摘要
研究旨在探讨智能电网技术在碳中和战略中的应用,通过对一个实验小区和对照小区的比较,评估了智能电网技术对电力使用、可再生能源比例、用电行为、电力网络效率和碳排放的影响。实验结果表明,智能电网技术对碳中和具有显著的潜力,可通过提高电力网络效率、促进可再生能源的使用以及引导用电行为来减少碳排放。
In order to improve the efficiency of high-voltage transmission line inspection,an improved Faster-RCNN algorithm combined with the optimized grey wolf algorithm-BP neural network fault diagnosis model is proposed.By adding a dynamic selection mechanism network with an attention mechanism to the feature extraction network,the relevant channel focusing on learning insulator features is constructed to improve the precise location of the inspection fault area.Meanwhile,the weights and thresholds in the optimised grey wolf algorithm are combined to improve the EMC fault diagnostic of the intelligent selector robot.After simulation tests,the Faster-RCNN algorithm combined with the optimised grey wolf algorithm(IGWO)traditional algorithm insulator inspection accuracy rate is improved by 3.56%,and the fault detection and location accuracy is higher.Therefore,the article combines the two algorithms optimised high-voltage transmission line intelligent inspection robot with higher detection accuracy,detection efficiency and stability,which can effectively meet the current power system high-voltage transmission line inspection and fault detection application requirements.
作者
李翠
纪艳菊
张玉
Li Cui;Ji Yanju;Zhang Yu(Beijing Hualian Electric Power Engineering Consulting Co.,Ltd.,Beijing 100071,China)
出处
《现代工业经济和信息化》
2023年第12期146-149,共4页
Modern Industrial Economy and Informationization
关键词
智能电网技术
碳中和
可再生能源
电力网络效率
Faster-RCNN algorithm
IFWO algorithm
high-voltage transmission line
intelligent inspection
fault location
