Substation equipment state dynamic prediction configuration method and system combined with multi-source characteristics

Abstract

The invention discloses a substation equipment state dynamic prediction configuration method and system combined with multi-source features, relates to the technical field of substation equipment, and comprises a substation electrical equipment operation state monitoring technology, a substation electrical equipment operation state automatic prediction method and a substation electrical equipment big data maintenance technology. In the invention, the automatic prediction method for the operation state of the electrical equipment of the transformer substation depends on an artificial big data technology, and accurate prediction of the operation state of the equipment is realized through learning and analysis of historical data, so that the fault diagnosis and prediction of the electrical equipment of the transformer substation is realized, and the fault diagnosis and prediction of the electrical equipment of the transformer substation is realized. The operation state of the equipment can be monitored in real time, early warning can be carried out in time when the equipment is abnormal, the influence of equipment faults on the whole power system is avoided, the automatic prediction method provides a reference basis for maintenance and replacement of the equipment, the service life of the equipment is prolonged, and the stability of the equipment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of substation equipment, and in particular to a method and system for dynamically predicting and configuring substation equipment status in combination with multi-source features. Background Art

[0002] With the development of power systems and the application of intelligent technologies, substations, as an important part of power systems, play an important role in power conversion, distribution and protection. However, due to the long-term operation of substation equipment and the impact of the environment, failures are inevitable. These failures may lead to abnormal operation of the power system, power outages, and even equipment damage, causing serious economic losses and safety risks.

[0003] The existing substation equipment status dynamic prediction configuration method and system combining multi-source characteristics, the traditional monitoring method mainly relies on manual inspection and regular maintenance, which is not only inefficient, but also difficult to achieve real-time monitoring and preventive maintenance, which has brought certain adverse effects on people's use process. In order to solve the shortcomings of the existing technology, we propose a substation equipment status dynamic prediction configuration method and system combining multi-source characteristics. Summary of the invention

[0005] The main purpose of the present invention is to provide a method and system for dynamic prediction and configuration of substation equipment status combining multi-source characteristics, which can effectively solve the problems in the background technology.

[0006] The present invention is implemented by the following technical solutions: a method and system for dynamic prediction and configuration of substation equipment status combined with multi-source characteristics, including:

[0007] Monitoring technology for the operation status of electrical equipment in substations;

[0008] Automatic prediction method for the operating status of electrical equipment in substations;

[0009] Big data maintenance technology for substation electrical equipment;

[0010] Fault diagnosis and prediction methods for substation electrical equipment;

[0011] Application in fault diagnosis and prediction of electrical equipment in substations.

[0012] Preferably, the technology for monitoring the operating status of substation electrical equipment includes the following steps:

[0013] Regular inspection method: formulate inspection plans and cycles, and conduct inspections strictly according to the plans, carefully check the appearance and internal conditions of each substation equipment, and promptly inspect and find problems. Inspection personnel should report them in a timely manner and repair them under the guidance of maintenance personnel to ensure the safe and stable operation of the equipment;

[0014] Monitoring equipment method: By installing sensors and monitoring devices, the operating data of the equipment is transmitted to the monitoring center in real time. Through remote access, the operating status of the equipment can be mastered at any time, and early warning can be given to help operation and maintenance personnel take corresponding measures before the equipment fails to reduce the risk of accidents. By introducing artificial big data and big data analysis technology, big data monitoring and management of electrical equipment can be achieved;

[0015] Temperature monitoring: The operation of equipment generates a lot of heat. When the temperature is too high, it will lead to serious consequences such as equipment overload, line short circuit, and fire. It is necessary to monitor the temperature of the equipment, detect temperature abnormalities in time, and take corresponding measures. Temperature monitoring relies on sensors to convert temperature into electrical signals and transmit them to the monitoring system, thereby realizing temperature monitoring of electrical equipment. Temperature monitoring of electrical equipment in substations can detect temperature abnormalities in time, take corresponding measures, and avoid accidents.

[0016] Vibration monitoring: It can detect the existence of mechanical failures or metal fatigue, take corresponding maintenance measures to prevent more serious failures of electrical equipment, and help evaluate the operating status and performance of electrical equipment. By monitoring and analyzing the vibration of electrical equipment, it can determine the normal operation of the equipment and the existence of abnormal conditions, providing an important basis for the maintenance and operation of the equipment;

[0017] Gas monitoring: monitor and analyze the gas in the substation. If there are abnormal conditions caused by arc, partial discharge, and overload, advanced sensors and monitoring devices can be used to monitor the gas composition and concentration in the substation in real time and continuously. The monitored gas composition and concentration can be analyzed to determine the problems with electrical equipment and take corresponding measures in time to prevent equipment failure. Timely and accurate monitoring and identification of gas composition can prevent equipment failure.

