Massive time series data visualization method for transient analysis of power system

Abstract

The invention discloses a massive curve visualization method for transient analysis of a power system. The method comprises the following steps: firstly, reading time series data involved in transientstability analysis of the power system and performing amplitude alignment on the time series data; converting the time series into a character string by adopting an approximate calculation method, indexing according to a Trie tree, and performing quick clustering by querying the Trie tree; wherein the clustering center is used as time series representativeness and is drawn into a curve; then, calculating the overall density distribution of the time series through a kernel density estimation algorithm by means of the parallel capacity of the GPU; providing interaction tools to classify viewingor select regions of interest to view time series values. According to the method, the density value is mapped to the color space to be visualized through the kernel density estimation method according to the density distribution of the curves in the screen space, and shielding between the curves and visual disorder are eliminated. For thousands of curves, the whole visualization process can be completed within tens of milliseconds, and the fluency of interaction is ensured.

Description

technical field [0001] The invention relates to the field of power system transient analysis, in particular to a massive time series data visualization method for power system transient analysis. Background technique [0002] The power system transient analysis process can be summarized as an iterative process of "controlling parameters, performing calculations, monitoring and analyzing key components, and readjusting parameters". The current transient analysis mainly uses the function of the curve reading room to open multiple monitoring windows at the same time, and place several (<10) monitoring curves side by side in each monitoring window. figure 1 Shows the time series visualization views placed side by side in the PSASP curve reading room, usually only a maximum of 8 time series curves are displayed in a curve window. [0003] Analyzing transient time series data with the help of curve reading room tools will mainly cause two problems: first, due to the limitation...

AI Technical Summary

Problems solved by technology

[0003] Analyzing transient time series data with the help of curve reading room tools will mainly cause two problems: first, due to the limitation of human brain memory, the number of curves that can be observed simultaneously visually is limited; second, relying on the experience of domain experts to monitor and analyze key components misjudgment or misjudgment

[0004] Simple and direct visualization of massive curves usually overlaps and occludes each other, which can easily cause visual confusion and bring great trouble to analysis

Studies have shown that it is not easy to distinguish if ten curves are displayed at the same time without additional processing

When the number of curves reaches dozens, even if you adjust the color, transparency, etc. to increase the recognition and layering of the curves, you still cannot get a good visual effect

Images