Intelligent power distribution network monitoring system and method

Abstract

The invention provides an intelligent power distribution network monitoring system and method, and relates to the technical field of power distribution network monitoring, and the specific scheme is that the intelligent power distribution network monitoring system comprises a pole-used sensing terminal, a line-used edge computing gateway and a background server, the data acquisition modules are respectively used for acquiring dynamic hidden danger data and static hidden danger images near the telegraph pole and sending the dynamic hidden danger data and the static hidden danger images to the line edge computing gateway; the online edge computing gateway is used for carrying out hidden danger identification on the dynamic hidden danger data and the static hidden danger image and sending an identification result to the background server; the background server is used for carrying out corresponding early warning measures according to the identification result; according to the invention, dynamic hidden dangers and static hidden dangers are identified, so that the maintenance efficiency of the power distribution network is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power distribution network monitoring, and in particular, relates to an intelligent power distribution network monitoring system and method. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As an important part of smart grid construction, smart distribution network has the characteristics of large number of towers, long line mileage, and complex and changeable environment around the channel. At the same time, it faces hazards such as phase-to-phase short circuit or grounding fault, line fault caused by external damage, and power outage caused by bird damage. At the same time, due to the low level of intelligence of on-site equipment, traditional drones, power transmission monitoring and other facilities are economically and technically difficult to apply on distribution poles. At present, the method of personnel inspection has problems such as high work intensity, long work cycle, shortage of personnel, inability to quickly locate faults and prevent them in advance.

[0004] With the advancement of smart grid construction, new services such as unmanned operation, visual operation, and online monitoring of substation status have emerged. These services generally feature large bandwidth, mobility, and large connections, which cannot be met by wired communication methods.

[0005] There are many deficiencies in the existing technologies for distribution network monitoring, among which the system energy consumption and delay problems are particularly prominent. In terms of system energy consumption, the existing monitoring equipment consumes a lot of electricity during continuous operation. In addition to the energy consumption of auxiliary equipment such as the cooling system, the overall energy consumption level is high, which increases operating costs and has a certain impact on the environment. The delay problem is mainly reflected in the real-time transmission of data. Due to equipment performance limitations, insufficient network bandwidth or poor transmission line quality, monitoring data is delayed in the collection, transmission, and processing links, affecting the accuracy and timeliness of monitoring; these deficiencies limit the effectiveness of the distribution network monitoring system and urgently need to be solved through technological innovation and optimization. Summary of the invention

[0006] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides an intelligent distribution network monitoring system and method, which can identify dynamic hidden dangers and static hidden dangers, thereby improving the efficiency of distribution network maintenance and repair.

[0007] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0008] A first aspect of the present invention provides a smart distribution network monitoring system.

[0009] A smart distribution network monitoring system includes a pole sensing terminal, a line edge computing gateway and a background server:

[0010] The pole-use sensing terminal includes a radar integration module and an image acquisition module, which are respectively used to collect dynamic hidden danger data and static hidden danger images near the poles, and send them to the line-use edge computing gateway;

[0011] The line edge computing gateway is used to identify hidden dangers on dynamic hidden danger data and static hidden danger images, and send the identification results to the background server;

[0012] The backend server is used to take corresponding early warning measures according to the recognition results.

[0013] Furthermore, the pole sensing terminal is installed on the utility pole and also includes a satellite positioning module, a LoRa temperature integration module, and a tilt integration module;

[0014] The satellite positioning module is used to obtain the positioning coordinates of the pole-use sensing terminal;

[0015] The LoRa temperature integrated module is used to collect wire temperature data and send it to the pole sensing terminal via LoRa;

[0016] The tilt integrated module is used to obtain the tilt angle of the utility pole.

[0017] Furthermore, the dynamic hidden danger data is the distance, distance change rate, azimuth, and altitude information of the dynamic target collected by the radar integrated module.

[0018] Furthermore, the line uses an edge computing gateway to identify hidden dangers on dynamic hidden danger data, specifically:

[0019] Based on the distance, distance change rate, direction, and height information of the dynamic target, it is determined whether the dynamic target poses a risk of external damage to the utility pole, forming a dynamic recognition result.

[0020] Furthermore, the line uses an edge computing gateway to identify hidden dangers on static hidden danger images, specifically:

[0021] Extract features from the received static hidden danger image;

[0022] Based on the extracted features, static target recognition is performed on the static hidden danger image to form a static recognition result.

[0023] Furthermore, the corresponding early warning measures are taken according to the identification results, specifically:

[0024] Based on the dynamic recognition results, early warning measures such as sound and light alarms and voice broadcasts are taken for dynamic targets with hidden dangers of external damage;

[0025] Based on the static recognition result, according to the positioning coordinates of the pole sensing terminal, the corresponding on-site maintenance personnel are reminded to confirm and repair the static hidden dangers.

