Electric power monitoring system with AI analysis technology

Abstract

The invention belongs to the technical field of power monitoring, and discloses a power monitoring system with an AI analysis technology, and the system consists of a data collection module, a data transmission module, an AI analysis module, and a monitoring and early warning module. The AI analysis module can be combined with multi-source temperature, sound, current and voltage data, comprehensive diagnosis is performed through a multi-mode learning and integrated learning method, so that the fault type and position are identified more accurately, abnormal data are processed and analyzed in real time through the monitoring and early warning system, and the fault diagnosis accuracy is improved through the intelligent decision-making system. Compared with a traditional power monitoring system, the monitoring system not only can quickly and accurately identify and process the power failure, but also can continuously improve the judgment and processing capability of the monitoring system on the power failure through subsequent learning and optimization, so that the operation and maintenance efficiency is improved. And the workload of operation and maintenance personnel is reduced.

Description

Technical Field

[0001] The present invention belongs to the technical field of power monitoring, and specifically relates to a power monitoring system with AI analysis technology. Background Art

[0002] Power equipment status monitoring is one of the key technologies to ensure the safe, stable and economical operation of the power system. It can monitor the operating status of power equipment in real time, including key parameters such as voltage, current, temperature, etc. Once an abnormality or potential failure is found, the system will immediately issue an alarm to remind the operation and maintenance personnel to take timely measures, thereby effectively avoiding the impact of equipment failure on the power system and improving the safety and reliability of the system.

[0003] The current power monitoring system will detect the operating parameters of the power transmission equipment. After the detected data is converted into digital signals, it is transmitted to the power monitoring host through the communication network. The power monitoring host will display the processed data in the form of a graphical interface or report for monitoring personnel to view in real time so as to promptly discover and deal with abnormal situations.

[0004] Although the system can realize the functions of data collection, data processing and control management of power transmission equipment, the operation and maintenance personnel need to analyze a large amount of data when monitoring the power, and formulate targeted solutions by analyzing the abnormal status of the equipment, which greatly increases the workload of the operation and maintenance personnel. In addition, the operation and maintenance personnel need to make decisions and judgments on the abnormal status of the equipment based on their own experience. The operation and maintenance personnel have fewer references and comparisons, which is prone to errors, so it needs to be improved and optimized. Summary of the invention

[0005] The purpose of the present invention is to provide a power monitoring system with AI analysis technology to solve the problems raised in the above background technology.

[0006] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: a power monitoring system with AI analysis technology, the power monitoring system is composed of a data acquisition module, a data transmission module, an AI analysis module and a monitoring and early warning module;

[0007] The data acquisition module includes an electric power monitor and an acoustic temperature monitor, the data transmission module is implemented by an Internet of Things gateway, the AI ​​analysis module is composed of a data extraction system and a fault analysis system, and the monitoring and early warning system includes a visualization interface, an intelligent decision-making system and an early warning system.

[0008] Preferably, in the power monitoring system, the operation process of the AI ​​analysis module is:

[0009] A1, when the power transmission equipment is running, the data acquisition module will transmit the collected data to the AI ​​analysis module through the IoT gateway;

[0010] A2, the AI ​​analysis module will extract the data transmitted through the data extraction system;

[0011] A3, further, obtain the equipment temperature change characteristics, equipment operation noise characteristics and current and voltage waveform characteristics;

[0012] A4, at this time, the fault analysis system will compare and analyze the acquired features to find out the cause of the equipment abnormality;

[0013] A5, when the operating temperature of the equipment is higher than the normal value, the fault analysis system will draw the analysis results of heavy equipment load and heat dissipation failure;

[0014] A6, when the wavelength of noise generated during equipment operation is higher than normal, the analysis structure of equipment looseness and electromagnetic anomaly will be obtained;

[0015] A7, when the current and voltage are high and low, the analysis structure of equipment overload short circuit and power supply shortage will be obtained respectively;

[0016] A8, the analysis results will be transmitted to the monitoring and early warning system in real time to assist operation and maintenance personnel in responding quickly.

[0017] Preferably, the AI ​​analysis module will combine the multi-source temperature, sound, current and voltage data extracted from the data extraction system, and use multimodal learning and integrated learning methods to perform comprehensive diagnosis to improve the accuracy and efficiency of fault identification.

