REDUCTION OF REAL POWER LOSS BY LAVA HERON OPTIMIZATION ALGORITHM

This paper presents a new Lava Heron Optimization (LHO) Algorithm for solving reactive power problem. This algorithm is inspired by the grab skill of the Lava Heron bird. Lava heron bird live in on the freshwater or saline water, swampy marshes or wetlands with tuft of trees mostly in low lying areas, where there are abundant convenience of fishes as their prey. By using the prey catching skill of the Lava Heron bird algorithm has been framed and utilized to minimize the real power loss. Proposed Lava Heron Optimization (LHO) Algorithm has been tested in standard IEEE 57,118 bus systems and simulation results demonstrate the commendable performance of the projected Lava Heron Optimization (LHO) Algorithm in reducing the real power loss.


Introduction
Optimal reactive power dispatch problem is one of the main problems in power systems. Main goal is to operate the system in secure mode and also to improve the economy of the system. Here the reactive power dispatch problem involves best utilization of the existing generator bus voltage magnitudes, transformer tap setting and the output of reactive power sources so as to minimize the loss and to enhance the voltage stability of the system. Various mathematical techniques have been utilized to Solve this optimal reactive power dispatch problem like the gradient method [1,2], Newton method [3] and linear programming [4][5][6][7].The gradient and Newton methods failed to handle inequality constraints. In last few years several biological and natural processes have been utilized in the methodologies of science and technology in an increasing manner. Among the most popular nature inspired approaches are Particle Swarm Optimization [8], the artificial immune systems [9], the Ant Colony Optimization [10], etc. Also, a number of swarm intelligence algorithms, based on the behaviour of the bees have been presented [11]. These algorithms are divided, mainly, in two categories according to their behaviour in the nature, the foraging behaviour and the mating behaviour. The most important methodologies that simulate the foraging behaviour of the bees are the Artificial Bee Colony (ABC) Algorithm proposed by [12], the Virtual Bee Algorithm proposed by [13], the Bee Colony Optimization Algorithm proposed by [14], the Beehive algorithm proposed by [15], the Bee Swarm Optimization Algorithm proposed by [16] and the Bees Algorithm proposed by [17]. This paper presents a new Lava Heron Optimization (LHO) Algorithm for solving reactive power problem. Lava heron bird live in on the freshwater or saline water, swampy marshes or wetlands with tuft of trees mostly in low lying areas, where there are abundant convenience of fishes as their prey. These highly territorial birds are found in intertidal zones and mangrove swamps on all of the islands of Galápagos Province. The lava heron stalks small crabs and fish slowly before quickly spearing and eating them. They have also been known to eat the flies that gather near cacti. Lava herons are typically seen hunched over and they have a sharp alarm call [18]. The performance of Lava Heron Optimization (LHO) Algorithm has been evaluated in standard IEEE 57,118 bus systems and the result analysis shows that our projected approach outperforms all approaches examined in this paper.

Problem Formulation
The main objective of the reactive power dispatch problem is to minimize the real power loss .

Minimization of Real Power Loss
The main objective is to minmize the real power loss (P loss ) in transmission lines of a power system. This is stated as follows.
Where n is the number of transmission lines, g k is the conductance of branch k, V i and V j are voltage magnitude at bus i and bus j, and θ ij is the voltage angle difference between bus i and bus j.

Minimization of Voltage Deviation
It is very important to minimize the Deviations in voltage magnitudes (VD) at load buses. This is stated as follows. Minimize VD = ∑ |V k − 1.0| nl k=1 (2) Where nl is the number of load busses and V k is the voltage magnitude at bus k.

System Constraints
In the minimization of active power ,there is two types of constraints, which one is equality and others are inequality . Load flow equality constraints: where, nb is the number of buses, P G and Q G are the real and reactive power of the generator, P D and Q D are the real and reactive load of the generator, and G ij and B ij are the mutual conductance and susceptance between bus i and bus j. Inequality constraints are as follows , Reactive power generation (Q Gi ) ≤ ≤ , ∈ (8) Transformers tap setting (T i ) inequality constraint: ≤ ≤ , ∈ (9) Transmission line flow (S Li ) inequality constraint: ≤ , ∈ (10) Where, nc, ng and nt are numbers of the switchable reactive power sources, generators and transformers.

Lava Heron Optimization (LHO)Algorithm
Lava heron bird live in on the freshwater or saline water, swampy marshes or wetlands with tuft of trees mostly in low lying areas, where there are abundant convenience of fishes as their prey. These highly territorial birds are found in intertidal zones and mangrove swamps on all of the islands of Galápagos Province. The lava heron stalks small crabs and fish slowly before quickly spearing and eating them. They have also been known to eat the flies that gather near cacti. Lava herons are typically seen hunched over and they have a sharp alarm call. These birds have little fear of humans. Unlike most herons, these birds nest in solitary pairs in either the lower branches of mangrove trees or under lava rocks. They can breed year-round, though typically from September to March, and can mate up to three times a year. The prey catching attribute of lava heron bird is being exploited as a meta-heuristic for solving the optimization problem.

