Transformer substation intelligent auxiliary method and system based on Internet of Things

Abstract

The invention discloses a transformer substation intelligent auxiliary method and system based on the Internet of Things, and the method comprises the steps: obtaining equipment state data and operation environment data of a transformer substation, and carrying out the first preprocessing of the data, and obtaining first data; second preprocessing is carried out on the first data to obtain second data, and the second data comprises the obtained substation equipment state and historical data and real-time data of the operation environment; and based on the historical data and the real-time data, utilizing a machine learning algorithm to predict an equipment fault, and performing equipment maintenance according to a fault analysis result. By optimizing data processing, enhancing safety measures, supporting real-time monitoring and other functions, the operation and maintenance efficiency and safety of the transformer substation are comprehensively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power Internet of Things, and in particular to an intelligent assistance method and system for a substation based on the Internet of Things. Background Art

[0002] Power IoT technology has been widely used in the operation and maintenance of power system equipment, including the widespread deployment of smart devices such as sensors and smart terminals. The communication between these devices usually adopts the Message Queuing Telemetry Transport (MQTT) message protocol, but the specific information content and information body in the protocol have not been uniformly standardized. This brings potential technical risks to the daily operation, maintenance, and replacement of power IoT equipment.

[0003] However, in practical applications, unified information bodies and related standards for power system transmission have not yet been established. Power IoT devices currently lack consistent models and information body definitions, which limits the operational reliability and basic data sharing capabilities of the IoT system, and is not conducive to the compatibility, scalability and maintainability of power IoT devices. With the gradual realization of advanced applications such as substation smart patrol, the need for communication standardization of substation IoT devices has become increasingly urgent. Therefore, in addition to the need for communication standardization, it is also necessary to conduct functional verification of IoT platform and edge device communication, communication system test architecture, application data structure and consistency testing of application transmission process. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an Internet of Things-based substation intelligent assistance method and system to solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, an embodiment of the present invention provides a substation intelligent assistance method based on the Internet of Things, comprising: acquiring equipment status data and operating environment data of the substation, and performing a first preprocessing on the data to obtain first data;

[0009] Performing a second preprocessing on the first data to obtain second data, wherein the second data includes the acquired historical data and real-time data of the substation equipment status and operating environment;

[0010] Based on the historical data and real-time data, machine learning algorithms are used to predict equipment failures, and equipment maintenance is performed based on the failure analysis results.

[0011] As a preferred solution of the intelligent assistance method for substation based on Internet of Things described in the present invention, obtaining the equipment status data and operating environment data of the substation includes: the equipment status data includes current, voltage, power, vibration and oil level data; the operating environment data includes temperature, humidity and light data.

[0012] As a preferred solution of the intelligent assistance method for substation based on Internet of Things described in the present invention, wherein: performing a first preprocessing on the data to obtain the first data includes: performing data cleaning on the data, eliminating outliers and noise, and compressing the data to reduce the transmission bandwidth requirement to obtain the first data.

[0013] As a preferred solution of the substation intelligent assistance method based on the Internet of Things described in the present invention, wherein: performing a second preprocessing on the first data to obtain the second data includes: using a file format standard parsing engine to read configuration information and command parameters; performing integrity check and validity verification on the first data; converting the first data into a unified format to obtain the second data;

[0014] The second data includes the acquired historical data and real-time data of the substation equipment status and operating environment.

[0015] As a preferred solution of the intelligent auxiliary method for substation based on the Internet of Things described in the present invention, wherein: based on the historical data and real-time data, using a machine learning algorithm to predict equipment failures, and performing equipment maintenance according to the failure analysis results includes:

[0016] Use machine learning algorithms to build fault prediction models and combine real-time and historical data to identify fault patterns;

[0017] When the predicted value exceeds the preset threshold, an alarm is triggered and the equipment fails; when the predicted value does not exceed the preset threshold, the equipment operates normally;

[0018] When a device failure triggers an alarm, the warning information will be pushed to the operation and maintenance personnel.

