快速准确的电力系统扰动检测能够为后续扰动分析提供有效的指导信息，而广域测量系统(wide area measurement system，WAMS)的广泛应用为扰动检测提供了有力的数据基础。基于PMU量测数据，该文提出一种考虑PMU不良数据的扰动事件检测方法。首先分析PMU异常数据行为特性，揭示扰动事件与不良数据的差异性特征。进一步，提出一种基于差分Teager-Kaiser能量算子与3Sigma准则相结合的PMU异常数据初筛方法，避免了低强度扰动漏检和扰动的重复检测问题。接着，利用动态时间规整和最大互信息系数分别计算不同 PMU 间的时空相似性，以及同一台 PMU内不同量测间的相关性，并以此作为表征扰动事件和不良数据差异的特征。最后，通过局部离群概率算法对得到的综合度量指标进行分析，可实现在含有不良数据场景下的扰动事件准确检测。基于IEEE 39系统，实际电网模型以及PMU实测数据，验证所提方法具有较好准确性、实时性以及泛化能力。
Power System Disturbance Detection Method Considering PMU Data Quality Problems
Fast and accurate power system disturbance detection can provide effective guidance information for subsequent disturbance analysis,and the wide area measurement system(WAMS)is widely used to provide a powerful data base for disturbance detection.Based on PMU measurement data,this paper proposes a disturbance event detection method considering PMU bad data.First,the behavioral characteristics of PMU abnormal data are analyzed to reveal the differential characteristics of disturbance events and PMU bad data.Furthermore,a PMU abnormal data initial screening method based on the combination of differential Teager-Kaiser energy operator and 3Sigma criterion is proposed to avoid the problems of low intensity disturbance miss detection and repeated detection of disturbances.Then,the dynamic time warping and the maximal information coefficient are used to calculate the spatio-temporal similarity among different PMUs and the correlation among different measurements within the same PMU,respectively.And it is used as features to characterize the differences of disturbance events and PMU bad data.Finally,the obtained comprehensive metrics are analyzed by a local outlier probability algorithm to achieve accurate detection of disturbance events in scenarios containing PMU bad data.Based on the IEEE 39 system,the actual grid model and the filed PMU data,the proposed method is verified to have good accuracy,real-time and generalization capability.
synchronous phasor measurementdisturbance detectiondata quality problemsbad data
国家科技期刊平台
NSTL
万方数据
维普
