Transparent detection and optimization diagnosis system for low-voltage power distribution network in high-altitude area

Abstract

The invention belongs to the technical field of power system monitoring and management, and particularly relates to a transparent detection and optimization diagnosis system for a low-voltage power distribution network in a high-altitude area. Comprising a multi-parameter sensing sensor unit, a lightweight edge gateway unit, a distribution network full-situation data model unit, a distribution network operation and maintenance service mobile software unit and a master control module, and through cooperative work of master control of the lightweight edge gateway unit, the Internet of Things and an MCU module, stability of data transfer and primary processing is guaranteed; the cloud edge cooperative computing model optimizes resource allocation and supports online upgrading and data security protection; the system can stably operate in power distribution networks in different high-altitude areas, adapts to complex environment and working condition changes, effectively solves the problem of operation and maintenance of the power grids in the high-altitude areas, realizes remote real-time monitoring and operation by operation and maintenance personnel by means of a mobile software unit, improves the fine management level, and promotes the development of the power distribution networks in the intelligentization and automation directions.

Description

Technical Field

[0001] The invention belongs to the technical field of power system monitoring and management, and specifically is a transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas. Background Art

[0002] In the process of building new power systems, the widespread access to new energy sources has led to significant changes in the power supply and load structure of distribution networks, and has put forward higher requirements for the management of distribution networks.

[0003] Especially in high-altitude areas, low-voltage distribution networks not only face the same problem of insufficient intelligent construction as other regions, but also need to deal with the challenges brought by the harsh natural environment, such as low temperature, strong ultraviolet rays, low air pressure, etc. These factors have an adverse effect on equipment performance and the accuracy and stability of detection and diagnostic technology.

[0004] The current low-voltage power distribution system has a low level of automation and intelligence, which makes it difficult to meet the needs of refined management. Innovative solutions are urgently needed to achieve transparent and intelligent management and control of low-voltage distribution networks in high-altitude areas, and to improve power supply quality and operation and maintenance efficiency. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, the technical solution adopted by the present invention to solve its technical problems is: a transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas, including a multi-parameter perception sensor unit, a lightweight edge gateway unit, a distribution network full situation data model unit, and a distribution network operation and maintenance business mobile software unit, wherein:

[0006] The multi-parameter sensing sensor unit is connected to the lightweight edge gateway unit by wireless or wired communication, and the collected operating data of temperature, partial discharge, current, voltage, and meteorology of the low-voltage distribution network equipment and key nodes are transmitted to the lightweight edge gateway unit in real time;

[0007] The lightweight edge gateway unit is connected to the distribution network full situation data model unit through network communication, and sends the valid data after preliminary processing, analysis and screening to the distribution network full situation data model unit for further processing and analysis;

[0008] The distribution network full situation data model unit is connected to the distribution network operation and maintenance business mobile software unit through network communication, and the diagnosis results, line status information, and fault warning data are transmitted to the distribution network operation and maintenance business mobile software unit for operation and maintenance personnel to view and process.

[0009] The distribution network operation and maintenance service mobile software unit interacts with the lightweight edge gateway unit through the network. The operation and maintenance personnel send control instructions to the lightweight edge gateway unit according to actual needs, such as adjusting sensor acquisition parameters and obtaining specific data instructions; the lightweight edge gateway unit performs corresponding operations after receiving the instructions and feeds back the results;

[0010] Furthermore, the multi-parameter sensing sensor unit includes a low-light level, low-power, intelligent wireless temperature measurement sensor. The low-light level, low-power, intelligent wireless temperature measurement sensor adopts a throat clamp dual-mode structure and is made of soft magnetic materials. The low-light level, low-power, intelligent wireless temperature measurement sensor is powered by CT energy extraction, uses an STM8L051 single-chip microcomputer as a controller and is equipped with a DS18B20 digital temperature sensor to collect temperature data. It communicates with the lightweight edge gateway unit through RF433 wireless ad hoc networking technology, and is used to monitor the temperature inside the distribution network line clamp, the transformer column in the substation, the ring network cabinet busbar and the cable grounding box. The measurement temperature range is -55°C to +125°C, and the inherent temperature measurement error is 1°C.

[0011] Furthermore, the multi-parameter sensing sensor unit also includes a partial discharge sensor based on RF433 wireless communication, which communicates with the lightweight edge gateway unit in an ad hoc network and is used to detect partial discharge in switch cabinets and ring network cabinets. It has low latency, low power consumption and anti-interference characteristics, and its communication protocol follows the system preset specifications to ensure stable data transmission.

[0012] Furthermore, the multi-parameter sensing sensor unit also includes a low-power micro camera based on wifi wireless communication; the low-power micro camera based on wifi wireless communication has a power switch controlled by the wifi module of the lightweight edge gateway unit, which is used to monitor the overhead lines and substation conditions of the distribution network. When the low-power micro camera based on wifi wireless communication sends and receives data, it logically judges the data frame header, address frame, CRC check code and instruction corresponding data segment length to ensure accurate data interaction.

