| Citation: |
DAI Yueming, YANG Lufeng, TONG Xiongmin. Real-time Section State Verification Method of Energy Management System Low Voltage Equipment Based on Infrared Image and Deep Learning[J]. Infrared Technology , 2024, 46(12): 1464-1470.
|
Real-time cross-sectional state verification of low-voltage equipment in an energy management system (EMS) is difficult in the maintenance of power equipment. Based on infrared images and deep learning, a real-time cross-sectional state verification method for low-voltage EMS equipment was proposed. The seed region growth method segments the red and green components of the real-time cross-section of the EMS low-voltage equipment in the infrared focal plane image to determine the abnormal area of the EMS low-voltage equipment. The invariant moment of the target shape in the abnormal area is extracted, and the difference in the active power of each branch before and after the disconnection of the EMS low-voltage equipment branch is used as the feature vector of the real-time section state check. A DC power flow model based on a deep neural network is constructed. The feature vector was input into the model, and the real-time section-state power flow calculation results were the output. The real-time section state power flow in abnormal areas was analyzed to determine if it was outside the limit, and a real-time section state check was completed. Experiments show that this method can determine the abnormal area of EMS low-voltage equipment. In addition, this method can accurately check the power flow of the real-time section state of the equipment to ensure the safety of equipment maintenance. Applying this method can improve the line qualification rate and voltage qualification rate.
| [1] |
张旭, 陈云龙, 王仪贤, 等. 基于潮流断面修正的含风电电网无功-电压分区方法[J]. 电力自动化设备, 2019, 39(10): 48-54.
ZHANG Xu, CHEN Yunlong, WANG Yixian, et al. Reactive power-voltage partitioning of power grid with wind power based on correction of power flow section[J]. Electric Power Automation Equipment, 2019, 39(10): 48-54.
|
| [2] |
高旭, 马迎新, 王可, 等. 基于连通状态矩阵的智能变电站安措校核方法[J]. 电力自动化设备, 2019, 39(7): 195-202.
GAO Xu, MA Yingxin, WANG Ke, et al. Verification for security measures of smart substation based on communication state matrix[J]. Electric Power Automation Equipment, 2019, 39(7): 195-202.
|
| [3] |
吴迪, 王正风, 刘航. 全时间维度调度计划安全校核的设计与应用[J]. 中国电力, 2020, 53(5): 48-55.
WU Di, WANG Zhengfeng, LIU Hang. Design and application of full-time dimension dispatching schedule safety check[J]. Electric Power, 2020, 53(5): 48-55.
|
| [4] |
程雄, 唐应玲, 申建建, 等. 电力市场环境下大规模水电站群月度交易电量分解与校核方法[J]. 中国电机工程学报, 2020, 40(8): 2514-2525.
CHENG Xiong, TANG Yingling, SHEN Jianjian, et al. Decomposition and checking method for large-scale hydropower plants monthly trading energy in electricity market[J]. Proceedings of the CSEE, 2020, 40(8): 2514-2525.
|
| [5] |
黄国栋, 杨军峰, 丁强, 等. 基于电量裕度的中长期电量安全校核方法[J]. 电力系统保护与控制, 2020, 48(2): 115-121.
HUANG Guodong, YANG Junfeng, DING Qiang, et al. Medium and long-term electric quantity security check method based on power quantity margin[J]. Power System Protection and Control, 2020, 48(2): 115-121.
|
| [6] |
应俊, 蔡月明, 杜红卫, 等. 配电网络模型二次校核方法[J]. 电力系统自动化, 2019, 43(7): 185-191.
YING Jun, CAI Yueming, DU Hongwei, et al. Secondary verification method of distribution network model[J]. Automation of Electric Power Systems, 2019, 43(7): 185-191.
|
| [7] |
梁辰, 孙建文, 王兰玉, 等. 应用聚类和证据理论实现变电站保护压板状态校核[J]. 电网技术, 2020, 44(6): 2343-2349.
