Opposition based red wolf algorithm for solving optimal reactive power problem

Received Feb 12, 2021 Revised Apr 2, 2021 Accepted Apr 24, 2021 This paper projects opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each Red wolf has a flag vector in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO). In this projected algorithm Red wolf optimization algorithm has been intermingled with opposition based learning (OBL). By this amalgamate procedure the convergence speed of the projected algorithm will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the proposed algorithm reduced the real power loss considerably.


INTRODUCTION
In this work the key objective is Actual power loss reduction. Optimal reactive power problem has been solved by a variety of methods [1]- [6]. However many technical hitches 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 key problem is some algorithms stuck in local optimal solution & failed to balance the exploration & exploitation during the search of global solution. This paper projects opposition based red wolf optimization (ORWO) algorithm for solving optimal reactive power problem. Each red wolf has a flag vector, in the algorithm, and length is equivalent to the whole sum of numbers which features in the dataset of the wolf optimization (WO) [17]- [19]. In this projected algorithm red wolf optimization algorithm has been intermingled with opposition based learning (OBL) [20]. By this convergence speed will be increased. To discover an improved candidate solution, the concurrent consideration of a probable and its corresponding opposite are estimated which is closer to the global optimum than an arbitrary candidate solution. Proposed algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the proposed algorithm reduced the real power loss considerably.

PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss: Voltage deviation given as (2).

OPPOSITION BASED 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 enhanced solutions. In order to scientifically replicate the encompassing behavior of red wolves, equation (10)  In order to mathematically simulate the hunting behavior of red wolves, equations (11), (12), (13) are proposed, The position of a red wolf was updated by (13) & (14) is used to discrete the position.
Int J Adv Appl Sci ISSN: 2252-8814  Opposition based red wolf algorithm for solving optimal reactive power problem (Lenin Kanagasabai)

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Where i, indicates the jth position of the ith Red wolf, , is features of the wolf. Opposition based learning (OBL) is one of the influential optimization tools to boost the convergence speed of different optimization techniques. The thriving implementation of the OBL engages evaluation of opposite population and existing population in the similar generation to discover the superior candidate solution of a given reactive power problem. The conception of opposite number requirements is to be defined to explain OBL. Let ( ∈ [ , ]) be a real number and the (opposite number) can be defined as (15).
In the exploration space it has been extended as (16).
Where (  Step 9: Using jumping rate, the opposite population are generated from the current population.
Step 10: Select nP number of fittest solution from the combination of the current and the opposite population.
Step 11: Repeat step 6 to step 10 until the maximum number of iteration is reached.
Step 12: Output the best solution.

Commence
Initialize the parameters Initialize b, H ⃗⃗⃗⃗ and F ⃗⃗ ; beginning positions of Red wolves has been stimulated. Generate opposite population; for j=; population size for i=1; number of control variables = + − Classify the existing population and the opposite population from best to worst Depending upon the population size nP, choose nP number of fittest solutions from the existing and the opposite population Modernize the control variables By utilizing the jumping rate, opposite population are generated from the existing population: 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 ⃗⃗⃗⃗ and F ⃗⃗ ; Fitness of Red wolves has been calculated The assessment of red wolves " "," " and " " has to be revised k=k+1; End while Revise the value of" "as the optimal characteristic division; End

SIMULATION RESULTS
At first in standard IEEE 14 bus system the validity of the proposed opposition based red wolf optimization (ORWO) algorithm has been tested & comparison results are presented in Table 1. Figure 1 Provide the details of Comparison of real power loss.  Then IEEE 300 bus system [22] is used as test system to validate the performance of the Opposition based Red Wolf Optimization (ORWO) algorithm. Table 2 shows the comparison of real power loss obtained after optimization. Figure 2 gives the comparison of real power values. Real power loss has been considerably reduced when compared to the other standard reported algorithms.

CONCLUSION
Opposition based red wolf optimization (ORWO) algorithm has successfully solved the reactive power optimization problem. In this projected algorithm red wolf optimization algorithm has been intermingled with opposition based learning (OBL). By this amalgamate procedure the convergence speed of the projected algorithm has been increased. Proposed algorithm has been tested in standard IEEE 14,300 bus test system and simulation results show the proposed opposition based red wolf optimization (ORWO) algorithm reduced the real power loss considerably.