[0018] Preferably, the power monitor is integrated at the monitoring point of the power line to collect current and voltage data in real time. The acoustic temperature monitor includes an infrared camera and a microphone. The infrared camera faces the transformer and cable connector of the power equipment to monitor temperature changes in real time. The microphone is used to capture abnormal sounds during equipment operation.

[0019] Preferably, the power monitor and the acoustic temperature monitor are both equipped with a communication interface and a WiFi wireless communication module to achieve dual-mode data transmission.

[0020] Preferably, the decision-making process of the intelligent decision-making system is:

[0021] B1, the AI ​​analysis module analyzes equipment anomalies, and the analysis results are transmitted to the monitoring and early warning system in real time;

[0022] B2, the intelligent decision-making system automatically retrieves the best recovery plan based on the type and location of the equipment anomaly and through AI;

[0023] B3, when receiving a report of heavy equipment load and heat dissipation failure, it provides solutions for replacing worn parts and cleaning the radiator, and displays them on the visual interface;

[0024] B4, when receiving equipment looseness and electromagnetic anomalies, provide solutions for equipment reinforcement and electromagnetic component replacement, and display them on the visual interface;

[0025] B5, when receiving a device overload short circuit, provides a solution for shutting down the power supply and repairing and replacing the short circuit node, and displays it on the visual interface;

[0026] B6,When the intelligent decision-making system detects equipment abnormality, it will issue an alarm through the early warning system.

[0027] Preferably, the visualization interface provides an intuitive data display interface, including real-time data monitoring, historical data analysis and alarm information.

[0028] Preferably, when the intelligent decision-making system detects an equipment abnormality, the early warning system sends an alarm message to the operation and maintenance personnel, including the fault type, location and severity.

[0029] The beneficial effects of the present invention are as follows:

[0030] 1. The present invention can combine temperature, sound, current and voltage data from multiple sources through the AI ​​analysis module, and perform comprehensive diagnosis through multimodal learning and integrated learning methods, so as to more accurately identify the type and location of the fault. The abnormal data can be processed and analyzed in real time through the monitoring and early warning system, and the best solution can be retrieved through the intelligent decision-making system for reference and comparison by the operation and maintenance personnel, so as to assist the operation and maintenance personnel in performing operation and maintenance operations. Compared with the traditional power monitoring system, the monitoring system can not only quickly and accurately identify and handle power faults, but also continuously improve its own judgment and handling capabilities of power faults through subsequent learning, thereby reducing the workload of the operation and maintenance personnel.

[0031] 2. The present invention, through the setting of the monitoring system and the visual interface in the monitoring and early warning system, enables it to notify relevant personnel in multiple ways such as sound, light signals, text messages and emails, and provides detailed abnormal information and possible causes of failures to provide decision support for equipment management and maintenance. It is convenient and fast, and helps to achieve comprehensive monitoring and intelligent identification of power equipment, and improve the operating efficiency and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of the power monitoring system of the present invention. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] like Figure 1 As shown, an embodiment of the present invention provides a power monitoring system with AI analysis technology, and the power monitoring system is composed of a data acquisition module, a data transmission module, an AI analysis module and a monitoring and early warning module;

[0035] The data acquisition module includes an electric power monitor and an acoustic temperature monitor. The data transmission module is implemented by the Internet of Things gateway. The AI ​​analysis module consists of a data extraction system and a fault analysis system. The monitoring and early warning system includes a visual interface, an intelligent decision-making system and an early warning system.

[0036] The AI ​​analysis module can combine temperature, sound, current and voltage data from multiple sources, and perform comprehensive diagnosis through multimodal learning and integrated learning methods, so as to more accurately identify the type and location of the fault. The monitoring and early warning system can process and analyze abnormal data in real time and retrieve the best solution through the intelligent decision-making system for reference and comparison by operation and maintenance personnel, to assist operation and maintenance personnel in performing operation and maintenance work. Compared with traditional power monitoring systems, this monitoring system can not only quickly and accurately identify and handle power faults, but also continuously improve its own judgment and handling capabilities of power faults through subsequent learning, thereby reducing the workload of operation and maintenance personnel.

