Method and device for processing data of phasor measurement device of power system and electronic equipment

Abstract

The invention discloses a method and a device for processing data of a phasor measurement device of a power system and electronic equipment. The method comprises the following steps: acquiring PMU data generated by the phasor measurement device; screening the PMU data by adopting an iterative random forest algorithm to obtain first data; and filling the first data by using the trained dynamic neural network to obtain a data processing result. The PMU data is determined as a time sequence for data filling by adopting the dynamic neural network, so that the precision and efficiency of PMU data filling are improved, and the construction of complete PMU data is realized.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method, a device and an electronic device for data processing of a power system phasor measurement device. Background technique [0002] In recent years, with the improvement of national big data, artificial intelligence and other data mining capabilities, the types and scale of data have become increasingly large. Therefore, the analysis and prediction of data provides infinite space for the development of power grid enterprises in various aspects such as strategic decision-making and future development. However, in the actual operation of power grid enterprises, massive amounts of data are generated every minute and every second. Due to the limitations of instrument accuracy, noise effects and human errors, a large amount of data may be lost or discrepant. PMU (phasor measurement unit) is a device used to measure and output synchronization vectors and perform dynam...

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Problems solved by technology

However, when the PMU data is generated, the actual data must be disturbed by many adverse conditions, so the analysis of the PMU data will have an impact on the power grid analysis and stability analysis

[0003] At present, for the problem of bad PMU data processing, the data is predicted by basic mathematical theory methods or modern machine learning methods, but data screening is generally lacking in the process of PMU data prediction, and the impact of these wrong data on data prediction is often fatal.

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