应用优化限制带宽经验模态分解法识别电力变压器绕组模态参数

摘要

电力变压器绕组的模态参数识别与绕组结构振动特性及其优化设计、绕组振动故障诊断密切相关，因此，准确识别电力变压器绕组的模态参数意义重大。根据某10 kV实体变压器绕组的轴向模态实验结果，提出一种基于粒子群的优化带宽限制经验模态分解算法对变压器绕组的模态参数进行识别。该方法首先在实测振动信号的经验模态分解中引入屏蔽信号，然后使用粒子群优化算法确定最佳的屏蔽信号频率，从而有效地抑制了现有经验模态分解算法中的模态混叠现象，提高了绕组模态参数的准确率。与目前通用的频域识别方法PolyMax法的识别结果的对比结果表明：该方法能够准确地识别出变压器绕组的前四阶固有频率和阻尼比，且具有较强的抗干扰能力，适合于识别变压器绕组这类结构复杂的模态参数。%Modal parameters of power transformer winding are closely related to its vibration feature,dynamic optimal design and winding vibration fault diagnosis.An improved Restrained Band Empirical Mode Decomposition (BREMD) based on Particle Swarm Optimization (PSO ) was proposed to identify the modal parameter of power transformer winding,where a masking signal was introduced in the process of mode decomposition of vibration signal and the PSO algorithm was applied to determine the best frequency of the masking signal.Then the problem of mode mixing in the existing EMD method is restrained effectively and the accuracy of parameter identification of power transformer winding is improved consequently.When compared to the widely used frequency domain method of PloyMax,it is seen that by the proposed method the first four natural frequencies and its corresponding damping ratios can be achieved successfully and accurately with strong anti-interference feature.Therefore,it can be applied effectively to identify the modal parameters of power transformer winding with complex structures.