陈厚合;张赫;王长江;魏俊红;张艳军;张嵩;

• 1:东北电力大学电气工程学院
• 2:华电电力科学研究院有限公司东北分公司
• 3:国网辽宁省电力有限公司

摘要(Abstract)：

为有效预测大扰动过程中直流送端系统的暂态过电压,提出一种基于卷积神经网络(convolutionalneural networks,CNN)的直流送端系统暂态过电压估算方法。首先,基于CNN输入特征构建的基本原理,搭建具有多层隐含层的非线性网络结构,将广域量测装置采集的各节点电压、相角及功率作为输入层,依据电网节点的拓补关系及故障发生到切除的时间顺序进行拼接,得到表征电网状态的矩阵。然后,优化调整CNN的超参数,采用梯度下降法进行有监督训练,通过逐层优化输入层与卷积层之间的权重矩阵,实现关键特征值的自动提取,同时利用CNN的深层架构构建暂态过电压与输入数据间的映射模型,快速准确地估算直流送端系统暂态过电压。最后,对修改后的Nordic32交直流混合系统和广东电网系统进行分析,验证该方法的有效性和准确性。

关键词(KeyWords)： 高压直流;卷积神经网络;暂态过电压;人工智能

Abstract:

Keywords:

基金项目(Foundation): 国家电网公司科技项目“千万千瓦级分层接入直流送受端系统动态行为机理和协调控制措施研究”(SGTYHT17-JS-199)~~

作者(Author): 陈厚合;张赫;王长江;魏俊红;张艳军;张嵩;

Email:

DOI: 10.13335/j.1000-3673.pst.2019.1555

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