Method and system for predicting health state of digital power supply and protection distribution network equipment, and medium

Abstract

The invention provides a method and a system for predicting the health state of digital guarantee power supply distribution network equipment, and a medium. The method comprises the following steps: collecting feature information and operation feature data of target distribution network equipment to generate a distribution network equipment digital twinborn body and virtually displaying the distribution network equipment digital twinborn body, obtaining electric energy demand data of a target power supply system, and combining the collected twinborn body to perform distribution network equipment operation data of virtual power supply on the power supply system, processing to obtain first and second prediction data of the distribution network equipment and power supply performance indexes of the distribution network, comparing the first and second prediction data and the power supply performance indexes with corresponding thresholds to obtain first, second and third health state levels, and recording the first, second and third health state levels; therefore, the health prediction technology of the digital power supply protection distribution network equipment in the virtual distribution network operation state is realized.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network equipment, and more specifically, to a method, system, and medium for predicting the health status of digital power distribution network equipment. Background Art

[0002] With the continuous growth of electricity demand and the increasing complexity of distribution network systems, ensuring the reliable operation of distribution network equipment is crucial for digital power supply security. Traditional distribution network equipment health status monitoring methods often rely on a single data source, making it difficult to comprehensively and accurately assess the true status of the equipment, resulting in inaccurate prediction results and the inability to timely detect potential fault hazards, affecting power supply reliability.

[0003] In response to the above problems, effective technical solutions are urgently needed. Summary of the Invention

[0004] The purpose of this application is to provide a method, system and medium for predicting the health status of digital power supply distribution network equipment. By collecting the characteristic information and operation characteristic data of the target distribution network equipment, a digital twin of the distribution network equipment can be generated and virtually displayed, the power demand data of the target power supply system can be obtained, and the operation data of the distribution network equipment for virtual power supply to the power supply system can be combined with the collected twin. The first and second prediction data of the distribution network equipment and the distribution network power supply performance indicators are processed to obtain the first, second and third health status levels, which are then compared with the corresponding thresholds to obtain and record them; thereby realizing the health prediction technology of the digital power supply distribution network equipment under the virtual distribution network operation state.

[0005] This application also provides a method for predicting the health status of digital power distribution network equipment, including the following steps: Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; Generate digital twins of distribution network equipment based on feature information and operational feature data, and perform virtual displays; Obtaining power demand data of a target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; Processing the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index; A threshold comparison is performed based on the first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them.

[0006] Optionally, in the digital power distribution network equipment health status prediction method described in the present application, collecting characteristic information of the target distribution network equipment and obtaining operating characteristic data of the target distribution network equipment within a first preset time period includes: Collect characteristic information of target distribution network equipment, including equipment model, performance parameters, power and self-test status information; Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

[0007] Optionally, in the digital power supply distribution network equipment health status prediction method described in the present application, collecting virtual operation information of the distribution network equipment digital twin performing virtual power supply to the target power supply system and extracting distribution network equipment operation data include: Performing virtual distribution network operations based on the digital twin of the distribution network equipment and the target power supply system; Acquire virtual operation information of the virtual distribution network operation within the second preset time period; Extract distribution network equipment operation data based on virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; The equipment statistical data includes the number of fault alarms and the number of downtimes; The power supply data includes virtual power supply amount and virtual power supply load.

[0008] Optionally, in the digital power supply distribution network equipment health status prediction method described in the present application, the processing of the distribution network equipment operation data to obtain the first prediction data and the second prediction data of the distribution network equipment includes: The voltage fluctuation rate, three-phase imbalance, and harmonic content are processed by a preset equipment health assessment model to obtain first prediction data of the distribution network equipment; A weighted statistical analysis is performed based on the number of fault alarms and the number of downtimes to obtain second prediction data for the distribution network equipment.

[0009] Optionally, in the digital power distribution network equipment health status prediction method described in the present application, the processing of the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index includes: Processing is performed according to the virtual power supply amount and virtual power supply load as well as the power consumption and power load to obtain a distribution network power supply performance indicator.

