Distribution network topology data intelligent monitoring system based on enterprise platform and implementation method thereof

Abstract

The invention discloses a distribution network topology data intelligent monitoring system based on an enterprise platform and an implementation method thereof, and the system comprises a data collection module which is used for collecting topology data in a distribution network; the data transmission module is connected with the data acquisition module and transmits the acquired topological data to an enterprise platform; and the enterprise middle station is used for receiving and storing the topological data and analyzing and processing the topological data. According to the system, the intelligent sensor is adopted, the system has the functions of self-calibration, self-diagnosis and self-adaption to environment change, data accuracy and reliability are ensured, wireless cooperative verification can be achieved, data transmission is efficient, stable and safe, advanced technologies are fused, and a data analysis unit of an enterprise platform accurately recognizes a mode and improves efficiency by means of a deep learning algorithm and an intelligent optimization algorithm; and external data prediction can be combined, the intelligent monitoring module can perform real-time display and intelligent decision making, multi-user and personalized customization are supported, the intelligent level of distribution network monitoring is greatly improved, and stable operation of the distribution network is guaranteed.

Description

Technical Field

[0001] The present invention relates to the field of smart grid technology, and in particular to a distribution network topology data intelligent monitoring system based on an enterprise middle station and an implementation method thereof. Background Art

[0002] In today's era of rapid development of power systems, the distribution network is a key link in power transmission, and the stability and reliability of its topology are crucial. Traditional distribution network monitoring systems often have limitations in data collection. Data collection is not comprehensive and the accuracy is not high, making it difficult to accurately reflect the actual operating status of the distribution network in real time. At the same time, the data transmission process faces problems such as low efficiency, poor stability, and insufficient security, which can easily lead to data loss or tampering, affecting the accuracy and reliability of monitoring.

[0003] In the analysis and processing link, the algorithms of traditional systems are relatively simple, and it is difficult to cope with the complex and changeable distribution network topology data. The ability to identify potential problems and abnormal patterns is limited, and it cannot meet the needs of modern power systems for intelligent monitoring. In addition, traditional monitoring systems are relatively weak in intelligent decision-making. When abnormal situations occur, manual analysis and processing are often required, which is slow to respond and inefficient.

[0004] The emergence of enterprise middle platform technology has brought new opportunities for distribution network topology data monitoring. Enterprise middle platform has powerful data integration, analysis and processing and resource sharing capabilities, which can effectively integrate various data resources in the distribution network and provide strong support for intelligent monitoring. However, the existing distribution network topology data monitoring system based on enterprise middle platform still has some shortcomings. For example, in the data acquisition module, the performance and function of the sensor need to be improved. Summary of the invention

[0005] The purpose of the present invention is to provide an intelligent monitoring system for distribution network topology data based on an enterprise middle station and an implementation method thereof, so as to solve the problems in the above-mentioned background technology.

[0006] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a distribution network topology data intelligent monitoring system based on an enterprise middle station, comprising a data acquisition module, a data transmission module, an enterprise middle station and an intelligent monitoring module;

[0007] The data acquisition module is used to collect topological data in the distribution network. The module is composed of high-precision and high-sensitivity intelligent sensors, which are distributed at various corresponding positions of the distribution network, so as to realize real-time and accurate collection of multi-dimensional topological data such as voltage, current, and power. The intelligent sensor also has the function of self-adapting to environmental changes and can automatically adjust the collection parameters according to different environmental conditions such as temperature, humidity, and electromagnetic field to ensure the accuracy and reliability of the data;

[0008] The data transmission module is connected to the data acquisition module to transmit the collected topological data to the enterprise middle station. The module adopts advanced wired and wireless hybrid communication technology to ensure the efficiency and stability of data transmission. It also has data encryption and error correction functions to ensure the security and integrity of data. The data transmission module also includes quantum encryption technology to further improve the security of data transmission and prevent data from being stolen or tampered. The quantum encryption technology uses the principles of quantum mechanics to ensure the security of the key.

