[1]李浩然,王子滔.基于改进长短期记忆网络的配电开关晨操状态评估技术[J].机械与电子,2025,(06):25-30.
　LI Haoran,WANG Zitao.A Status Assessment Technology of Distribution Switches During Morning Operation Based on the Optimization of Long Short-term Memory Network[J].Machinery & Electronics,2025,(06):25-30.
点击复制

基于改进长短期记忆网络的配电开关晨操状态评估技术()
分享到：

《机械与电子》[ISSN:1001-2257/CN:52-1052/TH]

卷:
期数:
2025年06期
页码:
25-30
栏目:
研究与设计
出版日期:
2025-06-27

文章信息/Info

Title:
A Status Assessment Technology of Distribution Switches During Morning Operation Based on the Optimization of Long Short-term Memory Network
文章编号:
1001-2257 ( 2025 ) 06-0025-06
作者:
李浩然; 王子滔
深圳供电局有限公司,广东 深圳 518000
Author(s):
LI Haoran ;  WANG Zitao
( Shenzhen Power Supply Bureau Co. , Ltd. , Shenzhen 518000 , China )
关键词:
配电开关; 晨操; 健康状态评估; 瞪羚算法; 长短期记忆网络
Keywords:
distribution switches ;  morning operation ;  health status assessment ;  gazelle algorithm ;  long short-term memory network
分类号:
TM73 ;TP18
文献标志码:
A
摘要:
随着配电自动化技术不断发展,对配电开关运行可靠性提出了更高的要求,然而当前配电开关晨操时健康状态评估准确率低,无法保障电网安全、稳定、可靠运行。基于此,提出一种基于瞪羚算法优化长短期记忆网络的配电开关晨操健康状态评估方法。首先,分析影响开关健康状态因素;其次,使用最大信息系数把跟配电开关健康状态最相关的因素提取出来,作为长短期记忆网络输入;最后,对长短期记忆网络进行改进,设计瞪羚算法 长短期记忆网络的配电开关健康状态评估模型,通过瞪羚算法优化长短期记忆网络超参数、注意力机制重点关注重要特征的方式提高模型的训练精度和效率。实验验证,所提方法有效提升了配电开关晨操时健康状态评估能力,保证设备安全可靠运行。
Abstract:
With the continuous development of distribution automation technology , higher requirements have been put forward for the reliability of distribution switch operation.However , the current accuracy of health status assessment during morning operation of distribution switches is low , which cannot guarantee the safe , stable , and reliable operation of the power grid.In view of this problem , a method for assessing the health status of distribution switches during morning operation by using the gazelle algorithm to optimize long short-term memory network is proposed.Firstly , the factors that affect the health status of the switch is analyzed ; secondly , the maximum information coefficient is used to extract the factors most related to the health status of the distribution switch as the inputs for the long short-term memory network ; finally , a health status assessment model for distribution switches by using the gazelle algorithm to support long short-term memory network is designed , so as to optimize the long short-term memory network.The accuracy and efficiency of model ’ s training are improved by optimizing the hyperparameters of the long short-term memory network and focusing on important features under attention mechanism through the gazelle algorithm.Experimental verification shows that the method proposed effectively improves the ability to assess the health status of distribution switches during morning operation , ensuring the safety and reliability of equipment operation.

参考文献/References:

[ 1 ] 孙鹏,耿苏杰,王秀利 . 基于时效评分函数和贝叶斯概率的电力变压器状态实时评估[ J ] . 高电压技术, 2018 ,44 ( 4 ): 1069-1077.

