Electrical equipment operation and maintenance management system based on power supply and distribution project

Abstract

The invention provides an electrical equipment operation and maintenance management system based on a power supply and distribution project, and relates to the technical field of electrical equipment management. The electrical equipment operation and maintenance management system based on the power supply and distribution project comprises a data acquisition layer which acquires operation data, including related parameters of current, voltage, temperature and humidity, of electrical equipment in real time through a sensor and an intelligent instrument; the data transmission layer is used for transmitting the acquired data to a central server by utilizing a wireless communication technology; according to the data processing layer, the central server stores, analyzes and processes the received data, and fault prediction and health state evaluation are carried out by adopting big data analysis and a machine learning algorithm. By using a machine learning algorithm, analyzing historical data and establishing an equipment fault prediction model, potential faults can be identified in advance, a fault diagnosis report can be provided, maintenance personnel can be guided to maintain, the reliability of equipment is improved, and the service life of the equipment is prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment management, and in particular to an electrical equipment operation and maintenance management system based on power supply and distribution engineering. Background Art

[0002] With the continuous growth of global energy demand and the increasing complexity of power systems, the scale of power supply and distribution projects has continued to expand, and the number and types of electrical equipment have also continued to increase. With the rapid development of information technologies such as the Internet of Things, big data, cloud computing and artificial intelligence, intelligent operation and maintenance management has become an inevitable trend in the development of power supply and distribution projects. By utilizing these advanced technologies, intelligent operation and maintenance management systems can achieve real-time monitoring, fault prediction, health status assessment and remote control of electrical equipment, thereby improving the reliability and operation and maintenance efficiency of the power system and reducing operation and maintenance costs.

[0003] At present, some power companies and research institutions at home and abroad have carried out research and application of intelligent operation and maintenance management systems, and have achieved certain results. Some systems have realized remote monitoring of electrical equipment and basic data collection functions, but there are still shortcomings. The existing systems have limited data processing and analysis capabilities, and are unable to perform deep data mining and fault prediction, and are difficult to meet the operation and maintenance needs of complex power systems. Some systems have only realized data collection and simple display, lacking intelligent fault prediction, health status assessment and decision support functions. There is a lack of unified interfaces and data standards between different systems, resulting in low system integration and difficulties in data sharing and function expansion. Therefore, technical personnel in this field provide an electrical equipment operation and maintenance management system based on power supply and distribution projects to solve the problems raised in the above technical solutions. Summary of the invention

[0004] 1. Technical issues to be solved

[0005] In view of the deficiencies in the prior art, the present invention provides an electrical equipment operation and maintenance management system based on power supply and distribution engineering, which solves the problems of insufficient data processing capability, low intelligence and low system integration in the prior system.

[0006] (II) Technical solution

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electrical equipment operation and maintenance management system based on power supply and distribution engineering, comprising:

[0008] The data collection layer collects the operating data of electrical equipment in real time through sensors and smart meters, including relevant parameters of current, voltage, temperature and humidity;

[0009] The data transmission layer uses wireless communication technology to transmit the collected data to the central server;

[0010] Data processing layer: The central server stores, analyzes and processes the received data, and uses big data analysis and machine learning algorithms to predict faults and assess health status;

[0011] The user interface layer provides web and mobile applications for operation and maintenance personnel to monitor device status in real time, receive alarm information, view historical data, and generate reports.

[0012] Preferably, the data collection layer comprises the following steps:

[0013] S1. Sensor selection and installation. According to the type of electrical equipment and monitoring requirements, select appropriate sensor types, including current sensors, voltage sensors, temperature sensors and humidity sensors. Install sensors at key nodes of the power supply and distribution project, including but not limited to transformers, distribution cabinets, switchgear and cable connectors. Smart meters have data storage and preliminary processing functions, can convert collected data into digital signals, and transmit them through communication modules;

[0014] S2. Data collection frequency and accuracy. Different data collection frequencies are set according to the importance and change speed of the monitored parameters. The specific collection formula is:

[0015]

[0016] Among them, D daily is the daily data volume, F i is the collection frequency of the i-th data, S i is the size of the i-th type of data, n is the number of data types, and 86400 is the number of seconds in a day;

[0017] S3. Data collection and preprocessing. The sensor collects the operating data of the electrical equipment in real time and performs preliminary processing through the data acquisition module. Data preprocessing includes data cleaning, filtering and calibration. The relevant calculation formula is:

[0018] Data cleaning:

[0019] V valid =V raw ×(1-R error )

[0020] Among them, V valid is the effective data volume, V raw is the original data volume, R error is the data error rate;

[0021] Data filtering:

[0022] V filtered (t) = α × Vraω (t)+(1-α)×V filtered (t-1)

[0023] Among them, V filtered (t) is the filtered data, V raω (t) is the original data, α is the filter coefficient;

[0024] Data calibration:

[0025] V calibrated =a×V raω +b

[0026] Among them, V calibrated is the calibrated data, V raω is the original data, a and b are the calibration coefficients.

