Development of A Spatiotemporal Database for Evolution Analysis of the Moscow Backbone Power Grid

Abstract

Currently in the field of transport geography, the spatial evolution of electrical networks remain globally understudied. Publicly available data sources, including remote sensing data, have made it possible to collect spatial data on electrical networks, but at the same time a suitable data structure for storing them has not been defined. The main purpose of this study was the collection and structuring of spatiotemporal data on electric networks with the possibility of their further processing and analysis. To collect data, we used publicly available remote sensing and geoinformation systems, archival schemes and maps, as well as other documents related to the Moscow power grid. Additionally, we developed a web service for data publication and visualization. We conducted a small morphological analysis of the evolution of the network to show the possibilities of working with the database using a Python script. For example, we found that the portion of new lines has been declining since 1950s and in the 2010s the portion of partial reconstruction reached its maximum. Thus, the developed data structure and the database itself provide ample opportunities for the analysis and interpretation of the spatiotemporal development of electric networks. This can be used as a basis to study other territories. The main results of the study are published on the web service where the user can interactively choose a year and two forms of power lines representation to visualize on a map.

Possibilities of using official data in spatio-temporal modeling of main electrical networks

“…Each segment was assigned a different year of construction based on field surveys, archival material, and the years of commissioning mentioned in the Scheme and Power Development Program. The modeling technique was described in more detail in [7]. Similar to the study [6], we have identified various spatiotemporal patterns, which are shown in table 1.…”

Clustering Mining Algorithm of Internet of Things Database Based on Python Language

“…They now have access to considerably more potent tools for expanding their firm. Python has now become a well-developed and most frequently used program in the database field [8,9]. Because it has very rich resources and powerful computing functions, it can help to complete the work more effectively in the mining process.…”

Data for Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection

“…In [18], a dataset and a procedure for the optimization of the charging configuration of electric vehicles in low-voltage grids are presented. The main purpose of [19] is the collection and structuring of spatio-temporal data from electrical networks for further processing and analysis. In [20], a dataset, a measurement platform for RGB images, and electrical data of a photovoltaic panel with different irradiation and shading conditions are presented.…”