LIANG Chen, SUN Jianwen, WANG Lanyu, et al. Application of clustering and evidence theory to checking the status of substation protection platen[J]. Power System Technology, 2020, 44(6): 2343-2349.
|
| [8] |
刘齐, 王茂军, 高强, 等. 基于红外成像技术的电气设备故障检测[J]. 电测与仪表, 2019, 56(10): 122-126.
LIU Qi, WANG Maojun, GAO Qiang, et al. Fault detection of electrical equipment based on infrared imaging technology[J]. Electrical Measurement & Instrumentation, 2019, 56(10): 122-126.
|
| [9] |
周毓敏, 邓韦斯, 吴云亮, 等. 基于深度学习的断面约束概率快速预警方法[J]. 南方电网技术, 2020, 14(9): 45-52.
ZHOU Yumin, DENG Weisi, WU Yunliang, et al. Fast probabilistic pre-warning method for transmission capacity constraints based on deep learning[J]. Southern Power System Technology, 2020, 14(9): 45-52.
|
| [10] |
王有元, 李后英, 梁玄鸿, 等. 基于红外图像的变电设备热缺陷自调整残差网络诊断模型[J]. 高电压技术, 2020, 46(9): 3000-3007.
WANG Youyuan, LI Houying, LIANG Xuanhong, et al. Self-adjusting residual network diagnosis model for substation equipment thermal defects based on infrared image[J]. High Voltage Engineering, 2020, 46(9): 3000-3007.
|
| [11] |
周正钦, 冯振新, 周东国, 等. 基于扩展Meanshift电气设备发热故障区域提取方法[J]. 红外技术, 2019, 41(1): 78-83. http://hwjs.nvir.cn/article/id/hwjs201901012
ZHOU Zhengqin, FENG Zhenxin, ZHOU Dongguo, et al. Fault region extraction of electrical equipment in infrared image by using an extended mean shift method[J]. Infrared Technology, 2019, 41(1): 78-83. http://hwjs.nvir.cn/article/id/hwjs201901012
|
| [12] |
蔡晖, 陈倩, 刘宇, 等. 苏州同里新能源小镇的交/直流混合配电网潮流计算方法研究[J]. 电力电容器与无功补偿, 2020, 41(2): 110-115.
CAI Hui, CHEN Qian, LIU Yu, et al. Study on power flow calculation method for AC/DC hybrid distribution network of Tongli new-energy town in Suzhou[J]. Power Capacitor & Reactive Power Compensation, 2020, 41(2): 110-115.
|
| [13] |
吴桂联, 张林垚, 林婷婷, 等. 不同运行模式下区域综合能源系统多能潮流计算方法[J]. 电力系统及其自动化学报, 2019, 31(7): 95-104.
WU Guilian, ZHANG Linyao, LIN Tingting, et al. Calculation method for multi-energy power flow in district-level integrated energy system in different operation modes[J]. Proceedings of the CSU-EPSA, 2019, 31(7): 95-104.
|
| [14] |
王红壮, 刘天琪, 何川, 等. 基于改进脆弱线路辨识的关键输电断面确定方法[J]. 现代电力, 2020, 37(3): 294-302.
WANG Hongzhuang, LIU Tianqi, HE Chuan, et al. Key transmission section determination method based on improved vulnerable line identification[J]. Modern Electric Power, 2020, 37(3): 294-302.
|
| [15] |
唐建兴, 杨君军, 马覃峰, 等. 考虑自动装置控制策略的故障后潮流快速计算方法[J]. 南方电网技术, 2020, 14(5): 51-58.
TANG Jianxing, YANG Junjun, MA Qinfeng, et al. Rapid power flow calculation method after fault considering control strategy of automatic equipment[J]. Southern Power System Technology, 2020, 14(5): 51-58.
|
Supported by: Beijing Renhe Information Technology Co., Ltd. 
DownLoad: