基于参数优化的扩容电力系统结构稳定的自愈控制

发布时间：2018-09-03 09:47

【摘要】：针对一类互联电力系统在线扩容时的自动发电控制(AGC)设计,采用动态输出反馈和遗传算法提出了一种能保证扩容后系统在拓扑结构突然改变时稳定运行的结构自愈优化控制方法。这种方法通过对扩容系统的建模,采用线性矩阵不等式方法设计了使其结构稳定的鲁棒分散控制器,并用遗传算法优化了问题求解中的相关参数,避免了设计中参数选择的盲目性。在优化过程中选取了综合评价系统时域响应性能的适应度函数来优化控制器设计中的互联约束矩阵,达到了改善系统性能指标的目的。文中针对一类互联电力系统模型进行了仿真研究,考虑在原区域电力系统的基础上在线增加一个新的区域,对新区域的自动发电控制律采用所提出方法进行了设计,仿真结果表明该方法达到了扩容后系统稳定的结构自愈控制要求,且采用遗传算法优化后的系统性能明显优于优化前的,证明了该方法的有效性。
[Abstract]:Aiming at the design of automatic generation control (AGC) for a class of interconnected power systems with on-line capacity expansion, By using dynamic output feedback and genetic algorithm, a structural self-healing optimization control method is proposed, which can guarantee the stable operation of the expanded system when the topology changes suddenly. By modeling the expanded system, the robust decentralized controller is designed by using linear matrix inequality (LMI) method, and the relevant parameters in solving the problem are optimized by genetic algorithm. The blindness of parameter selection in design is avoided. In the process of optimization, the fitness function of comprehensive evaluation system in time domain response performance is selected to optimize the interconnected constraint matrix in controller design, and the purpose of improving the system performance index is achieved. In this paper, the simulation of a class of interconnected power system models is carried out. A new area is added online to the original power system, and the proposed method is used to design the automatic generation control law in the new area. The simulation results show that the proposed method meets the requirements of stable structural self-healing control, and the performance of the optimized system by genetic algorithm is obviously better than that before the optimization, which proves the effectiveness of the proposed method.
【作者单位】：辽宁科技大学电子与信息工程学院;辽宁科技大学理学院;
【基金】：国家自然科学基金资助项目(61273011)
【分类号】：TM712

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