[0010] Optionally, in the digital power distribution network equipment health status prediction method described in the present application, the threshold comparison is performed based on the first prediction data and the second prediction data of the distribution network equipment and the distribution network power supply performance index, and the first, second, and third health status levels are obtained and recorded, including: Perform threshold comparisons based on the first prediction data and the second prediction data of the distribution network device, the distribution network power supply performance index, and the corresponding preset first threshold value of the distribution network device, the preset second threshold value of the distribution network device, and the preset third threshold value of the distribution network device to obtain first, second, and third threshold value comparison results; According to the comparison results of the first, second and third threshold values, the first, second and third health status levels are obtained accordingly; The first, second, and third health status levels are recorded, and performance adjustments are made to the distribution network equipment.

[0011] In a second aspect, the present application provides a digital power supply distribution network equipment health status prediction system, the system comprising: a memory and a processor, the memory including a program of a digital power supply distribution network equipment health status prediction method, the program of the digital power supply distribution network equipment health status prediction method being executed by the processor to implement the following steps: Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; Generate digital twins of distribution network equipment based on feature information and operational feature data, and perform virtual displays; Obtaining power demand data of a target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; Processing the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index; A threshold comparison is performed based on the first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them.

[0012] Optionally, in the digital power distribution network equipment health status prediction system described in the present application, collecting characteristic information of the target distribution network equipment and obtaining operating characteristic data of the target distribution network equipment within a first preset time period includes: Collect characteristic information of target distribution network equipment, including equipment model, performance parameters, power and self-test status information; Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

[0013] Optionally, in the digital power supply distribution network equipment health status prediction system described in the present application, collecting virtual operation information of the distribution network equipment digital twin performing virtual power supply to the target power supply system and extracting distribution network equipment operation data include: Performing virtual distribution network operations based on the digital twin of the distribution network equipment and the target power supply system; Acquire virtual operation information of the virtual distribution network operation within the second preset time period; Extract distribution network equipment operation data based on virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; The equipment statistical data includes the number of fault alarms and the number of downtimes; The power supply data includes virtual power supply amount and virtual power supply load.

[0014] On the third aspect, the present application also provides a computer-readable storage medium, which stores a program for a digital power supply distribution network equipment health status prediction method. When the program for a digital power supply distribution network equipment health status prediction method is executed by a processor, the steps of the digital power supply distribution network equipment health status prediction method as described in any one of the above items are implemented.

[0015] From the above, it can be seen that the digital power supply distribution network equipment health status prediction method, system and medium disclosed in the present invention generate a digital twin of the distribution network equipment and virtually display it by collecting the characteristic information and operation characteristic data of the target distribution network equipment, obtain the power demand data of the target power supply system, and combine the collected twin with the distribution network equipment operation data of the virtual power supply system to obtain the first and second prediction data of the distribution network equipment, as well as the distribution network power supply performance indicators, and then compare them with the corresponding thresholds to obtain the first, second and third health status levels and record them; thereby realizing the health prediction technology of the digital power supply distribution network equipment under the virtual distribution network operation state.

[0016] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or understood by practicing the embodiments of the present application. The objectives and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flowchart of a method for predicting the health status of digital power distribution network equipment provided in an embodiment of the present application; Figure 2 A flowchart of the method for predicting the health status of digital power distribution network equipment provided in an embodiment of the present application for obtaining operating characteristic data of target distribution network equipment; Figure 3 A flowchart of extracting distribution network equipment operating data for the digital power distribution network equipment health status prediction method provided in an embodiment of the present application; Figure 4 A flowchart of obtaining first prediction data and second prediction data of distribution network equipment in the digital power supply distribution network equipment health status prediction method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work fall within the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined or explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and should not be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 This is a flow chart of a method for predicting the health status of digital power distribution network equipment in some embodiments of the present application. The method is used in terminal devices such as computers and mobile terminals. The method includes the following steps: S11. Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; S12. Generate a digital twin of the distribution network equipment based on the characteristic information and operation characteristic data, and perform a virtual display; S13, obtaining power demand data of the target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; S14. Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; S15. Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; S16. Processing the distribution network equipment operation data and the power demand data to obtain a distribution network power supply performance indicator; S17. Perform threshold comparison based on the first prediction data and the second prediction data of the distribution network equipment and the distribution network power supply performance index, obtain first, second, and third health status levels, and record them.

