations. Additionally, the situation for ending the procedure may be within assured stage of time. Proposed MPO algorithm is appraised in IEEE 30 bus system and IEEE 14, 30, 57, 118, 300 bus test systems without considering the voltage constancy index. True power loss lessening, voltage divergence curtailing, and voltage constancy index augmentation have been attained.

REFERENCES

[1] Lee K., Park Y.M., Ortiz J.L. Fuel-cost minimization for both real and reactive-power dispatches. IEE Proceedings C, 1984, vol. 131, iss. 3, pp. 85-93.

DOI: https://doi.org/10.1049/ip-c.1984.0012

[2] Deeb N., Shahidehpour M. An efficient technique for reactive power dispatch using a revised linear programming approach. Electr. Power Syst. Res., 1988, vol. 15, iss. 2, pp. 121-134. DOI: https://doi.org/10.1016/0378-7796(88)90016-8

[3] Bjelogrlic M., Calovic M.S., Ristanovic P., et al. Application of Newton's optimal power flow in voltage/reactive power control IEEE Trans. Power Syst., 1990, vol. 5, iss. 4, pp. 1447-1454. DOI: https://doi.org/10.1109/59.99399

[4] Granville S. Optimal reactive dispatch through interior point methods. IEEE Trans. Power Syst., 1994, vol. 9, iss. 1, pp. 136-146. DOI: https://doi.org/10.1109/59.317548

[5] Grudinin N. Reactive power optimization using successive quadratic programming method. IEEE Trans. Power Syst., 1998, vol. 13, iss. 4, pp. 1219-1225.

DOI: https://doi.org/10.1109/59.736232

[6] Mavaddat N., Michailidou K., Dennis J., et al. Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. Am. J. Hum. Genet., 2019, vol. 104, iss. 1, pp. 21-34. DOI: https://doi.org/10.1016/j.ajhg.2018.11.002

[7] Kanimozhi U., Ganapathy S., Manjula D., et al. An intelligent risk prediction system for breast cancer using fuzzy temporal rules. Natl. Acad. Sci. Lett., 2019, vol. 42, no. 3, pp. 227-232. DOI: https://doi.org/10.1007/s40009-018-0732-0

[8] Gupta K., Janghel R. Dimensionality reduction-based breast cancer classification using machine learning. In: Verma N., Ghosh A. (eds). Computational Intelligence. Theories, Applications and Future Directions. Vol. 798. Singapore, Springer, 2019, pp. 133-146. DOI: https://doi.org/10.1007/978-981-13-1132-1_11

[9] Sangaiah I., Vincent Antony Kumar A. Improving medical diagnosis performance using hybrid feature selection via relieff and entropy based genetic search (RF-EGA) approach: application to breast cancer prediction. Cluster Comput., 2019, vol. 22, no. S3, pp. 6899-6906. DOI: https://doi.org/10.1007/s10586-018-1702-5

[10] Mouassa S., Bouktir T., Salhi A. Ant lion optimizer for solving optimal reactive power dispatch problem in power systems. Eng. Sci. Technol. an Int. J., 2017, vol. 20, iss. 3, pp. 885-895. DOI: https://doi.org/10.1016/j.jestch.2017.03.006

[11] Mandal B., Roy P.K. Optimal reactive power dispatch using quasi-oppositional teaching learning based optimization. Int. J. Electr. Power Energy Syst., 2013, vol. 53, pp. 123-134. DOI: https://doi.org/10.1016/j.ijepes.2013.04.011

[12] Khazali H., Kalantar M. Optimal reactive power dispatch based on harmony search algorithm. Int. J. Electr. Power Energy Syst., 2011, vol. 33, iss. 3, pp. 684-692.

DOI: https://doi.org/10.1016/j.ijepes.2010.11.018

[13] Tran H.V., Pham T.V., Pham L.H., et al. Finding optimal reactive power dispatch solutions by using a novel improved stochastic fractal search optimization algorithm. TELKOMNIKA, 2019, vol. 17, no. 5, pp. 2517-2526.

DOI: http://doi.org/10.12928/telkomnika.v17i5.10767

[14] Polprasert J., Ongsakul W., Dieu V.N. Optimal reactive power dispatch using improved pseudo-gradient search particle swarm optimization. Electr. Power Compon. Syst., 2016, vol. 44, iss. 5, pp. 518-532.

