Distribution network maintenance topology identification system based on multi-source data fusion

Abstract

The invention relates to a distribution network maintenance topology identification system based on multi-source data fusion, and the system achieves the effective integration and precise processing of multi-source power distribution network data through the cooperative work of a multi-source data collection module, a data preprocessing module and a measurement generation module, and remarkably improves the temporal-spatial resolution of the state quantity of a distribution network. According to the method, a confidence evaluation system is constructed, and an algorithm confidence evaluation system is used for carrying out confidence scoring on an output identification structure, so that the uncertainty of an identification result can be quantified, and the availability and reliability of the system in an actual complex environment are greatly improved; through seamless connection and data interaction between a database and a marketing system and a scheduling system, the applicability and effectiveness of the system in an actual maintenance scene are ensured, accurate and reliable topology identification support can be provided for power distribution network maintenance, and the maintenance efficiency and the power supply reliability are improved; the method has the advantages of being high in self-adaption, accurate and reliable.

Description

Technical Field

[0001] This invention belongs to the field of distribution network maintenance technology, specifically relating to a distribution network maintenance topology identification system based on multi-source data fusion. Background Technology

[0002] In distribution network maintenance, accurate topology analysis and parameter identification are crucial for ensuring maintenance efficiency and power supply reliability. However, in practical applications, the diverse data sources, varying accuracy, and inconsistent time scales in distribution networks lead to insufficient spatiotemporal resolution of distribution network status variables. Furthermore, existing parameter identification models exhibit poor adaptability under complex sampling conditions, and their identification accuracy and reliability fail to meet actual maintenance needs, thus hindering the provision of precise topology parameter support and impacting the scientific rigor and efficiency of distribution network maintenance. Therefore, to address these issues, it is essential to develop a highly adaptive, accurate, and reliable distribution network maintenance topology identification system based on multi-source data fusion. Summary of the Invention

[0003] The purpose of this invention is to overcome the shortcomings of the prior art and provide a highly adaptive, accurate and reliable distribution network maintenance topology identification system based on multi-source data fusion.

[0004] The objective of this invention is achieved as follows: a distribution network maintenance topology identification system based on multi-source data fusion, comprising a multi-source data acquisition module, a data preprocessing module, a measurement generation module, a topology parameter identification module, an algorithm optimization module, a graph model database construction module, and a verification module. The multi-source data acquisition module is configured with a multi-protocol data interface for synchronously acquiring multi-source heterogeneous data from SCADA systems, GIS systems, smart instruments, and user power consumption data systems. The data preprocessing module can preprocess the acquired multi-source heterogeneous data to ensure the integrity and reliability of the data. The measurement generation module can establish a multi-viewpoint super-resolution measurement model based on the pre-processed multi-source heterogeneous data. Subsequently, the multi-viewpoint super-resolution measurement model is trained using the spatial topological relationships provided by GIS image data and the time-series measurement data provided by intelligent instrument data. Finally, deep learning technology is used to reconstruct high-resolution measurement information from low-resolution data using the trained multi-viewpoint super-resolution measurement model, thereby making up for the shortcomings of direct measurement. The topology parameter identification module can establish a distribution network topology physical feature library, and map and match physical features such as line connection relationships and equipment type parameters with the hierarchical structure and activation function characteristics in the neural network structure to form a dual-branch collaborative identification model. One branch realizes the dynamic identification of the topology structure, and the other branch completes the accurate estimation of electrical parameters. Then, through cross-branch feature interaction, the collaborative optimization identification of topology and parameters is realized. The algorithm optimization module can conduct an in-depth analysis of the mechanism of the existing power distribution network parameter identification model based on transfer learning technology, propose a highly available parameter identification algorithm that adapts to complex sampling conditions, and then construct an algorithm confidence evaluation system through validation set performance monitoring, model correction and hidden pattern extraction technology during supervised training. The algorithm confidence evaluation system is then used to score the confidence of the output identification structure, thereby enhancing the accuracy and reliability of parameter identification. The diagram database construction module can be used to establish a distribution network diagram database and realize seamless connection and data interaction between the database and the marketing system and the dispatching system; The verification module can test the performance of the algorithm in a real environment based on the data provided by the graph database, and collect on-site test data in real time. Then, it compares and analyzes the on-site test data with the system identification results to generate a parameter identification error report.