[0013] Furthermore, the multi-parameter sensing sensor unit also includes an inductive power supply current and voltage sensor; the inductive power supply current and voltage sensor includes a CT power supply circuit and a Rogowski coil. The CT power supply circuit obtains power from the power line through electromagnetic induction to power the sensor. The Rogowski coil is used to measure the load current of the distribution network line. The gateway communicates with the current monitoring module through the LoRa module to control the power supply and obtain data. The CT power supply circuit has functional modules such as rectification and filtering, DC / DC conversion, overvoltage protection, power-off detection and backup battery.

[0014] Further, the lightweight edge gateway unit includes a main control module, an Internet of Things module and an MCU module;

[0015] The main control module uses the MTK6739 chip to achieve overall control and data processing of the gateway, including data aggregation, control and coordination of sensor work, management of communication with lightweight edge gateway units, and data preprocessing and preliminary analysis, specifically including:

[0016] Data aggregation: Receive data from various sensors such as low-light low-power intelligent wireless temperature measurement sensors, partial discharge sensors based on RF433 wireless communication, low-power micro cameras based on wifi wireless communication, inductive current and voltage sensors, and meteorological sensors. The meteorological sensors transmit the collected high-precision temperature and humidity and other meteorological data to the main control module through the RS485 interface. The main control module serves as a data aggregation point to centrally collect data from different sensors.

[0017] Control and coordination: Control the data collection frequency of each sensor, flexibly adjust it according to actual needs, coordinate the data collection and transmission order between sensors, ensure the orderly operation of the entire sensor unit, and avoid data conflicts and transmission confusion; for example, the data collection frequency of meteorological sensors can be set to adapt to different monitoring scenarios and data requirements;

[0018] Communication management: responsible for managing the communication link with the lightweight edge gateway unit, encapsulating the aggregated sensor data according to the communication protocol and format preset by the system, and then accurately transmitting it to the lightweight edge gateway unit. At the same time, it receives instructions from the lightweight edge gateway unit or other upper-level units, such as instructions for adjusting sensor parameters, instructions for querying specific data, etc., and distributes these instructions to the corresponding sensors or performs corresponding operations to achieve effective communication and interaction between various levels of the system;

[0019] Data preprocessing and preliminary analysis: Perform preliminary preprocessing on the received data, such as simple data verification, error detection and correction, to improve the quality and reliability of the data. It is also possible to perform preliminary analysis on the meteorological data, such as judging whether the data is within a reasonable range. If abnormal data is found, it will be marked in time or corresponding preliminary processing measures will be taken to provide a more accurate data basis for the subsequent in-depth analysis of the distribution network full situation data model unit;

[0020] The IoT module supports RF433, wifi, RS485 and ETH communication modes to exchange data with front-end sensors and the main station. RF433 wireless communication can communicate reliably in complex electromagnetic environments, and the communication protocol and data format meet system requirements.

[0021] The MCU module uses the STM8L051F3P6 chip to ensure stable operation of the system and perform operations such as power-on control and watchdog functions.

[0022] Furthermore, the lightweight edge gateway unit also includes a cloud-edge collaborative computing model, which performs edge computing on the collected data, flexibly adjusts computing strategies and resource allocation according to application scenarios and data characteristics, screens useful data for uploading, supports online upgrades and updates, and has a data security protection mechanism to prevent data leakage and tampering.

[0023] Furthermore, the distribution network full situation data model unit includes a data acquisition module, a feature extraction module, a fault diagnosis module and a result display module;

[0024] The data acquisition module includes two or more sensors to collect multi-dimensional data and pre-process them. The sensor layout is reasonable to ensure comprehensive and accurate collection, and the data transmission adopts a high-speed and stable communication protocol.

[0025] The feature extraction module uses a deep learning algorithm to extract key fault features;

[0026] The fault diagnosis module combines historical data and expert experience to diagnose faults, supports diagnosis of multiple fault types, and can analyze causes and provide treatment suggestions;

[0027] The result display module displays the diagnosis results in the form of reports and the like.

[0028] Furthermore, the distribution network operation and maintenance service mobile software unit includes an equipment operation parameter monitoring module, an early warning module, a work order management module, a data statistical analysis module and a user management module;

[0029] The equipment operation parameter monitoring module displays the distribution network equipment operation parameters in various ways;

[0030] The warning module displays warning levels and reminds operation and maintenance personnel in various ways;

[0031] The work order management module realizes the process management of work orders and is closely integrated with the operation and maintenance business;

[0032] The data statistical analysis module provides data statistical analysis and report generation functions;

[0033] The user management module supports multi-level authority management and user information management functions.

[0034] Furthermore, the multi-parameter sensing sensor unit also includes a meteorological sensor; the meteorological sensor uses a high-precision temperature and humidity sensor, which is placed in a radiation-proof louver to enhance shielding, and is connected to the main control module through an RS485 interface. The power supply system is independent and contains lithium batteries, charge and discharge management modules and solar panels, which can adapt to high-altitude harsh climates, the acquisition frequency can be set, the power supply system has overcharge and over-discharge protection, stable and reliable communication, and accurate data transmission.

