Hardware device of GIS fault intelligent judgment system

Abstract

The utility model relates to a hardware device of an intelligent GIS (Geographic Information System) fault judgment system. The hardware device mainly comprises an STM32F407 single chip microcomputer module, an OV2640 camera module, a Marvell 88W8782 WIFI (Wireless Fidelity) module, an ADV212 image compression module and an SD (Secure Digital) card module. The STM32F407 single-chip microcomputer module is taken as a center, and other modules are connected with the single-chip microcomputer module. According to the hardware device of the GIS fault intelligent judgment system, a single-chip microcomputer controls a camera to collect data of a current transformer of GIS equipment, and then the single-chip microcomputer compresses collected images and stores the images in an SD card. And finally, the single-chip microcomputer uploads the compressed image to a cloud server for storage through WIFI, and an administrator can check the operation condition of the GIS equipment in real time and make a judgment. Through the modularized design, the reliability is high, the system stability and the yield are improved, and the cost is reduced.

Description

Technical Field

[0001] The utility model relates to an intelligent judgment system for GIS faults. Background Art

[0002] Gas-insulated switchgear (GIS) equipment has been widely used in the construction of substation in each region. GIS equipment is one of the main operating equipment of the substation, which has the advantages of small floor area, compact structure, high reliability, convenient installation, flexible configuration, high safety, small maintenance workload and strong environmental adaptability. However, faults of GIS equipment cannot be avoided.

[0003] There are mainly two reasons for GIS equipment faults: ①Complex maintenance. Due to the limitation of power outage resources, the maintenance and detection work of GIS equipment defects must be completed efficiently within a limited time. Since the conductor of GIS equipment is enclosed in a metal tank, the working space is narrow. Most of the maintenance work requires a crane to disassemble the tank, and the process is relatively complex. ②The opening and closing positions of the disconnector and earthing switch cannot be directly seen. When the equipment is transferred to maintenance operation, the disconnectors on each side must be reliably separated and the earthing switch must be reliably grounded. Otherwise, relevant staff may be at risk of electric shock. The disconnector and earthing switch of GIS equipment are enclosed in a metal tank, and the opening and closing states of the disconnector and earthing switch cannot be directly observed. Only indirect means such as the opening and closing indicator signs of the mechanism box, the indication of the connecting rod crank arm, the indicator lights of the switch control cubicle, and the opening and closing position signals of the monitoring background can be relied on to judge. These indirect means cannot be completely reliable, and there is a risk of incomplete opening and closing.

[0004] Therefore, the present utility model designs a hardware device for an intelligent judgment system for GIS faults. The OV2640 camera is controlled by the STM32F407 single-chip microcomputer to collect data of the current transformer of the GIS equipment. Then the single-chip microcomputer compresses the collected images and saves them to the SD card. Finally, the single-chip microcomputer uploads the compressed images to the cloud server through WIFI for storage and enables the administrator to view the operation status of the GIS equipment in real time and make judgments. This device can monitor the working condition of the GIS equipment in real time and make judgments, which can avoid the occurrence of faults to a certain extent. Even if a fault occurs, the cause and location of the fault can be quickly determined, enabling the maintenance to be completed efficiently within a limited time. Summary of the Invention

[0005] A hardware device of an intelligent judgment system for GIS faults. The hardware device includes an STM32F407 single-chip microcomputer module, an OV2640 camera module, a Marvell 88W8782 WIFI module, an ADV212 image compression module, and an SD card module. The STM32F407 single-chip microcomputer module controls the OV2640 camera module to collect data of the current transformer of the GIS device. Then, the ADV212 image compression module compresses the collected images and saves them to the SD card. Finally, the STM32F407 single-chip microcomputer module uploads the compressed images to the cloud server for storage through the Marvell 88W8782 WIFI module, and uses a convolutional neural network to classify and judge the images and give real-time judgment results.

[0006] The present utility model has the following advantages and effects compared with the prior art:

[0007] The present utility model compresses the real-time acquired images before uploading, saving the bandwidth of the upload network and contributing to real-time judgment.

[0008] The present utility model adopts an advanced convolutional neural network method. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is a schematic diagram of the system flow of the present system;

[0010] Figure 2 It is a schematic diagram of the overall hardware framework of the system;

[0011] Figure 3 It is a schematic diagram of the reset circuit;

[0012] Figure 4 It is a schematic diagram of the power supply circuit;

[0013] Figure 5 It is a schematic diagram of the OV2640 connection;

[0014] Figure 6 It is a schematic diagram of the internal structure of the ADV212;

[0015] Figure 7 It is a schematic diagram of the ADV212 connection;

[0016] Figure 8 It is a schematic diagram of the 88W8782 connection. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following describes in detail the specific embodiments of the hardware device of an intelligent judgment system for GIS faults with reference to the accompanying drawings.

