A power operation fault monitoring and early warning system based on digital twin

Abstract

The present invention relates to the field of power monitoring technology, and specifically to a power operation fault monitoring and early warning system based on digital twins, comprising: a positioning acquisition module, for acquiring operation data of a power bus, the operation data including current, voltage, temperature, and vibration; generating a positioning data set according to the topological structure of the power bus; the positioning data set including the operation data, the topological structure of the power bus, and the actual connection relationship; a risk point positioning module, for analyzing the current state of the power bus according to the positioning data set, acquiring the fluctuation proportion coefficient and the topological structure judgment coefficient of the power bus, and identifying the risk points during the operation of the power bus based on the fluctuation proportion coefficient and the topological structure judgment coefficient; and improving the safety and stability of the power system.

Description

Technical Field

[0001] The present invention relates to the field of power monitoring technology, and specifically to a power operation fault monitoring and early warning system based on digital twins. Background Art

[0002] During power system operation, the power bus, as a key link in the transmission and distribution of electrical energy, has a significant impact on the stability and security of the power system. However, due to the complexity and diversity of the power bus operating environment, its operating data often exhibits nonlinear and time-varying characteristics, posing significant challenges to risk point monitoring and early warning.

[0003] Traditional power bus monitoring methods rely primarily on manual inspections and simple threshold alarms. Manual inspections suffer from low monitoring efficiency and delayed response times, making it difficult to promptly identify and address risk points. Simple threshold alarms, on the other hand, lack precision and flexibility and are susceptible to external interference and false alarms.

[0004] For example, Chinese patent publication number CN112256922A discloses a method for quickly identifying faulty power outages, including constructing a ledger tree topology model based on the topological structure of the distribution network; constructing a hierarchical data storage model of the ledger tree topology model based on the ledger tree topology model; determining the circuit breakers, and constructing a circuit breaker data storage model in combination with the real-time on-off status of each circuit breaker; when a fault occurs, obtaining a circuit breaker set based on the previous level tracing target in the circuit breaker data storage model, and obtaining a power supply path set in combination with the previous level tracing target in the hierarchical data storage model; obtaining the current value of each device before and after the fault report in the power supply path set to calculate the equipment load rate of each power supply path, and screening out the optimal fault troubleshooting path based on the equipment load rate.

[0005] For example, Chinese patent publication number CN116595693A discloses a method for analyzing the survivability of power optical cable networks, which relates to the field of power optical cable technology. It addresses the problems of existing power optical cable network survivability analysis methods, such as the failure to effectively utilize node spatial location information, the limitations of analysis results, and the high complexity of the algorithm. The method of the present invention can accurately determine the most vulnerable area of ​​the power optical cable network under different target damage levels, and uses the damage radius of the minimum damage circle under different target damage levels to express the survivability of the power optical cable network in the face of spatial range damage of that damage level. By analyzing the nodes covered by the minimum damage circle under different damage levels, it can guide the design of strategies for the power optical cable network to respond to natural disasters or malicious attacks; by observing the location of the minimum damage circle, it helps to locate network problems in special circumstances.

[0006] Existing technologies describe how to track and handle risks in power systems. However, when a power system is monitored using a power bus, it is not only necessary to identify the damage and power supply paths at these risk points, but also to adaptively locate the current risk points in real time and verify whether the current positioning is accurate and whether there is a correlation between the located risk points, so as to achieve control of the overall power system and improve the accuracy of early warnings. Summary of the Invention

[0007] In order to solve the above technical problems, the technical solution adopted by the present invention is: a power operation fault monitoring and early warning system based on digital twins, including: a positioning acquisition module, used to obtain the operation data of the power bus, the operation data including current, voltage, temperature, and vibration; the operation data is generated according to the topological structure of the power bus to generate a positioning data set; the positioning data set includes the operation data, the topological structure of the power bus, and the actual connection relationship.

[0008] The risk point positioning module is used to analyze the current state of the power bus according to the positioning data set, obtain the fluctuation proportion coefficient and topology structure judgment coefficient of the power bus, and identify the risk points during the operation of the power bus based on the fluctuation proportion coefficient and topology structure judgment coefficient.

[0009] The fault location module is used to identify risk points, determine the logical associations and fault levels between risk points, and obtain the correlation coefficients between the logical associations in the risk points and the actual operating nodes.

[0010] The positioning compensation module is used to determine the positioning error generated by the risk point during positioning based on the logical association between the risk points, and obtain the positioning compensation coefficient related to the positioning error.

[0011] The loss assessment module is used to obtain the spatial position and damage data of the power bus corresponding to the risk point when locating the risk point, and identify the warning loss degree of the risk point.