[0035] The beneficial effects of the present invention are as follows:

[0036] The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas described in the present invention ensures the stability of data transfer and preliminary processing through the collaborative work of the main control, Internet of Things and MCU modules of the lightweight edge gateway unit; the cloud-edge collaborative computing model optimizes resource allocation, supports online upgrades and data security protection; it can operate stably in distribution networks in different high-altitude areas, adapt to complex environments and changes in working conditions, and effectively solve the problems of power grid operation and maintenance in high-altitude areas.

[0037] The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas described in the present invention realizes transparent control of the entire process of low-voltage distribution networks, forming a closed loop from data collection to operation and maintenance management; the intelligent diagnosis and prediction functions of the distribution network full-situation data model unit provide a basis for planning and transformation; operation and maintenance personnel use mobile software units to achieve remote real-time monitoring and operation, improve the level of refined management, and promote the development of distribution networks towards intelligence and automation.

[0038] The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas described in the present invention has efficient and accurate data transmission, and the cloud-edge collaborative computing model reduces the pressure on the master station. The precise diagnosis of the distribution network's full-situation data model unit and the convenient operation of the distribution network operation and maintenance business mobile software unit jointly improve the operation and maintenance efficiency and quality, ensure the safe and stable operation of the low-voltage distribution network, and have high promotion value and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] Figure 1 is a system block diagram of the present invention;

[0041] Figure 2 It is the schematic diagram of the MCU module of the present invention;

[0042] Figure 3 This is a block diagram of the CT power supply principle of the present invention;

[0043] Figure 4 It is a design diagram of the lightweight gateway architecture of the present invention;

[0044] Figure 5 It is the electrical schematic diagram of RF433 of the present invention;

[0045] Figure 6 It is a design drawing of the low-light, low-power intelligent wireless temperature measurement sensor of the present invention. DETAILED DESCRIPTION

[0046] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0047] like Figure 1-Figure 6 As shown, the present invention proposes a transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas, including a multi-parameter perception sensor unit, a lightweight edge gateway unit, a distribution network full-situation data model unit and a distribution network operation and maintenance business mobile software unit. The units are connected through a specific communication method to achieve efficient data transmission and interaction, and jointly complete the comprehensive monitoring and management of the low-voltage distribution network.

[0048] The multi-parameter sensing sensor unit includes various types of sensors such as low-light low-power intelligent wireless temperature measurement sensors, partial discharge sensors based on RF433 wireless communication, low-power micro cameras based on wifi wireless communication, inductive power supply current and voltage sensors, and meteorological sensors, which are used to collect key operating data such as temperature, partial discharge, current, voltage, meteorology, etc. of low-voltage distribution network equipment and key nodes.

[0049] The low-power intelligent wireless temperature sensor adopts a throat clamp dual-mode structure and special soft magnetic materials, is powered by CT, uses the STM8L051 microcontroller as the controller and is equipped with a DS18B20 digital temperature sensor. It can accurately monitor the temperature inside the distribution network cable clamp, transformer column, ring network cabinet busbar and cable grounding box. The measurement range is -55℃ to +125℃, and the inherent temperature measurement error is 1℃.

[0050] The RF433 wireless communication partial discharge sensor is developed based on advanced pulse current method, geomagnetic wave and UHF technology. It communicates with lightweight edge gateway in self-organizing network. It has low latency, low power consumption and strong anti-interference characteristics, and can effectively detect partial discharge in switch cabinets and ring network cabinets.

[0051] The low-power micro camera based on WiFi wireless communication controls the power switch by the WiFi module of the lightweight edge gateway. It is used to monitor the status of the overhead lines and substations of the distribution network in real time. Its data sending and receiving follows specific logic to ensure the accuracy of data interaction.

[0052] The CT power supply circuit of the inductive power supply current and voltage sensor uses electromagnetic induction to obtain power from the power line to power the sensor. The Rogowski coil is used to accurately measure the load current of the distribution network line. The gateway communicates with the current monitoring module through the LoRa module to achieve power control and data acquisition. The CT power supply circuit has complete functional modules such as rectification and filtering, DC / DC conversion, overvoltage protection, power-off detection and backup battery.

[0053] The meteorological sensor uses a high-precision temperature and humidity sensor, which is placed in a radiation-proof louver to enhance the shielding effect. It is connected to the main control module through the RS485 interface. The power supply system is independent and includes a lithium battery, a charge and discharge management module and a solar panel. It can adapt to the harsh climate at high altitudes. The collection frequency can be flexibly set. The power supply system has overcharge and over-discharge protection, and the communication is stable and reliable to ensure accurate data transmission.

[0054] The main control module is used for:

[0055] Data aggregation: As a data aggregation hub, it receives and integrates data from various sensors to ensure centralized data management.

[0056] Control and coordination: Flexibly adjust the data collection frequency of each sensor according to actual needs, coordinate the data collection and transmission sequence between sensors, ensure the orderly operation of the entire sensor unit, and avoid data conflicts and transmission confusion.