[0022] It should be noted that the characteristic information of the target distribution network equipment is collected, and the operating characteristic data of the target distribution network equipment in the first historical preset time period is obtained. A digital virtual twin of the distribution network equipment is generated based on the characteristic information and the operating characteristic data, and virtual operation and display are performed. At the same time, the electric energy demand data of the target power supply system corresponding to the target distribution network equipment in the second future preset time period that needs to be predicted is obtained, including power consumption and power load. Then, the digital twin of the distribution network equipment is used to perform virtual power supply according to the power distribution demand of the target power supply system, and the virtual operation information of the twin's virtual power supply to the power supply system is obtained. The virtual operation data of the distribution network equipment is extracted, and the distribution network equipment operation data is processed to obtain the first prediction data of the distribution network equipment and the second prediction data of the distribution network equipment respectively. The distribution network equipment operation data is combined with the electric energy demand data for calculation and processing to obtain the distribution network power supply performance index. Then, the threshold value comparison is performed based on the three data and indicators obtained, and the virtual predicted health status level of the distribution network equipment in the second preset time period is obtained according to the threshold interval that falls into, thereby realizing the technology of virtual prediction of the health status of the distribution network equipment.

[0023] Please refer to Figure 2 , Figure 2 This is a flowchart of obtaining operating characteristic data of a target distribution network device in a method for predicting the health status of a digital power distribution network device in some embodiments of the present application. According to an embodiment of the present invention, collecting characteristic information of the target distribution network device and obtaining operating characteristic data of the target distribution network device within a first preset time period includes: S21. Collect characteristic information of the target distribution network equipment, including equipment model, performance parameters, power, and self-test status information; S22. Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

[0024] It should be noted that the self-test status information is the feedback information obtained during the self-test process of the distribution network equipment, including feedback on abnormal performance, coupling status, and operation data monitoring. The operation characteristic data of the distribution network equipment includes records of the equipment's electrical parameters, equipment operating status, and maintenance records.

[0025] Please refer to Figure 3 , Figure 3 This is a flowchart of obtaining the physical characteristic index of the device in the method for predicting the health status of the digital power distribution network equipment in some embodiments of the present application. According to an embodiment of the present invention, the virtual operation information of the virtual power supply of the target power supply system by the digital twin of the distribution network equipment is collected, and the distribution network equipment operation data is extracted, including: S31. Performing a virtual distribution network operation based on the digital twin of the distribution network equipment and the target power supply system; S32. Acquire virtual operation information of the virtual distribution network operation within the second preset time period; S33. Extracting distribution network equipment operation data based on the virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; S34. The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; S35. The equipment statistical data includes the number of fault alarms and the number of downtimes; S36. The power supply data includes a virtual power supply amount and a virtual power supply load.

[0026] It should be noted that the virtual operation information of the distribution network equipment and the power supply system performing virtual distribution network operations is obtained and the data of the virtual operation of the equipment is extracted. The electrical performance includes voltage fluctuation rate: the voltage output fluctuation rate between the rated voltage and the maximum / minimum phase voltage and the average phase voltage, which is voltage fluctuation rate (%) = (maximum phase voltage - minimum phase voltage) / average phase voltage × 100%, three-phase imbalance: (maximum current - minimum current) / maximum current × 100%, harmonic content: calculated by total harmonic distortion (THD) based on the effective values ​​of multiple harmonic components, that is, the ratio of the square root of the sum of the squares of the effective values ​​of each harmonic component to the effective value of the fundamental wave.

