摘要
风电能源的不稳定性导致暂态电流行波特征复杂多变,行波信号在频域上的分布极为分散,难以提取其准确特征,严重影响阈值动态调整的灵敏度。为了有效适应故障场景的动态变化,针对风电外送输电网络,设计了暂态电流行波特征动态阈值设定方法。利用行波传感器捕捉故障发生时的暂态电流行波信号,并采用经验模态分解(EMD)和希尔伯特变换(HT)准确提取信号的频域特征。将阈值设定问题转化为目标优化问题,将故障特征与参考暂态电流行波信号特征之间的相似度最大值设定为目标函数。利用粒子群算法迭代求解目标函数,得出不同故障类型下的特征阈值,以适应实际工况的动态变化。结果表明,应用该方法后,阈值动态调整的灵敏度最高可达到0.92,说明该方法能够更好地适应故障场景的动态变化。
The instability of wind power energy leads to complex and variable transient current traveling wave characteristics,and the distribution of traveling wave signals in the frequency domain is extremely scattered,making it difficult to extract accurate features and seriously affecting the sensitivity of threshold dynamic adjustment.Therefore,in order to effectively adapt to the dynamic changes of fault scenarios,a dynamic threshold setting method for transient current traveling wave characteristics was designed for wind power transmission networks.A traveling wave sensor is used to capture the transient current traveling wave signal when a fault occurs,and EMD and HT are used to accurately extract the frequency domain characteristics of the signal.The threshold setting problem is transformed into an objective optimization problem,and the maximum similarity between the fault characteristics and the reference transient current traveling wave signal characteristics is set as the objective function.The objective function is iteratively solved using particle swarm optimization algorithm to obtain feature thresholds for different types of faults,in order to adapt to the dynamic changes in actual working conditions.The results show that after applying this method,the sensitivity of threshold dynamic adjustment can reach up to 0.92,indicating that this method can better adapt to the dynamic changes of fault scenarios.
作者
曹丹
冯浩杰
笪玥
周浩然
刁杨华
CAO Dan;FENG Haojie;DA Yue;ZHOU Haoran;DIAO Yanghua(State Grid Zhenjiang Power Supply Company,Zhenjiang 212000,China)
出处
《电子设计工程》
2025年第11期59-62,67,共5页
Electronic Design Engineering
基金
国网江苏省电力有限公司科技项目(J20240015)。
关键词
风电外送输电网络
暂态电流行波信号
特征提取
粒子群算法
阈值设定
wind power transmission network
transient current traveling wave signal
feature extraction
particle swarm optimization algorithm
threshold setting