[0057] Communication management: Responsible for establishing a stable communication link with the lightweight edge gateway unit, encapsulating and accurately transmitting the aggregated sensor data according to the preset communication protocol and format, and receiving instructions from the upper-level unit and distributing them to the corresponding sensors for execution.

[0058] Data preprocessing and preliminary analysis: Perform preliminary processing on the received data, such as data verification, error detection and correction, preliminary analysis of meteorological data, etc., judge the rationality of the data, mark abnormal data and take preliminary processing measures to provide a better data foundation for subsequent in-depth analysis.

[0059] The lightweight edge gateway unit includes the following:

[0060] The main control module uses the MTK6739 chip, which is responsible for the overall control and data processing of the entire gateway.

[0061] The IoT module supports multiple broadband and narrowband communication methods such as RF433, wifi, RS485 and ETH to realize data interaction with front-end sensors and the main station. Among them, RF433 wireless communication has reliable communication capabilities in complex electromagnetic environments, and the communication protocol and data format strictly meet system requirements.

[0062] The MCU module uses the STM8L051F3P6 chip, which mainly ensures the stability of system operation and performs key operations such as power-on control, watchdog function, serial port command monitoring, and communication monitoring with intelligent modules.

[0063] The cloud-edge collaborative computing model includes:

[0064] Perform edge computing on the collected data, dynamically adjust computing strategies and resource allocation according to different application scenarios and data characteristics, screen out valuable data for uploading, and effectively reduce data sensing pressure and main station data storage burden.

[0065] It supports online upgrade and update functions, which can promptly adapt to changes in business needs and technological development trends. It also has a powerful data security protection mechanism to prevent data leakage and tampering, ensuring the security and integrity of system data.

[0066] The distribution network full situation data model unit includes:

[0067] Data collection and processing process:

[0068] The data acquisition module collects multi-dimensional data such as current, voltage, temperature, humidity, wind speed, wind direction, wire clamp temperature, wildfire, etc. of the distribution network lines through a variety of carefully arranged sensors, and performs comprehensive pre-processing operations such as data cleaning, denoising, and normalization on the collected data to ensure data accuracy and availability.

[0069] The feature extraction module uses advanced algorithm models such as deep learning and neural networks to perform deep feature extraction on preprocessed data and accurately mine key features related to faults.

[0070] The fault diagnosis module uses cutting-edge technologies such as pattern recognition and classification algorithms based on the extracted key features, combined with rich historical data and expert experience, to accurately identify and locate distribution network line faults, deeply analyze the causes of the faults, and provide scientific and reasonable handling suggestions.

[0071] The result display module presents the diagnostic results to users in the form of intuitive tables and reports, clearly showing the fault conditions of distribution network equipment and corresponding processing suggestions, allowing users to quickly understand the distribution network operation status.

[0072] Model features and advantages:

[0073] The data model has the ability of self-learning and updating, which can continuously optimize fault diagnosis knowledge and continuously improve the accuracy and reliability of fault diagnosis.

[0074] It supports the diagnosis of various complex fault types, including short circuit, open circuit, overload, ground fault and other common fault types, providing comprehensive technical support for distribution network operation and maintenance.

[0075] The distribution network operation and maintenance service mobile software unit includes:

[0076] Functional modules:

[0077] The equipment operation parameter monitoring module presents the operating parameters of the distribution network equipment, such as voltage, current, temperature, etc., in real time in a variety of display methods (such as lists, curves, charts, etc.), making it convenient for operation and maintenance personnel to intuitively and comprehensively understand the equipment operation status.

[0078] The early warning module categorizes and displays the early warning information of distribution network equipment according to different early warning levels, and ensures that operation and maintenance personnel obtain early warning information in a timely manner through various reminder methods such as sound and vibration, so as to take prompt response measures.

[0079] The work order management module realizes the full-process management functions such as work order submission, processing, approval, and query. It is closely integrated with the distribution network operation and maintenance business, effectively improving the efficiency and quality of work order processing, and realizing the rapid circulation and accurate processing of work orders.

[0080] The data statistics and analysis module conducts in-depth statistical analysis on the operating data of distribution network equipment, provides rich data query functions and generates detailed reports, providing strong data support for operation and maintenance decisions.

[0081] The user management module supports multi-level user authority management, strictly restricts the access and operation of system function modules by users with different authorities, ensures system security and data confidentiality, and provides convenient functions such as user information modification and password retrieval to enhance user experience.

[0082] By achieving efficient interaction with the lightweight edge gateway unit through the network, operation and maintenance personnel can send various control instructions to the lightweight edge gateway unit according to actual work needs, such as adjusting sensor acquisition parameters, obtaining specific data, etc. After receiving the instructions, the lightweight edge gateway unit will quickly execute and provide timely feedback, which greatly improves the flexibility and efficiency of operation and maintenance work.