[0018] Preferably, the method for automatically predicting the operating status of electrical equipment in a substation comprises the following steps:

[0019] Data-driven prediction methods: timely discover potential failure risks, take necessary maintenance measures in advance, and avoid power outages caused by equipment failures. By processing and mining data, hidden patterns and trends can be identified to achieve future predictions.

[0020] Data preprocessing techniques: data cleaning, data conversion and data normalization;

[0021] Data cleaning: Eliminate outliers, missing values, and duplicate values ​​to ensure the accuracy and completeness of the data;

[0022] Data transformation: transforming raw data to fit machine learning algorithms;

[0023] Data normalization: Standardize data of different scales to avoid overfitting or underfitting of the algorithm.

[0024] Preferably, the big data maintenance technology for substation electrical equipment includes the following steps:

[0025] Operation status analysis and prediction: By installing sensors at various key locations in the substation, various data generated during the operation of the equipment can be obtained in real time. Through processing and analysis, potential faults or abnormal conditions can be discovered. Through a large number of new types obtained from data collection, various fault diagnosis and prediction models can be established. Through machine learning algorithms and artificial big data technology, training and optimization can be carried out to accurately judge and predict the operation status of the equipment. Through the fault diagnosis and prediction model, potential fault risks can be discovered in time, necessary maintenance measures can be taken in advance, and power outages caused by equipment operation failures can be avoided;

[0026] Remote monitoring and operation: Substations can realize information transmission and interaction between devices through wireless networks, which can provide higher data transmission rates and more reliable connection quality. By real-time monitoring and collecting operating data of substation electrical equipment and applying advanced algorithms for real-time analysis, abnormal conditions of equipment can be quickly discovered and corresponding processing suggestions can be provided. The remote diagnosis and operation center supports remote operation functions, and operation and maintenance personnel can remotely control the switch status and adjust parameters of the equipment through the system.

[0027] Preferably, the fault diagnosis and prediction method for substation electrical equipment comprises the following steps:

[0028] Data-driven: Machine learning methods can learn and discover patterns from large amounts of data without relying on manual experience, and can automatically extract and analyze features in the data;

[0029] High prediction accuracy: By training models, machine learning methods can accurately predict the time and location of faults, improve the accuracy of fault prediction, and take measures to repair them in advance;

[0030] Real-time monitoring: Machine learning-based methods can monitor the status and performance parameters of substation equipment in real time, detect abnormal conditions in a timely manner, and perform fault diagnosis and prediction;

[0031] Automated processing: Machine learning methods can realize automated fault diagnosis and prediction, reducing the time and cost of manual intervention and processing.

[0032] Preferably, the application in fault diagnosis and prediction of substation electrical equipment includes the following steps:

[0033] Processing complex data: Deep learning can process and analyze a variety of complex data forms including images, sounds, and texts, and can obtain more comprehensive status information of substation equipment;

[0034] Strong feature extraction capability: Deep learning networks have strong feature extraction capabilities and can automatically learn and extract key features from data without the need for manual feature extraction;

[0035] High accuracy: Deep learning methods can achieve high prediction accuracy through multi-layer neural network training, improving the accuracy of fault diagnosis and prediction;

[0036] Real-time and adaptability: Deep learning methods can achieve real-time fault diagnosis and prediction, adapt and optimize models during continuous training and learning, and improve the real-time and adaptability of predictions.

[0037] Beneficial Effects

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The automated prediction method for the operating status of electrical equipment in substations is widely used. This prediction method mainly relies on artificial big data technology, such as machine learning and deep learning. By learning and analyzing a large amount of historical data, it can accurately predict the operating status of equipment. In specific applications, this automated prediction method can not only monitor the operating status of equipment in real time, but also provide timely warnings when equipment abnormalities occur, effectively avoiding the impact of equipment failures on the entire power system. In addition, the automated prediction method can also provide important reference for equipment maintenance and replacement, thereby improving the service life and stability of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 It is the overall structure flow chart of the present invention;

[0041] Figure 2 is a flow chart of the monitoring technology of the present invention;

[0042] Figure 3 is a flow chart of the automated prediction method of the present invention;

[0043] Figure 4 is a flowchart of the big data maintenance technology of the present invention;

[0044] Figure 5 is a flow chart of the fault diagnosis and prediction method of the present invention;

[0045] Figure 6 It is an application flow chart of the fault diagnosis and prediction of the present invention. DETAILED DESCRIPTION

[0046] 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.