[0027] Please refer to Figure 4 , Figure 4 This is a flowchart of obtaining first prediction data and second prediction data of a distribution network device in a digital power distribution network device health status prediction method in some embodiments of the present application. According to an embodiment of the present invention, the processing of the distribution network device operating data to obtain the first prediction data and the second prediction data of the distribution network device includes: S41. Processing the voltage fluctuation rate, three-phase imbalance, and harmonic content using a preset equipment health assessment model to obtain first prediction data for distribution network equipment; S42: Perform weighted statistics based on the fault alarm times and the downtime times to obtain second prediction data of the distribution network equipment.

[0028] It should be noted that in order to evaluate and predict the health status of distribution network equipment in virtual distribution operation, the voltage fluctuation rate, three-phase imbalance and harmonic content are processed through the preset equipment health assessment model to obtain the first prediction data of the distribution network equipment. The preset equipment health assessment model is a model for predicting the operation monitoring status of distribution network equipment obtained by training the voltage fluctuation rate, three-phase imbalance, harmonic content and health result data of the sample data of a large number of historical samples. The second prediction data of the distribution network equipment is obtained by weighted addition calculation based on the number of alarms and the number of downtimes combined with the preset reliability coefficient and quality inspection yield coefficient of the distribution network equipment.

[0029] According to an embodiment of the present invention, the processing of the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index includes: Processing is performed according to the virtual power supply amount and virtual power supply load as well as the power consumption and power load to obtain a distribution network power supply performance indicator.

[0030] It should be noted that in order to detect the performance of the virtual distribution network power supply operation between the distribution network equipment and the target power supply system, the distribution network adaptability is tested through the distribution network power supply performance index. The distribution network power supply performance index = (virtual power supply - power consumption)² + (virtual power supply load - power load)².

[0031] According to an embodiment of the present invention, the threshold comparison is performed based on the first prediction data of the distribution network device, the second prediction data of the distribution network device, and the distribution network power supply performance index to obtain the first, second, and third health status levels, and record them, including: Perform threshold comparisons based on the first prediction data and the second prediction data of the distribution network device, the distribution network power supply performance index, and the corresponding preset first threshold value of the distribution network device, the preset second threshold value of the distribution network device, and the preset third threshold value of the distribution network device to obtain first, second, and third threshold value comparison results; According to the comparison results of the first, second and third threshold values, the first, second and third health status levels are obtained accordingly; The first, second, and third health status levels are recorded, and performance adjustments are made to the distribution network equipment.

[0032] It should be noted that, finally, the obtained first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index are respectively compared with the corresponding preset first threshold value of the distribution network equipment, the preset second threshold value of the distribution network equipment, and the preset third threshold value of the distribution network equipment, and the first, second, and third threshold comparison results are obtained respectively. The preset first / second / third threshold values ​​of the distribution network equipment are threshold interval segments, corresponding to the health status level. The corresponding health status level is obtained according to the target interval segment in which the threshold comparison falls. For example, the threshold interval segment corresponding to the preset first threshold value of the distribution network equipment is divided into [0, 0.37), [0.37, 0.55), [0.55, 0.79), [0.79, 1.0], which correspond to the first health status levels of four, three, two, and one respectively. If the threshold comparison result of the first prediction data of the distribution network equipment falls into the second interval segment, its first health status level is level three. Then, the first, second, and third health status levels obtained by comparison are recorded, and the distribution network equipment is controlled for power supply.

[0033] According to an embodiment of the present invention, the further embodiment includes: Processing the operating characteristic data using edge computing and matter-element extension theory evaluation methods to obtain a first equipment health prediction result; Processing the operating characteristic data using multivariate information and fuzzy mathematical evaluation methods to obtain a second equipment health prediction result; Performing statistical processing on the first device health prediction result and the second device health prediction result to obtain standard deviation data; If the standard deviation data is less than a first preset standard deviation, averaging the first device health prediction result and the second device health prediction result to obtain an average value, and using the average value as the device health correction prediction result; Compare the device health correction prediction result with the preset device health threshold to obtain a threshold comparison result; The health status level of the target distribution network device is obtained according to the interval range of the threshold comparison result.

