KEYWORDS: Control systems, Signal processing, Fire, Data modeling, Telecommunications, Environmental monitoring, Process control, Visualization, Data communications, Image processing
In response to the issues of insufficient depth and breadth of equipment monitoring and significant differences in various data collection, research is conducted on the data diversion and collection architecture technology for the full business scenario of substation equipment monitoring. A partitioned distributed and elastic collection framework is proposed, and on-demand data penetration and retrieval technology is studied to support remote transparent access of various businesses to substations and integrated comprehensive monitoring of substation main and auxiliary equipment.
With the continuous improvement of automation technology, unmanned substations are developing rapidly throughout China. In order to more conveniently guarantee the basic operation and management of unattended substations, centralized control systems for substations are increasingly used in major real-time systems. In order to ensure the effective conduct of centralized control, State Grid Corporation of China proposed construction of a new generation of central control station equipment monitoring system. As a result, a huge amount of electricity data is generated in real time and need to be processed continuously. For instance, historical and current electricity data are analyzed differently, so that real-time analysis helps users to make quick decisions about centralized electricity regulation. This paper addresses the real-time processing and analysis of data in the centralized control system of a distributed substation from the perspective of real-time data processing. Specifically, our proposal is focused on accomplishing real-time distance processing of spatial-temporal data representations (graphs can represent many structured forms of data). To accelerate this computation, we propose a set of spatial indexing techniques, as well as the implementation of a complete KNN query system on this basis, and our approach has experimentally proven to be superior in terms of performance taken in constructing both.
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