[0047] like Figure 1-6 As shown, a method and system for dynamic prediction configuration of substation equipment status combining multi-source characteristics include:

[0048] Monitoring technology for the operation status of electrical equipment in substations;

[0049] Automatic prediction method for the operating status of electrical equipment in substations;

[0050] Big data maintenance technology for substation electrical equipment;

[0051] Fault diagnosis and prediction methods for substation electrical equipment;

[0052] Application in fault diagnosis and prediction of electrical equipment in substations.

[0053] The technology for monitoring the operating status of substation electrical equipment includes the following steps:

[0054] Regular inspection method: formulate inspection plans and cycles, and conduct inspections strictly according to the plans, carefully check the appearance and internal conditions of each substation equipment, and promptly inspect and find problems. Inspection personnel should report them in a timely manner and repair them under the guidance of maintenance personnel to ensure the safe and stable operation of the equipment;

[0055] Monitoring equipment method: By installing sensors and monitoring devices, the operating data of the equipment is transmitted to the monitoring center in real time. Through remote access, the operating status of the equipment can be mastered at any time, and early warning can be given to help operation and maintenance personnel take corresponding measures before the equipment fails to reduce the risk of accidents. By introducing artificial big data and big data analysis technology, big data monitoring and management of electrical equipment can be achieved;

[0056] Temperature monitoring: The operation of equipment generates a lot of heat. When the temperature is too high, it will lead to serious consequences such as equipment overload, line short circuit, and fire. It is necessary to monitor the temperature of the equipment, detect temperature abnormalities in time, and take corresponding measures. Temperature monitoring relies on sensors to convert temperature into electrical signals and transmit them to the monitoring system, thereby realizing temperature monitoring of electrical equipment. Temperature monitoring of electrical equipment in substations can detect temperature abnormalities in time, take corresponding measures, and avoid accidents.

[0057] Vibration monitoring: It can detect the existence of mechanical failures or metal fatigue, take corresponding maintenance measures to prevent more serious failures of electrical equipment, and help evaluate the operating status and performance of electrical equipment. By monitoring and analyzing the vibration of electrical equipment, it can determine the normal operation of the equipment and the existence of abnormal conditions, providing an important basis for the maintenance and operation of the equipment;

[0058] Gas monitoring: monitor and analyze the gas in the substation. If there are abnormal conditions caused by arc, partial discharge, and overload, advanced sensors and monitoring devices can be used to monitor the gas composition and concentration in the substation in real time and continuously. The monitored gas composition and concentration can be analyzed to determine the problems with electrical equipment and take corresponding measures in time to prevent equipment failure. Timely and accurate monitoring and identification of gas composition can prevent equipment failure.

[0059] The method for automatically predicting the operating status of electrical equipment in a substation includes the following steps:

[0060] Data-driven prediction methods: timely discover potential failure risks, take necessary maintenance measures in advance, and avoid power outages caused by equipment failures. By processing and mining data, hidden patterns and trends can be identified to achieve future predictions.

[0061] Data preprocessing techniques: data cleaning, data conversion and data normalization;

[0062] Data cleaning: Eliminate outliers, missing values, and duplicate values ​​to ensure the accuracy and completeness of the data;

[0063] Data transformation: transforming raw data to fit machine learning algorithms;

[0064] Data normalization: Standardize data of different scales to avoid overfitting or underfitting of the algorithm.

[0065] The big data maintenance technology for substation electrical equipment includes the following steps:

[0066] Operation status analysis and prediction: By installing sensors at various key locations in the substation, various data generated during the operation of the equipment can be obtained in real time. Through processing and analysis, potential faults or abnormal conditions can be discovered. Through a large number of new types obtained from data collection, various fault diagnosis and prediction models can be established. Through machine learning algorithms and artificial big data technology, training and optimization can be carried out to accurately judge and predict the operation status of the equipment. Through the fault diagnosis and prediction model, potential fault risks can be discovered in time, necessary maintenance measures can be taken in advance, and power outages caused by equipment operation failures can be avoided;

[0067] Remote monitoring and operation: Substations can realize information transmission and interaction between devices through wireless networks, which can provide higher data transmission rates and more reliable connection quality. By real-time monitoring and collecting operating data of substation electrical equipment and applying advanced algorithms for real-time analysis, abnormal conditions of equipment can be quickly discovered and corresponding processing suggestions can be provided. The remote diagnosis and operation center supports remote operation functions, and operation and maintenance personnel can remotely control the switch status and adjust parameters of the equipment through the system.