[0019] As a preferred solution of the intelligent auxiliary method for substation based on Internet of Things described in the present invention, it also includes: when the operation and maintenance personnel receive the early warning information, dynamically adjust the maintenance cycle according to the actual operating status of the equipment; establish a spare parts inventory management system to replace parts.

[0020] As a preferred solution of the intelligent auxiliary method for substation based on Internet of Things described in the present invention, it also includes: remotely adjusting equipment parameters to perform equipment maintenance according to the model prediction results.

[0021] In a second aspect, the present invention provides a substation intelligent auxiliary system based on the Internet of Things, comprising:

[0022] A data first processing module, used for acquiring equipment status data and operating environment data of the substation, and performing a first preprocessing on the data to obtain first data;

[0023] A second data processing module, used for performing a second preprocessing on the first data to obtain second data, wherein the second data includes the acquired historical data and real-time data of the substation equipment status and operating environment;

[0024] The fault prediction module is used to predict equipment failures based on the historical data and real-time data using a machine learning algorithm, and perform equipment maintenance according to the fault analysis results.

[0025] In a third aspect, the present invention provides an electronic device, comprising:

[0026] Memory and processor;

[0027] The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the substation intelligent assistance method based on the Internet of Things are implemented.

[0028] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the substation intelligent assistance method based on the Internet of Things.

[0029] Compared with the prior art, the present invention has the following beneficial effects: the present invention comprehensively improves the operation and maintenance efficiency and safety of substations by optimizing data processing, enhancing safety measures and supporting real-time monitoring and other functions. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0031] Figure 1The present invention is a flowchart of an intelligent substation assistance method and system based on the Internet of Things according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0033] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0034] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0035] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0036] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0037] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0038] Example 1

[0039] Reference Figure 1 , is an embodiment of the present invention, which provides a substation intelligent assistance method based on the Internet of Things, including:

[0040] S100: Acquire equipment status data and operating environment data of the substation, and perform first preprocessing on the data to obtain first data;

[0041] In an embodiment of the present application, obtaining equipment status data and operating environment data of a substation includes: the equipment status data includes current, voltage, power, vibration and oil level data; the operating environment data includes temperature, humidity and light data.

[0042] It should be noted that the equipment status data and operating environment data of the substation are collected through the sensing layer; in the embodiment of the present application, the sensing layer includes an environmental monitoring module and an equipment status monitoring module; wherein the environmental monitoring module deploys temperature sensors, humidity sensors, and light sensors; the equipment status monitoring module deploys current sensors, voltage sensors, power sensors, vibration sensors, and oil level sensors, and its sensors directly interface with the equipment or use non-invasive monitoring methods to obtain equipment operation data; ensure that the sensors operate at a safe voltage to avoid interfering with the normal operation of the equipment.

[0043] In an optional embodiment, the environmental monitoring module also deploys various environmental monitoring sensors such as air pressure, wind speed, wind direction, rainfall, etc., and regularly calibrates the sensors to ensure the accuracy of data collection; and sends the data to the central node via low-power Bluetooth / wireless sensor network and other methods.

[0044] In an embodiment of the present application, performing a first preprocessing on the data to obtain the first data includes: performing data cleaning on the data, removing outliers and noise, and compressing the data to reduce the transmission bandwidth requirement to obtain the first data.

[0045] It should be noted that the first preprocessing of the data in the embodiment of the present application is to implement preliminary data encryption to protect information security, and further ensure that data is not lost or duplicated by using the QoS mechanism of the MQTT protocol; ensure coverage of remote areas through network topology optimization and signal enhancers, implement network monitoring, detect connection stability in real time, and automatically restore faulty connections.

[0046] Specifically, the stable transmission of the data processing process is completed through the communication layer, which includes a data transmission module and a network management module, wherein the data transmission module includes: the MQTT client is responsible for establishing a connection with the message broker server and publishing / subscribing messages;

[0047] The encryption component is used to protect data transmission security using the TLS / SSL encryption standard;

[0048] The compression component is used to compress data to reduce transmission delay and bandwidth consumption;

[0049] The quality of service control submodule is used to ensure the reliability and sequence of message transmission;

[0050] The last will and testament submodule is used to send last will messages when there are network or device anomalies to notify system problems;

[0051] Among them, the network management module includes: the network topology optimization submodule is used to dynamically adjust the network structure to improve the transmission efficiency;

[0052] Signal boosters are used to set up signal boosters to solve coverage issues in remote areas;

[0053] Network diagnostic tools are used to monitor network status and promptly detect and repair fault points;

[0054] Load balancers are used to distribute network traffic to avoid overload and congestion.

