首页|违约用电检测与预警系统的设计与优化

违约用电检测与预警系统的设计与优化

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为解决当前电力系统违约用电行为日趋严重、且缺乏有效的检测手段与预警系统的问题,设计了 一种违约用电检测与预警系统.首先采用线性插值算法对低压用户电表异常数据进行数据清洗补正,以减少因配电台区量测数据缺失对模型的干扰,结合线损波动率、线损与电流曲线的变点时间进行关联分析,确定台区是否存在违约用电行为.同时引入空间维度辨识、时间维度辨识、关系维度辨识来检测低压用户是否存在窃电异常,并计及用户窃电时间和用电容量等特性,提供预估窃电量.最后,对电力设备监测和预警方法实施优化处理,提高系统性能.研究结果表明,在8 d时间段时,本文方法通过优化处理后的节能量为50 J,说明本文设计的预警系统具有更高的准确度和鲁棒性.
Design and Optimization of Default Electricity Usage Detecting and Early-warning System
In order to solve the problem of the behavior of default electricity usage increasingly serious and lack of effective de-tection means and early-warning system,a default electricity usage detecting and early-warning system is designed.Firstly,the linear interpolation algorithm is used to clean and correct the abnormal data of low-voltage household meter,so as to reduce the interference of the model caused by the missing measured data in the distribution station area,and the relationship among the line loss fluctuation rate,the line loss and the change-point time of current curve are analyzed to determine whether there is any behavior of default electricity usage.At the same time,the spatial dimension identification,the time dimension identification and the relation dimension identification are introduced to detect whether there is electricity stealing anomaly in low voltage users.In addition,the characteristics of user electricity theft time and electricity consumption capacity are taking into account to provide estimated quatity of electricity-stealing.Finally,the monitoring and early warning methods of power equipment are optimized to improve the system performance.The result shows that the proposed method can save 50 J energy in 8 d time,which shows that the early warning system designed in this paper has higher accuracy and robustness.

default electricity usage detectionearly warning systemlinear interpolation algorithmelectricity theft analysis al-gorithmoptimization processing

刘安磊、马迅、贾旭超、王锦腾、魏涛

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国网河北省电力有限公司营销服务中心,河北 石家庄 050035

违约用电检测 预警系统 线性插值算法 窃电分析算法 优化处理

2024

河北电力技术
河北省电机工程学会,河北省电力研究院

河北电力技术

影响因子:0.306
ISSN:1001-9898
年,卷(期):2024.43(4)
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