Real power loss diminution by predestination of particles wavering search algorithm

Received Nov 15, 2019 Revised Jan 17, 2020 Accepted Feb 11, 2020 In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradientbased progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.


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] 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. In this work, Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Particles will arbitrarily move in the exploration space in many algorithms which has been already applied to many optimization problems. In the PPS algorithm particles are distributed in the exploration space consistently. In an atom how the electrons positioned in the centre accordingly particles are in the exploration space. Normally the movement of the particle is based on gradient and swarming motion [16,17]. When the gradient method failed then swarming is executed by inducing the particle shift towards the global most excellent position by modernizing the velocity. Validity of the Proposed Predestination of Particles Wavering Search (PPS) algorithm has been tested in standard IEEE 14, 30, 57,118, 300 bus systems and results show the projected PPS reduced the 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:

PREDESTINATION OF PARTICLES WAVERING SEARCH ALGORITHM
Predestination of Particles Wavering Search (PPS) algorithm has been modeled based on the motion of the particles in the exploration space. Particles will arbitrarily move in the exploration space in many algorithms which has been already applied to many optimization problems. In the PPS algorithm particles are distributed in the exploration space consistently. In an atom how the electrons positioned in the centre accordingly particles are in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles velocity has been initiated as follows, Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. Particle moves from point of slope 1 to 2 then it end's in negative fitness slope and when the particle velocity is multiplied by the value -0.50, subsequently the particle moves from 2 to 3 then sequentially it end's in positive fitness slope, through this motion particle reach 4 afterwards a negative fitness slope attained again by the particle then once again by -0.50 the particle velocity will be multiplied. Next at 5 particle will attain, now the particle fitness will be positive slope, then in the same way particle continues its motion and it reach the point 6 . Once particle reaches the local optimal point then the velocity will be reversed again. When the gradient method failed then swarming is executed by inducing the particle shift towards the global most excellent position by modernizing the velocity as given below, When the progress develop into constructive subsequently particle prolong to discover any more local optimal solution, and this procedure persist until maximum number of evaluation has been attained. Predestination of Particles Wavering Search (PPS) algorithm defined as follows, Step 1 In the exploration space Initiate the particle's position with reference to boundary limits Step 2: i=1; k =1 Step 3: Iterative procedure: With respect to upper and lower boundaries particle positions are initiated While (i < = sum of particles) Particles possible combinations has to be discovered For c=1: sum of combinations With respect to positions and combinations alter the positions of the particle as elevated values i ++ End for k ++ if (k > dimensions) / when no boundary combinations are found then leave the loop / Break End if End while Step 4: Between two particles which has been already initiated some more particles are present, then factor based procedure is applied to reorganize the particle positions Particles number are factorized f=factor (n) ; n = sum of particles ; f is an array to store the factor values Iterative procedure: While (i <= n) For c=1: sum of factors (with reference to length of "f") For j=1: dimensions (p) Then with suitable parameters projected Predestination of Particles Wavering Search (PPS) algorithm is applied to solve the optimal reactive power problem as shown below, Step 1: Initialization of parameters Step 2: In the exploration space Initiate the particle's position with reference to boundary limits Step 3: Particles fitness values are computed and most excellent particle will be identified Step 4: Velocity of the particles are initialized through Step 5: Iterative procedure While (computation number < maximum number of computation) For i = 1; sum of particles By augmenting the velocity to the present position determine new-fangled position With reference to new-fangled position particle fitness should be calculated Augmentations of computation counter, and then modernize global most excellent solution When (slope = = unknown) then modernize slope of the particle with reference to new fitness to be positive or negative; Otherwise when (slope = = positive) When (new-fangled fitness inferior than previous fitness); Then modernize velocity by " − 2 " ; modernize the slope with reference to new-fangled fitness to be negative; otherwise (slope = = negative) When (new-fangled fitness inferior than the previous fitness) Then modernize velocity by + ( − 2 ⁄ ) Update slope to be unknown Step 6: Global most excellent particle position found with fitness value Step 7; Output the result

SIMULATION RESULTS
In standard IEEE 14 bus system the validity of the projected Predestination of Particles Wavering Search (PPS) algorithm 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 projected Predestination of Particles Wavering Search (PPS) algorithm 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. Then the proposed Predestination of Particles Wavering Search (PPS) algorithm has been tested, in IEEE 57 Bus system. Table 7 shows the constraints of control variables, Table 8 shows the limits of reactive power generators and comparison results are presented in Table 9.  Then the Predestination of Particles Wavering Search (PPS) algorithm has been tested, in IEEE 118 Bus system. Table 10 shows the constraints of control variables and comparison results are presented in Table 11.  Then IEEE 300 bus system [18] is used as test system to authenticate the good performance of the Predestination of Particles Wavering Search (PPS) algorithm. Table 12 shows the comparison of real power loss obtained after optimization.

CONCLUSION
In this work Predestination of Particles Wavering Search (PPS) algorithm successfully solved the optimal reactive power problem. In the PPS algorithm particles are distributed in the exploration space consistently. In an atom how the electrons positioned in the centre accordingly particles are in the exploration space. Normally the movement of the particle is based on gradient and swarming motion. Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned. In standard IEEE 14, 30, 57,118, 300 bus systems Predestination of Particles Wavering Search (PPS) algorithm have been tested and power loss has been reduced efficiently.