[0068] The fault diagnosis and prediction method for substation electrical equipment includes the following steps:

[0069] Data-driven: Machine learning methods can learn and discover patterns from large amounts of data without relying on manual experience, and can automatically extract and analyze features in the data;

[0070] High prediction accuracy: By training models, machine learning methods can accurately predict the time and location of faults, improve the accuracy of fault prediction, and take measures to repair them in advance;

[0071] Real-time monitoring: Machine learning-based methods can monitor the status and performance parameters of substation equipment in real time, detect abnormal conditions in a timely manner, and perform fault diagnosis and prediction;

[0072] Automated processing: Machine learning methods can realize automated fault diagnosis and prediction, reducing the time and cost of manual intervention and processing.

[0073] The application of fault diagnosis and prediction of substation electrical equipment includes the following steps:

[0074] Processing complex data: Deep learning can process and analyze a variety of complex data forms including images, sounds, and texts, and can obtain more comprehensive status information of substation equipment;

[0075] Strong feature extraction capability: Deep learning networks have strong feature extraction capabilities and can automatically learn and extract key features from data without the need for manual feature extraction;

[0076] High accuracy: Deep learning methods can achieve high prediction accuracy through multi-layer neural network training, improving the accuracy of fault diagnosis and prediction;

[0077] Real-time and adaptability: Deep learning methods can achieve real-time fault diagnosis and prediction, adapt and optimize models during continuous training and learning, and improve the real-time and adaptability of predictions.

[0078] In summary, the advantages of the present invention are:

[0079] The automated prediction method for the operating status of electrical equipment in substations is widely used. This prediction method mainly relies on artificial big data technology, such as machine learning and deep learning. By learning and analyzing a large amount of historical data, it can accurately predict the operating status of equipment. In specific applications, this automated prediction method can not only monitor the operating status of equipment in real time, but also provide timely warnings when equipment abnormalities occur, effectively avoiding the impact of equipment failures on the entire power system. In addition, the automated prediction method can also provide important reference for equipment maintenance and replacement, thereby improving the service life and stability of the equipment.

[0080] 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.

[0081] 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 method and system for dynamic prediction and configuration of substation equipment status combining multi-source characteristics, characterized in that: include: Monitoring technology for the operation status of electrical equipment in substations; Automatic prediction method for the operating status of electrical equipment in substations; Big data maintenance technology for substation electrical equipment; Fault diagnosis and prediction methods for substation electrical equipment; Application in fault diagnosis and prediction of electrical equipment in substations.

2. The method and system for dynamic prediction and configuration of substation equipment status combining multi-source features according to claim 1 is characterized in that: The technology for monitoring the operating status of electrical equipment in a substation comprises the following steps: Regular inspection method: formulate inspection plans and cycles, and conduct inspections strictly according to the plans, carefully check the appearance and internal conditions of each substation equipment, and promptly inspect and find problems. Inspection personnel should report them in a timely manner and repair them under the guidance of maintenance personnel to ensure the safe and stable operation of the equipment; Monitoring equipment method: By installing sensors and monitoring devices, the operating data of the equipment is transmitted to the monitoring center in real time. Through remote access, the operating status of the equipment can be mastered at any time, and early warning can be given to help operation and maintenance personnel take corresponding measures before the equipment fails to reduce the risk of accidents. By introducing artificial big data and big data analysis technology, big data monitoring and management of electrical equipment can be achieved; Temperature monitoring: The operation of equipment generates a lot of heat. When the temperature is too high, it will lead to serious consequences such as equipment overload, line short circuit, and fire. It is necessary to monitor the temperature of the equipment, detect temperature abnormalities in time, and take corresponding measures. Temperature monitoring relies on sensors to convert temperature into electrical signals and transmit them to the monitoring system, thereby realizing temperature monitoring of electrical equipment. Temperature monitoring of electrical equipment in substations can detect temperature abnormalities in time, take corresponding measures, and avoid accidents. Vibration monitoring: It can detect the existence of mechanical failures or metal fatigue, take corresponding maintenance measures to prevent more serious failures of electrical equipment, and help evaluate the operating status and performance of electrical equipment. By monitoring and analyzing the vibration of electrical equipment, it can determine the normal operation of the equipment and the existence of abnormal conditions, providing an important basis for the maintenance and operation of the equipment; Gas monitoring: monitor and analyze the gas in the substation. If there are abnormal conditions caused by arc, partial discharge, and overload, advanced sensors and monitoring devices can be used to monitor the gas composition and concentration in the substation in real time and continuously. The monitored gas composition and concentration can be analyzed to determine the problems with electrical equipment and take corresponding measures in time to prevent equipment failure. Timely and accurate monitoring and identification of gas composition can prevent equipment failure.