DOI: https://doi.org/10.1080/15325008.2015.1112449

[15] Duong T.L., Duong M.Q., Phan V.-D., et al. Optimal reactive power flow for large-scale power systems using an effective metaheuristic algorithm. J. Electr. Comput. Eng., 2020, vol. 2020, art. 6382507. DOI: https://doi.org/10.1155/2020/6382507

[16] Bhattacharya A., Chattopadhyay P.K. Solution of optimal reactive power flow using biogeography-based optimization. Int. J. Electr. Electron. Eng., 2010, vol. 4, no. 3, pp.621-629.

[17] Duman S., Sönmez Y., Gûvenç U., et al. Optimal reactive power dispatch using a gravitational search algorithm. IET Gener. Transm. Distrib., 2012, vol. 6, iss. 6, pp. 563576. DOI: https://doi.org/10.1049/iet-gtd.2011.0681

[18] Li Wu. Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and lévy walk. J. Mod. Power Syst. Clean Energy, 2014, vol. 2, no. 4, pp. 308-318.

DOI: https://doi.org/10.1007/s40565-014-0076-9

[19] MATPOWER 4.1 IEEE 30-bus and 118-bus test system. 2019. Available at: https://matpower.org

[20] Dai C., Chen W., Zhu Y., et al. Seeker optimization algorithm for optimal reactive power dispatch. IEEE Trans. Power Syst., 2009, vol. 24, iss. 3, pp. 1218-1231. DOI: https://doi.org/10.1109/TPWRS.2009.2021226

[21] Subbaraj P., Rajnarayan P.N. Optimal reactive power dispatch using self-adaptive real coded genetic algorithm. Electr. Pow. Syst. Res., 2009, vol. 79, iss. 2, pp. 374-381. DOI: https://doi.org/10.1016/jj.epsr.2008.07.008

[22] Pandya S., Roy R. Particle swarm optimization based optimal reactive power dispatch. Proc. ICECCT, 2015. DOI: https://doi.org/10.1109/ICECCT.2015.7225981

[23] Hussain A.N., Abdullah A.A., Neda O.M. Modified particle swarm optimization for solution of reactive power dispatch. Res. J. Appl. Sci. Eng. Technol., 2018, vol. 15, iss. 8, pp. 316-327. DOI: http://dx.doi.org/10.19026/rjaset.15.5917

[24] Vishnu M., Kumar T.K.S. An improved solution for reactive power dispatch problem using diversity-enhanced particle swarm optimization. Energies, 2020, vol. 13, iss. 11, art. 2862, pp. 2-21. DOI: https://doi.org/10.3390/en13112862

[25] Basu M. Improved particle swarm optimization for global optimization of unimodal and multimodal functions. J. Inst. Eng. India Ser. B., 2016, vol. 97, no. 4, pp. 525-535. DOI: https://doi.org/10.1007/s40031-015-0204-6

[26] Arya R., Purey P. Active power rescheduling for avoiding voltage collapse using modified bare bones particle swarm optimization. J. Inst. Eng. India Ser. B, 2016, vol. 97, no. 2, pp. 109-113. DOI: https://doi.org/ 10.1007/s40031-015-0209-1

[27] Jain N.K., Nangia U., Jain J. A review of particle swarm optimization. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 4, pp. 407-411.

DOI: https://doi.org/10.1007/s40031-018-0323-y

[28] Kela K.B., Arya L.D. Reliability optimization of radial distribution systems employing differential evolution and bare bones particle swarm optimization. J. Inst. Eng. India Ser. B, 2014, vol. 95, no. 3, pp. 231-239.

DOI: https://doi.org/10.1007/s40031-014-0094-z

[29] Jain N.K., Nangia U., Jain J. Economic load dispatch using adaptive social acceleration constant based particle swarm optimization. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 5, pp. 431-439. DOI: https://doi.org/10.1007/s40031-018-0322-z

[30] Verma H.K., Pal S. Modified sigmoid function based gray scale image contrast enhancement using particle swarm optimization. J. Inst. Eng. India Ser. B, 2016, vol. 97, no. 2, pp. 243-251. DOI: https://doi.org/10.1007/s40031-014-0175-z

[31] Chiu C.C., Chen C.H., Fan Y.S. Image reconstruction of a buried conductor by modified particle swarm optimization. IETE J. Res., 2012, vol. 58, no. 4, pp. 284-291.

