基于多尺度数学形态学和高低频能量比值的海上风电场内部瞬态过电压特征分析

目前,海上风电场集电系统因电气设备频繁操作或故障所引起的高频瞬态过电压尤为严重。为识别不同类型下海上风电场内部过电压的瞬态特性,该文首先提出一种多尺度数学形态学信号特征提取方法,构建形态学结构元素新算子,运用多尺度数学形态分解方法提取瞬态过电压的高低频成分,构建适用于识别海上风电场内部瞬态过电压类型的时域特征量。再基于所构造的高频特征量和高低频能量比值识别特征量,结合支持向量机分类器模型对典型的内部瞬态过电压分类识别。仿真和试验研究表明,所提出的数学形态学算法相对于传统的小波算法,构造的特征量区分度更明显,可以准确地识别过电压类型,为海上风电场变电站电气设备的电压保护整定和绝缘配合奠定基础。

Feature Analysis for Transient Overvoltage in Offshore Wind Farm Based on High and Low Frequency Energy Rate Using Multi-scale Mathematical Morphology

At present, the high-frequency transient overvoltage caused by the frequent operations on electrical equipment or faults in offshore wind farms, is particularly severe. In order to analyze the transient characteristics of internal overvoltage in offshore wind farms, this paper firstly proposed a signal feature extraction method based on mathematical morphology, constructing a new morphological structure operator and utilizing multi-scale mathematical morphological decomposition to extract the high and low frequency information of transient overvoltage. Then two time-domain identification feature indexes were constructed for identifying the type of transient overvoltage in offshore wind farms. Finally, based on the high frequency feature index and the high and low frequency energy rate feature index proposed by this paper, different types of internal transient overvoltage classification could be classified using the support vector machine classifier model. The results show that compared with the traditional Wavelet algorithm, the feature extracted by the proposed mathematical morphology algorithm had a clearer degree of discrimination, which could accurately identify the overvoltage types, laying a foundation for the protection setting and insulation coordination for the electrical equipment in the offshore wind farm substations.