声信号特征融合的变压器故障判断方法

针对变压器故障种类繁多、故障样本不足的问题，提出了一种声信号特征融合的变压器故障判断方法. 在正常变压器中提取大量正常声信号，从中找到符合变压器特性的特征. 在时域、频域等不同空间域中对采集的变压器正常声信号进行特征提取，由最大相关-最小冗余算法找到最符合变压器声信号的特征集，并对特征集利用加权熵主成分法得到融合指标. 对某变电站220 kV和110 kV变压器正常信号与故障仿真信号在噪声干扰的情况下对融合指标进行了比较，结果表明:该方法获取的融合指标在正常与仿真故障情况下区分明显，具有良好的识别效果，验证了该方法的可行性. 

Transformer Fault Judgment Method Based on Acoustic Signal Feature Fusion

Aiming at the problems of various transformer faults and insufficient fault samples, a transformer fault judgment method based on acoustic signal feature fusion was proposed. A large number of normal sound signals were extracted from the normal transformer to find out the characteristics of the transformer. In different spatial domains such as time domain and frequency domain, feature extraction was carried out on the collected normal transformer acoustic signals, and the feature set that was most consistent with the transformer acoustic signals was found by the maximum-correlating-minimum-redundancy algorithm, and then the weighted entropy principal component method was used to obtain the fusion index for the feature set. The fusion index was compared with the normal signal and fault simulation signal of 220 kV and 110 kV transformer of a substation under the condition of noise interference. The results show that the fusion index obtained by this method can distinguish clearly between normal and simulation fault, and has good recognition effect, which verifies the feasibility of this method.