Power distribution network abnormity monitoring and positioning method based on monitoring data space-time correlation

Abstract

The invention discloses a power distribution network abnormity monitoring and positioning method based on monitoring data space-time correlation. The method comprises the following steps: S1, collecting operation state information of a feeder line in a power distribution network; s2, processing the operation state information of the feeder line in the power distribution network to obtain a data matrix; s3, constructing an empirical characteristic value distribution model based on the data matrix; s4, constructing an empirical characteristic value distribution model based on a residual matrix space-time correlation structure; s5, solving the minimum value of the spectral distance between the two empirical feature value distribution models, and taking an estimation parameter set when the minimum value is obtained as an optimal estimation parameter; and S6, measuring the change of the space-time correlation through the optimal estimation parameter, and monitoring and positioning the abnormity of the power distribution network according to the change of the space-time correlation. According to the method, priori knowledge about the complex topology of the power distribution network does not need to be foreseen, and the method has very high robustness for tiny random fluctuation and measurement errors in the network and is beneficial to reducing the false alarm rate.

Description

technical field [0001] The invention relates to the field of distribution networks, in particular to a distribution network abnormality monitoring and positioning method based on the space-time correlation of monitoring data. Background technique [0002] In the distribution network, the abnormalities caused by fault disturbances manifest as intermittent, asymmetrical and sporadic spikes with random sizes, presenting complex, nonlinear and dynamic characteristics. In addition, the distribution network has many branches and topological structures. Traditional model-based anomaly monitoring and location methods often need to make certain assumptions and simplifications, and cannot comprehensively and accurately monitor and locate anomalies in distribution networks. [0003] With the deployment of a large number of on-line monitoring equipment for the distribution network, a large amount of operating data can be obtained. In order to make full use of these data, a large number...

AI Technical Summary

Problems solved by technology

From the perspective of data structure, the system's operating status can be evaluated by analyzing the space-time correlation of monitoring data, and the monitoring and positioning of abnormal conditions in the distribution network can be realized. What is temporal correlation? (2) How to mine the spatial-temporal correlation of monitoring data? (3) What is the relationship between the space-time correlation of monitoring data and the system operation status? Due to the complexity of the online monitoring data structure of the distribution network, it is currently difficult to analyze it accurately with simple techniques

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