[0012] The beneficial effects of the present invention are: 1. The present invention obtains the operating data of the power bus, such as current, voltage, temperature, vibration, etc., in real time through the positioning acquisition module, and generates a positioning data set based on the topological structure of the power bus, providing data support for the precise positioning of risk points.

[0013] 2. The present invention uses a risk point positioning module to analyze the current state of the power bus according to the positioning data set, obtain the fluctuation proportion coefficient and the topology structure judgment coefficient, so as to accurately identify the risk points during the operation of the power bus and issue an early warning signal.

[0014] 3. The present invention uses a fault location module to identify risk points, determine the logical associations and fault levels between risk points, and obtain node correlation coefficients, providing a scientific basis for quantitative evaluation of fault levels.

[0015] 4. The present invention adopts a positioning compensation module to determine the positioning error generated by the risk point during positioning based on the logical association between the risk points, and calculates the positioning compensation coefficient to correct the positioning result, thereby improving the accuracy of positioning.

[0016] 5. The present invention uses a loss assessment module to obtain the spatial position and damage data of the power bus corresponding to the risk point location, identify the warning loss degree, and provide an effective reference basis for decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings and examples.

[0018] Figure 1 It is a system framework diagram of a power operation fault monitoring and early warning system based on digital twins.

[0019] Figure 2 It is a system diagram of a power operation fault monitoring and early warning system based on digital twins.

[0020] Figure 3 It is a flow chart of the risk point location module of the power operation fault monitoring and early warning system based on digital twin.

[0021] Figure 4 It is a flow chart of the positioning compensation module of the power operation fault monitoring and early warning system based on digital twin. DETAILED DESCRIPTION

[0022] The following embodiments of the present invention are described in detail. The embodiments described below are exemplary and are only used to explain the present invention, and are not to be construed as limiting the present invention. Where specific techniques or conditions are not specified in the embodiments, the techniques or conditions described in the literature in the art or in the product specifications shall be followed.

[0023] See Figure 1 ,A power operation fault monitoring and early warning system based on digital twins, includes: a positioning acquisition module, a risk point positioning module, a fault positioning module, a positioning compensation module, and a loss assessment module.

[0024] The positioning acquisition module obtains the operating data of the power bus in real time, including current, voltage, temperature, vibration, etc., and generates a positioning data set based on the topological structure of the power bus; and transmits the positioning data set to the risk point positioning module.

[0025] The risk point positioning module receives the positioning data set transmitted by the positioning acquisition module, analyzes the current state of the power bus, obtains the fluctuation proportion coefficient and the topology structure judgment coefficient, and identifies the risk points during the operation of the power bus; and transmits the risk point information including the location of the risk point, fluctuation proportion coefficient, topology structure judgment coefficient, etc. to the fault positioning module and the positioning compensation module.

[0026] The fault location module receives the risk point information transmitted by the risk point location module, identifies the risk points, determines the logical association and fault degree between the risk points, obtains the correlation coefficient between the logical association and the actual operating node; and transmits the fault point information including location, fault degree, logical association, etc. to the positioning compensation module and the loss assessment module.

[0027] The positioning compensation module receives the fault point information transmitted by the fault positioning module, determines the positioning error based on the logical association between the risk points, and calculates the positioning compensation coefficient; and transmits the positioning compensation coefficient to the fault positioning module and the loss assessment module.

[0028] The loss assessment module receives information transmitted by the fault location module and the positioning compensation module, identifies the early warning loss degree of the risk point, and outputs the loss assessment results to the system administrator or operation and maintenance personnel so that corresponding processing measures can be taken.

[0029] like Figure 2 As shown, the positioning acquisition module is used to obtain the operating data of the power bus, which includes current, voltage, temperature, and vibration; the operating data is generated according to the topological structure of the power bus to generate a positioning data set; the positioning data set includes the operating data, the topological structure of the power bus, and the actual connection relationship. At this time, the actual connection relationship represents the actual position of the power bus and the corresponding connected equipment.

[0030] The risk point positioning module is used to analyze the current state of the power bus according to the positioning data set, obtain the fluctuation proportion coefficient and topology structure judgment coefficient of the power bus, and identify the risk points during the operation of the power bus based on the fluctuation proportion coefficient and topology structure judgment coefficient.

[0031] The fault location module is used to identify risk points, determine the logical associations and fault levels between risk points, and obtain the correlation coefficients between the logical associations in the risk points and the actual operating nodes.

[0032] The positioning compensation module is used to determine the positioning error generated by the risk point during positioning based on the logical association between the risk points, and obtain the positioning compensation coefficient related to the positioning error.

