Big data visualization-based intelligent monitoring method for power transformation and distribution station and cloud monitoring platform

Abstract

The invention discloses a big data visualization-based intelligent monitoring method for a power transformation and distribution station and a cloud monitoring platform, and the method comprises the steps of: carrying out the position numbering of each transformer cabinet in an indoor power transformation and distribution station, acquiring the oil level of a transformer in each transformer cabinet, and comparing and analyzing whether the transformer in each transformer cabinet needs to be refueled or not, informing a worker to perform refueling processing on the transformer needing refueling, at the same time detecting the transformer oil temperature in each transformer cabinet and each quality index numerical value of a transformer oil sample, and performing comparison to obtain the transformer oil temperature difference value in each transformer cabinet and the difference value of each quality index numerical value of the transformer oil sample, detecting the operation sound decibel of each transformer cabinet, calculating the comprehensive operation safety influence coefficient of each transformer cabinet, performing comparison with a set operation safety influence coefficient threshold value, and screening the position number of each transformer cabinet in a dangerous operation state and performing display, thus improving the operation safety and reliability of the transformer cabinet in the indoor power transformation and distribution station.

Description

technical field [0001] The invention relates to the field of substation monitoring technology, and relates to an intelligent monitoring method and a cloud monitoring platform for a substation based on big data visualization. Background technique [0002] The transformer cabinet is an important part of the indoor substation and the main voltage regulation device in the power system. Whether the transformer cabinet can operate normally is related to the stability and safety of the power system, so it is of great significance to monitor the transformer cabinet in the indoor substation. [0003] At present, the monitoring of transformer cabinets in existing indoor substations mainly adopts manual timing patrol inspection, that is, manual timing on-site monitoring of transformer oil level changes in transformer cabinets, which not only wastes a lot of human resources costs, but also has the characteristics of poor real-time performance. There is a problem that the transformer oi...

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Problems solved by technology

[0003] At present, the monitoring of transformer cabinets in existing indoor substations mainly adopts manual timing patrol inspection, that is, manual timing on-site monitoring of transformer oil level changes in transformer cabinets, which not only wastes a lot of human resources costs, but also has the characteristics of poor real-time performance. There is a problem that the transformer oil level is too low to be detected in time, which leads to the lack of insulation and cooling functions of the transformer, thereby accelerating the damage of the transformer, reducing the operation reliability of the transformer cabinet, and increasing the operation and maintenance costs of the indoor substation. At the same time, it cannot be detected in time The operation safety state of the transformer cabinet has the problem that the transformer cabinet cannot respond immediately when the transformer cabinet is in a dangerous operation state, thereby reducing the operation safety of the transformer cabinet in the indoor substation and affecting the power supply quality and stability of the power system. In order to solve For the above problems, we now design an intelligent monitoring method and cloud monitoring platform for substations based on big data visualization

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