[0026] Furthermore, it also includes: on-site maintenance personnel log in to the backend server through WAPI authentication during daily inspections.

[0027] A second aspect of the present invention provides a smart distribution network monitoring method.

[0028] A smart distribution network monitoring method, comprising:

[0029] The radar integrated module and image acquisition module in the pole-based sensing terminal are used to collect dynamic hidden danger data and static hidden danger images near the poles, and send them to the line-based edge computing gateway.

[0030] The line uses an edge computing gateway to identify hidden dangers on dynamic hidden danger data and static hidden danger images, and sends the identification results to the backend server;

[0031] The backend server takes corresponding early warning measures according to the identification results.

[0032] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in a smart distribution network monitoring system as described in the first aspect of the present invention.

[0033] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in a smart distribution network monitoring system as described in the first aspect of the present invention are implemented.

[0034] One or more of the above technical solutions have the following beneficial effects:

[0035] The present invention collects dynamic hidden danger data and static hidden danger images through the pole-based sensing terminal, and the line-based edge computing gateway identifies hidden dangers in the dynamic hidden danger data and static hidden danger images, and sends the identification results to the cloud or back-end server, so as to more effectively utilize bandwidth and storage resources, thereby reducing the amount of data sent to the cloud and back-end servers, and reducing costs and bandwidth consumption.

[0036] The present invention utilizes radar, temperature module and camera to collect dynamic hidden danger data and static hidden danger images respectively, and identifies the hidden dangers of dynamic birds and vehicles damaging electric poles and the impact of static bird nests and plastic hanging objects on transmission lines, thereby realizing comprehensive monitoring of the distribution network.

[0037] The present invention utilizes WAPI authentication to authenticate personal and terminal information entering the environmental monitoring system and the lighting control system, preventing hackers from maliciously damaging the distribution network and improving security.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0040] Figure 1 This is a system structure diagram of the first embodiment.

[0041] Figure 2 This is a schematic diagram of the installation of the first embodiment. DETAILED DESCRIPTION

[0042] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] Embodiment 1

[0045] In one embodiment of the present disclosure, a smart distribution network monitoring system is provided. Figure 1 As shown in the figure, it mainly includes sound and light alarms, cameras, lighting, pole sensing terminals, line edge computing gateways, backend servers, etc. The specific installation locations are as follows: Figure 2 As shown in the figure, assuming that the distribution network line tower is 12 meters high, the smart distribution network monitoring system is installed at 6 meters. Seven towers form a monitoring unit, which includes one edge computing node and seven pole-use sensing terminals. The line edge computing gateway is deployed on the middle tower as an edge computing node. The pole-use sensing terminals are installed on each tower. The two types of devices are locally networked through a trusted WLAN.

[0046] The pole sensing terminal includes a radar integrated module, a satellite positioning module, a LoRa temperature integrated module, a tilt integrated module, a DC power supply module, an image acquisition module and a long-distance transmission networking module.

[0047] Pole sensing terminals are used to collect dynamic and static hidden danger data. Dynamic hidden danger data include the distance, size and other data of abnormal line operation caused by close-range ground vehicles, high-altitude birds, etc. Static hidden danger data include the temperature of the tension section joints on the 10kV line, the temperature of the 10kV cable outlet interface, the inclination angle of the pole and other data.

[0048] The camera module of the pole sensing terminal is used to collect static hidden danger image data, which is divided into timed image collection and real-time call-based image data according to different collection methods. Both are image data in a specific direction and within a specified range, specifically bird nesting, line hanging objects, external damage hazards, and tree obstacle data.

[0049] The satellite positioning module is used to obtain the positioning coordinates of the pole-use sensing terminal, so that the background server can obtain the location information of the pole-use sensing terminal.

[0050] The LoRa temperature integrated module is composed of a temperature acquisition tag and a LoRa receiving module, wherein the temperature acquisition tag is independently fixed at the distribution cable clamp, and the temperature acquisition tag is internally provided with a temperature sensing head and LoRa wireless transmission. "LoRa" is a long-range radio (Long Range Radio), and its biggest feature is that it can transmit farther than other wireless methods under the same power consumption conditions, achieving the unity of low power consumption and long distance. It is 3-5 times larger than the traditional wireless radio frequency communication distance under the same power consumption. The collected wire temperature data at the cable clamp is sent to the LoRa receiving module of the pole sensing terminal through LoRa. The temperature data will first be parsed out on the sensing terminal, and then given to the edge computing gateway through the long-distance transmission networking module. The edge computing gateway sets a pre-set temperature threshold and issues an early warning for excessive wire temperature.

[0051] The tilt integrated module is used to monitor the tilt angle of the utility pole and issue an early warning when the tilt angle is too large according to a preset angle threshold.