[0033] The loss assessment module is used to obtain the spatial position and damage data of the power bus corresponding to the risk point when locating the risk point, and identify the warning loss degree of the risk point.

[0034] In one embodiment of the present invention, a positioning acquisition module is used to obtain operating data of a power bus, the operating data including current, voltage, temperature, and vibration; the operating data is generated into a positioning data set according to the topological structure of the power bus; the positioning data set includes the operating data, the topological structure of the power bus, and the actual connection relationship.

[0035] In this module, the topology of the power bus shows the IoT connection between the power bus and other electrical equipment, as well as the logical relationship of the bus in the power system, such as power distribution, load connection, etc., to indicate how the current power bus is connected to the equipment.

[0036] In this topology, the location of the power bus is described in detail, and sensors are used to obtain the operating data of the power bus at each location. For example, voltage and current sensors are used to obtain current and voltage data, temperature sensors are used to obtain the operating temperature of the power bus, and vibration sensors are used to obtain the vibration of the physical structure where the power bus is located to determine whether the structure supporting the power bus is stable.

[0037] In one embodiment of the present invention, a risk point positioning module is used to analyze the current state of the power bus based on the positioning data set, obtain the fluctuation proportion coefficient and topology structure judgment coefficient of the power bus, and identify the risk points during the operation of the power bus based on the fluctuation proportion coefficient and the topology structure judgment coefficient.

[0038] When locating risk points, the current, voltage, temperature, and vibration will be identified in turn, and points in the operating data where fluctuations exceed the preset fluctuation ratio will be identified. The topological position of the power bus will be verified to be normal. Points with abnormal topological positions and exceeding the preset fluctuation ratio will be regarded as risk points at this time.

[0039] like Figure 3 As shown, the implementation method of analyzing the state of the power bus includes obtaining the fluctuation proportional coefficient of the power bus based on the operation data.

[0040] Based on the topological structure of the power bus, a topological structure judgment coefficient of the power bus is obtained.

[0041] Based on the fluctuation ratio coefficient and topology structure judgment coefficient, the risk points of the power bus are output.

[0042] The fluctuation ratio coefficient is a comprehensive coefficient obtained after identifying the current, voltage, temperature, and vibration. This coefficient includes the risk points in the current, voltage, temperature, and vibration. These risk points can be verified in the form of average value, maximum value, and fluctuation range when identifying. At this time, in order to further describe the possible fluctuations, different situations will be selected for these four values ​​for processing; for example, the current and voltage tend to operate stably, and the overall data needs to be stable. The average value and fluctuation range can be used for identification; the temperature tends to identify abnormal temperatures, and the maximum value can be used for identification; vibration is easy to identify when the structure is unstable. At this time, it is expressed according to the frequency of occurrence and frequency interval to screen out the risk points in the fluctuation ratio.

[0043] The fluctuation proportional coefficient is expressed as sequentially obtaining a first proportional coefficient related to current and voltage, a second proportional coefficient related to temperature, and a third proportional coefficient related to vibration, and combining the first proportional coefficient, the second proportional coefficient, and the third proportional coefficient into the fluctuation proportional coefficient.

[0044] Therefore, the processing method for the first proportional coefficient related to the current and voltage in the fluctuation proportional coefficient is expressed as obtaining the index values ​​of the standard deviation, average value and fluctuation range of the current and voltage, and calculating the first proportional coefficient.

[0045] ;in, represents the first proportionality coefficient, represents the standard deviation of the current, represents the average value of the current, The index value indicating the fluctuation range of the current, which represents the difference between the upper limit and the lower limit of the fluctuation range; Indicates the standard value of the current fluctuation range index value. At this time, the standard value of the current fluctuation range index value indicates the average value corresponding to the fluctuation range index value in the historical data; represents the standard deviation of the voltage, represents the average value of the voltage, An indicator value indicating the voltage fluctuation range. Indicates the standard value of the voltage fluctuation range index value. At this time, the voltage fluctuation range index value is consistent with the current fluctuation range index value. The average value of the fluctuation range in the historical data is selected as the standard value, and the difference between the upper limit and the lower limit is used as the index value; represents the weight coefficient of current, Indicates the voltage weight coefficient. The weight coefficient is set to 0.5 and 0.5 in the order of current and voltage. Represents an exponential constant.

[0046] The second proportional coefficient related to temperature in the fluctuation proportional coefficient is expressed as follows: the maximum value and the average value of the temperature are obtained, and the second proportional coefficient is calculated.

[0047] ;in, represents the second proportionality coefficient, Indicates the maximum temperature, Indicates the average temperature. By comparing the maximum value with the average value, we can identify the proportion by which the current temperature exceeds the threshold when a corresponding abnormality occurs.