[0034] It should be noted that in addition to obtaining the health status prediction of distribution network equipment through the prediction method of digital twin technology, it can also be obtained through edge computing and matter-element extension theory evaluation method and multivariate information and fuzzy mathematics evaluation method. Among them, the evaluation method based on edge computing and matter-element extension theory is to use edge computing technology, combined with matter-element extension theory, consider multiple factors such as safety, economy, reliability, etc., and quantitatively evaluate the health status of the distribution network by establishing an evaluation system and determining indicator weights. The multivariate information and fuzzy mathematics evaluation method comprehensively considers the health of the equipment layer and the grid layer, introduces fuzzy mathematics theory, and uses the membership function to evaluate the health level. It also combines the improved DS evidence theory model to perform uncertainty reasoning and information fusion on the two. Finally, the standard deviation is obtained based on the obtained first equipment health prediction result and the second equipment health prediction result. If the standard deviation data is less than the first preset standard deviation, the average is calculated and the average value is used as the final equipment health correction prediction result, thereby improving the accuracy of the health prediction.

[0035] In a second aspect, the present invention further discloses a digital power supply distribution network equipment health status prediction system, comprising a memory and a processor, wherein the memory includes a digital power supply distribution network equipment health status prediction method program, and when the digital power supply distribution network equipment health status prediction method program is executed by the processor, the following steps are implemented: Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; Generate digital twins of distribution network equipment based on feature information and operational feature data, and perform virtual displays; Obtaining power demand data of a target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; Processing the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index; A threshold comparison is performed based on the first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them.

[0036] It should be noted that the characteristic information of the target distribution network equipment is collected, and the operating characteristic data of the target distribution network equipment in the first historical preset time period is obtained. A digital virtual twin of the distribution network equipment is generated based on the characteristic information and the operating characteristic data, and virtual operation and display are performed. At the same time, the electric energy demand data of the target power supply system corresponding to the target distribution network equipment in the second future preset time period that needs to be predicted is obtained, including power consumption and power load. Then, the digital twin of the distribution network equipment is used to perform virtual power supply according to the power distribution demand of the target power supply system, and the virtual operation information of the twin's virtual power supply to the power supply system is obtained. The virtual operation data of the distribution network equipment is extracted, and the distribution network equipment operation data is processed to obtain the first prediction data of the distribution network equipment and the second prediction data of the distribution network equipment respectively. The distribution network equipment operation data is combined with the electric energy demand data for calculation and processing to obtain the distribution network power supply performance index. Then, the threshold value comparison is performed based on the three data and indicators obtained, and the virtual predicted health status level of the distribution network equipment in the second preset time period is obtained according to the threshold interval that falls into, thereby realizing the technology of virtual prediction of the health status of the distribution network equipment.

[0037] According to an embodiment of the present invention, collecting characteristic information of a target distribution network device and obtaining operating characteristic data of the target distribution network device within a first preset time period includes: Collect characteristic information of target distribution network equipment, including equipment model, performance parameters, power and self-test status information; Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

[0038] It should be noted that the self-test status information is the feedback information obtained during the self-test process of the distribution network equipment, including feedback on abnormal performance, coupling status, and operation data monitoring. The operation characteristic data of the distribution network equipment includes records of the equipment's electrical parameters, equipment operating status, and maintenance records.

[0039] According to an embodiment of the present invention, collecting virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system and extracting distribution network device operation data includes: Performing virtual distribution network operations based on the digital twin of the distribution network equipment and the target power supply system; Acquire virtual operation information of the virtual distribution network operation within the second preset time period; Extract distribution network equipment operation data based on virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; The equipment statistical data includes the number of fault alarms and the number of downtimes; The power supply data includes virtual power supply amount and virtual power supply load.