[0083] The technical effects of the present invention will be further described below by way of specific embodiments:

[0084] Overall system construction and deployment:

[0085] Installation and configuration of multi-parameter perception sensor unit:

[0086] Install low-light, low-power, intelligent wireless temperature sensors at key equipment and nodes of the low-voltage distribution network. For example, at locations such as distribution network line clamps, transformer columns in substations, ring network cabinet busbars, and cable grounding boxes, select appropriate installation methods based on the equipment structure and operating characteristics to ensure that the sensor can closely contact the monitoring part and accurately measure the temperature; firmly fix the sensor through the throat clamp dual-mode structure, and adjust the position of the special soft magnetic material to enable it to work stably in a current environment above 5A, and there is no heating and current blind spot phenomenon at a high current of 5000A; correctly connect the sensor's CT energy acquisition device to the high-voltage distribution network line to achieve power acquisition and power the internal circuit.

[0087] Install partial discharge sensors based on RF433 wireless communication in switch cabinets and ring network cabinets; determine the best installation position of the sensor based on the cabinet structure and electrical layout to ensure that partial discharge signals can be effectively detected; after the sensor is installed, set up self-organizing network communication to establish a stable RF433 wireless communication connection with the lightweight edge gateway, follow the system's preset communication protocol, ensure reliable data transmission, and realize real-time monitoring of partial discharge conditions.

[0088] Install low-power micro cameras based on WiFi wireless communication at appropriate locations on overhead lines and substations in the distribution network; the camera installation should take into account coverage and shooting angles to ensure clear monitoring of line and substation conditions; pair the camera's WiFi module with the WiFi module of a lightweight edge gateway to achieve power control and data interaction; program the camera's logic for sending and receiving data to ensure that it can accurately determine the data frame header, address frame, CRC checksum, and instruction corresponding data segment length during data transmission, thereby ensuring the accuracy and integrity of data interaction.

[0089] Install the CT power supply circuit part of the inductive power supply current and voltage sensor on the power line, and ensure that the installation position is convenient for electromagnetic induction power supply without affecting the normal operation of the line; correctly install the Rogowski coil in the distribution network line to measure the load current; configure the communication parameters of the gateway's LoRa module and the current monitoring module to achieve remote control and data acquisition of the power switch, ensure the normal operation of the CT power supply circuit's rectification and filtering, DC / DC conversion, overvoltage protection, power-off detection and backup battery and other functional modules, and provide a stable power supply for the sensor.

[0090] Install meteorological sensors at appropriate locations, such as near poles or around substations, place them in radiation-proof louvers and secure them firmly; use RS485 communication cables to connect the meteorological sensors to the main control module of the multi-parameter sensing sensor unit, and complete the communication interface configuration in the main control module; install and debug the power supply system of the meteorological sensor, including connecting a 20AH lithium battery, a charge and discharge management module, and a 30W solar panel, setting reasonable charge and discharge parameters to ensure stable operation of the power supply system, and setting the meteorological data collection frequency according to actual needs.

[0091] Lightweight Edge Gateway Unit Setup and Debugging:

[0092] Install the main control module (MTK6739 chip) of the lightweight edge gateway unit in the gateway device, connect related hardware components such as memory, storage devices, etc. Install and configure the Internet of Things module, set the corresponding communication parameters for RF433, wifi, RS485 and ETH communication methods, and ensure stable and reliable communication with the front-end sensor and the back-end main station. In particular, the RF433 wireless communication module optimizes the antenna parameters and communication protocol settings according to the characteristics of the electromagnetic environment in high-altitude areas to improve its reliability and anti-interference ability in complex environments.

[0093] Install the MCU module (STM8L051F3P6 chip) in the gateway device, write and burn the relevant control program to realize the functions of power-on control, watchdog function, serial port command monitoring and communication monitoring with the intelligent module; set the power-on control logic to ensure that the system starts normally in the predetermined order after the power is turned on; configure the watchdog timer parameters so that it can monitor the system operation status in real time. When the system is abnormal or crashes, reset the system in time to ensure the continuous and stable operation of the system; program the serial port command monitoring function, set a reasonable command format and response rules, and judge the serial port data reception of the host module according to the preset rules. If the expected serial port data is not received within the specified time, execute the corresponding reset action to ensure normal system communication; at the same time, set the query command monitoring function of the intelligent module, determine the query cycle and fault judgment conditions, and when the query command is not received for more than the specified period, judge the fault in time and take reset operation, and perform a maximum of 3 resets to ensure stable communication between the system and the intelligent module.

[0094] Deploy the cloud-edge collaborative computing model in the lightweight edge gateway unit; initialize the edge computing strategy according to the actual application scenario requirements, including data screening rules, computing resource allocation schemes, etc. Establish a communication connection with the cloud, configure data upload and download channels, and ensure smooth data interaction between edge computing and cloud computing. During system operation, dynamically adjust computing strategies and resource allocation according to real-time data characteristics and business demand changes to achieve efficient data processing and analysis; at the same time, regularly check the online upgrade and update function to ensure that the system can obtain new models, algorithms and software updates pushed by the cloud in a timely manner, and achieve dynamic adaptive adjustments without affecting the normal operation of the system to maintain the system's optimal performance.

