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Transient stability constrained optimal power flow using teaching learning based optimization

机译:使用基于教学学习的优化来限制暂态稳定的最佳潮流

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Transient Stability Constrained Optimal Power Flow (TSCOPF) constitutes one of the most computational-intensive applications; it is used for power system preventive control against blackouts triggered by transient instability after a contingency. In this paper, a novel Optimal Power Flow (OPF) is proposed by adding the Transient Stability (TS) constraints into the conventional OPF problem, a Teaching-Learning-Based Optimization (TLBO) is proposed to solve the OPF problem. The objective function is to minimize the total cost of fuel for all generators. The proposed methodology has been tested on standard test systems the IEEE 30-bus network model. The simulation results are compared to those obtained with other conventional and new methods found in recent works.
机译:暂态稳定约束的最佳潮流(TSCOPF)构成了计算量最大的应用之一。它用于电力系统的预防性控制,以防止意外情况后由于瞬态不稳定而引起的停电。本文通过将瞬态稳定性(TS)约束添加到常规OPF问题中,提出了一种新型的最优潮流(OPF),并提出了一种基于教学-学习的优化(TLBO)来解决OPF问题。目标功能是使所有发电机的总燃料成本最小化。所提出的方法已在IEEE 30总线网络模型的标准测试系统上进行了测试。将仿真结果与近期工作中发现的其他常规方法和新方法所获得的结果进行了比较。

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