[0009] The enterprise middle station receives and stores the topology data, and analyzes and processes the topology data. The enterprise middle station includes a data storage unit, a data analysis unit and a data management unit. The data storage unit adopts a distributed storage architecture, which can quickly store and retrieve a large amount of topology data; the data analysis unit uses an innovative deep learning algorithm model to perform in-depth analysis of the topology data. The algorithm model is trained with a large amount of historical data and can automatically identify the normal mode and abnormal mode of the distribution network topology.

[0010] Preferably, the wired communication method of the data transmission module includes high-speed Ethernet technology, and the wireless communication method adopts 5G communication technology. The data transmission module also includes an intelligent routing function, which can automatically select the optimal transmission path according to network conditions.

[0011] Preferably, the data analysis unit of the enterprise middle station also includes a data prediction function, which can predict the future distribution network topology status based on historical data and current data trends, providing a basis for taking preventive measures in advance.

[0012] Preferably, the intelligent monitoring module includes a monitoring display unit and an alarm unit. The monitoring display unit is used to display the distribution network topology data and monitoring status in real time. The monitoring display unit supports simultaneous access by multiple users and can be customized according to user needs to display different information and charts. The alarm unit is used to issue an audible and visual alarm when an abnormality is detected and to notify relevant personnel in a timely manner.

[0013] The above-mentioned method for implementing the distribution network topology data intelligent monitoring system based on the enterprise middle station includes the following steps:

[0014] The topological data in the distribution network is collected by the smart sensor in the data collection module. The smart sensor performs self-calibration and self-diagnosis while collecting data to ensure the accuracy of the data; the smart sensor also automatically adjusts the collection parameters according to environmental changes, and cooperates with other sensors to achieve mutual verification of data and fault diagnosis;

[0015] The data transmission module is used to transmit the collected topological data to the enterprise middle station. The data is encrypted and error-corrected during transmission. At the same time, quantum encryption technology is introduced to improve the security of data transmission. The data transmission module also has an intelligent routing function and can automatically select the optimal transmission path according to the network conditions.

[0016] The enterprise middle station receives and stores the topological data, and the data analysis unit uses a deep learning algorithm model to analyze and process the topological data, including data preprocessing, feature extraction, model training and optimization.

[0017] The beneficial effects of the present invention are as follows:

[0018] In the present invention, in terms of data collection, a specific type of intelligent sensor is used, which has the functions of self-calibration, self-diagnosis and adaptive environmental changes, ensuring the accuracy and reliability of data collection. The sensors can also collaborate through wireless communication to perform data verification and fault diagnosis. In data transmission, wired and wireless hybrid communication technology is used and quantum encryption technology and intelligent routing functions are introduced to ensure the efficiency, stability and security of data transmission. The data analysis unit of the enterprise middle station can accurately identify the normal and abnormal modes of the distribution network topology and continuously adjust parameters to improve analysis efficiency through innovative deep learning algorithm models and intelligent optimization algorithms. It also has data prediction functions and can be integrated with external data sources to improve prediction accuracy. The intelligent monitoring module can not only display the distribution network topology data and monitoring status in real time, but also has intelligent decision-making functions. When an abnormality occurs, it can automatically analyze the cause and provide solutions. The monitoring display unit supports simultaneous access by multiple users and personalized customization. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a general schematic diagram of the system of the present invention;

[0020] Figure 2 This is a schematic diagram of a data acquisition module of the present invention;

[0021] Figure 3 This is a schematic diagram of a data transmission module of the present invention;

[0022] Figure 4 This is a schematic diagram of the enterprise platform of the present invention;

[0023] Figure 5 This is a schematic diagram of the intelligent monitoring module of the present invention;

[0024] Figure 6 It is a schematic diagram of the steps of implementing the method of the present invention. DETAILED DESCRIPTION

[0025] 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.

