基于分类-回归卷积神经网络的新能源电力系统可靠性评估方法

Renewable Power System Reliability Evaluation Method Based on Classification-regression Convolutional Neural Networks

Monte Carlo模拟(Monte Carlo simulation,MCS)在复杂电力系统可靠性评估中广泛应用,但计算效率较低。针对此,该文提出一种基于卷积神经网络(conventional neural network,CNN)的可靠性评估方法,在时序MCS框架下采用CNN加速系统状态评估计算。首先,构造反映系统运行状态的特征向量,建立基于CNN的系统失负荷量回归模型;其次,针对可靠性评估样本不均衡、回归训练效率低的问题,进一步建立系统状态分类器,形成基于CNN的分类-回归模型;此外,针对CNN训练样本和实际评估样本不一致的问题,提出分类结果矫正机制,进一步提升模型的实用性;最后,通过改编IEEE-RTS系统的计算分析验证了所提方法的有效性和优越性。

Monte Carlo simulation(MCS)is widely utilized in the reliability evaluation of composite power systems while suffers from low computational efficiency.This paper proposes a data-driven reliability evaluation method based on the convolutional neural network(CNN).The CNN is adopted in the sequential MCS framework to improve its efficiency.In this paper,the features reflecting the operating state of the system are firstly constructed and the optimal load shedding model based on CNN is established.To solve the problems of unbalanced reliability evaluation samples and low training efficiency of regression model,the system state classifier is then established and the classification-regression model based on CNN is formulated.What’s more,the correction mechanism of classification results is introduced to further promote the practicality of the classification-regression model.The validity and effectiveness of the proposed method are verified on the modified IEEE-RTS79 and IEEE-RTS96.