REDUCTION OF ACTIVE POWER LOSS BY IMPROVED TABU SEARCH ALGORITHM

In this paper, an Improved Tabu Search (ITS) algorithm has been proposed to solve the optimal reactive power problem. In this work Tabu Searchhas been hybridized with Simulated Annealing algorithm to solve the optimal reactive power problem. Hybridization of these two algorithms improves the exploration & exploitation capabilities during the search. Proposed Improved Tabu Search (ITS) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.


Introduction
Optimal reactive power problem is key problem in secure & economic operations of power system. The sources of the reactive power are the generators, synchronous condensers, capacitors, static compensators and tap changing transformers. The problem that has to be solved in a reactive power optimization is to determine the required reactive generation at various locations so as to optimize the objective function. 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. It involves a non linear optimization problem. Various mathematical techniques have been adopted to solve this optimal reactive power dispatch problem. These include the gradient method [1][2], Newton method [3] and linear programming [4][5][6][7]. The gradient and Newton methods suffer from the difficulty in handling inequality constraints. To apply linear programming, the input-output function is to be expressed as a set of linear functions which may lead to loss of accuracy. Recently Global Optimization techniques such as genetic algorithms have been proposed to solve the reactive power flow problem [8,9]. This paper proposes an Improved Tabu Search (ITS) algorithm has been proposed to solve the optimal reactive power problem. In this work Tabu Search-has been hybridized with Simulated Annealing algorithm to solve the optimal reactive power problem. Http://www.granthaalayah.com ©International Journal of Research -GRANTHAALAYAH [2] Hybridization of these two algorithms improves the exploration & exploitation capabilities during the search. Proposed Improved Tabu Search (ITS) algorithm has been tested in Standard IEEE 57,118 bus systems & real power loss has been comparatively reduced with voltage profiles are within the limits.

Active Power Loss
The objective of the reactive power dispatch is to minimize the active power loss in the transmission network, which can be described 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.

Voltage Profile Improvement
For minimizing the voltage deviation in PQ buses, the objective function becomes: Where VD -voltage deviation,ω v -is a weighting factor of voltage deviation.
Voltage deviation given by: Where Npq-number of load buses

Equality Constraint
The equality constraint of the problem is represented by the power balance equation, where the total power generation must cover the total power demand and the power losses: Where PG-total power generation, PDtotal power demand.

Inequality Constraints
The inequality constraints in the power system as well as the limits created to ensure system security. Upper and lower bounds on the active power of slack bus (Pg), and reactive power of generators (Qg) are written in mathematically as follows: Http://www.granthaalayah.com ©International Journal of Research -GRANTHAALAYAH [3] Upper and lower bounds on the bus voltage magnitudes (Vi): Upper and lower bounds on the transformers tap ratios (Ti): Upper and lower bounds on the compensators reactive powers (Qc): Where N is the total number of buses, NT is the total number of Transformers; Nc is the total number of shunt reactive compensators.

Tabu Search
Tabu search (TS), which was firstly developed by Glover [10][11][12]. Neighbourhood explorations take a potential solution to a problem and authenticate its instantaneous local opportunities, which is, solutions that are similar except for one or two minor details to recognize a better-quality solution. Local search methods incline to become stuck in suboptimal regions. Tabu search takes benefit of the performance of these approaches by using memory structures, which elucidate the visited solutions .If a potential solution has been already visited within a certain short-term period or if it has already violated a rule, it is marked as "tabu" (forbidden) so that the algorithm would not reconsider that possibility.
1) Arbitrarily develop an initial solution 2) Calculate neighbourhood 3) Choose a candidate move 4) Is candidate tabu? If yes then go to step 4a or go to step 5 4a. Will solution be the absolute best? Or go to step 4b 4b. Reject candidate move and adjust the neighbourhood then go to step 3. 5) Update solution by incorporating the candidate move, set z value 6) Have we reached the stopping criteria?
6a. if yes -Stop and report the best solution found during search 6b. if nogo to step 2.

Tabu Search Algorithm
Step 1. Let S be the preliminary feasible solution and Z its objective function value; then, set S* = S, Z* = Z, max short-term memory (STM) = 5, and max iteration = 1,000; iter = 1. Best O value = O value.
Step 5. Compute the objective function value (Z) of the new permutation.
Step 7. If iter ≤ max iteration, then replicate step 2; or else, print Z* and S*.

Simulated Annealing
Simulated annealing (SA) is a standard probabilistic metaheuristic for combinatorial optimization problem of locating a good guesstimate to the global optimum of a given function in an attractive great exploration space. For certain problems, SA may be more efficient than exhaustive enumeration rather than the best possible solution. Paul et al [13] reported that for a number of varied problem instances, SA could perform better for higher quality targets while TS performs better for lower quality targets. Http://www.granthaalayah.com ©International Journal of Research -GRANTHAALAYAH [5]

Hybridized Tabu Search -Simulated Annealing Algorithm for Solving Optimal Reactive Power Problem
Both the simulated annealing and tabu search algorithms has been hybridized.
Step 1 to step 6 main part of hybridization to handle the reactive power problem.

Simulation Results
At first Improved Tabu Search (ITS) 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. Http://www.granthaalayah.com ©International Journal of Research -GRANTHAALAYAH [6] The preliminary conditions for the IEEE-57 bus power system are given as follows: Pload = 12.102 p.u. Qload = 3.014 p.u. The total initial generations and power losses are obtained as follows: ∑ = 12.408 p.u. ∑ = 3.3142 p.u. Ploss = 0.25826 p.u. Qloss = -1.2038 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.   Then Improved Tabu Search (ITS) algorithm has been tested in standard IEEE 118-bus test system [16]. The system has 54 generator buses, 64 load buses, 186 branches and 9 of them are with the tap setting transformers. The limits of voltage on generator buses are 0.95 -1.1 per-unit., and on load buses are 0.95 -1.05 per-unit. The limit of transformer rate is 0.9 -1.1, with the changes step of 0.025. The limitations of reactive power source are listed in Table 4, with the change in step of 0.01.  Http://www.granthaalayah.com ©International Journal of Research -GRANTHAALAYAH [8] The statistical comparison results of 50 trial runs have been list in Table 5 and the results clearly show the better performance of proposed Improved Tabu Search (ITS) algorithm in reducing the real power loss.

Conclusion
In this paper, an Improved Tabu Search (ITS) algorithm has been successfully solved the optimal reactive power problem. Hybridization of these two algorithms improved the exploration & exploitation capabilities during the search. In order to evaluate the validity of the proposed Improved Tabu Search (ITS) algorithm, it has been tested on Standard IEEE 57,118 bus systems and simulation results reveal about the good performance of the proposed algorithm in reducing real power loss and voltage profiles are within the limits.