无平衡节点孤岛运行微电网的连续潮流计算

连续潮流(continuous power flow,CPF)是电力系统电压稳定分析的有效工具,也是解决常规潮流中病态潮流问题的方法之一。针对无平衡节点孤岛运行微电网系统的无平衡节点、且有下垂控制分布式电源装置的特性,提出一种无平衡节点孤岛运行微电网CPF计算方法。采用不要求雅可比矩阵非奇异,且具有全局收敛性的LM-TR方法求解初始点。预测环节采用结合局部参数化方法的切线法。校正环节提出新型的超球面参数化方法,并采用结合传统牛顿法和带Armijo型线性搜索牛顿法的组合牛顿法进行校正,以保证CPF校正计算成功,及实现整个CPF过程中在较高计算精度下一直采用较大定步长预测。对改造后的37节点和17节点无平衡节点孤岛运行微电网系统采用所提方法进行CPF计算,验证了其正确性和有效性。

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.