[0040] It should be noted that the virtual operation information of the distribution network equipment and the power supply system performing virtual distribution network operations is obtained and the data of the virtual operation of the equipment is extracted. The electrical performance includes voltage fluctuation rate: the voltage output fluctuation rate between the rated voltage and the maximum / minimum phase voltage and the average phase voltage, which is voltage fluctuation rate (%) = (maximum phase voltage - minimum phase voltage) / average phase voltage × 100%, three-phase imbalance: (maximum current - minimum current) / maximum current × 100%, harmonic content: calculated by total harmonic distortion (THD) based on the effective values ​​of multiple harmonic components, that is, the ratio of the square root of the sum of the squares of the effective values ​​of each harmonic component to the effective value of the fundamental wave.

[0041] According to an embodiment of the present invention, the step of processing the distribution network device operation data to obtain the first prediction data and the second prediction data of the distribution network device includes: The voltage fluctuation rate, three-phase imbalance, and harmonic content are processed by a preset equipment health assessment model to obtain first prediction data of the distribution network equipment; A weighted statistical analysis is performed based on the number of fault alarms and the number of downtimes to obtain second prediction data for the distribution network equipment.

[0042] It should be noted that in order to evaluate and predict the health status of distribution network equipment in virtual distribution operation, the voltage fluctuation rate, three-phase imbalance and harmonic content are processed through the preset equipment health assessment model to obtain the first prediction data of the distribution network equipment. The preset equipment health assessment model is a model for predicting the operation monitoring status of distribution network equipment obtained by training the voltage fluctuation rate, three-phase imbalance, harmonic content and health result data of the sample data of a large number of historical samples. The second prediction data of the distribution network equipment is obtained by weighted addition calculation based on the number of alarms and the number of downtimes combined with the preset reliability coefficient and quality inspection yield coefficient of the distribution network equipment.

[0043] According to an embodiment of the present invention, the processing of the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index includes: Processing is performed according to the virtual power supply amount and virtual power supply load as well as the power consumption and power load to obtain a distribution network power supply performance indicator.

[0044] It should be noted that in order to detect the performance of the virtual distribution network power supply operation between the distribution network equipment and the target power supply system, the distribution network adaptability is tested through the distribution network power supply performance index. The distribution network power supply performance index = (virtual power supply - power consumption)² + (virtual power supply load - power load)².

[0045] According to an embodiment of the present invention, the threshold comparison is performed based on the first prediction data of the distribution network device, the second prediction data of the distribution network device, and the distribution network power supply performance index to obtain the first, second, and third health status levels, and record them, including: Perform threshold comparisons based on the first prediction data and the second prediction data of the distribution network device, the distribution network power supply performance index, and the corresponding preset first threshold value of the distribution network device, the preset second threshold value of the distribution network device, and the preset third threshold value of the distribution network device to obtain first, second, and third threshold value comparison results; According to the comparison results of the first, second and third threshold values, the first, second and third health status levels are obtained accordingly; The first, second, and third health status levels are recorded, and performance adjustments are made to the distribution network equipment.

[0046] It should be noted that, finally, the obtained first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index are respectively compared with the corresponding preset first threshold value of the distribution network equipment, the preset second threshold value of the distribution network equipment, and the preset third threshold value of the distribution network equipment, and the first, second, and third threshold comparison results are obtained respectively. The preset first / second / third threshold values ​​of the distribution network equipment are threshold interval segments, corresponding to the health status level. The corresponding health status level is obtained according to the target interval segment in which the threshold comparison falls. For example, the threshold interval segment corresponding to the preset first threshold value of the distribution network equipment is divided into [0, 0.37), [0.37, 0.55), [0.55, 0.79), [0.79, 1.0], which correspond to the first health status levels of four, three, two, and one respectively. If the threshold comparison result of the first prediction data of the distribution network equipment falls into the second interval segment, its first health status level is level three. Then, the first, second, and third health status levels obtained by comparison are recorded, and the distribution network equipment is controlled for power supply.