[0052] The image acquisition module cooperates with the camera and the lighting to collect and preprocess static hidden danger images, specifically:

[0053] Cameras and lighting are installed on each utility pole on the distribution network. The cameras will capture the power distribution conditions and surrounding environment near each utility pole and transmit them to the image acquisition module in the pole sensing terminal; the image acquisition module pre-processes the image, including denoising, contrast enhancement, brightness adjustment, etc.

[0054] The edge computing gateway is used to extract color, texture, shape and other features from the preprocessed image. The color feature uses a color histogram (such as RGB, HSV or Lab color space) to represent the color distribution of the image. The texture feature uses a gray-level co-occurrence matrix (GLCM) to extract texture features such as energy, contrast, entropy, etc. The shape feature uses an edge detection algorithm (such as Canny, Sobel or Laplacian) to extract the edge of the image. The bird's nest has a specific shape and texture, and is identified by shape features (such as contour, roundness, etc.) and texture features (such as GLCM contrast, correlation, etc.). Plastic hanging objects have obvious color features, shape features (such as long strips, irregular shapes, etc.) and texture features (such as smooth surfaces); based on the extracted features, the image is identified as a static target such as a bird's nest and a plastic hanging object to form a static recognition result.

[0055] Based on the static identification results and the positioning coordinates of the pole sensing terminal, the backend server reminds the corresponding on-site maintenance personnel to confirm and repair the static hidden dangers, specifically:

[0056] Backstage staff use the environmental monitoring system to check data transmitted to the backstage server or cloud. When bird nests, hanging plastic objects or other safety hazards are found near the electrical poles on the distribution network, the environmental monitoring system will send maintenance information and positioning notifications of the inspection location to the mobile phones of on-site maintenance personnel, so that on-site maintenance personnel can confirm and repair in time, thereby improving the efficiency of distribution network maintenance and inspection.

[0057] The radar integrated module is used to collect dynamic hidden danger data near utility poles, namely, the distance from dynamic targets such as vehicles and birds around the utility poles to the utility poles, the rate of change of distance, direction, height and other information.

[0058] The edge computing gateway for the line monitors dynamic targets such as vehicles and birds around the utility poles in real time based on the dynamic hidden danger data uploaded by the radar integrated module, standardizes the data format, ensures that all dynamic target information (distance, distance change rate, azimuth, height) follows a unified format, and uses machine learning algorithms (such as support vector machines, decision trees, neural networks, etc.) to classify targets according to the radar echo characteristics of dynamic targets, and classifies targets into categories such as vehicles and birds. Set a distance threshold. For example, targets within 10 meters from the utility pole need to be paid special attention. If the dynamic target is within the set distance threshold, proceed to the next step of judgment, analyze the target's distance change rate, azimuth information, and height information, and combine the above factors to make a comprehensive judgment on the dynamic target. Determine whether the dynamic target has hidden dangers of external damage to the utility pole and form a dynamic identification result.

[0059] The environmental monitoring system of the backend server takes early warning measures in the form of sound and light alarms and voice broadcasts for dynamic targets with potential external damage risks. For example, it activates the sound and light alarm to drive away birds at the electric pole, and broadcasts voice reminders to nearby vehicles to pay attention to driving safety.

[0060] The intelligent distribution network monitoring system of this embodiment also has high security. During daily inspections, on-site maintenance personnel log in to the backend server through WAPI authentication and enter the trusted wireless access controller, firewall and trusted wireless access network management authentication system; the trusted wireless access controller can achieve global unified management and centralized RF, access and security policy settings; the firewall can greatly improve the security of the internal network, reduce risks by filtering unsafe services, and monitor and audit network access; the firewall can greatly improve the security of the internal network, reduce risks by filtering unsafe services, and monitor and audit network access; the trusted wireless access network management authentication system can authenticate the identity of devices, users, applications, and systems and realize single sign-on, ensure the authenticity and credibility of the identity, protect network security, and avoid users from frequently verifying their identities during use.

[0061] Specifically, during WAP I authentication, it is necessary to obtain AP public key authentication activation on the STA terminal, send an access authentication request to the AP, and feedback the trusted ASU identity, AP certificate, and ECDH parameter information. Then the AP will verify the certificate signature, verify the STA certificate, and issue a certificate authentication request. Then, the AP certificate and STA certificate are verified and an AP challenge and a STA challenge are issued to generate a temporary public key Y and private key Yp, ECDH calculation, and BK calculation. Then, a certificate authentication response will be issued to verify the signature, compare the challenge, and perform cryptographic calculation to obtain the verification result. If passed, an access authentication response will be issued.