[0027] Preferably, the data transmission layer comprises the following steps:

[0028] S1. Selection of wireless communication technology: According to the actual situation of the power supply and distribution project, select appropriate wireless communication technology, including LoRa, NB-IoT, and Zigbee. The specific calculation formula for calculating the coverage of network nodes is as follows:

[0029]

[0030] Among them, R coverage is the coverage, P tx is the transmission power, G tx is the transmitting antenna gain, G rx is the receiving antenna gain, λ is the wavelength, P rx is the receiving sensitivity;

[0031] S2. Network architecture design involves a reasonable network architecture to ensure the reliability and real-time performance of data transmission, using a star, tree or mesh network structure, selecting according to the site environment and equipment distribution, setting up relay nodes, expanding network coverage, and ensuring the stability of data transmission;

[0032] S3. Data transmission protocol: Use standardized data transmission protocol to ensure data security and reliability. Calculate the overhead of different protocols and choose the protocol with the lowest overhead. The calculation formula is:

[0033]

[0034] Among them, O protocol is the protocol overhead, D header is the protocol header size, D packet is the packet size;

[0035] S4. Data security and encryption: Encrypt the transmitted data, use AES and DES encryption algorithms to ensure data security, use TLS / SSL protocol to encrypt data transmission to prevent data from being stolen or tampered with, and set up access control mechanisms to ensure that only authorized users can access the data.

[0036] Preferably, the data processing layer comprises the following steps:

[0037] S1. Data storage and management: A distributed database system is used to store and manage the collected data. The database system should have high availability, high scalability and high performance, and be able to process large-scale data. The data storage format should use a structured data format to facilitate subsequent data processing and analysis.

[0038] S2. Data processing and analysis: A big data processing framework is used to perform distributed processing and analysis on data. The data processing process includes data cleaning, data conversion, and data loading. Data analysis includes statistical analysis and machine learning analysis. The specific calculation steps are as follows:

[0039] Missing value filling:

[0040]

[0041] Among them, V filled is the value after filling, V i is the valid data value, n is the number of valid data;

[0042] Data normalization:

[0043]

[0044] Among them, V normalized is the normalized value, V is the original data value, V min is the minimum value of the data, V max is the maximum value of the data;

[0045] S3. Fault prediction and health status assessment: Use machine learning algorithms to analyze historical data, establish equipment fault prediction models, use historical data to train the models, adjust model parameters, improve prediction accuracy, use test data to evaluate the models, calculate the model's accuracy, recall rate, and F1 value indicators, and evaluate the health status of the equipment based on the prediction model to predict the remaining life of the equipment.

[0046] S4. Real-time data processing uses stream processing technology to quickly process and analyze real-time data. The stream processing process includes data collection, data transmission, data processing and data storage. Real-time data processing should have the characteristics of low latency and high throughput to ensure the real-time and reliability of data.

[0047] Preferably, the user interface layer comprises the following steps:

[0048] S1. Web application, develop a Web-based monitoring and management platform, provide a user-friendly interface for operation and maintenance personnel to monitor equipment status in real time, receive alarm information, view historical data and generate reports. It should have real-time monitoring, alarm management, historical data query, report generation and user management, real-time display of equipment operation status, including current, voltage, temperature and humidity parameters, involving alarm thresholds, receive alarm information, view alarm history records, query equipment historical operation data, generate trend charts, generate various reports, manage user permissions, and ensure data security;

[0049] S2. Mobile application: Develop mobile applications to facilitate operation and maintenance personnel to monitor equipment status anytime and anywhere. Mobile applications have the following functions: real-time monitoring, alarm notification, historical data query and remote control;

[0050] S3. User interface design adopts responsive design to ensure that the interfaces of web and mobile applications can be well displayed on different devices. The interface design is concise and clear, the operation is simple and easy to use, and it provides rich charts and visualization tools to facilitate users to view and analyze data.

[0051] (III) Beneficial effects

[0052] The present invention provides an electrical equipment operation and maintenance management system based on power supply and distribution engineering. It has the following beneficial effects:

[0053] 1. In the present invention, the operating data of electrical equipment is collected in real time through sensors and intelligent instruments, which avoids the tediousness and inefficiency of manual inspections, can quickly process and analyze massive data, extract valuable information, and improve operation and maintenance efficiency. The system can monitor the operating status of the equipment in real time, and automatically trigger an alarm when the parameters are abnormal, promptly notify relevant personnel to handle it, and reduce fault response time.