[0095] Construction and training of the distribution network full situation data model unit:

[0096] Build the software environment of the distribution network full-situation data model unit on the data center server, install the required deep learning framework, database management system and other software components; configure the data acquisition module so that it can connect with the multi-parameter perception sensor unit through network communication, and receive multi-dimensional data such as current, voltage, temperature, humidity, wind speed, wind direction, wire clamp temperature, wildfire, etc. from various sensors. According to the actual layout of the distribution network line and the distribution of equipment, optimize the sensor layout plan to ensure that the collected data fully and accurately reflects the actual operating status of the distribution network line.

[0097] Preprocess the raw data received by the data acquisition module. Write a data cleaning program to identify and remove outliers and noise interference in the data, and repair missing data through filtering algorithms, data interpolation and other methods; use normalization algorithms to unify data of different magnitudes into the same numerical range to improve data quality and availability. Use deep learning, neural network and other algorithm models to write feature extraction programs, perform deep feature extraction on preprocessed data, and mine key feature information related to faults hidden in the data.

[0098] Based on a large amount of historical data and expert experience, a fault diagnosis model is constructed; the extracted key features are analyzed and processed using pattern recognition, classification algorithms and other technologies to achieve accurate identification and rapid location of distribution network line faults. For common fault types such as short circuit, open circuit, overload, and grounding fault, corresponding diagnostic models are established respectively, and trained and optimized through actual case data to improve the accuracy and reliability of the model; at the same time, the self-learning and updating mechanism of the model is designed so that it can continuously adjust the model parameters according to new fault cases and operating data, optimize the diagnostic algorithm, and improve the fault diagnosis capability.

[0099] Develop a result display module to present the diagnosis results to users in the form of intuitive and easy-to-understand reports. Design a visual interface to clearly present key information such as the fault status, fault location, fault type, and corresponding processing suggestions of the distribution network equipment through charts and graphics; establish a user interaction function to allow operation and maintenance personnel to query and analyze historical data, real-time data, and diagnosis results according to actual needs, providing strong support for operation and maintenance decisions.

[0100] Development and deployment of mobile software units for distribution network operation and maintenance services:

[0101] Based on mobile device operating systems (such as Android or iOS), use corresponding development tools and technologies to develop distribution network operation and maintenance business mobile software units; design the software user interface, including equipment operation parameter monitoring interface, early warning interface, work order management interface, data statistics analysis interface and user management interface, etc., to ensure that the interface is simple, intuitive, and easy to use, and conforms to the operating habits of operation and maintenance personnel.

[0102] In the equipment operation parameter monitoring module, a data receiving and display program is written so that it can communicate with the lightweight edge gateway unit through the network and obtain the operation parameters of the distribution network equipment in real time, such as voltage, current, temperature, etc.; a variety of data display methods are adopted, such as listing the values ​​of various parameters in detail in the form of a list, displaying the trend of parameter changes over time in the form of a curve, and performing parameter comparison and analysis in the form of a chart, etc., so that operation and maintenance personnel can grasp the equipment operation status in real time and comprehensively from different angles; set the parameter abnormal alarm threshold, and when the monitored parameters exceed the normal range, trigger the early warning mechanism in time, and display the early warning information on the early warning interface.

[0103] An early warning module is developed to monitor and judge possible faults or abnormal situations in real time according to the operating status of distribution network equipment and preset early warning rules. When the early warning conditions are met, they are classified and displayed according to different warning levels. Serious faults are indicated by red eye-catching prompts, sound alarms and vibration reminders, and general faults are indicated by yellow prompts. At the same time, detailed fault information and handling suggestions are provided on the early warning interface to facilitate operation and maintenance personnel to quickly understand the situation and take corresponding measures.

[0104] Build a work order management module to realize the information management of the whole process including work order submission, processing, approval, and query. Design a work order filling template. After the operation and maintenance personnel find a problem on site, they can quickly submit the work order through the mobile software, and record the fault situation, equipment location, processing requirements and other information in detail; after the work order is submitted, the system automatically pushes it to the corresponding processing personnel, who can view the work order details on the mobile software and process it, and update the work order status in real time during the processing. Approval personnel can approve the work order online through the mobile software to ensure that the processing process complies with the specifications; provide a work order query function, and the operation and maintenance personnel can query the work order history and current progress according to the work order number, time range, processing status and other conditions, so as to realize the transparency and efficient collaboration of the whole process of work order processing.

[0105] In the data statistics and analysis module, write data statistics and analysis programs to obtain distribution network equipment operation data from the data center and conduct comprehensive and in-depth statistical analysis; provide rich data query functions, query by time range, query by equipment type, query by region, etc., to facilitate operation and maintenance personnel to obtain the required data; generate detailed reports based on the analysis results, such as equipment operation status reports, fault statistics reports, operation and maintenance workload reports, etc., and use charts to display data analysis results, intuitively presenting the distribution network operation status and trends. Through in-depth mining and analysis of data, provide strong data support for operation and maintenance management decisions, help operation and maintenance managers discover potential problems and laws in distribution network operation, and formulate scientific and reasonable operation and maintenance strategies and plans.