[0055] S200: performing a second preprocessing on the first data to obtain second data, where the second data includes historical data and real-time data of the substation equipment status and operating environment;

[0056] In an embodiment of the present application, performing a second preprocessing on the first data to obtain the second data includes: using a file format standard parsing engine to read configuration information and command parameters; performing integrity check and validity verification on the first data; converting the first data into a unified format to obtain the second data;

[0057] The second data includes the acquired historical data and real-time data of the substation equipment status and operating environment.

[0058] It should be noted that in the embodiment of the present application, the second preprocessing is performed by the data processing layer, and the data processing layer includes a data parsing module and a data analysis and processing module, wherein the data parsing module includes:

[0059] The OpenXML parsing engine is used to parse device configuration and command parameters based on the OpenXML format;

[0060] The data check submodule is used to check the integrity and validity of the data and eliminate errors and noise;

[0061] The data formatting submodule is used to convert the raw data into a unified format for subsequent processing;

[0062] Among them, the data analysis and processing module includes: the real-time data processing submodule is used to quickly process real-time data streams to support immediate decision-making;

[0063] The historical data processing submodule is used to store and manage historical data for trend analysis and reporting;

[0064] The machine learning submodule is used to apply machine learning algorithms to predict equipment failures and maintenance needs;

[0065] Data visualization tools are used to graphically display data and analysis results.

[0066] S300: Based on historical data and real-time data, use machine learning algorithms to predict equipment failures and perform equipment maintenance based on the failure analysis results.

[0067] In the embodiment of the present application, based on historical data and real-time data, a machine learning algorithm is used to predict equipment failures, and equipment maintenance is performed according to the failure analysis results, including:

[0068] Use machine learning algorithms to build fault prediction models and combine real-time and historical data to identify fault patterns;

[0069] When the predicted value exceeds the preset threshold, an alarm is triggered and the equipment fails; when the predicted value does not exceed the preset threshold, the equipment operates normally;

[0070] When a device failure triggers an alarm, the warning information will be pushed to the operation and maintenance personnel.

[0071] In an optional embodiment, statistical analysis and time series analysis methods are used to discover data trends; machine learning algorithms, such as decision trees, neural networks, etc., are applied to perform fault pattern recognition; and data visualization technology is used to help quickly understand complex data.

[0072] In an embodiment of the present application, it also includes: when the operation and maintenance personnel receive the early warning information, they dynamically adjust the maintenance cycle according to the actual operating status of the equipment; and establish a spare parts inventory management system to replace parts.

[0073] In an optional embodiment, the maintenance cycle is dynamically adjusted according to the actual operating conditions of the equipment; preventive maintenance is used instead of traditional periodic maintenance to reduce unnecessary maintenance costs; and a spare parts inventory management system is established to ensure that parts can be quickly replaced when needed.

[0074] In an embodiment of the present application, it also includes: remotely adjusting device parameters to perform equipment maintenance based on model prediction results.

[0075] In an optional embodiment, a graphical interface is provided to allow authorized users to remotely view the status of the device and control the device; remote control functions such as starting, stopping, and adjusting parameters of the device are realized; all remote operation logs are recorded for auditing and fault tracking. A series of operations are automatically executed according to preset conditions, such as automatically starting the cooling system when the temperature exceeds the standard; automatic processing procedures are configured for common fault conditions to reduce the need for manual intervention; scheduled tasks are executed, such as automatic equipment maintenance during the low-peak period at night.

