Real power loss reduction by tundra wolf algorithm

Received Nov 16, 2019 Revised Jan 17, 2020 Accepted Feb 11, 2020 In this work Tundra wolf algorithm (TWA) is proposed to solve the optimal reactive power problem. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents’ numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the proposed algorithm reduced the real power loss effectively.


INTRODUCTION
Reactive power problem plays an important role in secure and economic operations of power system. Numerous types of methods [1][2][3][4][5][6] have been utilized to solve the optimal reactive power problem. However many scientific difficulties are found while solving problem due to an assortment of constraints. Evolutionary techniques [7][8][9][10][11][12][13][14][15][16][17] are applied to solve the reactive power problem. This paper proposes Tundra wolf algorithm (TWA) to solve optimal reactive power problem. At first, searching agents has been aggravated to scatter all over the extensive range of probing space to discover the probable prey as an alternative of crowding in the region of the regular local optimal. This phase is also termed as exploration period. In the subsequent exploitation phase, searching agents should have the ability to influence the information of the probable prey to converge in the direction of the global optimal value. In general tracking or hunting action is solitary possessed alpha, beta and delta Tundra wolf while the remaining Tundra wolves are indebted to go behind them that include omega Tundra wolf. In sequence to augment the exploration capability of the search agents, several alterations have been suggested. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30, bus test systems and simulation results show the projected algorithm reduced the real power loss effectively.

PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss: Voltage deviation given as follows: Voltage deviation given by: Constraint (Equality), Constraints (Inequality),

TUNDRA WOLF ALGORITHM
In the proposed Tundra wolf algorithm (TWA) hunting behavior of the Tundra wolf has been imitated to design the algorithm for solving the optimal reactive power problem. In Tundra wolf algorithm, the movement of wolf is described by, The state of wolves are adjusted by, When the value of "A" are located in [-1, 1] capriciously, which indicate the procedure of local search perceptibly in this phase the Tundra wolves attack towards the prey. Tundra wolves are forced to make a global search When | A | > 1. Through the parameter "a" fluctuation range of "A" can be decreased. In the projected Tundra wolf algorithm (TWA) in order to avoid the searching agents from trapping into the local optimal the converging towards global optimal is divided based on two different conditions. At first, searching agents has been aggravated to scatter all over the extensive range of probing space to discover the probable prey as an alternative of crowding in the region of the regular local optimal. This phase is also termed as exploration period. In the subsequent exploitation phase, searching agents should have the ability to influence the information of the probable prey to converge in the direction of the global optimal value.
In general tracking or hunting action is solitary possessed alpha, beta and delta Tundra wolf while the remaining Tundra wolves are indebted to go behind them that include omega Tundra wolf. In sequence to augment the exploration capability of the search agents, several alterations have been suggested. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. Escalating the searching agents numbers will perk up the exploration capability of the Tundra wolf wolves in an extensive range. Also it makes the search agents to spread widely during exploration phase. The mode of hunting action done by Tundra wolf will increase the efficiency and time will be saved.

SIMULATION RESULTS
At first in standard IEEE 14 bus system [18] the validity of the proposed Tundra wolf algorithm (TWA) has been tested, Table 1 shows the constraints of control variables Table 2 shows the limits of reactive power generators and comparison results are presented in Table 3.
Then the proposed Tundra wolf algorithm (TWA) has been tested, in IEEE 30 Bus system. Table 4 shows the constraints of control variables, Table 5 shows the limits of reactive power generators and comparison results are presented in Table 6.

CONCLUSION
In this paper Tundra wolf algorithm (TWA) successfully solved the optimal reactive power problem. Proposed algorithm perk up the exploration capability of the Tundra wolf wolves in an extensive mode. Also it makes the search agents to spread widely during exploration phase. In the proposed Tundra wolf algorithm (TWA) omega tundra wolf has been taken as searching agent as an alternative of indebted to pursue the first three most excellent candidates. This mode of hunting action increases the efficiency. Proposed Tundra wolf algorithm (TWA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show the projected algorithm reduced the real power loss. Percentage of real power loss reduction has been improved when compared to other standard algorithms.