考虑代价敏感的AC-LSTM暂态稳定评估

AC-LSTM transient stability assessment considering cost-sensitivity

电力系统稳定样本与失稳样本的失衡会导致数据驱动型暂稳评估模型对失稳样本的漏分率增加,由于失稳样本漏判的代价远高于稳定样本误判的代价,因此提出一种引入代价敏感机制的AC-LSTM电力系统暂态稳定评估模型。通过改进Adaboost算法,引入代价敏感函数对样本权重进行更新,更好地考虑了少数类样本对整体分类准确率的影响,降低不稳定样本的漏分率。并进一步将改进的Adaboost算法和长短期记忆网络(long short-term memory,LSTM)相结合以提高分类器的综合性能。在IEEE39和IEEE140节点系统上的仿真结果表明,所提模型较其他模型具有良好的适应性和泛化能力,提升了评估模型的综合性能,其抗噪能力也优于其他模型。

When the data-driven transient stability assessment model is used to judge the stability of power system,there can be an increase in the false positive rate of unstable samples.This is due to the imbalance between stable samples and unstable samples.Also the cost of misjudgment of unstable samples is much higher.An AC-LSTM transient stability assessment model with a cost-sensitive mechanism is proposed.By improving the Adaboost algorithm and introducing a cost-sensitive function to update the sample weights,the influence of minority samples on the overall classification accuracy is eliminated to minimize the false positive rate of unstable samples.In addition,the improved AdaBoost algorithm is integrated with long short-term memory(LSTM)to improve the comprehensive performance of the classifier.The simulation results on the IEEE39-bus and IEEE140-bus systems show that the model proposed has better adaptability and generalization ability than others,and the comprehensive evaluation performance is improved.In addition,the anti-noise ability is also better than other models.