[0076] It should be noted that the embodiment of the present application provides users with an intuitive operation interface and real-time feedback through the application layer for equipment maintenance. The application layer includes an equipment monitoring module, a fault prediction and maintenance module, and a remote control and adjustment module, wherein the equipment monitoring module includes:

[0077] The real-time monitoring interface submodule is used to display the current status and alarm information of the equipment;

[0078] The historical data query submodule is used to retrieve and view historical operation and event records;

[0079] The equipment health scoring submodule is used to score the equipment based on various indicators, which can directly reflect the health status;

[0080] Among them, the fault prediction and maintenance module includes: the fault warning submodule is used to analyze data in real time and issue fault warning signals;

[0081] The maintenance plan scheduling submodule is used to formulate and automatically schedule preventive maintenance work;

[0082] Among them, the remote control and adjustment module includes:

[0083] The remote operation interface is used to allow remote control of equipment switches and parameter adjustment operations;

[0084] The automation script execution submodule is used to trigger the automation control script on a scheduled or conditional basis;

[0085] The device performance tuning submodule is used to adjust device settings to optimize performance based on data analysis results.

[0086] In an optional implementation, all transmitted and stored data is encrypted using modern encryption standards such as AES; multi-layer firewalls are set up and intrusion detection systems are deployed to defend against external attacks; network security drills and vulnerability scans are conducted regularly to detect potential security risks in advance; digital signature technology is used to verify data integrity to prevent data tampering during transmission; strict access control policies are implemented, with different levels of users having different permission restrictions; and detailed security event logs are recorded to facilitate subsequent tracking and analysis of the causes of security incidents.

[0087] In an optional implementation, the substation also ensures the security and integrity of data by introducing digital signature technology and serial number mechanism into the security layer of the Internet of Things in the overall detection and operation.

[0088] Specifically, the security layer includes a data security module and a network security module, wherein the data security module includes:

[0089] The digital signature verification submodule is used to verify the signature of the received data to ensure that the data has not been tampered with;

[0090] The sequence number anti-replay attack submodule is used to maintain the sequence number list to prevent replay attacks on old data;

[0091] The access control submodule is used to implement user authentication and permission control strategies;

[0092] The security audit log submodule is used to record all sensitive operations for later tracking and review;

[0093] The network security module includes:

[0094] Firewalls and intrusion detection systems are used to prevent unauthorized access and monitor for malicious activity;

[0095] VPN tunnel is used to provide a secure virtual private network connection for remote access;

[0096] The DDoS defense submodule is used to protect the system from distributed denial of service attacks.

[0097] It should be noted that the present invention uses machine learning and data analysis technology to achieve fault prediction and maintenance suggestions, and improve operation and maintenance efficiency; the data processing layer performs data processing and analysis through the OpenXML parsing engine, supports batch testing and automated detection, thereby improving detection efficiency and accuracy; the application layer provides functional modules such as equipment monitoring, fault prediction, remote control and maintenance, and provides users with intuitive operation interfaces and real-time feedback. Further integration of multiple sensors and advanced communication technologies can fully monitor the operating status of the substation; the sensor layer includes environmental monitoring and equipment status monitoring modules to ensure real-time acquisition of comprehensive environmental and equipment data. The communication layer uses the standardized MQTT protocol for data transmission to ensure efficient and secure data transmission; the data processing layer constructs a data processing module based on OpenXML, which is responsible for data parsing, processing and analysis. Further introduction of digital signature technology and serial number mechanism to ensure the security and integrity of data transmission; the security layer includes two main aspects: data security and network security, and implements data encryption, access control and firewall protection measures to ensure the overall security of the system.

[0098] Example 2

[0099] The above embodiment is a schematic scheme of a substation intelligent assistance method based on the Internet of Things. It should be noted that the technical solution of the substation intelligent assistance system based on the Internet of Things and the technical solution of the substation intelligent assistance method based on the Internet of Things belong to the same concept. The details of the technical solution of the substation intelligent assistance system based on the Internet of Things in this embodiment that are not described in detail can be referred to the description of the technical solution of the substation intelligent assistance method based on the Internet of Things.

