Remote transmission system for 110kV cable temperature and grounding wire current

Abstract

The invention relates to the technical field of automation, and discloses a 110kV cable temperature and ground wire current remote transmission system, which comprises a data acquisition module used for acquiring the temperature of an intermediate head of a 110kV cable and the ground wire current through a temperature sensor and a current sensor; the energy supply module is used for supplying power to the system by using a solar panel and a battery; the data transmission module is used for wirelessly transmitting data through the LORA wireless communication module and an external antenna; the data receiving and processing module is used for placing a wireless receiving device in an adjacent power distribution substation, and transmitting the received data to a refining transformer monitoring computer through a dispatching automation optical fiber network; and the background monitoring software module is used for displaying and analyzing the operation state of the cable by monitoring refining transformer monitoring software operated on the computer. Through the data remote transmission system, real-time monitoring of the operation state of the cable is realized, and reliable data analysis data is provided for abnormal analysis, accident judgment, equipment predictive maintenance and the like.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of automation, in particular to a 110kV cable temperature and grounding line current remote transmission system. BACKGROUND

[0002] The 110kV high-voltage cable in a refinery is a main equipment for power transmission, and should have good insulation performance. The cable end and accessories should have good insulation performance and sealing performance, and can effectively prevent leakage and moisture from entering. It is very important to closely monitor the operation state of the cable, timely find abnormal conditions, carry out predictive maintenance, and ensure the reliable operation of the cable.

[0003] The 110kV macro-refining A and B lines in a refinery use a combination of cable trench and bridge laying. Some cables are located in the upper power plant, and the operating environment is uncontrollable. The ambient temperature is low in winter, and water leakage in the surrounding pipelines has occurred many times, flowing into the cable trench and freezing. Insulation damage caused by laying and cable stress at the corner of the bridge greatly increase the unreliability of cable operation, and easily cause cable burning accidents, causing some production devices to shut down, resulting in huge economic losses.

[0004] The current method is to carry out inspection work, and to inspect the cable full length once a week to check the cable stress at the bridge corner point, and to measure the cable intermediate head temperature and grounding line current. However, this manual inspection method cannot achieve real-time monitoring of the cable, and has many disadvantages.

[0005] Therefore, a 110kV cable temperature and grounding line current remote transmission system is provided by those skilled in the art to solve the problems in the background art. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a 110kV cable temperature and grounding line current remote transmission system, which solves the problem of low efficiency of manual inspection in the prior art.

[0007] To achieve the above purpose, the present application is implemented by the following technical scheme: a 110kV cable temperature and grounding line current remote transmission system, comprising:

[0008] The data acquisition module is used to acquire the temperature and grounding line current of the 110kV cable intermediate head through the temperature sensor and the current sensor;

[0009] The energy supply module is used to supply power to the system using solar panels and batteries;

[0010] The data transmission module is used to wirelessly transmit data through the LORA wireless communication module and the external antenna;

[0011] The data receiving and processing module is used to place wireless receiving devices in adjacent substations, and after receiving the data, it is transmitted to the refinery and petrochemical plant monitoring computer through the dispatching automation optical fiber network;

[0012] The background monitoring software module is used to display and analyze the cable operating state through the running of the refinery and petrochemical plant monitoring software on the monitoring computer;

[0013] The intelligent prediction module is used to receive real-time and historical data transmitted from the data acquisition module, analyze the data through the built-in machine learning algorithm, predict potential failures and performance degradation, and generate warnings and maintenance recommendations for the automated response module;

[0014] The automated response module is used to receive warnings and failure predictions from the intelligent prediction module, automatically execute response measures according to preset strategies, and record all automatic response decisions and execution details.

[0015] Preferably, the data transmission module wirelessly transmits data including temperature data and ground line current data transmitted through the RS485 bus.

[0016] Preferably, the cable operating state displayed by the refinery and petrochemical plant monitoring software in the background monitoring software module includes the 110kV cable intermediate head grounding current value, temperature value, and generates an exponential curve.

[0017] Preferably, the response measures in the automated response module include adjusting device operating parameters or starting emergency procedures.

[0018] Preferably, the machine learning algorithm used by the intelligent prediction module is a Transformer model.

[0019] Preferably, the Transformer model calculation step includes:

[0020] S1, data preparation: collect and clean historical temperature and current data of the cable system;

[0021] S2, feature establishment: including timestamp, historical readings and environmental temperature changes;

[0022] S3, model training: use historical data to train the Transformer model to identify patterns and trends in the data;

[0023] S4, verification and testing: test the predictive performance of the model on real-time data;

[0024] S5, deployment and monitoring: deploy the trained model to the intelligent prediction module, analyze data in real time and generate prediction data.