[0018] Figure 1 As shown, Figure 1This is the schematic diagram of the system process. The camera captures images of the working environment of the GIS device. The single-chip microcomputer compresses the images and finally sends the images to the cloud server for the administrator to view the operation status of the GIS device in real time and make judgments.

[0019] Figure 2 As shown Figure 2 This is the schematic diagram of the overall hardware framework of the system. The SD card is connected to STM32F407 through the SDIO interface to achieve the storage expansion of the system; OV2640 is connected to STM32F407 through the DCMI interface to achieve the image acquisition of the system; ADV212 is connected to STM32F407 through multiple GPIO ports to achieve the compression of the system images; 88W8782 is connected to STM32F407 through the SDIO port to achieve the transmission of the system images; The ASM1117 power chip is connected to STM32F407 through the VCC port to finally achieve the stability of the power supply for each module; The reset circuit is connected to the NRST pin of STM32F407 to prevent system errors through reset operations.

[0020] Figure 3 As shown Figure 3 This is the schematic diagram of the reset circuit. A control switch is added between the NRST reset output pin of STM32F407 and the power supply VCC to form a manual reset circuit. The +3.3V power supply is connected to the NRST pin through a 10K resistor, and the NRST pin is at a high level; when the reset switch SW is pressed, VCC and ground are connected, the NRST pin becomes low level, and the single-chip microcomputer is in the reset state, resetting all internal registers and on-chip SRAM; when the switch SW is released, STM32F407 completes the reset operation.

[0021] Figure 4 As shown Figure 4 This is the schematic diagram of the power supply circuit. The core part of this circuit is the ASM1117 chip. When the power supply voltage is connected to the V-in interface of this circuit, after the processing of the circuit, a stable +3.3V voltage with an accuracy of 1% can be output at the V-out interface for the system to use.

[0022] Figure 5 As shown Figure 5It is a schematic diagram of the connection of OV2640. The I2C communication pins SDA and SCL of OV2640 are connected to PB8 and PB9 of STM32F407. Under the control of the SCCB bus, the parameter settings and initialization of OV2640 are realized. The D0 - D7 pins of OV2640 are connected to the GPIO pins of STM32F407. PIXCLK, PWDN, HSYNC, and VSYNC are the clock signal input, power input, synchronization, and clock signal of OV2640 respectively, and they are also connected to the GPIO pins of STM32F407. STM32F407 directly receives and processes the image data from OV2640 through the numerous DCMI interfaces.

[0023] Figure 6 as shown Figure 6 It is a schematic diagram of the internal structure of ADV212. This chip mainly consists of a pixel interface, a wavelet transform engine, an entropy codec, an embedded processor, a memory system, and an internal DMA engine, etc. The input image and pixel data are input to the pixel interface, and the sampled values are transmitted through interlaced scanning to the wavelet transform engine. In the wavelet engine, each tile or frame will be decomposed into many sub - bands through 5 / 3 or 9 / 7 filters. The generated wavelet coefficients are written into the internal registers. The entropy codec encodes the image data into data compliant with the JPEG2000 standard. The internal DMA engine provides high - bandwidth transmission between memories and high - performance transmission between each module and the memory. The RISC processor has ROM and RAM corresponding to each program and data memory, an interrupt controller, a standard bus interface, and a timer counter.

[0024] Figure 7 as shown Figure 7 It is a schematic diagram of the connection of ADV212. The data transmission bus pins D0 - D15, chip - select pin CS, write pin WR, read pin RD, reset pin RESET, data valid pin VALID, and clock pin CLK of ADV212 are connected to the GPIO pins of STM32F407, and finally the compression of the image is realized.

[0025] Figure 8 as shown Figure 8 It is a schematic diagram of the connection of 88W8782. The data transmission pins D0 - D3, clock pin CK, command pin CMD, control pin CTL, and reset pin RST of the SDIO interface of 88W8782 are connected to STM32F407, enabling the system to have communication functions. By connecting to the network of the substation, the compressed image can be quickly sent to the cloud server.

[0026] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformations made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, are similarly included in the patent protection scope of the present invention.

Claims

1. The hardware device of an intelligent judgment system for GIS faults, characterized in that, The hardware device includes an STM32F407 single-chip microcomputer module, an OV2640 camera module, a Marvell 88W8782 WIFI module, an ADV212 image compression module, and an SD card module; the STM32F407 single-chip microcomputer module is used to control the OV2640 camera module to collect data of the current transformer of the GIS device, then the ADV212 image compression module compresses the collected images and saves them to the SD card. Finally, the STM32F407 single-chip microcomputer module uploads the compressed images to the cloud server for storage through the Marvell 88W8782 WIFI module, and uses a convolutional neural network to classify and judge the images and give real-time judgment results.

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