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A novel hybrid particle swarm optimization for economic dispatch with valve-point loading effects

机译：具有阀点负荷效应的经济调度新混合粒子群算法

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摘要

Economic dispatch (ED) is one of the important problems in the operation and management of the electric power systems which is formulated as an optimization problem. Modern heuristics stochastic optimization techniques appear to be efficient in solving ED problem without any restriction because of their ability to seek the global optimal solution. One of modern heuristic algorithms is particle swarm optimization (PSO). In PSO algorithm, particles change place to get close to the best position and find the global minimum point. Also, differential evolution (DE) is a robust statistical method for solving non-linear and non-convex optimization problem. The fast convergence of DE degrades its performance and reduces its search capability that leads to a higher probability towards obtaining a local optimum. In order to overcome this drawback a hybrid method is presented to solve the ED problem with valve-point loading effect by integrating the variable DE with the fuzzy adaptive PSO called FAPSO-VDE. DE is the main optimizer and the PSO is used to maintain the population diversity and prevent leading to misleading local optima for every improvement in the solution of the DE run. The parameters of proposed hybrid algorithm such as inertia weight, mutation and crossover factors are adaptively adjusted. The feasibility and effectiveness of the proposed hybrid algorithm is demonstrated for two case studies and results are compared with those of other methods. It is shown that FAPSO-VDE has high quality solution, superior convergence characteristics and shorter computation time.
机译：经济调度（ED）是电力系统运行和管理中的重要问题之一，被表述为优化问题。现代启发式随机优化技术似乎可以有效地解决ED问题，没有任何限制，因为它们具有寻求全局最优解的能力。现代启发式算法之一是粒子群优化（PSO）。在PSO算法中，粒子改变位置以接近最佳位置并找到全局最小点。而且，微分演化（DE）是解决非线性和非凸优化问题的可靠统计方法。 DE的快速收敛会降低其性能并降低其搜索能力，从而导致获得局部最优值的可能性更高。为了克服这个缺点，提出了一种混合方法，通过将变量DE与称为FAPSO-VDE的模糊自适应PSO集成来解决具有阀点负载效应的ED问题。 DE是主要的优化程序，PSO用于维护种群多样性并防止对DE运行解决方案的每一次改进都导致误导局部最优。自适应地调整了提出的混合算法的参数，如惯性权重，变异和交叉因子。在两个案例中证明了所提混合算法的可行性和有效性，并将结果与​​其他方法进行了比较。结果表明，FAPSO-VDE具有高质量的解决方案，优异的收敛性和较短的计算时间。

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