[0054] 2. In the present invention, the data transmission layer adopts redundant transmission paths and error detection mechanisms to ensure the reliability of data transmission. The system has fault self-healing capabilities and can automatically switch to backup paths or devices in the event of network failure or device failure to ensure continuous operation of the system.

[0055] 3. In the present invention, by utilizing machine learning algorithms to analyze historical data and establish an equipment failure prediction model, it is possible to identify potential failures in advance, provide fault diagnosis reports, guide maintenance personnel to perform repairs, and improve the reliability and service life of the equipment.

[0056] 4. In the present invention, by adopting modular design, each functional module is independent of each other, easy to expand and maintain, the system supports multiple communication protocols and data formats, convenient for integration and data exchange with other systems, the system provides Web and mobile applications, users can access the system through various devices such as PC, tablet and mobile phone, and monitor the device status anytime and anywhere. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic diagram of the overall system of the present invention;

[0058] Figure 2 It is a schematic diagram of the system flow of the data acquisition layer in the present invention;

[0059] Figure 3 It is a schematic diagram of the system flow of the data transmission layer in the present invention;

[0060] Figure 4 It is a schematic diagram of the system flow of the data processing layer in the present invention;

[0061] Figure 5 It is a flow chart of the user interface layer in the present invention. DETAILED DESCRIPTION

[0062] 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.

[0063] Embodiment 1:

[0064] like Figure 1-5 As shown, an embodiment of the present invention provides an electrical equipment operation and maintenance management system based on a power supply and distribution project, comprising:

[0065] The data collection layer collects the operating data of electrical equipment in real time through sensors and smart meters, including relevant parameters of current, voltage, temperature and humidity;

[0066] The data transmission layer uses wireless communication technology to transmit the collected data to the central server;

[0067] Data processing layer: The central server stores, analyzes and processes the received data, and uses big data analysis and machine learning algorithms to predict faults and assess health status;

[0068] The user interface layer provides web and mobile applications for operation and maintenance personnel to monitor device status in real time, receive alarm information, view historical data, and generate reports.

[0069] The data collection layer includes the following steps:

[0070] S1. Sensor selection and installation. According to the type of electrical equipment and monitoring requirements, select appropriate sensor types, including current sensors, voltage sensors, temperature sensors and humidity sensors. Install sensors at key nodes of the power supply and distribution project, including but not limited to transformers, distribution cabinets, switchgear and cable connectors. Smart meters have data storage and preliminary processing functions, can convert collected data into digital signals, and transmit them through communication modules;

[0071] S2. Data collection frequency and accuracy. Different data collection frequencies are set according to the importance and change speed of the monitored parameters. The specific collection formula is:

[0072]

[0073] Among them, D daily is the daily data volume, F i is the collection frequency of the i-th data, S i is the size of the i-th type of data, n is the number of data types, and 86400 is the number of seconds in a day;

[0074] S3. Data collection and preprocessing. The sensor collects the operating data of the electrical equipment in real time and performs preliminary processing through the data acquisition module. Data preprocessing includes data cleaning, filtering and calibration. The relevant calculation formula is:

[0075] Data cleaning:

[0076] V valid =V raw ×(1-R error )

[0077] Among them, V valid is the effective data volume, V raw is the original data volume, R error is the data error rate;

[0078] Data filtering:

[0079] V filtered (t) = α × V raω (t)+(1-α)×V filtered (t-1)

[0080] Among them, V filtered (t) is the filtered data, V raω (t) is the original data, α is the filter coefficient;

[0081] Data calibration:

[0082] V calibrated =α×V raω +b

[0083] Among them, V calibrated is the calibrated data, V raω is the original data, α and b are the calibration coefficients.