[32] Dash P.K., Panigrahi B.K., Hasan S. Hybrid particle swarm optimization and un-scented filtering technique for estimation of non-stationary signal parameters. IETE J. Res., 2009, vol. 55, no. 6, pp. 266-274.

[33] Fahimeh Z., Mehdi G., Mehdi H. Optimizing radio frequency identification networks planning by using particle swarm optimization algorithm with fuzzy logic controller and mutation. IETE J. Res., 2017, vol. 63, iss. 5, pp. 728-735.

DOI: https://doi.org/10.1080/03772063.2015.1083905

[34] Biswal P., Dash P.K., Panigrahi B.K. Time frequency analysis and non-stationary signal classification using PSO based fuzzy C-means algorithm. IETE J. Res., 2007, vol. 53, iss. 5, pp. 441-450. DOI: https://doi.org/10.1080/03772063.2007.10876159

[35] Chaitanya R.K., Raju G.S.N., Raju K.V.S.N., et al. Antenna pattern synthesis using the quasi-Newton method, firefly and particle swarm optimization techniques. IETE J. Res., 2019, vol. 68, iss. 2, pp. 1148-1156. DOI: https://doi.org/10.1080/03772063.2019.1643263

[36] Raval P.D., Pandya A.S. A hybrid PSO-ANN-based fault classification system for EHV transmission lines. IETE J. Res., 2022, vol. 68, iss. 4, pp. 3086-3099.

DOI: https://doi.org/10.1080/03772063.2020.1754299

[37] Kumar P., Silambarasan K. Enhancing the performance of healthcare service in IoT and cloud using optimized techniques. IETE J. Res., 2019, vol. 68, iss. 2, pp. 1475-1484. DOI: https://doi.org/10.1080/03772063.2019.1654934

[38] Barnali S., Kumar J.A., Satchidananda D. Adaptive improved binary PSO-based learnable Bayesian classifier for dimensionality reduced microarray data. Int. J. Med. Eng. Inform., 2019, vol. 11, iss. 3, pp. 265-285.

DOI: https://doi.org/10.1504/IJMEI.2019.10020712

[39] Ayoubi A., Sanie M.S., Kazemi M. Synchronisation of SA and AV node oscillators using PSO optimised RBF-based controllers and comparison with adaptive control. Int. J. Med. Eng. Inform., 2016, vol. 8, no. 3, pp. 210-224.

DOI: https://doi.org/10.1504/IJMEI.2016.077438

[40] Mishra A.K., Singh B. Performance optimization of PV-powered SRM-driven water pump using modified CUK converter. J. Inst. Eng. India Ser. B., 2019, vol. 100, no. 3, pp. 249-258. DOI: https://doi.org/10.1007/s40031-019-00381-4

[41] Singh S., Singh B., Bhuvaneswari G., et al. Unity power factor operated PFC converter based power supply for computers. J. Inst. Eng. India Ser. B, 2018, vol. 99, no. 1, pp. 49-60. DOI: https://doi.org/10.1007/s40031-017-0303-7

[42] Singh N., Kumar Y. Multiobjective economic load dispatch problem solved by new PSO. Adv. Electr. Electron. Eng., 2015, vol. 2015, art. 536040.

DOI: https://doi.org/10.1155/2015/536040

[43] Gupta A.R., Kumar A. Performance analysis of radial distribution systems with UPQC and D-STATCOM. J. Inst. Eng. India Ser. B., 2017, vol. 98, no. 4, pp. 415-422. DOI: https://doi.org/10.1007/s40031-016-0254-4

[44] Hazarika D., Hussain S.A. A voltage stability index for an interconnected power system based on network partitioning technique. J. Inst. Eng. India Ser. B., 2018, vol. 99, no. 6, pp. 565-573. DOI: https://doi.org/10.1007/s40031-018-0353-5

[45] Teeparthi K., Kumar D.M.V. An improved artificial physics optimization algorithm approach for static power system security analysis. J. Inst. Eng. India Ser. B., 2020, vol. 101, no. 4, pp. 347-359. DOI: https://doi.org/10.1007/s40031-020-00457-6

[46] Kumar Sharma A., Murty V.V.S.N. Analysis of mesh distribution systems considering load models and load growth impact with loops on system performance. J. Inst. Eng. India Ser. B., 2014, vol. 95, no. 4, pp. 295-318.