[0025] Preferably, the Transformer model calculation in the S3 step includes the following sub-steps:

[0026] S31. Input Processing and Encoding: Combine the steps of input embedding and positional encoding, and directly calculate the adjusted input representation:

[0027] X encoded =(X·W embed +PositionEncoding(pos))

[0028] Where X is the original data, X encoded It is the embedding weight, and PositionEncoding(pos) provides position information;

[0029] S32. Establish a multi-head self-attention mechanism: Combine the calculation of self-attention with the multi-head mechanism, and directly calculate the multi-head attention output:

[0030] MultiHead(X encoded = Concat(head1,…,head) h )·W O

[0031] Each head i The calculation is as follows:

[0032]

[0033] in, and The weight matrix for each head;

[0034] S33. Feedforward Networks and Residual Connections: Expressions for feedforward networks and residual connections:

[0035] Output = LayerNorm(X) encoded +FFN(MultiHead(X encoded )))

[0036] The feedforward network FFN is expressed as follows:

[0037] FFN(x)=max(0,xW1+b1)W2+b2;

[0038] S34, Output Prediction: Generate prediction results from the output of the last layer.

[0039] Preferably, the data acquisition module is electrically connected with the energy supply module, the data acquisition module is connected with the data transmission module through LORA wireless communication technology and RS485 bus system, the data receiving and processing module is connected with the background monitoring software module through an automatic optical fiber network, the intelligent prediction module is network-connected with the automatic response module, and the data transmission module is connected with the intelligent prediction module through LORA wireless communication technology.

[0040] The application provides a 110kV cable temperature and grounding wire current remote transmission system.

[0041] The application has the following beneficial effects:

[0042] 1. The application realizes real-time and accurate measurement, and remote transmission of temperature data and current data, and is suitable for measurement and remote transmission of high-voltage cable intermediate heads and grounding wire currents, and solves the problem of inconvenient power supply on site, compared with manual inspection, the data remote transmission system realizes real-time monitoring of the cable operation state, and provides reliable data analysis materials for analysis of abnormalities, judgment of accidents, and predictive maintenance of equipment.

[0043] 2. The application uses a solar cell panel to supply power for data acquisition and transmission equipment, ensures stable operation of the equipment even in remote or inconvenient power supply areas, thereby ensuring the continuity and accuracy of data acquisition, and has the combination of LORA wireless communication and RS485 bus technology, can reliably transmit data in a wide geographical range, and can ensure the stability and safety of data transmission even in areas with harsh environmental conditions or many interferences.

[0044] 3. The application analyzes real-time and historical data by using a Transformer model, can predict and identify potential cable faults and performance degradation, and the prediction function enables the maintenance team to take preventive measures before the fault occurs, avoiding large-scale power outages and expensive repair costs caused by cable faults. BRIEF DESCRIPTION OF DRAWINGS

[0045] Figure 1 It is a system framework perspective view of the application;

[0046] Figure 2 It is a data acquisition network schematic view of the application;

[0047] Figure 3 It is a data transmission network schematic view of the application;

[0048] Figure 4 It is a data acquisition device structure schematic view of the application;

[0049] Figure 5This is a schematic diagram of the background monitoring software interface of the present invention. Detailed Implementation

[0050] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example:

[0052] Please see the appendix Figure 1 - Appendix Figure 2 This invention provides a remote transmission system for 110kV cable temperature and grounding current, comprising:

[0053] The data acquisition module is used to collect the temperature and grounding current of the 110kV cable joint using temperature and current sensors. The module utilizes temperature and current sensors (CTs) installed at the 110kV cable joint for real-time monitoring. Temperature sensors, typically thermocouples or thermistors, capture changes in heat generated by current flow through the cable during operation. Current sensors monitor the current intensity passing through the cable to assess its load. Real-time monitoring data helps maintenance personnel understand the cable's operating status, identify overheating or overload conditions, and prevent cable faults and fire risks, thereby ensuring the safe and reliable operation of the power grid.

[0054] The energy supply module is used to power the system using solar panels and batteries; the energy supply module includes solar panels and batteries. The solar panels convert sunlight into electricity under sunshine conditions, which is stored in the batteries to provide continuous power to the data acquisition module and its sensors. Using renewable energy reduces dependence on traditional power sources, lowers system operating costs, and increases the flexibility of system deployment, making it suitable for remote areas or areas where power access is difficult.

[0055] A temperature sensor collects the temperature of the intermediate head, a CT detects the grounding wire current, and the STC8A8K microcontroller receives temperature data from the HI7038 current acquisition module and temperature data transmitted via RS485 bus. Data is then wirelessly transmitted via an external antenna connected to the LORA wireless communication module. Solar panels convert solar energy into electrical energy to charge the battery and power the entire circuit.