[0084] The data transfer layer includes the following steps:

[0085] S1. Selection of wireless communication technology: According to the actual situation of the power supply and distribution project, select appropriate wireless communication technology, including LoRa, NB-IoT, and Zigbee. The specific calculation formula for calculating the coverage of network nodes is as follows:

[0086]

[0087] Among them, R coverage is the coverage, P tx is the transmission power, G tx is the transmitting antenna gain, G rx is the receiving antenna gain, λ is the wavelength, P rx is the receiving sensitivity;

[0088] S2. Network architecture design involves a reasonable network architecture to ensure the reliability and real-time performance of data transmission, using a star, tree or mesh network structure, selecting according to the site environment and equipment distribution, setting up relay nodes, expanding network coverage, and ensuring the stability of data transmission;

[0089] S3. Data transmission protocol: Use standardized data transmission protocol to ensure data security and reliability. Calculate the overhead of different protocols and choose the protocol with the lowest overhead. The calculation formula is:

[0090]

[0091] Among them, O protocol is the protocol overhead, D header is the protocol header size, D packet is the packet size;

[0092] S4. Data security and encryption: Encrypt the transmitted data, use AES and DES encryption algorithms to ensure data security, use TLS / SSL protocol to encrypt data transmission to prevent data from being stolen or tampered with, and set up access control mechanisms to ensure that only authorized users can access the data.

[0093] The data processing layer includes the following steps:

[0094] S1. Data storage and management: A distributed database system is used to store and manage the collected data. The database system should have high availability, high scalability and high performance, and be able to process large-scale data. The data storage format should use a structured data format to facilitate subsequent data processing and analysis.

[0095] S2. Data processing and analysis: A big data processing framework is used to perform distributed processing and analysis on data. The data processing process includes data cleaning, data conversion, and data loading. Data analysis includes statistical analysis and machine learning analysis. The specific calculation steps are as follows:

[0096] Missing value filling:

[0097]

[0098] Among them, V filled is the value after filling, V i is the valid data value, n is the number of valid data;

[0099] Data normalization:

[0100]

[0101] Among them, V normalized is the normalized value, V is the original data value, V min is the minimum value of the data, V max is the maximum value of the data;

[0102] S3. Fault prediction and health status assessment: Use machine learning algorithms to analyze historical data, establish equipment fault prediction models, use historical data to train the models, adjust model parameters, improve prediction accuracy, use test data to evaluate the models, calculate the model's accuracy, recall rate, and F1 value indicators, and evaluate the health status of the equipment based on the prediction model to predict the remaining life of the equipment.

[0103] S4. Real-time data processing uses stream processing technology to quickly process and analyze real-time data. The stream processing process includes data collection, data transmission, data processing and data storage. Real-time data processing should have the characteristics of low latency and high throughput to ensure the real-time and reliability of data.

[0104] The user interface layer includes the following steps:

[0105] S1. Web application, develop a Web-based monitoring and management platform, provide a user-friendly interface for operation and maintenance personnel to monitor equipment status in real time, receive alarm information, view historical data and generate reports. It should have real-time monitoring, alarm management, historical data query, report generation and user management, real-time display of equipment operation status, including current, voltage, temperature and humidity parameters, involving alarm thresholds, receive alarm information, view alarm history records, query equipment historical operation data, generate trend charts, generate various reports, manage user permissions, and ensure data security;

[0106] S2. Mobile application: Develop mobile applications to facilitate operation and maintenance personnel to monitor equipment status anytime and anywhere. Mobile applications have the following functions: real-time monitoring, alarm notification, historical data query and remote control;

[0107] S3. User interface design adopts responsive design to ensure that the interfaces of web and mobile applications can be well displayed on different devices. The interface design is concise and clear, the operation is simple and easy to use, and it provides rich charts and visualization tools to facilitate users to view and analyze data.

[0108] 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. An electrical equipment operation and maintenance management system based on power supply and distribution engineering, characterized by: include: The data collection layer collects the operating data of electrical equipment in real time through sensors and smart meters, including relevant parameters of current, voltage, temperature and humidity; The data transmission layer uses wireless communication technology to transmit the collected data to the central server; Data processing layer: The central server stores, analyzes and processes the received data, and uses big data analysis and machine learning algorithms to predict faults and assess health status; The user interface layer provides web and mobile applications for operation and maintenance personnel to monitor device status in real time, receive alarm information, view historical data, and generate reports.

2. According to claim 1, an electrical equipment operation and maintenance management system based on power supply and distribution engineering is characterized by: The data collection layer includes the following steps: S1. Sensor selection and installation. According to the type of electrical equipment and monitoring requirements, select appropriate sensor types, including current sensors, voltage sensors, temperature sensors and humidity sensors. Install sensors at key nodes of the power supply and distribution project, including but not limited to transformers, distribution cabinets, switchgear and cable connectors. Smart meters have data storage and preliminary processing functions, can convert collected data into digital signals, and transmit them through communication modules; S2. Data collection frequency and accuracy. Different data collection frequencies are set according to the importance and change speed of the monitored parameters. The specific collection formula is: Among them, D daily is the daily data volume, F i is the collection frequency of the i-th data, S i is the size of the i-th type of data, n is the number of data types, and 86400 is the number of seconds in a day; S3. Data collection and preprocessing. The sensor collects the operating data of the electrical equipment in real time and performs preliminary processing through the data acquisition module. Data preprocessing includes data cleaning, filtering and calibration. The relevant calculation formula is: Data cleaning: V valid =V raw ×(1-R error ) Among them, V valid is the effective data volume, V raw is the original data volume, R error is the data error rate; Data filtering: V filtered (t)=α×V raω (t)+(1-α)×V filtered (t-1) Among them, V filtered (t) is the filtered data, V raω (t) is the original data, α is the filter coefficient; Data calibration: V calibrated =a×V raω +b Among them, V calibrated is the data after calibration, V raω is the original data, a and b are the calibration coefficients.

