基于深度学习的特高压直流闭锁故障智能调度决策

Intelligent Dispatch Decision-Making for UHVDC Blocking Fault Based on Deep Learning

针对特高压直流闭锁故障的处置策略问题,提出一种基于深度学习的故障特征建模方法及故障后电网调度策略生成方法,所提智能调控决策依据电网直流故障特征和运行环境信息,通过大数据驱动模型训练得到故障后的调度策略。首先根据故障环境信息,利用故障影响相关性提取有效故障信息,构建故障特征模型。然后介绍深度学习类神经网络原理和多层感知器模型,提出利用深度网络提取训练故障前后运行特征,自动生成调控策略的思路。之后利用反向传播算法构建深度学习框架,通过不断计算损失函数和准确率修正训练模型,自动生成有效故障处置策略。最后利用锦苏直流特高压线路相关的电力系统验证了所提方法的有效性。

For disposal of the UHVDC blocking faults, this paper proposes a deep-learning-based fault feature modeling method and a post-fault grid dispatching strategy generation method. The proposed intelligent dispatch decision-making is based on the DC fault characteristics and operating environment information of power systems, and the post-fault dispatching strategy is generated through training with the big data driven model. Firstly, based on the fault environment information, the effective fault information is extracted to construct the fault feature model. And then, the principle of deep-learning neural network and the multi-layer perceptron model are introduced, and the idea is proposed to use deep network to extract the running characteristics before and after the training fault and automatically generate the dispatching strategy. Thirdly, the back-propagation algorithm is used to construct the deep learning framework, and the effective fault-disposal strategy is automatically generated by continuously calculating the loss function and the accuracy correction training model. Finally, the effectiveness of the proposed method is verified using the related power system of the Jinsu UHV DC transmission line.