论文标题
数据驱动的电力系统安全评估
Data-Driven Security Assessment of the Electric Power System
论文作者
论文摘要
由于可再生能源渗透和减少系统惯性的显着增长，由于化石燃料发电厂的退出，可再生能源渗透和系统惯性减少，导致发电和负载类型的混合不断变化。这增加了电网计划和操作的技术挑战。这项研究介绍了一种新的分解方法，以说明使用常规机器学习工具短期计划的系统安全性。这项工作的直接价值在于，它提供了可扩展和计算上有效的指南，用于使用监督的学习工具评估首次摆动瞬态稳定性状态。为了对训练数据集上的最终模型进行公正的评估，在先前看不见的测试集上检查了提出的方法。它在测试设置中区分了稳定且不稳定的情况，仅误差为0.57％，并且在预测不稳定性时的精度很高，误差为6.8％，平均绝对误差为0.0145。
The transition to a new low emission energy future results in a changing mix of generation and load types due to significant growth in renewable energy penetration and reduction in system inertia due to the exit of ageing fossil fuel power plants. This increases technical challenges for electrical grid planning and operation. This study introduces a new decomposition approach to account for the system security for short term planning using conventional machine learning tools. The immediate value of this work is that it provides extendable and computationally efficient guidelines for using supervised learning tools to assess first swing transient stability status. To provide an unbiased evaluation of the final model fit on the training dataset, the proposed approach was examined on a previously unseen test set. It distinguished stable and unstable cases in the test set accurately, with only 0.57% error, and showed a high precision in predicting the time of instability, with 6.8% error and mean absolute error as small as 0.0145.