[0106] Design user management modules and build a comprehensive multi-level user authority management system; assign different system operation permissions according to the roles and responsibilities of operation and maintenance personnel. Ordinary operation and maintenance personnel can only view and process equipment data and work orders related to their work, while management personnel have higher permissions, such as equipment management, user management, data statistics and analysis, etc. Develop user information management functions, allowing users to modify personal information, set passwords, etc., and provide password retrieval functions to ensure that users can use the system smoothly and ensure system security and data confidentiality.

[0107] After the system is built, perform the following data collection and transmission tests:

[0108] Multi-parameter perception sensor unit data acquisition test:

[0109] Start the low-light low-power intelligent wireless temperature measurement sensor, and measure its temperature acquisition accuracy and stability under different current load conditions; by simulating the current changes in the actual distribution network operation, gradually increasing the current from low current to high current, observe the difference between the temperature value measured by the sensor and the actual temperature value (measured using a high-precision thermometer as a reference), and calculate the measurement error; at the same time, run the sensor for a long time, record the fluctuation of the temperature data, and evaluate its stability.

[0110] Conduct partial discharge detection tests on partial discharge sensors based on RF433 wireless communication; build a test platform for simulating partial discharge in switch cabinets and ring main units in a laboratory environment, set partial discharge signals of different types and intensities, and observe the sensor's detection sensitivity and accuracy for discharge signals. Verify the sensor's detection performance under different working conditions by changing the frequency, amplitude, waveform and other parameters of the discharge signal, record the characteristics of the discharge signal detected by the sensor, and compare and analyze it with the actual set discharge parameters.

[0111] Test the image acquisition and transmission functions of low-power micro cameras based on WiFi wireless communication. Under different lighting conditions and shooting scenes, control the camera to capture images and videos, and check whether the image quality, clarity, and color reproduction meet the requirements. At the same time, monitor indicators such as WiFi communication data transmission rate, delay, and packet loss rate between the camera and the lightweight edge gateway. By testing at different distances and interference environments, evaluate the communication performance of the camera in the actual distribution network monitoring scenario.

[0112] For the inductive current and voltage sensors, current and voltage measurement accuracy tests and power supply stability tests are performed. Use standard current and voltage sources to provide known current and voltage signals to the sensors, measure the output values ​​of the sensors, and calculate the measurement errors. At the same time, observe the stability of the power supply of the sensors during long-term operation, record the power supply voltage fluctuations, and check the switching and power supply capabilities of the backup battery when the main power supply is cut off.

[0113] Test the temperature and humidity measurement accuracy and data transmission reliability of the meteorological sensor; place the meteorological sensor in a test chamber with different temperature and humidity environments, compare the temperature and humidity values ​​measured by the sensor with the standard values ​​set in the test chamber, and calculate the measurement error. By running the sensor for a long time, monitor its data transmission with the main control module through the RS485 interface, check whether the data is transmitted accurately and completely, and whether there is any data loss or error.

[0114] Data transfer to lightweight edge gateway unit test:

[0115] When each sensor of the multi-parameter perception sensor unit collects data normally, check whether the data can be accurately and stably transmitted to the lightweight edge gateway unit; use network monitoring tools to observe the communication traffic and data packet transmission between the sensor and the gateway, and record error information and abnormal phenomena during the transmission process.

[0116] For the RF433 wireless communication mode, its transmission performance is tested in high-altitude areas. Representative high-altitude test sites are selected to arrange sensors and gateway equipment to simulate the actual distribution network operation environment, and the signal strength, communication distance and data transmission reliability of RF433 wireless communication in a complex electromagnetic environment are monitored. By adding interference sources and changing the terrain, the anti-interference ability and adaptability of RF433 wireless communication under harsh conditions are evaluated.

[0117] Test the ability of the lightweight edge gateway unit to receive and parse data from different types of sensors. Write a data receiving and parsing program on the gateway side to receive and parse the data from each sensor in real time, check whether the parsed data is consistent with the data collected by the sensor, and whether the data format meets the system requirements; verify whether the gateway can correctly identify the data types of different sensors, and store and process them according to the preset rules.

[0118] Example 3: System function verification and optimization:

[0119] Verification of fault diagnosis function of distribution network full situation data model unit:

[0120] The fault diagnosis function of the distribution network full-situation data model unit is verified using historical fault data and simulated fault data; the collected historical fault data (including multi-dimensional data such as current, voltage, temperature, etc. when the fault occurs) is input into the data model, the diagnostic results of the model are observed, compared with the actual fault situation, and the diagnostic accuracy is calculated. At the same time, a simulated fault occurrence device is used to artificially create different types of faults such as short circuit, open circuit, overload, and ground fault in the actual distribution network or simulated distribution network environment, collect real-time data when the fault occurs and input it into the data model to verify the model's diagnostic ability and positioning accuracy for actual faults.

