1. 网络空间安全学院
2. 广东电网有限责任公司佛山供电局
3. 武汉人才集团有限公司
纸质出版:2026
移动端阅览
汪 毅,胡正庭,钟建贵.基于电力计量数据的跨省输电线路异常线损分析及治理[J].电力大数据,2026,29(07):1-2.
WangYi, Huzhengting, zhongJiangui. Analysis and Treatment of Abnormal Line Loss in Cross Provincial Transmission Lines Based on Electricity Metering Data[J]. 2026, 29(7): 1--.
跨省输电线路是国家能源战略布局的关键组成部分,在优化能源资源配置、保障电力供应安全稳定方面发挥着至关重要的作用。针对现有研究中存在的跨省输电线路线损评价模型不完善、计量设备运行变差对线损的综合影响不明确、异常治理体系不健全等核心问题,开展了异常线损分析及治理关键技术研究,创新性构建了融合物理机理与优化注意力机制LSTM的多维影响量同期线损综合评价模型,系统分析了输电线路关口计量设备电容式电压互感器(capacitor voltage transformer
CVT)的运行变差对统计线损的综合影响,提出了跨省输电线路异常线损的高效综合治理技术体系并完成应用验证。验证结果表明,所构建的多维影响量同期线损综合评价模型能够有效提升线损评估的准确性,CVT的运行变差以及电能计量装置的整体误差对跨省长距离输电的统计线损具有不可忽视的影响,研究提出的异常线损综合治理技术体系,结合人工智能诊断与多部门协同,可显著提升治理效率与效果。
Inter-provincial transmission lines are a crucial component of the national energy strategic layout
playing a vital role in optimizing energy resource allocation and ensuring the safety and stability of power supply. Addressing the core issues in existing research
such as the imperfect evaluation model for line loss in cross-provincial transmission lines
the unclear comprehensive impact of operating variation of metering equipment on line loss
and the inadequate anomaly management system
we have conducted research on abnormal line loss analysis and key treatment technologies. We have innovatively constructed a comprehensive evaluation model for simultaneous line loss that integrates physical mechanisms and optimized attention mechanism LSTM
systematically analyzed the comprehensive impact of the operating variation of capacitor voltage transformers (CVTs)
which are metering devices at transmission line gateways
on statistical line loss
and proposed an efficient comprehensive management technology system for abnormal line loss in cross-provincial transmission lines
which has been verified through application. The verification results indicate that the constructed comprehensive evaluation model for multi-dimensional impact factors and simultaneous line loss can effectively enhance the accuracy of line loss assessment.The operating variation of CVT and the overall error of electric energy metering devices have a non-negligible impact on the statistical line loss of long-distance cross-provincial power transmission. The comprehensive management technology system for abnormal line loss proposed in the study
combined with artificial intelligence diagnosis and multi-sector collaboration
can significantly improve the efficiency and effectiveness of governance.
国家电网有限公司.国家电网公司能源互联网规划[R].北京:国家电网有限公司,2020.
江秀臣,许永鹏,李曜丞,等.新型电力系统背景下的输变电数字化转型[J].高电压技术,2022,48(1):1-10.
韩先才,孙昕,陈海波,等.中国特高压交流输电工程技术发展综述[J].中国电机工程学报,2020,40(14):4371-4386.
中国南方电网有限责任公司.数字电网白皮书[R].广州:中国南方电网有限责任公司,2020.
舒印彪.发展新型电力系统助力实现“双碳”目标[J].中国电力企业管理,2021(7):8-9.
周远翔,陈健宁,张灵,等.“双碳”与“新基建”背景下特高压输电技术的发展机遇[J].高电压技术,2021,47(7):2396-2408.
YANG B, DUAN J H, LIU Z J, et al. Exploring sustainable development of new power systems under dual carbon goals: control, optimization, and forecasting[J]. Energies, 2024, 17(16): 3909.
GROMPONE V G, JAKUBOWICZ J, MOREL J M, et al. LSD: a fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722-732.
