A substation intelligent detection system based on the ubiquitous power Internet of things
A technology of power Internet of things and detection systems, applied in information technology support systems, electrical components, circuit devices, etc., can solve problems such as the lack of effective monitoring power grid system detection systems, and achieve the goal of reducing communication expenses, reducing losses, and improving efficiency. Effect
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Embodiment 1
[0021] A substation intelligent detection system based on the ubiquitous power Internet of things is used for fault detection of the power grid system with the ubiquitous power Internet of things, such as figure 1 As shown, including, build the sensor table, the sensor table records the sensor L i name, detected device information, and detected values.
[0022] Establish the sensor early warning value table, the sensor early warning value table records each sensor L i detection value of Sensors include primary equipment operating temperature monitoring sensors, lightning arrester leakage current monitoring sensors, video monitoring image sensors, perimeter anti-intrusion sensors, tower anti-theft monitoring sensors, SF6 gas density monitoring sensors, fire smoke detection sensors and rain sensors .
[0023] Establish a sensor cooperation table, and the sensor cooperation table records each sensor L i Collaborative Sensor L i-k A list of collaborative sensors L i-k for d...
Embodiment 2
[0028] This embodiment is further improved on the basis of the first embodiment. Specifically, the power grid is divided into several regions according to the voltage level, the nodes whose voltage level changes are attributed to the higher voltage level area, and the area is regarded as a subnetwork. An edge server is established for each subnet, and the sensors in the subnet are connected to the corresponding edge server.
[0029] Create early warning portraits of equipment failures, such as image 3 As shown, the establishment of the equipment fault warning image includes the following steps: enumerate the sensor L that detects the equipment i ; get sensor L i detection value of The historical value of and the historical fault data of the equipment; calculate the sensor L i detection value of Correlation with equipment failure, selected detection value A number of sensors L whose correlation with the fault is higher than the set threshold i ; will be selected sens...
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