[0047] According to an embodiment of the present invention, the further embodiment includes: Processing the operating characteristic data using edge computing and matter-element extension theory evaluation methods to obtain a first equipment health prediction result; Processing the operating characteristic data using multivariate information and fuzzy mathematical evaluation methods to obtain a second equipment health prediction result; Performing statistical processing on the first device health prediction result and the second device health prediction result to obtain standard deviation data; If the standard deviation data is less than a first preset standard deviation, averaging the first device health prediction result and the second device health prediction result to obtain an average value, and using the average value as the device health correction prediction result; Compare the device health correction prediction result with the preset device health threshold to obtain a threshold comparison result; The health status level of the target distribution network device is obtained according to the interval range of the threshold comparison result.

[0048] It should be noted that in addition to obtaining the health status prediction of distribution network equipment through the prediction method of digital twin technology, it can also be obtained through edge computing and matter-element extension theory evaluation method and multivariate information and fuzzy mathematics evaluation method. Among them, the evaluation method based on edge computing and matter-element extension theory is to use edge computing technology, combined with matter-element extension theory, consider multiple factors such as safety, economy, reliability, etc., and quantitatively evaluate the health status of the distribution network by establishing an evaluation system and determining indicator weights. The multivariate information and fuzzy mathematics evaluation method comprehensively considers the health of the equipment layer and the grid layer, introduces fuzzy mathematics theory, and uses the membership function to evaluate the health level. It also combines the improved DS evidence theory model to perform uncertainty reasoning and information fusion on the two. Finally, the standard deviation is obtained based on the obtained first equipment health prediction result and the second equipment health prediction result. If the standard deviation data is less than the first preset standard deviation, the average is calculated and the average value is used as the final equipment health correction prediction result, thereby improving the accuracy of the health prediction.

[0049] The third aspect of the present invention provides a readable storage medium, which stores a program for a digital power supply and distribution network equipment health status prediction method. When the program for a digital power supply and distribution network equipment health status prediction method is executed by a processor, the steps of the digital power supply and distribution network equipment health status prediction method as described in any one of the above items are implemented.

[0050] The present invention discloses a method, system and medium for predicting the health status of digital power supply distribution network equipment. By collecting characteristic information and operation characteristic data of the target distribution network equipment, a digital twin of the distribution network equipment is generated and virtually displayed, the power demand data of the target power supply system is obtained, and the operation data of the distribution network equipment for virtual power supply to the power supply system is combined with the collected twin. The first and second prediction data of the distribution network equipment and the distribution network power supply performance indicators are processed to obtain the first, second and third health status levels, which are then compared with the corresponding thresholds to obtain and record the first, second and third health status levels; thereby realizing the health prediction technology of the digital power supply distribution network equipment under the virtual distribution network operation state.

[0051] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0052] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0053] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0054] Those skilled in the art will appreciate that all or part of the steps of the above-mentioned method embodiments may be implemented by hardware related to program instructions, and the aforementioned program may be stored in a readable storage medium. When the program is executed, the program executes the steps of the above-mentioned method embodiments. The aforementioned storage medium includes various media that can store program codes, such as mobile storage devices, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0055] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as standalone products, they can also be stored on a readable storage medium. Based on this understanding, the technical solutions of the embodiments of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This software product, stored on a storage medium, includes instructions for enabling a computer device (such as a personal computer, server, or network device) to execute all or part of the methods described in the various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as removable storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A digital power distribution network equipment health status prediction method, characterized in that: The following steps are involved: Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; Generate digital twins of distribution network equipment based on feature information and operational feature data, and perform virtual displays; Obtaining power demand data of a target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; Processing the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index; A threshold comparison is performed based on the first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them.

2. The method for predicting the health status of digital power distribution network equipment according to claim 1 is characterized in that: The collecting characteristic information of the target distribution network device and obtaining the operating characteristic data of the target distribution network device within a first preset time period includes: Collect characteristic information of target distribution network equipment, including equipment model, performance parameters, power and self-test status information; Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

3. The method for predicting the health status of digital power distribution network equipment according to claim 2 is characterized in that: The collecting virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system and extracting the distribution network device operation data includes: Performing virtual distribution network operations based on the digital twin of the distribution network equipment and the target power supply system; Acquire virtual operation information of the virtual distribution network operation within the second preset time period; Extract distribution network equipment operation data based on virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; The equipment statistical data includes the number of fault alarms and the number of downtimes; The power supply data includes virtual power supply amount and virtual power supply load.