3. According to claim 1, an electrical equipment operation and maintenance management system based on power supply and distribution engineering is characterized in that: The data transmission layer includes the following steps: S1. Selection of wireless communication technology: According to the actual situation of the power supply and distribution project, select appropriate wireless communication technology, including LoRa, NB-loT, Zigbee, and calculate the coverage of network nodes. The specific calculation formula is as follows: Among them, R coverage is the coverage, P tx is the transmission power, G tx is the transmitting antenna gain, G rx is the receiving antenna gain, λ is the wavelength, P rx is the receiving sensitivity; S2. Network architecture design involves a reasonable network architecture to ensure the reliability and real-time performance of data transmission, using a star, tree or mesh network structure, selecting according to the site environment and equipment distribution, setting up relay nodes, expanding network coverage, and ensuring the stability of data transmission; S3. Data transmission protocol: Use standardized data transmission protocol to ensure data security and reliability. Calculate the overhead of different protocols and choose the protocol with the lowest overhead. The calculation formula is: Among them, O protocol is the protocol overhead, D header is the protocol header size, D packet is the packet size; S4. Data security and encryption: Encrypt the transmitted data, use AES and DES encryption algorithms to ensure data security, use TLS / SSL protocol to encrypt data transmission to prevent data from being stolen or tampered with, and set up access control mechanisms to ensure that only authorized users can access the data.

4. The electrical equipment operation and maintenance management system based on power supply and distribution engineering according to claim 1 is characterized by: The data processing layer includes the following steps: S1. Data storage and management: A distributed database system is used to store and manage the collected data. The database system should have high availability, high scalability and high performance, and be able to process large-scale data. The data storage format should use a structured data format to facilitate subsequent data processing and analysis. S2. Data processing and analysis: A big data processing framework is used to perform distributed processing and analysis on data. The data processing process includes data cleaning, data conversion, and data loading. Data analysis includes statistical analysis and machine learning analysis. The specific calculation steps are as follows: Missing value filling: Among them, V filled is the value after filling, V i is the valid data value, n is the number of valid data; Data normalization: Among them, V normalized is the normalized value, V is the original data value, V min is the minimum value of the data, V max is the maximum value of the data; S3. Fault prediction and health status assessment: Use machine learning algorithms to analyze historical data, establish equipment fault prediction models, use historical data to train the models, adjust model parameters, improve prediction accuracy, use test data to evaluate the models, calculate the model's accuracy, recall rate, and F1 value indicators, and evaluate the health status of the equipment based on the prediction model to predict the remaining life of the equipment. S4. Real-time data processing uses stream processing technology to quickly process and analyze real-time data. The stream processing process includes data collection, data transmission, data processing and data storage. Real-time data processing should have the characteristics of low latency and high throughput to ensure the real-time and reliability of data.

5. The electrical equipment operation and maintenance management system based on power supply and distribution engineering according to claim 1, characterized in that: The user interface layer includes the following steps: S1. Web application, develop a Web-based monitoring and management platform, provide a user-friendly interface for operation and maintenance personnel to monitor equipment status in real time, receive alarm information, view historical data and generate reports. It should have real-time monitoring, alarm management, historical data query, report generation and user management, real-time display of equipment operation status, including current, voltage, temperature and humidity parameters, involving alarm thresholds, receive alarm information, view alarm history records, query equipment historical operation data, generate trend charts, generate various reports, manage user permissions, and ensure data security; S2. Mobile application: Develop mobile applications to facilitate operation and maintenance personnel to monitor equipment status anytime and anywhere. Mobile applications have the following functions: real-time monitoring, alarm notification, historical data query and remote control; S3. User interface design adopts responsive design to ensure that the interfaces of web and mobile applications can be well displayed on different devices. The interface design is concise and clear, the operation is simple and easy to use, and it provides rich charts and visualization tools to facilitate users to view and analyze data.

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