摘要
电力扰动数据蕴含大量系统和设备状态信息,基于电力扰动检测分析可实现系统和设备的状态感知。现有扰动检测方法以检测电能质量扰动和故障扰动为主,算法针对性强,通用性和适应性较差,尤其当扰动幅值小或持续时间短时,检测灵敏度较低。为此,提出一种通用的高灵敏度电力扰动检测方法,基于相邻周期差分信号的奇异值分布规律实现扰动检测,利用奇异值极差进行扰动与正常波形区分,提出基于历史奇异值极差序列参考值和差分信号分布特征的自适应阈值计算规则。通过大量仿真和现场实测数据对算法进行验证,结果表明算法在强噪声背景下对各类微弱扰动均具有较高检测灵敏性,证明了算法的正确性和有效性。
Power disturbance data contains a lot of information about the system and equipment conditions,and the detection and analysis of the power disturbance helps to realize the status perception of the system and equipment.The existing disturbance detections mainly focus on detecting the power quality disturbances and the fault disturbances,which are of strong pertinence and poor universality and adaptability.Specially when detecting low-amplitude or short-duration disturbances,the detection sensitivity is low.Therefore,a generic high-sensitivity power disturbance detection is proposed,which realizes disturbance detection based on the singular value distribution law of the adjacent cycle of differential signals.By using the singular value range(SVR)to distinguish the disturbance from the normal waveform,an adaptive threshold calculation rule based on the reference value of SVR sequence and the distribution characteristic of differential signals is proposed.Verified by a large number of simulation and field-measured data,the results show that the algorithm has high detection sensitivity for all kinds of weak disturbances under strong-noise backgrounds,which proves the correctness and effectiveness of the algorithm.
作者
王常智
张文海
刘亮
王杨
肖先勇
WANG Changzhi;ZHANG Wenhai;LIU Liang;WANG Yang;XIAO Xianyong(College of Electrical Engineering,Sichuan University,Chengdu 610065,Sichuan Province,China;Electric Power Research Institute of State Grid Jibei Electric Power Co.,Ltd.,Xicheng District,Beijing 100045,China)
出处
《电网技术》
EI
CSCD
北大核心
2023年第5期2147-2155,共9页
Power System Technology
基金
国家重点研发计划(2020YFF0305800)。
关键词
电力扰动
扰动检测
差分波形
奇异值分解
奇异值极差
power disturbance
disturbance detection
differential waveform
singular value decomposition
singular value range