[0026] The present invention provides Figure 1 The distribution network topology data intelligent monitoring system based on the enterprise middle station shown includes a data acquisition module, a data transmission module, an enterprise middle station and an intelligent monitoring module;

[0027] Data acquisition module such as Figure 2 As shown, it is used to collect topological data in the distribution network. The module is composed of high-precision and high-sensitivity intelligent sensors, which are distributed in various corresponding positions of the distribution network, so as to realize real-time and accurate collection of multi-dimensional topological data such as voltage, current, and power; the intelligent sensor also has the function of self-adapting to environmental changes, and can automatically adjust the collection parameters according to different environmental conditions such as temperature, humidity, and electromagnetic field to ensure the accuracy and reliability of the data;

[0028] Data transmission module such as Figure 3 As shown, it is connected with the data acquisition module to transmit the collected topological data to the enterprise middle station. The module adopts advanced wired and wireless hybrid communication technology to ensure the efficiency and stability of data transmission, and has data encryption and error correction functions to ensure the security and integrity of data; the data transmission module also includes quantum encryption technology to further improve the security of data transmission and prevent data from being stolen or tampered with. The quantum encryption technology uses the principles of quantum mechanics to ensure the security of the key;

[0029] Enterprise middle platform Figure 4 As shown, the topology data is received and stored, and the topology data is analyzed and processed. The enterprise middle platform includes a data storage unit, a data analysis unit and a data management unit. The data storage unit adopts a distributed storage architecture and can quickly store and retrieve a large amount of topology data; the data analysis unit uses an innovative deep learning algorithm model to perform in-depth analysis on the topology data. The algorithm model is trained with a large amount of historical data and can automatically identify the normal mode and abnormal mode of the distribution network topology.

[0030] The wired communication method of the data transmission module includes high-speed Ethernet technology, and the wireless communication method adopts 5G communication technology. The data transmission module also includes an intelligent routing function, which can automatically select the optimal transmission path according to the network conditions.

[0031] The data analysis unit of the enterprise middle station also includes a data prediction function, which can predict the future distribution network topology status based on historical data and current data trends, providing a basis for taking preventive measures in advance.

[0032] The intelligent monitoring module is as follows Figure 5 As shown, it includes a monitoring display unit and an alarm unit. The monitoring display unit is used to display the distribution network topology data and monitoring status in real time. The monitoring display unit supports simultaneous access by multiple users and can be customized according to user needs to display different information and charts; the alarm unit is used to issue an audible and visual alarm when an abnormality is found and to notify relevant personnel in a timely manner.

[0033] The present invention also provides a method for implementing a distribution network topology data intelligent monitoring system based on an enterprise middle station, such as Figure 6 As shown, the following steps are included:

[0034] The topological data in the distribution network is collected by the smart sensor in the data collection module. The smart sensor performs self-calibration and self-diagnosis while collecting data to ensure the accuracy of the data; the smart sensor also automatically adjusts the collection parameters according to environmental changes, and cooperates with other sensors to achieve mutual verification of data and fault diagnosis;

[0035] The data transmission module is used to transmit the collected topological data to the enterprise middle station. The data is encrypted and error-corrected during transmission. At the same time, quantum encryption technology is introduced to improve the security of data transmission. The data transmission module also has an intelligent routing function and can automatically select the optimal transmission path according to the network conditions.

[0036] The enterprise middle station receives and stores the topological data, and the data analysis unit uses a deep learning algorithm model to analyze and process the topological data, including data preprocessing, feature extraction, model training and optimization.

[0037] The intelligent sensors in the data acquisition module are distributed at corresponding locations of the distribution network to collect multi-dimensional topological data in real time.

[0038] The data transmission module adopts high-speed Ethernet technology and 5G communication technology for data transmission, ensuring the efficiency and stability of data transmission.

[0039] The data analysis unit of the enterprise middle station uses a specific deep learning algorithm to analyze and process the topological data to identify potential problems and anomalies and perform data prediction.

[0040] The monitoring display unit of the intelligent monitoring module is used to display the distribution network topology data and monitoring status in real time. The alarm unit sends out an audible and visual alarm when an abnormality is found, and promptly notifies relevant personnel.

