【摘 要】
：
为提高行波保护在各种工况下的可靠性,分析区内、外故障时线路两端的初始行波电流变化规律,提出一种基于多分辨奇异值分解和随机森林的同杆双回线路区内外故障识别方法.该方法通过提取故障后线路两端的行波电流数据进行相模变换,选取变换后的同向模量电流进行6层多分辨奇异值分解,计算每层的电流积分作为特征向量输入随机森林分类器模型中进行训练和测试,来识别区内、外故障.仿真结果表明,该方法在各种工况下都能够准确地进行故障识别,并在抗噪声干扰和CT饱和方面也具有良好的表现.
【机 构】
：
四川轻化工大学自动化与信息工程学院,自贡 643000;四川轻化工大学自动化与信息工程学院,自贡 643000;人工智能四川省重点实验室,自贡 643000;国网四川电力公司电力科学研究院,成都 61
论文部分内容阅读
为提高行波保护在各种工况下的可靠性,分析区内、外故障时线路两端的初始行波电流变化规律,提出一种基于多分辨奇异值分解和随机森林的同杆双回线路区内外故障识别方法.该方法通过提取故障后线路两端的行波电流数据进行相模变换,选取变换后的同向模量电流进行6层多分辨奇异值分解,计算每层的电流积分作为特征向量输入随机森林分类器模型中进行训练和测试,来识别区内、外故障.仿真结果表明,该方法在各种工况下都能够准确地进行故障识别,并在抗噪声干扰和CT饱和方面也具有良好的表现.
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