Power loss reduction by gryllidae optimization algorithm

This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed GOA approach is based on the chirping characteristics of Gryllidae. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.


INTRODUCTION
Reactive power problem plays a key role in secure and economic operations of power system. Optimal reactive power problem has been solved by variety of types of methods [1][2][3][4][5][6]. Nevertheless numerous 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, but the main problem is many algorithms get stuck in local optimal solution & failed to balance the Exploration & Exploitation during the search of global solution. This paper projects Gryllidae Optimization Algorithm (GOA) has been applied to solve optimal reactive power problem. Proposed algorithm based on the chirping characteristics of Gryllidae and formulated to solve the optimal reactive power problem. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Male Gryllidae create this sound by chirping the wings and known as stridulating. They attract each other by this sound for mating and keep the others from their nests. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.

PROBLEM FORMULATION
Objective of the problem is to reduce the true power loss: voltage deviation given by: constraint (equality) constraints (Inequality) P gslack min ≤ P gslack ≤ P gslack max (5)

GRYLLIDAE OPTIMIZATION ALGORITHM
Gryllidae optimization algorithm based on the chirping characteristics of Gryllidae and formulated to solve the optimal reactive power problem. In common, male Gryllidae chirp, on the other hand some female Gryllidae also do as well. Male Gryllidae draw the females by this sound which they produce. Male Gryllidae create this sound by chirping the wings and known as stridulating. They attract each other by this sound for mating and keep the others from their nests. Moreover, they caution the other Gryllidae against dangers with this sound. The hearing organs of the Gryllidae are housed in an expansion of their forelegs. Through this, they bias to the produced fluttering sounds. Temperature (Tf) in degrees Fahrenheit is calculated from the chirp (Nc (per minute)) by, frequency of the sound is given by, = atmosphere sound absorption is given by, = 7.4( 2 / )10 −8 (18) free filed sound pressure is given by, frequency, velocity, position value obtained by, random walk [18] is done through, in the period of the modernizing procedure, Euclidian distances (r) among all of the Gryllidae in the population were computed by,

SIMULATION RESULTS
At first in standard IEEE 14 bus system the validity of the proposed Gryllidae Optimization Algorithm (GOA) 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 Gryllidae Optimization Algorithm (GOA) simulated 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 Gryllidae Optimization Algorithm (GOA) successfully solved the optimal reactive power problem. Application of the Gryllidae Optimization Algorithm is developed by the inspiration of a type of insect; on the recognizable global engineering problems in the simulation-based nature which has recently participated to the meta-heuristic algorithm approach was confirmed. Proposed Gryllidae Optimization Algorithm (GOA) has been tested in standard IEEE 14, 30 bus test systems and simulation results show that the projected algorithms reduced the real power loss considerably.