[0041] Among them, the enterprise middle station receives and stores the topology data, analyzes and processes the topology data, and includes a data storage unit, a data analysis unit, and a data management unit. The data storage unit adopts a distributed storage architecture and can quickly store and retrieve a large amount of topology data. The data analysis unit uses an innovative deep learning algorithm model to deeply analyze the topology data. The algorithm model is trained with a large amount of historical data and can automatically identify the normal mode and abnormal mode of the distribution network topology.

[0042] The algorithm model formation process is as follows:

[0043] Data preprocessing: Clean and normalize the collected raw topological data to remove noise and outliers so that the data has a unified format and range.

[0044] Feature extraction: Use principal component analysis and other methods to extract key features of topological data, reduce data dimensions, and improve the efficiency and accuracy of the algorithm.

[0045] Model training: Use a deep neural network for model training, taking the extracted features as input and the corresponding labels (normal or abnormal mode) as output. The network weights are continuously adjusted through the back propagation algorithm so that the model can accurately predict the state of the distribution network topology.

[0046] Model optimization: Use optimization algorithms such as stochastic gradient descent to optimize the model to improve the convergence speed and generalization ability of the model.

[0047] The specific algorithm formula is as follows:

[0048] X={x1,x2,...,x n}, where x i Represents the i-th data sample. After feature extraction, the feature vector F = {f1, f2, ..., f m}where f j represents the jth feature, and the output of the deep neural network is Y = {y1, y2, ..., y k}, where y l Indicates the value of the lth output node, corresponding to different distribution network topology states.

[0049] The calculation formula of deep neural network is:

[0050] where w lj represents the weight connecting the jth feature and the lth output node, b l represents the bias of the lth output node, and g represents the activation function.

[0051] The present invention can improve the accuracy and reliability of data collection. The data collection module of the present invention is composed of high-precision and high-sensitivity intelligent sensors. These sensors have self-calibration and self-diagnosis functions, and can automatically detect their own status during operation to ensure the accuracy and reliability of collected data. At the same time, the sensors also have the function of self-adapting to environmental changes, and can automatically adjust the collection parameters according to different environmental conditions such as temperature, humidity, and electromagnetic field, further improving the accuracy of data collection. In addition, the sensors can cooperate through wireless communication to achieve mutual verification and fault diagnosis of data, thereby improving the reliability of data.

[0052] The present invention can ensure the efficiency, stability and security of data transmission. The data transmission module of the present invention adopts advanced wired and wireless hybrid communication technology to achieve high-speed, low-latency data transmission, thereby improving the efficiency and stability of data transmission. The introduction of quantum encryption technology further improves the security of data transmission and prevents data from being stolen or tampered with. The data transmission module also has an intelligent routing function, which can automatically select the optimal transmission path according to the network conditions, thereby improving the efficiency and reliability of data transmission.

[0053] The present invention can improve the accuracy and efficiency of data analysis and processing. The data analysis unit in the enterprise center of the present invention uses an innovative deep learning algorithm model to perform in-depth analysis on topological data, and can automatically identify normal and abnormal modes of distribution network topology. The algorithm model is formed through steps such as data preprocessing, feature extraction, model training and optimization, which improves the accuracy and efficiency of analysis. Equipped with an intelligent optimization algorithm, it can continuously adjust the algorithm parameters according to real-time data to further improve the accuracy and efficiency of analysis.

[0054] The present invention can enhance intelligent monitoring and decision-making capabilities. The intelligent monitoring module of the present invention performs intelligent monitoring based on the topological data processed by the enterprise middle station. When an abnormality is found, an alarm is issued, and relevant personnel are notified in a timely manner through text messages, emails, etc. The monitoring display unit adopts a high-resolution display screen, which can display the distribution network topology data and monitoring status in real time, including dynamic topological diagrams, data curves, etc., so that users can intuitively understand the operation of the distribution network. The intelligent monitoring module also has an intelligent decision-making function. When an abnormal situation is detected, it can automatically analyze the cause of the abnormality and propose corresponding solutions to provide decision support for operation and maintenance personnel.

