牵引供电系统异常扰动在线识别与评估算法

Online identification and evaluation algorithm of abnormal disturbance of traction power supply system

牵引供电系统中的异常电气扰动产生的暂态/稳态的过电压、过电流具有特征相近、频率分开范围大、捕捉困难等特点,造成扰动事件的类型判定与扰动的关键模态、参数辨识困难。因此,提出一种基于奇异谱分析和Hilbert变换的扰动特征提取算法,实现了对不同异常扰动类型的在线快速识别;改进了总体最小二乘求解的旋转不变技术参数估计(TLS-ESPRIT)算法以精确获取扰动的关键模态参数,并根据各扰动的特征及扰动的关键模态参数定义了扰动严重程度评估指标;利用上述算法对仿真数据和现场实测数据进行验证,结果表明所提算法可以有效、快速地识别牵引供电系统异常扰动类型并对其严重程度进行评估。

The transient/steady-state overvoltage and overcurrent generated by abnormal electrical disturbances in the traction power supply system have the characteristics of similar features,large frequency separation range,and difficulty in capturing.These characteristics make it difficult to determine the type of disturbance events and identify the key modes and parameters of the disturbance.Therefore,a feature extraction algorithm based on singular spectrum analysis and Hilbert transform is proposed,which realizes the online rapid identification of different abnormal disturbance types.Then the total least squares-estimation of signal parameters via rotational invariance techniques(TLS-ESPRIT)algorithm is improved to accurately evaluate the disturbance modal parameters.According to the characteristics of each disturbance and the key modal parameters of the disturbance,the evaluation index of disturbance severity is defined.The above algorithms are used to identify the simulated data and the measured data.The results show that the proposed algorithms can effectively and quickly identify the type of abnormal disturbance in traction power supply system and further evaluate its severity.