Research on Pressure Forecasting Method of SF<sub>6</sub> Density On-line Monitoring Device Based on BP Neural Network

LIU Haifeng; XU Jian; HU Weitao; LIU Zihao; SHI Xiaolong

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Research on Pressure Forecasting Method of SF₆ Density On-line Monitoring Device Based on BP Neural Network

更新时间：2025-04-27

- Research on Pressure Forecasting Method of SF₆ Density On-line Monitoring Device Based on BP Neural Network
- Vol. 40, Issue 4, Pages: 26-31(2021)
- 作者机构：
  
  国网河北省电力有限公司检修分公司
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM561;TP183
- Published：2021
- 稿件说明：
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LIU Haifeng, XU Jian, HU Weitao, et al. Research on Pressure Forecasting Method of SF₆ Density On-line Monitoring Device Based on BP Neural Network[J]. 2021, 40(4): 26-31.
DOI：

LIU Haifeng, XU Jian, HU Weitao, et al. Research on Pressure Forecasting Method of SF₆ Density On-line Monitoring Device Based on BP Neural Network[J]. 2021, 40(4): 26-31. DOI：

摘要

GIS设备SF

密度监测容易受环境因素影响产生一定的波动

对设备运行状态判定造成干扰

因此提出一种基于BP神经网络的SF

压力预测方法

通过分析环境温度及其变化情况、相对湿度、风速、天气类型和导体电流等内外部因素对SF

压力变化的影响

建立BP神经网络预测模型

对SF

在线监测装置的压力值进行预测

为监测设备正常运行提供数据参考

并给出了分析GIS设备压力降低原因的判别策略。选取某特高压变电站GIS设备的典型SF

在线监测装置气体压力和对应的自然环境及导体电流数据进行MATLAB仿真

结果表明

压力预测结果准确度可达98.5%以上

验证了该方法的可行性。

Abstract

The SF

pressure value of GIS equipment is easy to fluctuate under the influence of environmental factors

which affects the judgment on equipment operation state.This paper mainly studies the prediction method of SF

pressure based on BP neural network.By analyzing the influence of internal and external factors such as ambient temperature

temperature transition rate

relative humidity

wind speed

weather type and conductor current on the change of SF

pressure

the prediction model of BP neural network is established to predict the pressure value of SF

on-line monitoring device

which provides data reference for the normal operation of mo

nitoring device This paper also puts forward the discrimination strategy for the reason of pressure reduction on GIS equipment.By selecting the gas pressure and the corresponding natural environment and conductor current data of a typical SF

on-line monitoring device of GIS equipment in an UHV substation for simulation calculation

the results show that the accuracy of SF

pressure prediction results can reach more than 98.5%

therefore the correctness of theoretical analysis has been verified.

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Related Institution

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Research on Pressure Forecasting Method of SF6 Density On-line Monitoring Device Based on BP Neural Network

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摘要

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Research on Pressure Forecasting Method of SF₆ Density On-line Monitoring Device Based on BP Neural Network