电网停电计划的排期结果关系到电网安全稳定运行和检修工作的开展,是电网运行方式业务的重要组成。目前,已有计划排期方法缺乏对计划间存在冲突这一场景的考虑,且算法效率较低,难以满足停电计划排期的实际需求。为此,该文以工作量不均...电网停电计划的排期结果关系到电网安全稳定运行和检修工作的开展,是电网运行方式业务的重要组成。目前,已有计划排期方法缺乏对计划间存在冲突这一场景的考虑,且算法效率较低,难以满足停电计划排期的实际需求。为此,该文以工作量不均衡度、停电计划时间调整量、停电经济成本为目标,涵盖计划关联关系判别和优先级排序等过程,建立了考虑冲突的电网停电计划优化求解模型。在此基础上,通过对NSGA II算法(the second generation of non-dominated sorting genetic algorithm,NSGAII)进行性能改进,提出了基于约束的自适应NSGAII算法(constraint-basedadaptive NSGAII,CA-NSGAII),并将其用于模型求解。最后,在IEEE-300输电系统模型中模拟了月停电计划排期过程,验证了该文所提模型与实际情况更为贴近,所提求解算法更加准确高效。展开更多
In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied...In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.展开更多
文摘电网停电计划的排期结果关系到电网安全稳定运行和检修工作的开展,是电网运行方式业务的重要组成。目前,已有计划排期方法缺乏对计划间存在冲突这一场景的考虑,且算法效率较低,难以满足停电计划排期的实际需求。为此,该文以工作量不均衡度、停电计划时间调整量、停电经济成本为目标,涵盖计划关联关系判别和优先级排序等过程,建立了考虑冲突的电网停电计划优化求解模型。在此基础上,通过对NSGA II算法(the second generation of non-dominated sorting genetic algorithm,NSGAII)进行性能改进,提出了基于约束的自适应NSGAII算法(constraint-basedadaptive NSGAII,CA-NSGAII),并将其用于模型求解。最后,在IEEE-300输电系统模型中模拟了月停电计划排期过程,验证了该文所提模型与实际情况更为贴近,所提求解算法更加准确高效。
基金Projects(61203020,61403190)supported by the National Natural Science Foundation of ChinaProject(BK20141461)supported by the Jiangsu Province Natural Science Foundation,China
文摘In order to solve the non-linear and high-dimensional optimization problems more effectively, an improved self-adaptive membrane computing(ISMC) optimization algorithm was proposed. The proposed ISMC algorithm applied improved self-adaptive crossover and mutation formulae that can provide appropriate crossover operator and mutation operator based on different functions of the objects and the number of iterations. The performance of ISMC was tested by the benchmark functions. The simulation results for residue hydrogenating kinetics model parameter estimation show that the proposed method is superior to the traditional intelligent algorithms in terms of convergence accuracy and stability in solving the complex parameter optimization problems.