SWETAPADMA A, YADAV A. A novel decision tree regression-based fault distance estimation scheme for transmission lines[J]. IEEE Transactions on Power Delivery, 2017, 32(1): 234-245.
SINGH P K, SINGH N, NEGI R. Short-term wind power prediction using hybrid auto regressive integrated moving average model and dynamic particle swarm optimization[J].International Journal of Cognitive Informatics and Natural Intelligence,2021,5(2): 111- 138.
MOUTIS P, ALIZADEH-MOUSAVI O. Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements[J]. IEEE Transactions on Power Delivery, 2021, 36(4): 1952-1963.
梅府贤,李昀熠,万灿,等.计及交互功率不确定性和柔性负荷的输配协同优化调度[J].高电压技术.2024,50(8):3521-3534
曾宇,刘友波,高红均,等.基于模型预测控制的高压配电网负荷转供与储能电站协同运行[J].电网技术,2021,45(5):1902-1911.
吕军,栾文鹏,刘日亮,等.基于全面感知和软件定义的配电物联网体系架构[J].电网技术,2018,42(10):3108-3115.
江秀臣,刘亚东,傅晓飞,等.输配电设备泛在电力物联网建设思路与发展趋势[J].高电压技术,2019,45(5):1345-1351.
齐波,张鹏,张书琦,等.数字孪生技术在输变电设备状态评估中的应用现状与发展展望[J].高电压技术,2021,47(5):1522-1538.
郝帅,杨磊,马旭,等.基于注意力机制与跨尺度特征融合的YOLOv5输电线路故障检测[J].中国电机工程学报,2023,43(6):2319-2330.
赵振兵,张薇,戚银城,等.融合深度特征的输电线路金具缺陷因果分类方法[J].北京航空航天大学学报,2021,47(3):461-468.
曾勇斌,王星华,彭显刚,等.输电线路缺陷风险建模及其预测方法研究[J].电力系统保护与控制,2020,48(10):91-98.
周恩泽,樊灵孟,黄勇,等.基于火焰燃烧模型的输电线路山火跳闸风险分布评估[J].电网技术,2022,46(7):2778-2785.
李瑞生,张彦龙,翟登辉,等.基于改进SSD的输电线路销钉缺陷检测[J].高电压技术,2021,47(11):3795-3802.
张姝,王昊天,董骁翀,等.基于深度学习的输电线路螺栓检测技术[J].电网技术,2021,5(7):2821-2828.
吴军,白梁军,董晓虎,等.基于Cascade R-CNN算法的输电线路小目标缺陷检测方法[J].电网与清洁能源,2022,8(4):19-27,36.
ZHAO W Q, XU M F, CHENG X F, et al. An insulator in transmission lines recognition and fault detection model based on improved faster RCNN[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 5016408.
SHE L C, FAN Y D, WANG J G, et al. Insulator surface breakage recognition based on multiscale residual neural network[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 3524309.
ISLAM M, LEE G, HETTIWATTE S N, et al. Calculating a health index for power transformers using a subsystem-based GRNN approach[J]. IEEE Transactions on Power Delivery, 2018, 33(4): 1903-1912.
SONG H, DAI J J, SHENG G H, et al. GIS partial discharge pattern recognition via deep convolutional neural network under complex data source[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2018, 25(2): 678-685.
CHANG C K, LAI C S, WU R N. Decision tree rules for insulation condition assessment of pre-molded power cable joints with artificial defects[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2019, 26(5): 1636-1644.
国家电网有限公司.国家电网公司能源互联网规划[R].北京:国家电网有限公司,2020.
江秀臣,许永鹏,李曜丞,等.新型电力系统背景下的输变电数字化转型[J].高电压技术,2022,48(1):1-10.
韩先才,孙昕,陈海波,等.中国特高压交流输电工程技术发展综述[J].中国电机工程学报,2020,40(14):4371-4386.
中国南方电网有限责任公司.数字电网白皮书[R].广州:中国南方电网有限责任公司,2020.
舒印彪.发展新型电力系统助力实现“双碳”目标[J].中国电力企业管理,2021(7):8-9.
