راه حل الگوریتم مصنوعی کلنی زنبور برای جریان بهینه توان راکتیو

Artificial bee colony (ABC) algorithm is an optimization algorithm based on the intelligent foraging behavior of honeybee swarm. Optimal reactive power flow (ORPF) based on ABC algorithm to minimize active power loss in power systems is studied in this paper. The advantage of ABC algorithm is that it does not require these parameters, because it is very difficult to determine external parameters such as cross over rate and mutation rate as in case of genetic algorithm and differential evolution. The other advantage is that global search ability of the algorithm is implemented by introducing a neighborhood source production mechanism which is similar to mutation process. Because of these features, ABC algorithm attracts much attention in recent years and has been used successfully in many areas. ORPF problem is one of these areas. In this paper, proposed algorithm is tested on both standard IEEE 30-bus test system and IEEE 118-bus test system. To show the effectiveness of proposed algorithms, the obtained results are compared with different approaches as available in the literature.

Optimal reactive power flow (ORPF) that is a special case of the optimal power flow (OPF) problem is very important tool in terms of secure and economic operation of power systems. Control parameters of the optimal power flow (OPF) problem have a close relationship with the reactive power flow, such as voltage magnitudes of generator buses, shunt capacitors/reactors, output of static reactive power compensators, transformer tap-settings. In ORPF, the network active power loss is minimized and the voltage profile is improved while satisfying a given set of operating and physical constraints. Again in ORPF problem, the outputs of shunt capacitors/reactors and tap-settings of transformers are discrete variables and the other variables are continuous. Therefore, the reactive power flow problem is modeled as a large-scale mixed integer nonlinear programming (MINLP) problem. For solving the ORPF problem, the classical methods such as linear programming, nonlinear programming, quadratic programming, the mixed integer programming, the Newton method have been successfully used [1], [2], [3] and [4]. Recently, methods based on interior point techniques have been presenting encouraging results to handle the large-scale ORPF/OPF problems because these methods offer much faster convergence and noticeable convenience in handling inequality constraints in comparison with other methods [5], [6] and [7]. Nevertheless, these methods have severe troubles in handling the objective functions having multiple local minima. In all these practice, some simplification has been used to overcome the inherent limitations of the solution technique. Such simplifications frequently lead to a local minimum and sometimes result in a divergence. Recently, many new stochastic search methods have been developed for solving the global optimization problems. Many salient stochastic methods such as evolutionary programming (EP) [8], genetic algorithm (GA) [9], [10] and [11], evolutionary strategy (ES) [12], particle swarm optimization (PSO) [13], and tabu search (TS) [14] have been developed for solution of the ORPF problem since the mid-1990s. Such methods present extremely superiority in obtaining the global optimum and in handling discontinuous and non-convex objectives. However, many of these methods are not effective in managing optimization problems of integer and discrete nature. Such optimization problems can be solved by approximating the discrete and integer variables by continuous variables. Thus, the problem becomes an ordinary nonlinear programming one with continuous control parameters and the continuous values are reduced to the closest possible discrete or integer variable values. In practice, this method generally causes to the solutions that may be far from the globally optimal solution. Artificial bee colony (ABC) algorithm is a search method, which is inspired by the foraging behavior of honeybee swarm, and target discrete optimization problems. The ABC algorithm that was developed by Karaboga [15] is a population-based heuristic algorithm. In this algorithm, bees are members of a family which live in organized honeybee swarm. The bees consist of two groups. ABC algorithm has been applied to various optimization problems such as compute-industrial engineering, hydraulic engineering, aviation and space science and electronic engineering since 2005 [16], [17] and [18]. ABC algorithm was firstly applied to ORPF problem by Ozturk and is tested on IEEE 10 bus-test system in Ref. [19]. In this paper, various aspects of performance of ABC algorithm in solving the ORPF problem are analyzed using the IEEE 30-bus system and IEEE 118-bus system. The security constraints are also included in this study. The obtained results for two test systems confirm the superiority and efficiency of ABC algorithm in large-scale optimization problems with discontinuous objective functions and integer or discrete control variables.

The optimal reactive power flow is a global optimization problem of a non-continuous nonlinear function arising from large-scale industrial power systems. ABC algorithm for optimal reactive power flow problem is presented by this study in the first time. According to the simulation results, it is seen that this method is very effective and quite efficient for solving ORPF. From the simulation results, it has been seen that ABC algorithm converges to the global optimum. The optimization strategy is general and can be used to other power system optimization problems as well. The simulation results obtained give an efficient, feasible and optimal solution.