Electrical Performance Testing Method of Transformer Oil Based on Multi-frequency Ultrasonic Technology and Grey Wolf-optimized Neural Network

WANG Xu; SHENG Xikui; MU Kun; WANG Xin

doi:10.13296/j.1001-1609.hva.2020.08.018

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Electrical Performance Testing Method of Transformer Oil Based on Multi-frequency Ultrasonic Technology and Grey Wolf-optimized Neural Network

更新时间：2025-04-26

- Electrical Performance Testing Method of Transformer Oil Based on Multi-frequency Ultrasonic Technology and Grey Wolf-optimized Neural Network
- High VoltageApparatus Vol. 56, Issue 8, Pages: 114-120(2020)
- 作者机构：
  
  1. 上海交通大学电工与电子技术中心
  2. 国网吉林省电力有限公司延边供电公司
- 作者简介：
- 基金信息：
- DOI：10.13296/j.1001-1609.hva.2020.08.018
  CLC： TM407
- Published：2020
- 稿件说明：
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[1]王旭,生西奎,慕锟,王昕.基于多频超声波技术和GWO-BP算法的变压器油电气性能检测方法研究[J].高压电器,2020,56(08):114-120.

WANG Xu, SHENG Xikui, MU Kun, et al. Electrical Performance Testing Method of Transformer Oil Based on Multi-frequency Ultrasonic Technology and Grey Wolf-optimized Neural Network[J]. High voltageapparatus, 2020, 56(8): 114-120.
[1]王旭,生西奎,慕锟,王昕.基于多频超声波技术和GWO-BP算法的变压器油电气性能检测方法研究[J].高压电器,2020,56(08):114-120. DOI： 10.13296/j.1001-1609.hva.2020.08.018.

WANG Xu, SHENG Xikui, MU Kun, et al. Electrical Performance Testing Method of Transformer Oil Based on Multi-frequency Ultrasonic Technology and Grey Wolf-optimized Neural Network[J]. High voltageapparatus, 2020, 56(8): 114-120. DOI： 10.13296/j.1001-1609.hva.2020.08.018.

摘要

为了实现对变压器油电气性能快速准确地检测

提出了基于多频超声波（MFU）技术和灰狼优化BP神经网络（GWO-BP）的检测方法

利用不同频率超声波在变压器油中传播速度和衰减系数不同、对不同尺寸颗粒物检测效果不同的特点

综合了超声波在无损检测上的优势以及灰狼算法全局寻优能力强、预测精度高的优点。首先利用超声波在非均匀介质中的传播特性

准确地获取超声波在变压器油中的传播速度、衰减系数等特性参数;再利用灰狼算法对传统BP神经网络进行优化。然后利用灰狼优化的神经网络对样本进行训练

建立超声波传播特性参数和变压器油电气性能参数之间的映射关系

进而建立变压器油电气性能参数的检测模型

实现对多个电气性能参数快速准确的无损检测。通过实验

验证了该方法在检测变压器油电气性能参数上的可行性和有效性。

Abstract

To realize rapid and accurate detection of transformer oil’s electrical performance

a detection method based on the multi-frequency ultrasonic（MFU）technology and the grey wolf-optimized BP neural network（GWOBP）is proposed. This method makes use of the characteristics that the ultrasonic wave with different frequency has different propagation speed and attenuation coefficient in transformer oil

hence has different particle size detection effect. An this method combines the advantages of ultrasonic wave in nondestructive testing

the advantages of multifrequency ultrasonic technology in fast detection speed and comprehensive information acquisition

and the advantages of grey wolf-optimizer in global optimization and high prediction accuracy. Firstly

the propagation velocity and attenuation coefficient of ultrasonic wave in transformer oil are accurately obtained by using the propagation characteristics of ultrasonic wave in inhomogeneous medium. The grey wolf-optimizer is used to optimize the traditional BP neural network. Then

the grey wolf-optimized neural network is used to train the samples to establish the mapping relationship between the ultrasonic propagation characteristic parameters and the transformer oil’s electrical performance parameters. Furthermore

the detection model of the electrical performance parameters is established to realize fast and accurate nondestructive testing of multiple electrical performance parameters. The feasibility and effectiveness of the method in detecting the electrical performance parameters of transformer oil are verified by experiments.

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