3. The method and system for dynamic prediction and configuration of substation equipment status combining multi-source features according to claim 1 is characterized in that: The method for automatically predicting the operating status of electrical equipment in a substation comprises the following steps: Data-driven prediction methods: timely discover potential failure risks, take necessary maintenance measures in advance, and avoid power outages caused by equipment failures. By processing and mining data, hidden patterns and trends can be identified to achieve future predictions. Data preprocessing techniques: data cleaning, data conversion and data normalization; Data cleaning: Eliminate outliers, missing values, and duplicate values ​​to ensure the accuracy and completeness of the data; Data transformation: transforming raw data to fit machine learning algorithms; Data normalization: Standardize data of different scales to avoid overfitting or underfitting of the algorithm.

4. The method and system for dynamic prediction and configuration of substation equipment status combining multi-source features according to claim 1 is characterized in that: The big data maintenance technology for substation electrical equipment includes the following steps: Operation status analysis and prediction: By installing sensors at various key locations in the substation, various data generated during the operation of the equipment can be obtained in real time. Through processing and analysis, potential faults or abnormal conditions can be discovered. Through a large number of new types obtained from data collection, various fault diagnosis and prediction models can be established. Through machine learning algorithms and artificial big data technology, training and optimization can be carried out to accurately judge and predict the operation status of the equipment. Through the fault diagnosis and prediction model, potential fault risks can be discovered in time, necessary maintenance measures can be taken in advance, and power outages caused by equipment operation failures can be avoided; Remote monitoring and operation: Substations can realize information transmission and interaction between devices through wireless networks, which can provide higher data transmission rates and more reliable connection quality. By real-time monitoring and collecting operating data of substation electrical equipment and applying advanced algorithms for real-time analysis, abnormal conditions of equipment can be quickly discovered and corresponding processing suggestions can be provided. The remote diagnosis and operation center supports remote operation functions, and operation and maintenance personnel can remotely control the switch status and adjust parameters of the equipment through the system.

5. The method and system for dynamic prediction and configuration of substation equipment status combining multi-source features according to claim 1 is characterized in that: The method for fault diagnosis and prediction of substation electrical equipment comprises the following steps: Data-driven: Machine learning methods can learn and discover patterns from large amounts of data without relying on manual experience, and can automatically extract and analyze features in the data; High prediction accuracy: By training models, machine learning methods can accurately predict the time and location of faults, improve the accuracy of fault prediction, and take measures to repair them in advance; Real-time monitoring: Machine learning-based methods can monitor the status and performance parameters of substation equipment in real time, detect abnormal conditions in a timely manner, and perform fault diagnosis and prediction; Automated processing: Machine learning methods can realize automated fault diagnosis and prediction, reducing the time and cost of manual intervention and processing.

6. The method and system for dynamic prediction and configuration of substation equipment status combining multi-source features according to claim 1 is characterized in that: The application of fault diagnosis and prediction of substation electrical equipment includes the following steps: Processing complex data: Deep learning can process and analyze a variety of complex data forms including images, sounds, and texts, and can obtain more comprehensive status information of substation equipment; Strong feature extraction capability: Deep learning networks have strong feature extraction capabilities and can automatically learn and extract key features from data without the need for manual feature extraction; High accuracy: Deep learning methods can achieve high prediction accuracy through multi-layer neural network training, improving the accuracy of fault diagnosis and prediction; Real-time and adaptability: Deep learning methods can achieve real-time fault diagnosis and prediction, adapt and optimize models during continuous training and learning, and improve the real-time and adaptability of predictions.

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