[0100] In this embodiment, a substation intelligent auxiliary system based on the Internet of Things includes:

[0101] A data first processing module, used for acquiring equipment status data and operating environment data of the substation, and performing a first preprocessing on the data to obtain first data;

[0102] A data second processing module, used for performing a second preprocessing on the first data to obtain second data, wherein the second data includes historical data and real-time data of the substation equipment status and operating environment;

[0103] The fault prediction module is used to predict equipment failures based on historical data and real-time data using machine learning algorithms, and perform equipment maintenance based on the fault analysis results.

[0104] This embodiment also provides an electronic device, which is applicable to the case of the intelligent auxiliary method for substation based on the Internet of Things, including:

[0105] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the intelligent auxiliary method for substation based on the Internet of Things as proposed in the above embodiment.

[0106] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing the intelligent assistance method for a substation based on the Internet of Things as proposed in the above embodiment is implemented.

[0107] The storage medium proposed in this embodiment and the method for realizing the intelligent assistance of a substation based on the Internet of Things proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0108] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A substation intelligent assistance method based on the Internet of Things, characterized in that: include: Acquire equipment status data and operating environment data of the substation, and perform first preprocessing on the data to obtain first data; Performing a second preprocessing on the first data to obtain second data, wherein the second data includes the acquired historical data and real-time data of the substation equipment status and operating environment; Based on the historical data and real-time data, machine learning algorithms are used to predict equipment failures, and equipment maintenance is performed based on the failure analysis results.

2. The intelligent auxiliary method for substation based on Internet of Things according to claim 1, characterized in that: Acquiring equipment status data and operating environment data of the substation includes: the equipment status data includes current, voltage, power, vibration and oil level data; the operating environment data includes temperature, humidity and light data.

3. The intelligent auxiliary method for substation based on Internet of Things according to claim 2, characterized in that: Performing a first preprocessing on the data to obtain the first data includes: performing data cleaning on the data, removing outliers and noise, and compressing the data to reduce transmission bandwidth requirements to obtain the first data.

4. The intelligent auxiliary method for substation based on Internet of Things according to claim 3, characterized in that: The first data is subjected to a second preprocessing to obtain the second data, including: using a file format standard parsing engine to read configuration information and command parameters; performing integrity check and validity verification on the first data; converting the first data into a unified format to obtain the second data; The second data includes the acquired historical data and real-time data of the substation equipment status and operating environment.

5. The intelligent auxiliary method for substation based on Internet of Things according to claim 4, characterized in that: Based on the historical data and real-time data, machine learning algorithms are used to predict equipment failures. According to the failure analysis results, equipment maintenance is performed, including: Use machine learning algorithms to build fault prediction models and combine real-time and historical data to identify fault patterns; When the predicted value exceeds the preset threshold, an alarm is triggered and the equipment fails; when the predicted value does not exceed the preset threshold, the equipment operates normally; When a device failure triggers an alarm, the warning information will be pushed to the operation and maintenance personnel.

6. The intelligent auxiliary method for substation based on Internet of Things according to claim 5, characterized in that: Also includes: When the operation and maintenance personnel receive the warning information, they dynamically adjust the maintenance cycle according to the actual operating status of the equipment; Establish spare parts inventory management system to replace components.

7. The intelligent auxiliary method for substation based on Internet of Things according to claim 6, characterized in that: Also includes: Based on the model prediction results, remotely adjust equipment parameters to perform equipment maintenance.

8. An intelligent auxiliary system for substations based on the Internet of Things, characterized in that: include: A data first processing module, used for acquiring equipment status data and operating environment data of the substation, and performing a first preprocessing on the data to obtain first data; A second data processing module, used for performing a second preprocessing on the first data to obtain second data, wherein the second data includes the acquired historical data and real-time data of the substation equipment status and operating environment; The fault prediction module is used to predict equipment failures based on the historical data and real-time data using a machine learning algorithm, and perform equipment maintenance according to the fault analysis results.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions. When the computer-executable instructions are executed by the processor, the steps of the substation intelligent assistance method based on the Internet of Things according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the substation intelligent assistance method based on the Internet of Things as described in any one of claims 1 to 7.

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