REDUCTION OF REAL POWER LOSS BY UNIFIED ALGORITHM

In this paper


Introduction
To till date various methodologies has been applied to solve the Optimal Reactive Power problem. The key aspect of solving Reactive Power problem is to reduce the real power loss. Previously many types of mathematical methodologies like linear programming, gradient method (Alsac et al., 1973; Lee et al., 1985;Monticelli et al., 1987;Deeb et al., 1990;Hobson, 1980;Lee et al., 1993;Mangoli et al., 1993;Canizares et al., 1996) [1][2][3][4][5][6][7][8] has been utilized to solve the reactive power problem, but they lack in handling the constraints to reach a global optimization solution. In the next level various types of evolutionary algorithms (Berizzi et al., 2012;Roy et al., 2012;Hu et al., 2010;Eleftherios et al., 2010) [9][10][11][12] has been applied to solve the reactive power problem. But each and every algorithm has some merits and demerits. One algorithm good in exploration means, it lacks in exploitation and another algorithm good in exploitation means it lacks in exploration. Some algorithms are good in exploration and exploitation but the speed of convergence is poor. In this work Variable mesh optimization algorithm (VMO) and

2.1.Active Power Loss
The objective of the reactive power dispatch problem is to minimize the active power loss and can be written in equations as follows: Where F-objective function, PLpower loss, gk -conductance of branch,Vi and Vj are voltages at buses i,j, Nbr-total number of transmission lines in power systems.

2.2.Voltage Profile Improvement
To minimize the voltage deviation in PQ buses, the objective function (F) can be written as: Where VD -voltage deviation, ω v -is a weighting factor of voltage deviation. And the Voltage deviation given by: Where Npq-number of load buses

2.3.Equality Constraint
The equality constraint of the problem is indicated by the power balance equation as follows: Where PG-total power generation, PD -total power demand.

2.4.Inequality Constraints
The inequality constraint implies the limits on components in the power system in addition to the limits created to make sure system security. Upper and lower bounds on the active power of slack bus (Pg), and reactive power of generators (Qg) are written as follows: P gslack min ≤ P gslack ≤ P gslack max (5) Q gi min ≤ Q gi ≤ Q gi max , i ∈ N g (6) [Lenin *, Vol.5 (Iss. Upper and lower bounds on the bus voltage magnitudes (Vi) is given by: Upper and lower bounds on the transformers tap ratios (Ti) is given by: Upper and lower bounds on the compensators (Qc) is given by: Where N is the total number of buses, Ng is the total number of generators, NT is the total number of Transformers, Nc is the total number of shunt reactive compensators.

Variable Mesh Optimization
Variable mesh optimization algorithm (VMO) (Puris et al., 2011) [13] is a metaheuristic in which the population is sprinkled as a mesh. This mesh is self-possessed of Z nodes ( 1 , 2 , . . , ) that represent solutions in the exploration space. Each node is coded as a vector of M floating point numbers = ( 1 , 2 , . . , , . . , ) that denote the solution to the optimization problem. In the exploration progression developed by VMO, two operations are accomplished: the expansion and contraction procedures. During the expansion, new nodes are produced in the direction of local extreme, the global end and to the edge nodes. Based on an exclusive strategy, nodes are ordered bestowing to their quality in uphill order. Cleaning adaptive operator is then applied; each node is compared to its heirs eliminating those that do not surpass a threshold. The value of this threshold can be calculated as:

Differential Evolution
In Where the indices s1, s2, s3, s4, and s5 are homogenous different integers from 1 to , Y best denotes the best individual obtained so far &Y are the th vector of D and Y, rand indicates the term randomly and H is the constant respectively. The crossover operator is performed to produce a trial vector G according to each pair of Y and D after the mutant vector D is generated. The most Enhanced strategy is the binomial crossover described as follows: where E is called the crossover rate, l rand is arbitrarily sampled from 1 to N, and g , , d , , and y , are the th element of G , D , and Y , respectively.
Finally, DE utilize greedy mechanism to choose the best vector from each pair of Y and G . This can be defined as follows:

Proposed Unified Algorithm (UA) -Combination of VMO Algorithm and DE Algorithm
The Unified Algorithm (UA) metaheuristic employs VMO as the key core and insert the DE algorithm in order to augment the primary mesh of the subsequent iteration. The use of DE was decided to progress the superiority of the population at the end of the cleaning process done by VMO. The DE algorithm does not produce an arbitrary preliminary population but takes as its chief population the matrix resulting from the cleaning operation executed by VMO, giving out a population with greater quality individuals whose VMO starts a new-fangled iteration.

Start
Arbitrarily produce Z nodes for the primary mesh Pick the global best in the initial mesh Repeat For each node in primary mesh do Find its closest k nodes by their spatial locations Pick the finest neighbour as per the fitness values If present node is not the local best then Produce a new-fangled node toward the local best End if End for For each node in primary mesh but the global best do Produce a new node toward the global best End for Produce nodes from nodes in the mesh frontier Categorize nodes according to their fitness values Smear the adaptive clearing operator Select Z best nodes to build the primary mesh for the following iteration DE call using VMO population Stop criterion End

Simulation Results
Validity of proposed UA algorithm has been verified by testing in IEEE 30-bus, 41 branch system and it has 6 generator-bus voltage magnitudes, 4 transformer-tap settings, and 2 bus shunt reactive compensators. Bus 1 is taken as slack bus and 2, 5, 8, 11 and 13 are considered as PV generator buses and others are PQ load buses. Control variables limits are given in Table 1. In Table 2 the power limits of generators buses are listed.  Table 3 shows the proposed UA approach successfully kept the control variables within limits. Table 4 narrates about the performance of the proposed UA algorithm . Fig 1 shows about the voltage deviations during the iterations and Table 5 list out the overall comparison of the results of optimal solution obtained by various methods.

Conclusion
In this paper, Unified Algorithm (UA) by combination of Variable mesh optimization algorithm (VMO) with Differential Evolution (DE) has been successfully implemented to solve Optimal Reactive Power Dispatch problem. The proposed (HA) algorithm has been tested in the standard IEEE 30 bus system. Simulation results show the robustness of proposed Unified Algorithm (UA) by combination of Variable mesh optimization algorithm (VMO) with Differential Evolution (DE) for providing better optimal solution in decreasing the real power loss. The control variables obtained after the optimization by UA are well within the limits.