[0056] Please see the appendix Figure 3The data transmission module is used for wireless data transmission via the LoRa wireless communication module and an external antenna. The wirelessly transmitted data includes temperature data and grounding current data transmitted via an RS485 bus. LoRa is a low-power wide-area network communication technology suitable for long-distance, low-speed data transmission. The RS485 bus is used for data transmission in local area networks, supporting multi-device communication and enhancing data transmission reliability. The combination of LoRa and RS485 technologies results in wide and stable data transmission coverage, ensuring data integrity and real-time performance even in complex or highly interference-prone industrial environments.

[0057] The data receiving and processing module is used to place wireless receiving devices in adjacent substations. After receiving data, it transmits it to the refining and chemical substation monitoring computer through the dispatch automation fiber optic network. The data receiving and processing module is located in the substation and includes a wireless receiving device and an interface for connecting to the automation fiber optic network. This module is responsible for receiving information from the data transmission module and quickly sending the data to the monitoring center through the high-speed fiber optic network.

[0058] Please see the appendix Figure 5 During actual installation, wireless receiving devices can be placed in adjacent substations to receive data and transfer it to the dispatch automation fiber optic network. The system is independently programmed to display real-time grounding current and temperature values ​​of the 18 cable joints of the Honglian A and B lines and the Ranglian line on the refining substation monitoring computer, generating curves for easy comparison and analysis of three-phase data (A, B, and C) at the same location. Historical curves can also be queried, providing valuable reference data for equipment maintenance.

[0059] The background monitoring software module is used to run the refining and chemical substation monitoring software on the monitoring computer to display and analyze the cable operating status. The cable operating status displayed by the refining and chemical substation monitoring software in the background monitoring software module includes the grounding current value and temperature value of the 110kV cable intermediate joint, and generates an exponential curve.

[0060] The background monitoring software module is implemented through the refining and chemical transformer monitoring software. This software runs on the monitoring computer, displaying the cable's temperature and current data in real time, and generating an exponential curve graph of the cable's operation based on this data. The monitoring software not only provides intuitive data display but also helps operators analyze data trends and detect abnormal changes early through the generated graphs.

[0061] During operation, the temperature of the 110kV Honglian A and B lines and the intermediate joint of the refining cable are first collected by the temperature sensor in the data acquisition module. Then, the grounding current of the 110kV Honglian A and B lines and the refining cable is collected by the starting current sensor (CT). The field acquisition device is powered by solar panels and batteries. The data transmission module automatically uploads data every one minute. A wireless receiver is placed on the polypropylene section to receive the data, which is then transferred to the dispatch automation fiber optic network. The grounding current value and current change curve are displayed on the refining substation monitoring computer.

[0062] Please see the appendix Figure 4 The left side of the field acquisition device is the battery charging and discharging circuit, the three white and black wires on the left are the three-phase current inputs, the red and black wires on the right are the temperature inputs, and the blue circuit board on the right is the LORA wireless communication board with an external antenna for data transmission.

[0063] The intelligent prediction module receives real-time and historical data transmitted from the data acquisition module, analyzes the data using built-in machine learning algorithms, predicts potential faults and performance degradation, and generates warnings and maintenance suggestions for the automated response module. By learning the normal operating modes of the cable and historical anomalies, the module can identify patterns and trends in the data and predict possible future faults and performance degradation.

[0064] By enabling real-time monitoring and prediction, operators can be alerted before problems escalate into serious malfunctions, thereby reducing maintenance costs, extending equipment lifespan, optimizing resource utilization, and preventing unexpected downtime and production losses due to equipment failures.

[0065] The automated response module receives warnings and fault predictions from the intelligent prediction module, automatically executes response measures according to preset strategies, and records all decision and execution details of the automated response.

[0066] The automated response module's response measures include adjusting equipment operating parameters or activating emergency procedures. The implementation of this module enhances the automation level of the cable management system, enabling it to react quickly and accurately to potential faults, significantly improving response efficiency and overall system reliability. Furthermore, automatically recording response activities provides valuable data for fault analysis and future improvements, helping to continuously optimize response strategies and enhance system performance.

[0067] The machine learning algorithm used in the intelligent prediction module is the Transformer model.

