Enhanced whale optimization algorithm for active power loss diminution

Received Nov 8, 2019 Revised Nov 15, 2019 Accepted Jan 12, 2020 In this paper Enhanced whale Optimization Algorithm (EWO) proposed to solve the optimal reactive power problem. Whale optimization algorithm is modeled by Bubble-net hunting tactic. In the projected optimization algorithm an inertia weight ω ∈ [1, 0] has been introduced to perk up the search ability. Whales are commonly moving 10-16 meters down then through the bubbles which are created artificially then they encircle the prey and move upward towards the surface of sea. Proposed Enhanced whale optimization algorithm (EWO) is tested in standard IEEE 57 bus systems and power loss reduced considerably.


INTRODUCTION
In this work minimization of real power loss is key goal. A variety of conventional techniques has been already solved the problem [1][2][3][4][5][6] but many techniques underwent complexity in managing the in-equality constraints. Subsequently evolutionary techniques [7][8][9][10][11][12][13][14][15] have been successfully solved the problem. In this work Enhanced whale Optimization Algorithm (EWO) is applied to solve the optimal reactive power problem. Whale algorithm modelled by Bubble-net hunting strategy of whale [16] and with respect to current excellent candidate, solution will be obtained. Alike to Particle Swarm Optimization algorithm, an inertia weight; ω ∈ [1, 0] is introduced into whale optimization algorithm to augment the search and called as Enhanced whale optimization algorithm. Projected EWO algorithm evaluated in standard IEEE 57 bus systems and power loss has been reduced powerfully.

PROBLEM FORMULATION
Reduction real power loss is the key goal of this work and it has been written as follows:

ENHANCED WHALE OPTIMIZATION ALGORITH
Projected algorithm has been modelled through Bubble-net hunting strategy of whale. Normally bubbles form a '9-shaped path' through that whale encircle the prey during hunting. Whales normally move 10-16 meters down the sea then through the bubbles which created artificially in spiral shape by that it encircles the prey and move upward towards the surface of sea.
Encompassing prey equation after enclosing the prey whale evaluate its position, Diminishing encircling method; it is done by reducing the value of 'k' from 2.0 to 0.0. Then the capricious value of vector D ⃗⃗ will range from [-1, 1].
Modernization of spiral position; In this phase whale and prey position will be in helix-shaped then the movement is described by, In (15) describe the distance between "i" th whale to the prey and it point out the premium solution obtained so far. Movement of the whale in enclosed path or logarithmic path mode is described as, Prey exploration; D ⃗⃗ for prey exploration and value will be "1" or less than -1. With reference to the condition's exploration is done in the search,

ENHANCED WHALE OPTIMIZATION ALGORITHM
An inertia weight ω ∈ [1, 0] has been introduced in the whale optimization algorithm and by this modernized methodology surrounding of prey is defined by, In phase of modernization of spiral position helix shaped sequence created by whale and described as: Recoil circling produced by the whale is defined by,

SIMULATION RESULTS
Proposed enhanced Whale Optimization Algorithm (EWO) is tested in IEEE 57 Bus system [17]. Table 1 show control variables, Table 2 gives the reactive power generators, comparison of results is given in Table 3. Figure 1 shows the comparison of Real Power Loss and Figure 2 indicate about the Real power loss reduction in percentage.

CONCLUSION
Enhanced whale Optimization Algorithm (EWO) solved the optimal reactive power problem efficiently. To pick up the pace of convergence during the period of exploration an inertia weight ω ∈ [0,1] has been applied. Bubble-net hunting stratagem is used for modelling and most excellent candidate solution has been attained. In standard IEEE 57 bus test system Enhanced whale Optimization Algorithm (EWO) is tested and results shows that the projected algorithm reduced the real power loss efficiently. Reduction of real power loss value is 23.93 % when compared to the base value.