[0062] Among them, the signature algorithm is ECDSA-192, and the hash algorithm is SHA-256; the elliptic curve parameter field uses the O ID value 1.2.156.11235.1.1.2.1 to identify the curve parameters approved by the State Cryptography Administration for wireless LANs; and in the WAPI authentication-cryptographic calculation, ECDH calculation: the other party's public key, one's own public key => ECDH value (key seed); BK (basic password) calculation: BK_TXT = AP challenge + STA challenge + "base key expansion for key and add it a lnonce" HMAC-SHA256 (BK_TXT, ECDH value) => BK value; communication password calculation: MK_TXT = AP-MAC + AP challenge + STA-MAC + STA challenge + "pai rwi se key expansion for unicast and add it a lkeys and nonce" HMAC-SHA256 (MK_TXT, BK value) => unicast key.

[0063] Embodiment 2

[0064] In one embodiment of the present disclosure, a smart distribution network monitoring method is provided, comprising:

[0065] The radar integrated module and image acquisition module in the pole-based sensing terminal are used to collect dynamic hidden danger data and static hidden danger images near the poles, and send them to the line-based edge computing gateway.

[0066] The line uses an edge computing gateway to identify hidden dangers on dynamic hidden danger data and static hidden danger images, and sends the identification results to the backend server;

[0067] The backend server takes corresponding early warning measures according to the identification results.

[0068] Embodiment 3

[0069] The purpose of this embodiment is to provide a computer-readable storage medium.

[0070] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in a smart distribution network monitoring system as described in Embodiment 2 of the present disclosure.

[0071] Embodiment 4

[0072] The purpose of this embodiment is to provide an electronic device.

[0073] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a smart distribution network monitoring system as described in Embodiment 2 of the present disclosure are implemented.

[0074] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A smart distribution network monitoring system, characterized in that: Including pole-based sensing terminals, line-based edge computing gateways, and backend servers: The pole-use sensing terminal includes a radar integration module and an image acquisition module, which are respectively used to collect dynamic hidden danger data and static hidden danger images near the poles, and send them to the line-use edge computing gateway; The line edge computing gateway is used to identify hidden dangers on dynamic hidden danger data and static hidden danger images, and send the identification results to the background server; The backend server is used to take corresponding early warning measures according to the recognition results.

2. A smart distribution network monitoring system as claimed in claim 1, characterized in that: The pole sensing terminal also includes a satellite positioning module, a LoRa temperature integration module, and a tilt integration module; The satellite positioning module is used to obtain the positioning coordinates of the pole-use sensing terminal; The LoRa temperature integration module is used to collect wire temperature data and send it to the pole sensing terminal via LoRa; The tilt integrated module is used to obtain the tilt angle of the utility pole.

3. The intelligent distribution network monitoring system according to claim 1, characterized in that: The dynamic hidden danger data is the distance, distance change rate, direction, and altitude information of the dynamic target collected by the radar integrated module.

4. A smart distribution network monitoring system as claimed in claim 3, characterized in that: The line uses an edge computing gateway to identify hidden dangers in dynamic hidden danger data, specifically: Based on the distance, distance change rate, direction, and height information of the dynamic target, it is determined whether the dynamic target poses a risk of external damage to the utility pole, forming a dynamic recognition result.

5. The intelligent distribution network monitoring system according to claim 1, characterized in that: The line uses an edge computing gateway to identify hidden dangers in static hidden danger images, specifically: Extract features from the received static hidden danger image; Based on the extracted features, static target recognition is performed on the static hidden danger image to form a static recognition result.

6. A smart distribution network monitoring system as claimed in claim 5, characterized in that: The corresponding early warning measures are carried out according to the identification results, specifically: Based on the dynamic recognition results, early warning measures such as sound and light alarms and voice broadcasts are taken for dynamic targets with hidden dangers of external damage; Based on the static recognition result, according to the positioning coordinates of the pole sensing terminal, the corresponding on-site maintenance personnel are reminded to confirm and repair the static hidden dangers.

7. The intelligent distribution network monitoring system according to claim 1, characterized in that: Also includes: On-site maintenance personnel log in to the backend server through WAPI authentication during daily inspections.

8. A method for monitoring a smart distribution network, characterized in that: include: The radar integrated module and image acquisition module in the pole-based sensing terminal are used to collect dynamic hidden danger data and static hidden danger images near the poles, and send them to the line-based edge computing gateway. The line uses an edge computing gateway to identify hidden dangers on dynamic hidden danger data and static hidden danger images, and sends the identification results to the backend server; The backend server takes corresponding early warning measures according to the identification results.

9. An electronic device, comprising: a memory for non-transitory storage of computer readable instructions; a processor for executing the computer-readable instructions; Wherein, when the computer-readable instructions are executed by the processor, the method of claim 8 is performed.

10. A storage medium, characterized in that: The computer readable instructions are non-transitorily stored, wherein when the computer readable instructions are executed by a computer, the method of claim 8 is performed.

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