Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm
Operation State Identification Method for Converter Transformers Based on Vibration Detection Technology and Deep Belief Network Optimization Algorithm
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DOI：
10.3390/act10030056
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发表时间：
2021-03-01
期刊：
影响因子：
2.6
通讯作者：
Deng, Jun
中科院分区：
文献类型：
--
作者：
Wu, Yongye;Zhang, Zhanlong;Deng, Jun
换流变压器是高压直流输电系统中连接换流桥与交流系统的专用电力变压器。由于换流变压器的特殊结构，在其制造加工过程中，有必要对其运行状态进行测试，以确保其未来接入电网的安全性。大量研究表明，变压器的振动信号可以反映变压器的工作状态。因此，为了实现对换流变压器运行状态的有效识别，本文提出了一种基于振动检测技术和深度信念网络优化算法的换流变压器运行状态识别方法。本文首先介绍了振动检测技术的背景、原理和应用，利用带有压电加速度传感器、压电致动器和数据采集仪的振动测量系统，采集换流变压器在空载和有载状态下不同测点的振动信号。通过分析振动信号的时频特性，将快速傅立叶变换（FFT）、小波包分解（WPD）和时域指标（TDI）结合成一种融合特征提取方法，提取振动信号的特征值，从而构造信号的融合特征向量。考虑到深度学习在分类方面的优异性能，采用深度信念网络对信号的特征向量进行分类。为有效提高网络分类效率，引入麻雀搜索算法，基于麻雀种群的行为特征建立数学模型，并将该模型与深度信念网络相结合，实现网络参数的自适应优化和信号特征向量的准确分类。将该方法应用于500kv换流变压器进行了实验验证。实验结果表明，融合特征提取方法能够充分提取振动信号的特征，深度信念网络优化算法具有更高的分类精度和更好的运行效率，能够有效地实现对换流变压器运行状态的准确识别。此外，该方法实现了对振动传感器检测结果的精确响应，有助于未来换流变压器制造技术的改进。
The converter transformer is a special power transformer that connects the converter bridge to the AC system in the HVDC transmission system. Due to the special structure of the converter transformer, it is necessary to test its operation state during its manufacture and processing to ensure the safety of its future connection to the grid. Numerous studies have shown that vibration signals in transformers can reflect their operating state. Therefore, in order to achieve an effective identification of the operation state of the converter transformer, this paper proposes a method for identifying the operation state of the converter transformer based on vibration detection technology and a deep belief network optimization algorithm. This paper firstly describes the background, principle and application of vibration detection technology, using vibration measurement systems with piezoelectric acceleration sensors, piezoelectric actuators and data acquisition instruments to collect vibration signals at different measurement points on the converter transformer in states of no-load and on-load. By analyzing the time-frequency characteristics of the vibration signals, fast Fourier transform (FFT), wavelet packet decomposition (WPD) and time domain indexes (TDI) are combined into a fused feature extraction method to extract the eigenvalues of the vibration signals, so that the fused eigenvectors of the signals can be constructed. Considering the excellent performance of deep learning in classification, the deep belief network is used to classify the signals' eigenvectors. To effectively improve the network classification efficiency, the sparrow search algorithm was introduced to build a mathematical model based on the behavioral characteristics of sparrow populations and combine the model with a deep belief network, so as to achieve adaptive parameter optimization of the network and accurate classification of the signals' eigenvectors. The proposed method is applied to a 500 kV converter transformer for experimental verification. The experimental results show that the fused feature extraction method was able to fully extract the features of the vibration signal, and the deep belief network optimization algorithm had higher classification accuracy and better operational efficiency, and was able to effectively achieve accurate identification of the operation state of the converter transformer. In addition, the method achieved a precision response to the detection results of the vibration sensors, contributing to future improvements in converter transformer manufacturing technology.