周远翔,陈健宁,张灵,等.“双碳”与“新基建”背景下特高压输电技术的发展机遇[J].高电压技术,2021,47(7):2396-2408.
YANG B, DUAN J H, LIU Z J, et al. Exploring sustainable development of new power systems under dual carbon goals: control, optimization, and forecasting[J]. Energies, 2024, 17(16): 3909.
GROMPONE V G, JAKUBOWICZ J, MOREL J M, et al. LSD: a fast line segment detector with a false detection control[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(4): 722-732.
SWETAPADMA A, YADAV A. A novel decision tree regression-based fault distance estimation scheme for transmission lines[J]. IEEE Transactions on Power Delivery, 2017, 32(1): 234-245.
SINGH P K, SINGH N, NEGI R. Short-term wind power prediction using hybrid auto regressive integrated moving average model and dynamic particle swarm optimization[J].International Journal of Cognitive Informatics and Natural Intelligence,2021,5(2): 111- 138.
MOUTIS P, ALIZADEH-MOUSAVI O. Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements[J]. IEEE Transactions on Power Delivery, 2021, 36(4): 1952-1963.
梅府贤,李昀熠,万灿,等.计及交互功率不确定性和柔性负荷的输配协同优化调度[J].高电压技术.2024,50(8):3521-3534
曾宇,刘友波,高红均,等.基于模型预测控制的高压配电网负荷转供与储能电站协同运行[J].电网技术,2021,45(5):1902-1911.
吕军,栾文鹏,刘日亮,等.基于全面感知和软件定义的配电物联网体系架构[J].电网技术,2018,42(10):3108-3115.
江秀臣,刘亚东,傅晓飞,等.输配电设备泛在电力物联网建设思路与发展趋势[J].高电压技术,2019,45(5):1345-1351.
齐波,张鹏,张书琦,等.数字孪生技术在输变电设备状态评估中的应用现状与发展展望[J].高电压技术,2021,47(5):1522-1538.
郝帅,杨磊,马旭,等.基于注意力机制与跨尺度特征融合的YOLOv5输电线路故障检测[J].中国电机工程学报,2023,43(6):2319-2330.
赵振兵,张薇,戚银城,等.融合深度特征的输电线路金具缺陷因果分类方法[J].北京航空航天大学学报,2021,47(3):461-468.
曾勇斌,王星华,彭显刚,等.输电线路缺陷风险建模及其预测方法研究[J].电力系统保护与控制,2020,48(10):91-98.
周恩泽,樊灵孟,黄勇,等.基于火焰燃烧模型的输电线路山火跳闸风险分布评估[J].电网技术,2022,46(7):2778-2785.
李瑞生,张彦龙,翟登辉,等.基于改进SSD的输电线路销钉缺陷检测[J].高电压技术,2021,47(11):3795-3802.
张姝,王昊天,董骁翀,等.基于深度学习的输电线路螺栓检测技术[J].电网技术,2021,5(7):2821-2828.
吴军,白梁军,董晓虎,等.基于Cascade R-CNN算法的输电线路小目标缺陷检测方法[J].电网与清洁能源,2022,8(4):19-27,36.
ZHAO W Q, XU M F, CHENG X F, et al. An insulator in transmission lines recognition and fault detection model based on improved faster RCNN[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 5016408.
SHE L C, FAN Y D, WANG J G, et al. Insulator surface breakage recognition based on multiscale residual neural network[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70, 3524309.
ISLAM M, LEE G, HETTIWATTE S N, et al. Calculating a health index for power transformers using a subsystem-based GRNN approach[J]. IEEE Transactions on Power Delivery, 2018, 33(4): 1903-1912.
SONG H, DAI J J, SHENG G H, et al. GIS partial discharge pattern recognition via deep convolutional neural network under complex data source[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2018, 25(2): 678-685.
CHANG C K, LAI C S, WU R N. Decision tree rules for insulation condition assessment of pre-molded power cable joints with artificial defects[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2019, 26(5): 1636-1644.
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