Inducement procedure
The inducement process is similar to holding attraction in the beak by the lava heron bird, as it does and will dive at the appropriate place where there is possibility of a grab. Similarly in the calculation the inducement is a solution subset that is arbitrarily produced and is held by the lava heron bird prior to find a high-quality position through local exploration all over the complete solution set.
The three following progression depends on the happenings and it depends on the problem, its constraints, and the execution and partially on the possibility. Local exploration are given as follows, Fail To Spot -In this case the inducement gets established at one of its chosen place where it discover permanence and if the lava heron bird not succeeded to grab any prey and consequently the number of solutions in the solution set tends to raise. The absent node must be refurbished, in sequence to maintain the soundness of the solutions.
Grab -In this case the inducement helps the lava heron bird to grab a prey and thus the solution set elements remains steady and one appropriate element is added and one inappropriate element is eradicated.
Phony grab -Here the lava heron bird gets grip of a prey devoid of using inducement as occasionally fishes come close to the surface of the water. In this step an unsuitable element of the solution set is eradicated from the set. The exhaustion of a node in the form of a grab form the solution set must be reinstated for organization of soundness for definite constraints of the problems.

Modifying Locations
The Local exploration process can occur through assessment of all diminutive solution sets or part of extended enough solution sets. Depending on the solution sets for probable positions before it finds a appropriate one. This step is equivalent to the nature of the lava heron bird can sit very near the surface of the water such that at any point of time can take hold of a fish.

Magnetize Victim Swarm
In this step the point of discharge of the inducement will stay same but the complete set will swing to produce position for the most excellent agent to obtain the inducement and this will assist in an evolution like step where a move can change the solution particularly when the positions of the solution hold huge sense and the right progression is of greatest significance.
Articulate in a solution string represented as = { 1 , 2 , . . , } and the lined segments are characterized as { 1 , 2 , . . , } where each ⊆ and = {1,2, . . , } , Derivative Fitness = (11) And with development of linkages will continually reduce. In this case we can use each of the subsets as nodes for link foundation.
For each unremitting variable of the fitness function of the problem that is where D is the dimension of the problem 1 ∈ { 1 , 2 , . . , } and is the D th element in the t th iteration. It encompasses the subsequent equations prevailing the variation of the variable's variation and is self-adaptive, preceding iteration based and error sensitive based on the fitness function of the t and t-1 iteration. Where For our reactive power problem with J t < J t -1 or J t > Jt-1 where 1 is the i th variable for iteration t, J t is the fitness value at iteration t, y f and y r are the front and rear neighbours respectively where ∈ { } and ∈ { } , is the variations contributor with respect to the t th iteration generated from previous iteration t -1, ∈ is the constantly changing adaptive contributor which denotes what percentage of the must be incorporated into the variable.

Lava Heron Optimization (LHO) Algorithm for Solving Optimal Reactive Power Problem
Stage 1: Initiate the Solution set all its n (n = dimension of problem) events{y 1 , y 2 , . . , y n }.

Simulation Results
At first Lava Heron Optimization (LHO) Algorithm has been tested in standard IEEE-57 bus power system. The reactive power compensation buses are 18, 25 and 53. Bus 2, 3, 6, 8, 9 and 12 are PV buses and bus 1 is selected as slack-bus. The system variable limits are given in Table 1. The preliminary conditions for the IEEE-57 bus power system are given as follows: P load = 12.119 p.u. Q load = 3.059 p.u. The total initial generations and power losses are obtained as follows: ∑ = 12.442 p.u. ∑ = 3.3158 p.u. P loss = 0.25862 p.u. Q loss = -1.2064 p.u. Table 2 shows the various system control variables i.e. generator bus voltages, shunt capacitances and transformer tap settings obtained after optimization which are within the acceptable limits. In Table 3, shows the comparison of optimum results obtained from proposed methods with other optimization techniques. These results indicate the robustness of proposed approaches for providing better optimal solution in case of IEEE-57 bus system.     Table 4, with the change in step of 0.01.  The statistical comparison results of 50 trial runs have been list in Table 5 and the results clearly show the better performance of proposed Lava Heron Optimization (LHO) Algorithm in reducing the real power loss.

Conclusion
In this paper a novel approach Lava Heron Optimization (LHO) Algorithm used to solve optimal reactive power problem. Mainly inducement process, Modifying locations, Magnetize Victim Swarm has been utlized to solve the problem. The proposed approach has been tested in standard IEEE 57,118 bus systems. The simulation results indicate the efficiency and heftiness of the projected algorithm in reducing the real power loss.