[0005] Furthermore, the preprocessing operations for the multi-source heterogeneous data specifically include: removing data noise through an outlier detection model, performing data quality quantification assessment based on the entropy weight method, and using time series alignment technology to achieve time-scale unification of multi-source data.

[0006] Furthermore, the data preprocessing module is equipped with a storage unit, which can be used to store and back up the preprocessed data.

[0007] Furthermore, the multi-source data acquisition module acquires real-time operational data from the SCADA system, topological geographic data from the GIS system, high-precision measurement data from intelligent instruments, and historical operation and maintenance data from the user's electricity consumption data system.

[0008] Furthermore, the verification module is equipped with a decision-making unit, which uses the decision-making unit to generate a visual maintenance decision report based on the parameter identification error report.

[0009] The beneficial effects of this invention are as follows: Through the collaborative work of multiple modules, this invention achieves effective integration and accurate processing of multi-source distribution network data, significantly improves the spatiotemporal resolution of distribution network status quantities, fundamentally improves the identification capability in sparse measurement scenarios, and realizes more accurate estimation of key parameters such as electrical quantities and line impedance. Furthermore, by constructing a confidence assessment system and using the algorithm confidence assessment system to score the confidence of the output identification structure, the uncertainty of the identification results can be quantified, and early warning and self-correction can be performed for abnormal or unreliable results, which greatly improves the availability and reliability of the system in real complex environments (such as data missing and noise interference). Through seamless connection and data interaction between the database and the marketing and dispatch systems, the applicability and effectiveness of the system in actual maintenance scenarios are ensured. It can provide accurate and reliable topology identification support for distribution network maintenance, thereby improving maintenance efficiency and power supply reliability. By configuring the verification module, the performance of the algorithm in a real-world environment can be tested based on the data provided by the graph database, and a parameter identification error report can be generated. Overall, this invention has the advantages of strong adaptability and high accuracy and reliability. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the structure of the present invention. Detailed Implementation

[0011] The present invention will now be further described with reference to the accompanying drawings.

[0012] Example: Figure 1 As shown, a distribution network maintenance topology identification system based on multi-source data fusion includes a multi-source data acquisition module, a data preprocessing module, a measurement generation module, a topology parameter identification module, an algorithm optimization module, a graph model database construction module, and a verification module. The multi-source data acquisition module is configured with a multi-protocol data interface for acquiring real-time operating data from the SCADA system, topological geographic data from the GIS system, high-precision measurement data from intelligent instruments, and historical operation and maintenance data from the user power consumption data system. The data preprocessing module can preprocess the acquired multi-source heterogeneous data to ensure data integrity and reliability. Specifically, the preprocessing operations include: removing data noise using an outlier detection model, performing data quality quantification based on the entropy weight method, and using time series alignment technology to unify the timescales of the multi-source data. Furthermore, the data preprocessing module has an internal storage unit for storing and backing up the preprocessed data. The measurement generation module can establish a multi-viewpoint super-resolution measurement model based on the pre-processed multi-source heterogeneous data. Subsequently, the multi-viewpoint super-resolution measurement model is trained using the spatial topological relationships provided by GIS image data and the time-series measurement data provided by intelligent instrument data. Finally, deep learning technology is used to reconstruct high-resolution measurement information from low-resolution data using the trained multi-viewpoint super-resolution measurement model, thereby making up for the shortcomings of direct measurement. The topology parameter identification module can establish a distribution network topology physical feature library, and map and match physical features such as line connection relationships and equipment type parameters with the hierarchical structure and activation function characteristics in the neural network structure to form a dual-branch collaborative identification model. One branch realizes the dynamic identification of the topology structure, and the other branch completes the accurate estimation of electrical parameters. Then, through cross-branch feature interaction, the collaborative optimization identification of topology and parameters is realized. The algorithm optimization module can conduct an in-depth analysis of the mechanism of the existing power distribution network parameter identification model based on transfer learning technology, propose a highly available parameter identification algorithm that adapts to complex sampling conditions, and then construct an algorithm confidence evaluation system through validation set performance monitoring, model correction and hidden pattern extraction technology during supervised training. The algorithm confidence evaluation system is then used to score the confidence of the output identification structure, thereby enhancing the accuracy and reliability of parameter identification. The diagram database construction module can be used to establish a distribution network diagram database and realize seamless connection and data interaction between the database and the marketing system and the dispatching system; The verification module can test the performance of the algorithm in a real environment based on the data provided by the graph database, and collect on-site measured data in real time. Then, it compares and analyzes the on-site measured data with the system identification results to generate a parameter identification error report. In addition, the verification module is equipped with a decision unit, which uses the decision unit to formulate a visual maintenance decision report based on the parameter identification error report.