[ 2 ] 孔运运 . 智能技术在电力设备运维中的应用[ J ] . 电子技术,2024 , 53 ( 5 ): 388-389.
[ 3 ] TIMPERLEY J E , VALLEJOJ M.Condition assessment of electrical apparatus with EMI diagnostics [ J ] .IEEE Transactions on industry applications , 2017 , 53 ( 1 ):693-699.
[ 4 ] BALDUCCI B , MARINOVA D.Unstructured data in marketing [ J ] .Journal of the academy of marketing science , 2018 , 46 ( 4 ): 557-590.
[ 5 ] 赵会茹,李娜娜,郭森,等 . 配电网设备故障停电风险实时评估[ J ] . 电力自动化设备, 2014 , 34 ( 11 ): 89-94.
[ 6 ] 史常凯,宁昕,孙智涛,等 . 基于设备实时健康指数的配电网风险量化评估 [ J ] . 高电压技术, 2018 ,44 ( 2 ):534-540.
[ 7 ] 胡丽娟,刁赢龙,刘科研,等 . 基于大数据技术的配电网运行可靠性分析 [ J ] . 电网技术, 2017 , 41 ( 1 ):265-271.
[ 8 ] 刘科研,吴心忠,石琛,等 . 基于数据挖掘的配电网故障风险预警[ J ] . 电力自动化设备, 2018 , 38 ( 5 ): 155-160.
[ 9 ] 孙莹,高贺,李可军,等 . 基于多时段信息融合的配电变压器运行状态评估模型[ J ] . 高电压技术, 2016 , 42 ( 7 ):2054-2062.
[ 10 ] 张友强,寇凌峰,盛万兴,等 . 配电变压器运行状态评估的大数据分析方法[ J ] . 电网技术, 2016 , 40 ( 3 ): 768-773.
[ 11 ] 史常凯,宁昕,孙智涛,等 . 基于设备实时健康指数的配电网风险量化评估[ J ] . 高电压技术, 2018 , 44 ( 2 ):534-540.
[ 12 ] 黄曌,汪沨,谭阳红,等 . 基于健康度和重要度的配电网运行风险评估方法[ J ] . 电力自动化设备, 2016 , 36( 6 ): 136-141.
[ 13 ] 龚方亮,牟龙华,郭文明,等 . 基于 D-S 证据理论的配电终端状态诊断[ J ] . 电力系统保护与控制, 2018 , 46( 1 ): 30-36.
[ 14 ] 庄保乾,韩路,李晓虎,等 . 基于集成深度随机森林算法的智能电厂设备健康评估方法[ J ] . 计算机测量与控制,2024 , 32 ( 8 ): 322-328.
[ 15 ] 孙琦,任杰锶,王伟杰,等 . 鲸鱼优化算法优化卷积神经网络与双向长短期记忆网络的飞机液压系统优先阀故障诊断[ J ] . 液压与气动, 2024 , 48 ( 10 ): 107-116.
[ 16 ] CHOUEIRI A C , SATO D M V , SCALABRIN E E , et al.An extended model for remaining time prediction in manufacturing systems using process mining [ J ] .Journal of manufacturing systems , 2020 , 56 : 188-201.
[ 17 ] 邵海东,肖一鸣,颜深 . 仿真数据驱动的改进无监督域适应轴承故障诊断[ J ] . 机械工程学报, 2023 , 59 ( 3 ):76-85.
[ 18 ] 李贝蕾 . 面向汽车装配车间订单剩余完工时间预测方法研究[ D ] . 合肥:合肥工业大学,2022.
[ 19 ] LIU C , ZHU H , TANG D , et al.A transfer learning CNN-LSTM network-based production progress prediction approach in IIoT-enabled manufacturing [ J ] .International journal of production research , 2023 , 61 ( 12 ): 4045-4068.
[ 20 ] 张朝龙,赵筛筛,何怡刚 . 基于信息熵与 PSO-LSTM的锂电池组健康状态估计方法[ J ] . 机械工程学报,2022 , 58 ( 10 ): 180-190.
[ 21 ] 高凯,李勋豪,胡林等 . 基于多头注意力的CNN-LSTM 的换道意图预测[ J ] . 机械工程学报, 2022 , 58( 22 ): 369-378.
[ 22 ] 张凤博,马雪玲,董珍珍,等 . 基于 CNN 和 LSTM 的机器学习模型在测井岩性识别的应用[ J ] . 西安石油大学学报(自然科学版),2024 , 39 ( 5 ): 96-103 , 133.
[ 23 ] 景巍巍,史明明,郑仙,等 . 基于长短期记忆网络的多风光混杂谐波的分类溯源方法[ J ] . 电子器件, 2024 ,47 ( 5 ): 1324-1330.
[ 24 ] 邓毅,廖秋丽 . 基于改进瞪羚优化算法的机器人路径规划[ J ] . 机械设计与制造工程, 2024 , 53 ( 1 ): 51-54.
[ 25 ] 卢后洪,谢罗峰,朱杨洋,等 . 基于卷积神经网络和模态转换的磁瓦内部缺陷检测方法 [ J ] . 中国测试,2024 , 50 ( 2 ): 22-27.
[ 26 ] 陈伟伟,荆世博,边家瑜,等 . 基于长短期记忆神经网络的电力用电量预测[ J ] . 机械与电子, 2024 , 42 ( 5 ):18-23.

备注/Memo

备注/Memo:
收稿日期: 2024-10-10
基金项目:深圳供电局有限公司科技项目( 09000020240301030900108 )
作者简介:李浩然 ( 1989- ),男,广东中山人,硕士,高级工程师,研究方向为配网自动化;王子滔 ( 1995- ),男,广东中山人,工程师,研究方向为配网自动化。
更新日期/Last Update: 2025-07-02