DOI: https://doi.org/10.1007/s40031-014-0108-x

[47] Chejarla M.K.D., Matam S.K. Multiple solutions for optimal PMU placement using a topology-based method. J. Inst. Eng. India Ser. B., 2021, vol. 102, no. 2, pp. 249-259. DOI: https://doi.org/10.1007/s40031-020-00532-y

[48] Kumar J., Kumar N. FACTS devices impact on congestion mitigation of power system. J. Inst. Eng. India Ser. B., 2020, vol. 101, no. 3, pp. 239-254.

DOI: https://doi.org/10.1007/s40031-020-00450-z

[49] Gupta A.R., Kumar A. Comparison of deterministic and probabilistic radial distribution systems load flow. J. Inst. Eng. India Ser. B., 2017, vol. 98, no. 6, pp. 547-556. DOI: https://doi.org/10.1007/s40031-017-0288-2

[50] Kanagasabai L. Real power loss reduction by North American sapsucker algorithm. Int. J. Syst. Assur. Eng. Manag., 2022, vol. 13, no. 1, pp. 143-153.

DOI: https://doi.org/10.1007/s13198-021-01155-2

[51] Lenin K. Real power loss reduction by Duponchelia fovealis optimization and enriched squirrel search optimization algorithms. Soft. Comput., 2020, vol. 24, no. 23, pp. 17863-17873. DOI: https://doi.org/10.1007/s00500-020-05036-x

[52] Lenin K. Solving optimal reactive power problem by Alaskan Moose Hunting, Larus livens and Green Lourie Swarm optimization algorithms. Ain Shams Eng. J., 2020, vol. 11, iss. 4, pp. 1227-1235. DOI: https://doi.org/10.1016/j.asej.2020.03.019

[53] Omelchenko I.N., Lyakhovich D., Aleksandrov A.A., et al. Development of a design algorithm for the logistics system of product distribution of the mechanical engineering enterprise. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2020, no. 3 (132), pp. 62-69.

DOI: https://doi.org/10.18698/0236-3941-2020-3-62-69

[54] Omelchenko I., Zakharov M., Lyakhovich D., et al. [Organization of logistic systems of scientific productions: scientific research work of the master's student and evaluation of its results]. Sistemy upravleniya polnym zhiznennym tsiklom vysoko-tekhnologichnoy produktsii v mashinostroenii: novye istochniki rosta. Mater. III vseros. nauch.-prakt. konf. [Management Systems for the Full Life Cycle of High-Tech Products in Mechanical Engineering: New Sources of Growth. Materials of the III All-Russ. Sci.-Pract. Conf.]. Moscow, Pervoe ekonomicheskoe izdatelstvo Publ., 2020, pp. 252256 (in Russ.). DOI: https://doi.org/10.18334/9785912923258.252-256

[55] Omelchenko I., Lyakhovich D., Aleksandrov A., et al. [Problems and organizational and technical solutions of processing management problems of material and technical resources in a design-oriented organization]. Sistemy upravleniya polnym zhiznennym tsiklom vysokotekhnologichnoy produktsii v mashinostroenii: novye istochniki rosta. Mater. III vseros. nauch.-prakt. konf. [Management Systems for the Full Life Cycle of High-Tech Products in Mechanical Engineering: New Sources of Growth. Materials of the III All-Russ. Sci.-Pract. Conf.]. Moscow, Pervoe ekonomicheskoe izdatelstvo Publ., 2020, pp. 257-260 (in Russ.).

DOI: https://doi.org/10.18334/9785912923258.257-260

Kanagasabai Lenin — Dr. Sc. (Full), Professor, Department of Electrical and Electronics Engineering, Prasad V. Potluri Siddhartha Institute of Technology (Kanuru, Vijayawada, Andhra Pradesh, 520007 India).

Please cite this article as:

Kanagasabai L. Real power loss reduction by Maine Coon and Perognathinae based optimization algorithm. Herald of the Bauman Moscow State Technical University, Series Natural Sciences, 2023, no. 3 (108), pp. 61-84. DOI: https://doi.org/10.18698/1812-3368-2023-3-61-84