Method, device and equipment for processing bad data of power distribution network and storage medium

Abstract

The invention discloses a processing method and device for bad data of a power distribution network, equipment and a storage medium. The method comprises the following steps: acquiring measurement data in a preset time period in the power distribution network; inputting the measurement data into a preset identification model for data classification, and outputting the measurement data as normal data or bad data; determining a moment t at which bad data in the measurement data appear based on the identification result, and inputting a preset number of normal data before the moment t at which the bad data appear into a preset data correction model to obtain prediction data at the moment t; and replacing the bad data at the moment t with the prediction data at the moment t to obtain corrected measurement data. The technical problems that in the prior art, after bad data are identified by adopting a convolutional neural network model, the bad data after elimination are not supplemented or corrected, so that the data are incomplete, the observability of a power distribution network system is influenced, and the reliability of a power distribution network state estimation result is reduced are solved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a method, device, equipment and storage medium for processing bad data of distribution networks. Background technique [0002] In recent years, the distribution network system has achieved leapfrog development, and residents have increasingly stringent requirements on the quality and reliability of power grid power supply. However, because the real-time measurement system is not perfect, the measurement data such as node voltage, line circuit, and branch power obtained from the data acquisition and monitoring system (SCADA) will have errors, repetitions, and missing problems. Bad data will appear. The existence of bad data will have a negative impact on the operating state of the distribution network system, and then affect the performance of the distribution network system. [0003] Traditional bad data identification methods are mainly based on state est...

AI Technical Summary

Problems solved by technology

[0005] The present application provides a method, device, equipment and storage medium for processing bad data in distribution networks, which are used to solve the problem that the prior art uses convolutional neural network models to identify bad data, but does not supplement the bad data after elimination Or correction, which makes the data incomplete, thus affecting the observability of the distribution network system and reducing the reliability of the distribution network state estimation results.

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