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首页> 外文期刊>Electric Power Components and Systems >Preventive and Corrective Control Actions on Power Systems via Heuristic Optimization Methods with Consecutive Search Space Reduction
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Preventive and Corrective Control Actions on Power Systems via Heuristic Optimization Methods with Consecutive Search Space Reduction

机译：连续搜索空间减少的启发式优化方法对电力系统的预防和纠正控制措施

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摘要

This paper represents a new methodology to improve the performance of population-based optimization algorithms designed for corrective and preventive control actions enhancing power system dynamic security. Unlike many adaptive approaches employing the parameters such as population size, crossover or mutation rates, the proposed method for the performance improvement is based on the reduction of the search space size. In the method, optimization algorithms run consecutively, while the size of the search space is reduced according to the objective function values attained during the optimization process. In this study, generation rescheduling combined with load curtailment is applied as a preventive control action, whereas load shedding is selected as a corrective control. Each of these control actions is determined through the formulation of a security constrained optimization problem and its solution via population-based optimization algorithms. The proposed methodology is successfully applied to differential evolution, particle swarm optimization, artificial bee colony optimization, and big bang big crunch optimization methods for solving the optimization problems in a 16-generator-68-bus system and the Turkish Transmission System with 750 generators and 2600 buses. It is demonstrated that the proposed method provides better solutions with lesser computational complexity than the ones obtained by using fixed search space sizes.
机译：本文提出了一种新的方法，可以提高基于种群的优化算法的性能，该算法旨在为纠正和预防控制措施而设计，以增强电力系统的动态安全性。与许多采用诸如种群大小，交叉或突变率等参数的自适应方法不同，所提出的性能改进方法基于减小搜索空间的大小。在该方法中，优化算法连续运行，同时根据优化过程中获得的目标函数值减小搜索空间的大小。在这项研究中，将发电调度与削减负荷相结合用作预防控制措施，而选择削减负荷作为矫正控制措施。这些控制措施中的每一个都是通过制定安全受限的优化问题及其基于总体优化算法的解决方案来确定的。所提出的方法已成功应用于差分进化，粒子群优化，人工蜂群优化和大爆炸紧缩优化方法，以解决16发电机68客车系统和750台发电机的土耳其输电系统的优化问题。 2600辆巴士。结果表明，与使用固定搜索空间大小获得的方法相比，该方法可提供更好的解决方案，并且计算复杂度更低。

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