基于支持向量机和决策函数的暂态稳定评估方法

暂态稳定评估是保证电力系统安全稳定运行的关键点,为解决应用机器学习进行暂态稳定评估保守性不足的问题,提出了一种基于支持向量机和决策函数的暂态稳定评估方法。该方法以故障前潮流量为初始特征集,结合暂态安全稳定量化评估和统计理论方法,提取输入特征;通过支持向量机训练暂态稳定评估模型,得出评估模型的决策函数,并依据支持向量的决策值确定门槛值,保证评估结果保守性。新英格兰10机39节点测试系统和实际系统算例验证了所提方法的可靠性和实用性。

Transient stability assessment based on support vector machine and decision function

Machine learning has been extensively studied in transient stability assessment of power system. To ensure the conservativeness of assessment results, a transient stability assessment method based on support vector machine and decision function is proposed. Firstly, training samples are constructed by Monte Carlo sampling on the basis of the on-line operation mode of power grid at a certain time. Secondly, the input features are extracted by combining the quantitative evaluation and statistical theory of transient security stability with the initial feature set of power flow before fault. Thirdly, the parameters of SVM are determined by grid search and the correlation between input features and transient stability assessment results is trained to obtain decision values. Finally, the threshold values are determined according to the decision value of support vector to ensure the conservativeness of assessment results. The effectiveness of the proposed method is verified by New England 10-machine 39-bus and actual system.