[0037] Among them, in the power monitoring system, the operation process of the AI ​​analysis module is as follows:

[0038] A1, when the power transmission equipment is running, the data acquisition module will transmit the collected data to the AI ​​analysis module through the IoT gateway;

[0039] A2, the AI ​​analysis module will extract the data transmitted through the data extraction system;

[0040] A3, further, obtain the equipment temperature change characteristics, equipment operation noise characteristics and current and voltage waveform characteristics;

[0041] A4, at this time, the fault analysis system will compare and analyze the acquired features to find out the cause of the equipment abnormality;

[0042] A5, when the operating temperature of the equipment is higher than the normal value, the fault analysis system will draw the analysis results of heavy equipment load and heat dissipation failure;

[0043] Use time series analysis and anomaly detection algorithms to identify temperature anomalies that could indicate equipment overheating or cooling system failures;

[0044] A6, when the wavelength of noise generated during equipment operation is higher than normal, the analysis structure of equipment looseness and electromagnetic anomaly will be obtained;

[0045] Classify and identify sound signals through MFCC feature extraction and deep learning models to distinguish normal operating sounds from abnormal sounds, such as mechanical wear and arc discharge, indicating equipment looseness and electromagnetic anomalies;

[0046] A7, when the current and voltage are high and low, the analysis structure of equipment overload short circuit and power supply shortage will be obtained respectively;

[0047] Signal processing technology is used to analyze current and voltage waveforms, identify abnormal conditions such as harmonics, voltage fluctuations, and current overloads, and combine with AI learning models to predict changes in grid load, indicating equipment overload, short circuit, and insufficient power supply problems.

[0048] A8, the analysis results will be transmitted to the monitoring and early warning system in real time to assist operation and maintenance personnel in responding quickly.

[0049] Among them, the AI ​​analysis module will combine the multi-source temperature, sound, current and voltage data extracted from the data extraction system, and use multimodal learning and integrated learning methods to perform comprehensive diagnosis to improve the accuracy and efficiency of fault identification.

[0050] The introduction of AI analysis modules makes equipment fault identification more accurate, providing data support for subsequent decision-making processes.

[0051] Among them, the power monitor is integrated into the monitoring point of the power line to collect current and voltage data in real time. The sound and temperature monitor includes an infrared camera and a microphone. The infrared camera faces the transformer and cable connector of the power equipment to monitor temperature changes in real time. The microphone is used to capture abnormal sounds during equipment operation.

[0052] The power monitor and sound temperature monitor are responsible for obtaining equipment temperature, noise, and current and voltage changes, providing data support for subsequent AI analysis modules.

[0053] Among them, the power monitoring instrument and the sound and temperature monitoring instrument are both equipped with communication interfaces and WI-FI wireless communication modules to realize dual-mode data transmission.

[0054] Through the communication interface and WI-FI wireless communication module, the device can respond flexibly in different scenarios, improving the flexibility of data transmission. This dual-mode complementary design effectively reduces the risk of data transmission interruption and reduces the losses caused by failure of a single communication mode.

[0055] Among them, the decision-making process of the intelligent decision-making system is:

[0056] B1, the AI ​​analysis module analyzes equipment anomalies, and the analysis results are transmitted to the monitoring and early warning system in real time;

[0057] B2, the intelligent decision-making system automatically retrieves the best recovery plan based on the type and location of the equipment anomaly and through AI;

[0058] B3, when receiving a report of heavy equipment load and heat dissipation failure, it provides solutions for replacing worn parts and cleaning the radiator, and displays them on the visual interface;

[0059] Based on the analysis results of heavy equipment load and heat dissipation failure, the solutions in the database are judged and screened to obtain the best solution for replacing worn parts and cleaning the radiator;

[0060] B4, when receiving equipment looseness and electromagnetic anomalies, provide solutions for equipment reinforcement and electromagnetic component replacement, and display them on the visual interface;

[0061] Based on the analysis results of equipment looseness and electromagnetic anomalies, the solutions in the database are judged and screened to obtain the best solutions for equipment reinforcement and electromagnetic component replacement;

[0062] B5, when receiving a device overload short circuit, provides a solution for shutting down the power supply and repairing and replacing the short circuit node, and displays it on the visual interface;

[0063] Based on the analysis results of equipment overload and short circuit, the solutions in the database are judged and screened to obtain the best solution of shutting down the power supply and repairing and replacing the short-circuited node;

[0064] B6,When the intelligent decision-making system detects equipment abnormality, it will issue an alarm through the early warning system.

