BANKS CAPACITOR COMPENSATION FOR CRITICAL NODAL DETECTION BY AUGMENTED RED WOLF OPTIMIZATION ALGORITHM

In this paper Banks Capacitor Compensation for Critical Nodal Detections by Augmented Red Wolf Optimization Algorithm has been worked out. Projected ERWO algorithm hybridizes the wolf optimization (WO) algorithm with swarm based algorithm called as particle swarm optimization (PSO) algorithm. In the approach each Red wolf has a flag vector, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). Exploration capability of the projected Red wolf optimization algorithm has been enriched by hybridization of both WO with PSO. Efficiency of the projected Enriched Red wolf optimization (ERWO) is tested in standard IEEE 57 bus test system. Simulation study indicates Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses.


Introduction
Reactive power problem plays major role in improving secure& economic of power system operation & control. To determine the size, type, and location of capacitor banks to be installed on radial distribution feeders to achieve positive economic response is the objective of optimal capacitor placement problem. Loss reductions weighted against capacitors costs with constraints are in limits to obtain the economic benefits. A variety of methodologies [1][2][3][4][5][6] have been implemented to solve the problem, but difficulty found in handling the constraints. Now days various types of Evolutionary algorithms implemented to solve problem [7][8][9][10][11][12][13][14][15][16][17][18][19]. For last twenty years various types of programming and probabilistic based approach has been used to solve the problem. In this work Enriched Red wolf optimization (ERWO) algorithm has been implemented to work out the problem. Both Exploration & Exploitation has been improved. In basic Wolf optimization algorithm (WO) [20], exploration spaces are missing the diversity and the highquality diversity is needed to upgrade the performance of the algorithm to find an optimal solution. Particle swarm optimization (PSO) [21] has good feature of exploration ability and it has been hybridized with Wolf optimization algorithm (WO) to produce an enriched version called as Enriched Red wolf optimization (ERWO). PSO will aid to form better preliminary population to WO. In standard IEEE 57 bus test system efficiency of Enriched Red wolf optimization (ERWO) algorithm has been evaluated. Results indicate that Enriched Red wolf optimization (ERWO) algorithm performs well in tumbling the actual power losses.

Problem Formulation
Reducing power loss, bus voltage with specified limits with minimized cost is the objective function of capacitor placement. Voltage limits are considered as constraints. Due to the capacitor placement the annual cost and power losses are given by, Where F -total annual cost function ($), ZPL the annual cost per unit of power losses, ($/KW), PLtotal active power loss (KW), N-Number of buses.
Constraint of voltage is given as

Red Wolf Optimization
Red wolf optimization mimics the communal management and hunt deeds of Red wolves in nature. There are three fittest candidate solutions assumed as , and to lead the population toward promising regions of the exploration space in each iteration of red wolf optimization. is named for the rest of Red wolves and it will assist , and to encircle, hunt, and attack prey, that is, to find Enriched solutions. In order to scientifically replicate the encompassing behavior of Red wolves, the following equations are proposed: Where indicates the current iteration, H ⃗⃗⃗ = 2b ⃗ ⃗⃗⃗ . r 1 ⃗⃗⃗⃗ − b ⃗⃗ , F ⃗⃗ = 2. r 2 ⃗⃗⃗⃗, Y P the position vector of the prey, Y ⃗ ⃗⃗ is the position vector of a Red wolf, b ⃗⃗ is linearly decreased from 2.0 to 0, and r 1 ⃗⃗⃗⃗and r 2 ⃗⃗⃗⃗are arbitrary vectors in [0, 1].
Hunting behavior of Red wolves are mathematically simulated by following equations, In this work, a new Enriched Red wolf optimization (ERWO) algorithm is proposed to solve reactive power dispatch problem & the position of a Red wolf was updated & the following equation is used to discrete the position.
Where i, indicates the jth position of the ith Red wolf, , is features of the wolf.

Particle Swarm Optimization
In Particle swarm optimization (PSO) algorithm the positions and velocities of the Particles are modernized as follows: The current position of particle is y t i &search velocity is v t i . Global best-found position is. m t g . In uniformly distributed interval (0, 1) Rm 1 &Rm 2 are arbitrary numbers. Where cg 1 and cg 2 are scaling parameters.ω t is the particle inertia. The variableω t is modernized as ω t = (ω max − ω min ).
(t max −t) t max + ω min (15) Maximum and minimum of ω t is represented by ω max and ω min ; maximum number of iterations is given byt max . Until termination conditions are met this process will be repeated.

Enriched Red Wolf Optimization (ERWO) Algorithm for Solving Optimal Reactive Power Dispatch Problem
In this approach red wolves α,β and γ determine the position of the prey. ⃗ ⃗⃗ = 2b ⃗ ⃗⃗⃗ . r 1 ⃗⃗⃗⃗ − b ⃗⃗ directs the exploration & exploitation process by reducing the value from 2 to 0. When |H ⃗⃗⃗ | < 1 it converged towards the prey& If |H ⃗⃗⃗ | > 1 diverged away. The first best Minimum loss and variables are accumulated as"α"position, score & as like second best, third best accumulated as "β" and " γ" position & score.

Commence
Initialize the parameters Initialize b, H ⃗⃗⃗⃗ andF ⃗⃗ ; beginning positions of Red wolves has been stimulated. i = 1: population size j=1: n When (i, j) > 0.500 (i) = 1; Else (j) = 0; End if End for Work out the maximum fitness of Red wolves as follows, Primary maximum fitness of the Red wolf is designated as " " Second maximum fitness of the Red wolf is designated as " " Third maximum fitness of the Red wolf is designated as " " While k <maximum iteration For i = 1: population size Exact Location of the existing Red wolf has been revised periodically End for For i = 1: population size For i=1: n;If (i, j) > 0.500 (j) = 1; Else (j) = 0; End if End for Sporadically revise the values of b, H ⃗⃗⃗⃗ andF ⃗⃗ ; At this stage Fitness of Red wolves has been calculated The assessment of red wolves " "," " and " "has to be revised k=k+1; End while Re-examine the value of" "as the optimal characteristic division; End

Simulated Outcomes
The Enriched Red Wolf Optimization (ERWO) has been applied in standard IEEE 57 bus system.

Conclusion
Enriched Red wolf optimization (ERWO) approach effectively solved the problem. Exploration & Exploitation has been considerably improved through the proposed methodology. Proposed technique has been tested in standard IEEE 57 bus test system. Comparison of the real power loss has been done along with reactive power values.