[0013] In use, this invention first acquires real-time operational data from the SCADA system, topological geographic data from the GIS system, high-precision measurement data from intelligent instruments, and historical operation and maintenance data from the user's electricity consumption data system through the multi-protocol data interface configured in the multi-source data acquisition module. This acquired data is then used to form multi-source heterogeneous data. Next, the acquired multi-source heterogeneous data undergoes preprocessing to ensure data integrity and reliability. Specifically, the preprocessing includes: removing data noise using an outlier detection model, performing data quality quantification based on the entropy weight method, and using time series alignment technology to unify the time scale of the multi-source data. Afterwards, through... The data is backed up by the storage unit within the data preprocessing module. Then, using the measurement generation module and based on the preprocessed multi-source heterogeneous data, a multi-viewpoint super-resolution measurement model is established. Subsequently, the model is trained using spatial topological relationships provided by GIS imagery and time-series measurement data from intelligent instruments. Finally, deep learning technology is employed to reconstruct high-resolution measurement information from low-resolution data using the trained multi-viewpoint super-resolution measurement model, thus compensating for the shortcomings of direct measurement. Finally, the topology parameter identification module establishes a distribution network topology physical feature library, including line connection relationships and equipment type parameters. Physical features are mapped and matched with the hierarchical structure and activation function characteristics in the neural network structure to form a two-branch collaborative identification model. One branch realizes dynamic identification of the topology structure, and the other completes the accurate estimation of electrical parameters. Subsequently, through cross-branch feature interaction, collaborative optimization identification of topology and parameters is achieved. After completing the above operations, the mechanism of the existing distribution network parameter identification model is analyzed in depth using the algorithm optimization module and based on transfer learning technology. A highly available parameter identification algorithm adapted to complex sampling conditions is proposed. Subsequently, through supervised training, validation set performance monitoring, model correction, and hidden pattern extraction techniques, an algorithm confidence evaluation system is constructed, and the algorithm is then used... The confidence assessment system scores the confidence of the output identification structure, thereby enhancing the accuracy and reliability of parameter identification. Simultaneously, a distribution network diagram database is established through a diagram database construction module, enabling seamless connection and data interaction between this database and the marketing and dispatching systems. Finally, the verification module tests the algorithm's performance in a real-world environment based on data provided by the diagram database, and collects real-time field measurement data. The field measurement data is then compared and analyzed with the system identification results to generate a parameter identification error report. After the error report is generated, the decision-making unit within the verification module, based on the parameter identification error report, formulates a visualized maintenance decision report.