[0055] The present invention can realize personalized customization and multi-user simultaneous access. The monitoring display unit supports multi-user simultaneous access, which is convenient for personnel from different departments to understand the distribution network topology status in real time. It can be customized according to user needs, display different information and charts, and improve user experience.

[0056] The present invention can improve the accuracy of data prediction. The data analysis unit of the enterprise middle station has the function of data prediction, which can predict the future distribution network topology state based on historical data and current data trends, and provide a basis for taking preventive measures in advance. It can be integrated with external data sources to obtain meteorological, geographical and other information to further improve the accuracy of prediction.

[0057] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The intelligent monitoring system for distribution network topology data based on the enterprise middle station is characterized by: Including data collection module, data transmission module, enterprise middle platform and intelligent monitoring module; A data acquisition module is used to collect topological data in the distribution network. The data acquisition module is composed of intelligent sensors, which are distributed at various corresponding positions of the distribution network and are used to collect multi-dimensional topological data in real time. The intelligent sensors also have the function of self-adapting to environmental changes and automatically adjust the acquisition parameters according to different environmental conditions. A data transmission module is connected to the data acquisition module to transmit the collected topological data to the enterprise middle station. The data transmission module adopts wired and wireless hybrid communication technology and has data encryption and error correction functions. The data transmission module also includes quantum encryption technology, which uses the principles of quantum mechanics to ensure the security of the key. The enterprise middle station receives and stores the topology data, and analyzes and processes the topology data. The enterprise middle station includes a data storage unit, a data analysis unit and a data management unit. The data storage unit adopts a distributed storage architecture for storing and retrieving topology data; the data analysis unit uses a deep learning algorithm model to perform in-depth analysis on the topology data. The deep learning algorithm model is trained through historical data and can automatically identify normal and abnormal modes of the distribution network topology.

2. According to claim 1, the distribution network topology data intelligent monitoring system based on the enterprise middle station is characterized in that: The wired communication method of the data transmission module includes high-speed Ethernet technology, and the wireless communication method adopts 5G communication technology. The data transmission module also includes an intelligent routing function, which can automatically select the optimal transmission path according to the network conditions.

3. The distribution network topology data intelligent monitoring system based on the enterprise middle station according to claim 1 is characterized by: The data analysis unit of the enterprise middle station also includes a data prediction function, which can predict the future distribution network topology status based on historical data and current data trends.

4. The distribution network topology data intelligent monitoring system based on the enterprise middle station according to claim 1 is characterized in that: The intelligent monitoring module includes a monitoring display unit and an alarm unit. The monitoring display unit is used to display the distribution network topology data and monitoring status in real time. The monitoring display unit supports simultaneous access by multiple users and is customized according to user needs to display different information and charts. The alarm unit is used to issue an audible and visual alarm when an abnormality is found and to notify relevant personnel in a timely manner.

5. The implementation method of the distribution network topology data intelligent monitoring system based on the enterprise middle station is characterized in that: The intelligent monitoring system for distribution network topology data based on the enterprise middle station according to any one of claims 1 to 4 is implemented, comprising the following steps: The topological data in the distribution network is collected by the smart sensor in the data collection module. The smart sensor performs self-calibration and self-diagnosis while collecting data. The smart sensor also automatically adjusts the collection parameters according to environmental changes and cooperates with other sensors to achieve mutual verification of data and fault diagnosis. The data transmission module is used to transmit the collected topological data to the enterprise middle station. The data is encrypted and error-corrected during transmission. Quantum encryption technology is introduced. The data transmission module also has an intelligent routing function, which can automatically select the optimal transmission path according to the network conditions. The enterprise middle station receives and stores the topological data, and the data analysis unit uses a deep learning algorithm model to analyze and process the topological data, including data preprocessing, feature extraction, model training and optimization.

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