[0068] The calculation steps for the Transformer model include:

[0069] S1. Data Preparation: Collect and clean historical temperature and current data of the cable system;

[0070] S2. Feature establishment: including timestamps, historical readings, and changes in ambient temperature;

[0071] S3. Model Training: Train the Transformer model using historical data to identify patterns and trends in the data;

[0072] The Transformer model computation in step S3 includes the following sub-steps:

[0073] S31. Input Processing and Encoding: Combine the steps of input embedding and positional encoding, and directly calculate the adjusted input representation:

[0074] X encoded =(X·W embed +PositionEncoding(pos))

[0075] Where X is the original data, X encoded It is the embedding weight, and PositionEncoding(pos) provides position information;

[0076] S32. Establish a multi-head self-attention mechanism: Combine the calculation of self-attention with the multi-head mechanism, and directly calculate the multi-head attention output:

[0077] MultiHead(X encoded = Concat(head1,…,head) h )·W O

[0078] Each head i The calculation is as follows:

[0079]

[0080] in, and The weight matrix for each head;

[0081] S33. Feedforward Networks and Residual Connections: Expressions for feedforward networks and residual connections:

[0082] Output = LayerNorm(X) encoded +FFN(MultiHead(X encoded )))

[0083] The feedforward network FFN is expressed as follows:

[0084] FFN(x)=max(0,xW1+b1)W2+b2;

[0085] S34, Output Prediction: Generate prediction results from the output of the last layer.

[0086] S4. Validation and Testing: Test the model's predictive performance on real-time data;

[0087] S5. Deployment and Monitoring: Deploy the trained model to the intelligent prediction module to analyze data and generate prediction data in real time.

[0088] The data acquisition module and the energy supply module are electrically connected. The data acquisition module and the data transmission module are connected through LORA wireless communication technology and RS485 bus system. The data receiving and processing module and the background monitoring software module are connected through an automated fiber optic network. The intelligent prediction module and the automated response module are network connected. The data transmission module and the intelligent prediction module are connected through LORA wireless communication technology.

[0089] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote transmission system for 110kV cable temperature and grounding current, characterized in that, include: The data acquisition module is used to collect the temperature and grounding current of the 110kV cable joint through temperature and current sensors; The energy supply module is used to power the system using solar panels and batteries; The data transmission module is used for wireless data transmission via the LORA wireless communication module and an external antenna; The data receiving and processing module is used to place wireless receiving devices in adjacent substations. After receiving data, it transmits it to the refining and chemical substation monitoring computer through the dispatch automation fiber optic network. The background monitoring software module is used to run the refining and chemical transformer monitoring software on the monitoring computer to display and analyze the cable operating status; The intelligent prediction module receives real-time and historical data transmitted from the data acquisition module, performs data analysis through built-in machine learning algorithms, predicts potential failures and performance degradation, and generates warnings and maintenance suggestions for the automated response module. The automated response module receives warnings and fault predictions from the intelligent prediction module, automatically executes response measures according to preset strategies, and records all decision and execution details of the automated response.

2. The 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The data transmission module wirelessly transmits data including temperature data and grounding current data via an RS485 bus.

3. The 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The cable operating status displayed by the refining and chemical substation monitoring software in the background monitoring software module includes the grounding current value and temperature value of the 110kV cable mid-joint, and generates an exponential curve.

4. The 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The response measures in the automated response module include adjusting equipment operating parameters or activating emergency procedures.

5. A 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The intelligent prediction module uses the Transformer model as its machine learning algorithm.

6. The 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The Transformer model calculation steps include: S1. Data Preparation: Collect and clean historical temperature and current data of the cable system; S2. Feature establishment: including timestamps, historical readings, and changes in ambient temperature; S3. Model Training: Train the Transformer model using historical data to identify patterns and trends in the data; S4. Validation and Testing: Test the model's predictive performance on real-time data; S5. Deployment and Monitoring: Deploy the trained model to the intelligent prediction module to analyze data and generate prediction data in real time.

7. A 110kV cable temperature and grounding current remote transmission system according to claim 6, characterized in that, The Transformer model calculation in step S3 includes the following sub-steps: S31. Input Processing and Encoding: Combine the steps of input embedding and positional encoding, and directly calculate the adjusted input representation: X encoded =(X·W embed +PositionEncoding(pos)) Where X is the original data, X encoded It is the embedding weight, and PositionEncoding(pos) provides position information; S32. Establish a multi-head self-attention mechanism: Combine the calculation of self-attention with the multi-head mechanism, and directly calculate the multi-head attention output: MultiHead(X encoded )=Concat(head1,…,head h )·W O Each head i The calculation is as follows: in, and The weight matrix for each head; S33. Feedforward Networks and Residual Connections: Expressions for feedforward networks and residual connections: Output=LayerNorm(X encoded +FFN(MultiHead(X encoded ))) The feedforward network FFN is expressed as follows: FFN(x)=max(0,xW1+b1)W2+b2; S34, Output Prediction: Generate prediction results from the output of the last layer.

8. A 110kV cable temperature and grounding current remote transmission system according to claim 1, characterized in that, The data acquisition module and the energy supply module are electrically connected. The data acquisition module and the data transmission module are connected via LoRa wireless communication technology and an RS485 bus system. The data receiving and processing module and the background monitoring software module are connected via an automated fiber optic network. The intelligent prediction module and the automated response module are network connected. The data transmission module and the intelligent prediction module are connected via LoRa wireless communication technology.

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