[0065] Through the setting of monitoring system and visual interface in the monitoring and early warning system, it is possible to notify relevant personnel through sound, light signals, text messages and emails, and provide detailed abnormal information and possible causes of failures to provide decision support for equipment management and maintenance. It is convenient and fast, and helps to achieve comprehensive monitoring and intelligent identification of power equipment, and improve the operating efficiency and safety of the power system.

[0066] Among them, the visualization interface provides an intuitive data display interface, including real-time data monitoring, historical data analysis and alarm information.

[0067] Based on the AI ​​analysis results, decision support information such as maintenance suggestions and scheduling instructions are automatically generated in the visual interface to assist operation and maintenance personnel in responding quickly.

[0068] Among them, when the intelligent decision-making system detects equipment abnormalities, the early warning system sends alarm information to the operation and maintenance personnel, including the fault type, location and severity.

[0069] Relevant personnel are notified in a variety of ways, including sound, light signals, text messages, and emails, and detailed information about equipment abnormalities is provided so that operation and maintenance personnel can take timely measures to handle the situation.

[0070] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0071] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A power monitoring system with AI analysis technology, characterized by: The power monitoring system is composed of a data acquisition module, a data transmission module, an AI analysis module and a monitoring and early warning module; The data acquisition module includes an electric power monitor and an acoustic temperature monitor, the data transmission module is implemented by an Internet of Things gateway, the AI ​​analysis module is composed of a data extraction system and a fault analysis system, and the monitoring and early warning system includes a visualization interface, an intelligent decision-making system and an early warning system.

2. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: In the power monitoring system, the operation process of the AI ​​analysis module is as follows: A1, when the power transmission equipment is running, the data acquisition module will transmit the collected data to the AI ​​analysis module through the IoT gateway; A2, the AI ​​analysis module will extract the data transmitted through the data extraction system; A3, further, obtain the equipment temperature change characteristics, equipment operation noise characteristics and current and voltage waveform characteristics; A4, at this time, the fault analysis system will compare and analyze the acquired features to find out the cause of the equipment abnormality; A5, when the operating temperature of the equipment is higher than the normal value, the fault analysis system will draw the analysis results of heavy equipment load and heat dissipation failure; A6, when the wavelength of noise generated during equipment operation is higher than normal, the analysis structure of equipment looseness and electromagnetic anomaly will be obtained; A7, when the current and voltage are high and low, the analysis structure of equipment overload short circuit and power supply shortage will be obtained respectively; A8, the analysis results will be transmitted to the monitoring and early warning system in real time to assist operation and maintenance personnel in responding quickly.

3. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: The AI ​​analysis module will combine the multi-source temperature, sound, current and voltage data extracted from the data extraction system, use multimodal learning and integrated learning methods to perform comprehensive diagnosis, and improve the accuracy and efficiency of fault identification.

4. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: The power monitor is integrated at the monitoring point of the power line to collect current and voltage data in real time. The sound and temperature monitor includes an infrared camera and a microphone. The infrared camera faces the transformer and cable connector of the power equipment to monitor temperature changes in real time. The microphone is used to capture abnormal sounds during equipment operation.

5. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: The power monitoring instrument and the acoustic temperature monitoring instrument are both equipped with a communication interface and a WI-FI wireless communication module to realize dual-mode data transmission.

6. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: The decision-making process of the intelligent decision-making system is as follows: B1, the AI ​​analysis module analyzes equipment anomalies, and the analysis results are transmitted to the monitoring and early warning system in real time; B2, the intelligent decision-making system automatically retrieves the best recovery plan based on the type and location of the equipment anomaly and through AI; B3, when receiving a report of heavy equipment load and heat dissipation failure, it provides solutions for replacing worn parts and cleaning the radiator, and displays them on the visual interface; B4, when receiving equipment looseness and electromagnetic anomalies, provide solutions for equipment reinforcement and electromagnetic component replacement, and display them on the visual interface; B5, when receiving a device overload short circuit, provides a solution for shutting down the power supply and repairing and replacing the short circuit node, and displays it on the visual interface; B6,When the intelligent decision-making system detects equipment abnormality, it will issue an alarm through the early warning system.

7. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: The visualization interface provides an intuitive data display interface, including real-time data monitoring, historical data analysis and alarm information.

8. The power monitoring system with AI analysis technology according to claim 1 is characterized in that: When the intelligent decision-making system detects equipment abnormality, the early warning system sends alarm information to the operation and maintenance personnel, including the fault type, location and severity.

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