[0121] According to the verification results, the fault diagnosis model is optimized and adjusted. If the model is found to have inaccurate diagnosis or missed diagnosis, the reasons may be insufficient data feature extraction, inappropriate algorithm model selection, or unreasonable model parameter settings. For these problems, the data preprocessing algorithm is further optimized to extract more effective features; different deep learning algorithms are tried or existing algorithms are improved to improve the diagnostic performance of the model; by adjusting model parameters, such as the learning rate and number of iterations of the neural network, the model training process is optimized to improve the accuracy and reliability of the model.

[0122] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas, characterized in that: It includes multi-parameter perception sensor unit, lightweight edge gateway unit, distribution network full situation data model unit, distribution network operation and maintenance business mobile software unit, among which: The multi-parameter sensing sensor unit is connected to the lightweight edge gateway unit by wireless or wired communication, and the collected operating data of temperature, partial discharge, current, voltage, and meteorology of the low-voltage distribution network equipment and key nodes are transmitted to the lightweight edge gateway unit in real time; The lightweight edge gateway unit is connected to the distribution network full situation data model unit through network communication, and sends the valid data after preliminary processing, analysis and screening to the distribution network full situation data model unit for further processing and analysis; The distribution network full situation data model unit is connected to the distribution network operation and maintenance business mobile software unit through network communication, and the diagnosis results, line status information, and fault warning data are transmitted to the distribution network operation and maintenance business mobile software unit for operation and maintenance personnel to view and process. The distribution network operation and maintenance service mobile software unit interacts with the lightweight edge gateway unit through the network. The operation and maintenance personnel send control instructions to the lightweight edge gateway unit according to actual needs, such as adjusting sensor acquisition parameters and obtaining specific data instructions; the lightweight edge gateway unit performs corresponding operations after receiving the instructions and feeds back the results.

2. According to claim 1, a transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas is characterized by: The multi-parameter sensing sensor unit includes a low-light level, low-power, intelligent wireless temperature measurement sensor. The low-light level, low-power, intelligent wireless temperature measurement sensor adopts a throat clamp dual-mode structure and is made of soft magnetic materials. The low-light level, low-power, intelligent wireless temperature measurement sensor is powered by CT energy extraction, uses an STM8L051 microcontroller as a controller and is equipped with a DS18B20 digital temperature sensor to collect temperature data, and communicates with the lightweight edge gateway unit through RF433 wireless ad hoc networking technology.

3. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 2 is characterized in that: The multi-parameter sensing sensor unit also includes a local discharge sensor based on RF433 wireless communication, which communicates with the lightweight edge gateway unit in an ad hoc network.

4. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 3 is characterized in that: The multi-parameter sensing sensor unit also includes a low-power micro camera based on wifi wireless communication; the low-power micro camera based on wifi wireless communication has a power switch controlled by the wifi module of the lightweight edge gateway unit.

5. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 4 is characterized in that: The multi-parameter sensing sensor unit also includes an inductive power supply current and voltage sensor; the inductive power supply current and voltage sensor includes a CT power supply circuit and a Rogowski coil. The CT power supply circuit obtains power from the power line through electromagnetic induction to power the sensor. The Rogowski coil is used to measure the load current of the distribution network line. The gateway communicates with the current monitoring module via the LoRa module to control the power supply and obtain data.

6. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 4 is characterized in that: The lightweight edge gateway unit includes a main control module, an Internet of Things module and an MCU module; The main control module adopts MTK6739 chip to realize the overall control and data processing of the gateway; The IoT module supports RF433, wifi, RS485 and ETH communication modes to exchange data with front-end sensors and the main station; The MCU module adopts the STM8L051F3P6 chip to ensure stable operation of the system.

7. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 6 is characterized in that: The lightweight edge gateway unit also includes a cloud-edge collaborative computing model, which performs edge computing on the collected data.

8. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 1 is characterized in that: The distribution network full situation data model unit includes a data acquisition module, a feature extraction module, a fault diagnosis module and a result display module; The data acquisition module includes two or more sensors to collect multi-dimensional data and pre-process it, and the data transmission adopts a high-speed and stable communication protocol; The feature extraction module uses a deep learning algorithm to extract key fault features; The fault diagnosis module combines historical data and expert experience to diagnose faults, provide fault type diagnosis, analyze causes and provide treatment suggestions; The result display module displays the diagnosis result of the fault diagnosis module in the form of a report or the like.

9. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 1 is characterized in that: The distribution network operation and maintenance business mobile software unit includes an equipment operation parameter monitoring module, an early warning module, a work order management module, a data statistics analysis module and a user management module; The equipment operation parameter monitoring module is used to display the distribution network equipment operation parameters; The warning module displays and reminds the operation and maintenance personnel according to the warning level; The work order management module is used for work order process management; The data statistical analysis module provides data statistical analysis and report generation functions; The user management module supports multi-level authority management and user information management.

10. The transparent detection and optimization diagnosis system for low-voltage distribution networks in high-altitude areas according to claim 5 is characterized in that: The multi-parameter sensing sensor unit also includes a meteorological sensor; the meteorological sensor adopts a high-precision temperature and humidity sensor, which is placed in a radiation-proof louver to enhance shielding and is connected to the main control module through an RS485 interface.

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