4. The method for predicting the health status of digital power distribution network equipment according to claim 3 is characterized in that: The step of processing the distribution network equipment operation data to obtain the first prediction data and the second prediction data of the distribution network equipment includes: The voltage fluctuation rate, three-phase imbalance, and harmonic content are processed by a preset equipment health assessment model to obtain first prediction data of the distribution network equipment; A weighted statistical analysis is performed based on the number of fault alarms and the number of downtimes to obtain second prediction data for the distribution network equipment.

5. The method for predicting the health status of digital power supply distribution network equipment according to claim 4 is characterized in that: The processing according to the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index includes: Processing is performed according to the virtual power supply amount and virtual power supply load as well as the power consumption and power load to obtain a distribution network power supply performance indicator.

6. The method for predicting the health status of digital power distribution network equipment according to claim 5 is characterized in that: The comparing thresholds based on the first prediction data of the distribution network device, the second prediction data of the distribution network device, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them includes: Perform threshold comparisons based on the first prediction data and the second prediction data of the distribution network device, the distribution network power supply performance index, and the corresponding preset first threshold value of the distribution network device, the preset second threshold value of the distribution network device, and the preset third threshold value of the distribution network device to obtain first, second, and third threshold value comparison results; According to the comparison results of the first, second and third threshold values, the first, second and third health status levels are obtained accordingly; The first, second, and third health status levels are recorded, and performance adjustments are made to the distribution network equipment.

7. A digital power supply and distribution network equipment health status prediction system, characterized by: The system includes: a memory and a processor, wherein the memory includes a program of a digital power supply distribution network equipment health status prediction method, and when the program of the digital power supply distribution network equipment health status prediction method is executed by the processor, the following steps are implemented: Collect characteristic information of the target distribution network device and obtain operating characteristic data of the target distribution network device within a first preset time period; Generate digital twins of distribution network equipment based on feature information and operational feature data, and perform virtual displays; Obtaining power demand data of a target power supply system corresponding to the target distribution network device within a second preset time period, including power consumption and power load; Collect virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system, and extract distribution network device operation data; Processing the distribution network equipment operation data to obtain first prediction data and second prediction data of the distribution network equipment; Processing the distribution network equipment operation data and the power demand data to obtain the distribution network power supply performance index; A threshold comparison is performed based on the first prediction data of the distribution network equipment, the second prediction data of the distribution network equipment, and the distribution network power supply performance index to obtain the first, second, and third health status levels and record them.

8. The digital power supply and distribution network equipment health status prediction system according to claim 7 is characterized in that: The collecting characteristic information of the target distribution network device and obtaining the operating characteristic data of the target distribution network device within a first preset time period includes: Collect characteristic information of target distribution network equipment, including equipment model, performance parameters, power and self-test status information; Obtain operating characteristic data of the target distribution network equipment within a first preset time period, including electrical parameter record data, equipment status record data, and maintenance record data.

9. The digital power supply and distribution network equipment health status prediction system according to claim 8 is characterized in that: The collecting virtual operation information of the distribution network device digital twin performing virtual power supply to the target power supply system and extracting the distribution network device operation data includes: Performing virtual distribution network operations based on the digital twin of the distribution network equipment and the target power supply system; Acquire virtual operation information of the virtual distribution network operation within the second preset time period; Extract distribution network equipment operation data based on virtual operation information, including electrical performance parameters, equipment statistics, and power supply data; The electrical performance parameters include voltage fluctuation rate, three-phase imbalance, and harmonic content; The equipment statistical data includes the number of fault alarms and the number of downtimes; The power supply data includes virtual power supply amount and virtual power supply load.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a digital power supply and distribution network equipment health status prediction method, system and medium program. When the digital power supply and distribution network equipment health status prediction method, system and medium program are executed by a processor, the steps of the digital power supply and distribution network equipment health status prediction method as described in any one of claims 1 to 6 are implemented.

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