[0014] This invention achieves effective integration and accurate processing of multi-source distribution network data through the collaborative work of a multi-source data acquisition module, a data preprocessing module, and a measurement generation module. This significantly improves the spatiotemporal resolution of distribution network status quantities, fundamentally enhancing identification capabilities in sparse measurement scenarios and enabling more accurate estimation of key parameters such as electrical quantities and line impedance. Furthermore, by constructing a confidence assessment system through an algorithm optimization module and using this system to score the confidence of the output identification structure, the uncertainty of the identification results can be quantified, and early warnings and self-corrections can be provided for abnormal or unreliable results. This greatly enhances the system's performance in practical applications. The system's usability and reliability are enhanced under complex environments (such as missing data and noise interference). A distribution network diagram database is constructed using a diagram database construction module, enabling seamless connection and data interaction between this database and the marketing and dispatching systems. This ensures the system's applicability and effectiveness in actual maintenance scenarios, providing accurate and reliable topology identification support for distribution network maintenance, thus improving maintenance efficiency and power supply reliability. Through the verification module, the algorithm's performance in real-world environments can be tested based on the data provided by the diagram database, generating parameter identification error reports. Overall, this invention possesses the advantages of strong adaptive capability and high accuracy and reliability.

[0015] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A distribution network maintenance topology identification system based on multi-source data fusion, comprising a multi-source data acquisition module, a data preprocessing module, a measurement generation module, a topology parameter identification module, an algorithm optimization module, a graph model database construction module, and a verification module, characterized in that: The multi-source data acquisition module is configured with multi-protocol data interfaces to synchronously acquire multi-source heterogeneous data from SCADA systems, GIS systems, smart instruments, and user power consumption data systems. The data preprocessing module can preprocess the acquired multi-source heterogeneous data to ensure the integrity and reliability of the data. The measurement generation module can establish a multi-viewpoint super-resolution measurement model based on the pre-processed multi-source heterogeneous data. Subsequently, the multi-viewpoint super-resolution measurement model is trained using the spatial topological relationships provided by GIS image data and the time-series measurement data provided by intelligent instrument data. Finally, deep learning technology is used to reconstruct high-resolution measurement information from low-resolution data using the trained multi-viewpoint super-resolution measurement model, thereby making up for the shortcomings of direct measurement. The topology parameter identification module can establish a distribution network topology physical feature library, and map and match physical features such as line connection relationships and equipment type parameters with the hierarchical structure and activation function characteristics in the neural network structure to form a dual-branch collaborative identification model. One branch realizes the dynamic identification of the topology structure, and the other branch completes the accurate estimation of electrical parameters. Then, through cross-branch feature interaction, the collaborative optimization identification of topology and parameters is realized. The algorithm optimization module can conduct an in-depth analysis of the mechanism of the existing power distribution network parameter identification model based on transfer learning technology, propose a highly available parameter identification algorithm that adapts to complex sampling conditions, and then construct an algorithm confidence evaluation system through validation set performance monitoring, model correction and hidden pattern extraction technology during supervised training. The algorithm confidence evaluation system is then used to score the confidence of the output identification structure, thereby enhancing the accuracy and reliability of parameter identification. The diagram database construction module can be used to establish a distribution network diagram database and realize seamless connection and data interaction between the database and the marketing system and the dispatching system; The verification module can test the performance of the algorithm in a real environment based on the data provided by the graph database, and collect on-site test data in real time. Then, it compares and analyzes the on-site test data with the system identification results to generate a parameter identification error report.

2. The distribution network maintenance topology identification system based on multi-source data fusion as described in claim 1, characterized in that: The preprocessing operations for the multi-source heterogeneous data are as follows: removing data noise through an outlier detection model, performing data quality quantification assessment based on the entropy weight method, and using time series alignment technology to achieve time standardization of multi-source data.

3. The distribution network maintenance topology identification system based on multi-source data fusion as described in claim 1, characterized in that: The data preprocessing module has an internal storage unit, which can be used to store and back up the preprocessed data.

4. The distribution network maintenance topology identification system based on multi-source data fusion as described in claim 1, characterized in that: The multi-source data acquisition module obtains real-time operating data from the SCADA system, topological geographic data from the GIS system, high-precision measurement data from intelligent instruments, and historical operation and maintenance data from the user electricity consumption data system.

5. The distribution network maintenance topology identification system based on multi-source data fusion as described in claim 1, characterized in that: The verification module is equipped with a decision-making unit, which uses the error report of parameter identification to generate a visual maintenance decision report.

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