[0048] The third proportional coefficient related to vibration in the fluctuation proportional coefficient is expressed as follows: the occurrence frequency and frequency interval of the vibration are acquired to obtain the third proportional coefficient.

[0049] ;in, represents the third proportionality coefficient, represents the frequency of vibration, Indicates the standard value of the frequency of vibration. represents the frequency interval of vibration, Indicates the standard value of the frequency interval of vibration; The weight coefficient representing the frequency of occurrence of vibration, The weight coefficient representing the frequency interval of vibration is set to 0.6 and 0.4 in the order of occurrence frequency and frequency interval. The standard value of the above-mentioned vibration occurrence frequency is the average value of the vibration occurrence frequency in the historical data. The standard value of the frequency interval of vibration will select the average value of the frequency interval in the historical data.

[0050] At this time, the fluctuation proportional coefficient can be expressed as directly outputting the three coefficients after combination, without performing comprehensive calculation on the three coefficients, or the first proportional coefficient, the second proportional coefficient, and the third proportional coefficient can be comprehensively calculated to obtain the currently required fluctuation proportional coefficient.

[0051] For example, the volatility ratio It can be expressed as, ; When considering these coefficients comprehensively, the volatility ratio coefficient needs to consider the comprehensive situation of the three coefficients in order to obtain the coefficient of the corresponding average situation as much as possible to identify the risk points in the current situation.

[0052] When obtaining the topology judgment coefficient of the power bus, it is mainly judged whether the position of the power bus is abnormal, and the relative load of the equipment connected to the power bus to determine whether there is a risk point in the current power bus in the corresponding topology structure.

[0053] Therefore, the topology structure judgment coefficient is expressed as follows: according to the actual connection relationship of the power bus, a topology diagram is constructed; the topology diagram can be represented by a graph theory model, where nodes represent electrical equipment and edges represent the connection relationship between devices.

[0054] The position deviation between the actual position and the expected position of the node in the topology graph, as well as the load deviation between the actual load and the expected load of the node in the topology are calculated, and the load deviation and position deviation are combined into the topology structure judgment coefficient.

[0055] The location at this time can be obtained through images taken by drones or corresponding patrol robots. The load at this time represents the electric energy value consumed by the nodes in the topology diagram. The current and voltage sensors can be used to measure the electric energy value consumed at the corresponding nodes, and combined with the expected load to determine the risk points in the topology structure at this time.

[0056] Since the position of the corresponding power equipment will basically not change after the installation is completed, when obtaining the position deviation, in addition to adjusting the deviation according to the actual position of the power equipment, it can also be identified according to the location where the load deviation occurs, that is, the location where the load exceeds the average value is identified as the actual location of the node at this time, so as to determine the risk point where the corresponding deviation occurs.

[0057] The topology structure judgment coefficient is expressed as follows: obtain the Pearson correlation coefficient corresponding to the load deviation and the position deviation, and combine the Pearson correlation coefficient corresponding to the load deviation and the position deviation, the load deviation, and the position deviation to obtain the topology structure judgment coefficient.

[0058] ;in, represents the topological structure judgment coefficient, represents the Pearson correlation coefficient corresponding to load deviation and position deviation, represents the weight coefficient of the Pearson correlation coefficient, Indicates load deviation, Indicates the maximum value of load deviation, represents the weight of the load deviation, Indicates position deviation, Indicates the maximum value of position deviation, Represents the weight of the position deviation.

[0059] The Pearson correlation coefficient corresponding to the above-mentioned load deviation and position deviation is obtained by taking the load deviation and position deviation as input, and calculating the load deviation and position deviation with the average value of the load deviation and position deviation at the corresponding position in the historical data. At the same time, the position deviation is expressed as the distance difference at this time.

[0060] When taking the fluctuation proportion coefficient and the topology structure judgment coefficient as the basis to obtain the risk points of the power bus, the components of the fluctuation proportion coefficient and the topology structure judgment coefficient, as well as the size of the corresponding coefficient values, will be judged in turn. When a single coefficient value exceeds the standard threshold, the corresponding data will be regarded as a risk point. When the comprehensive coefficient value exceeds the standard threshold, the data corresponding to the comprehensive coefficient value will also be regarded as a risk point, so as to obtain all the currently identified risk points. This standard coefficient will use the average value of the corresponding data in the historical data to represent the standard threshold of the coefficient.

[0061] For example, the first proportional coefficient, the second proportional coefficient, the third proportional coefficient, the fluctuation proportional coefficient and the topological structure judgment coefficient are taken as basic conditions and compared with the average values ​​of the historical data corresponding to the first proportional coefficient, the second proportional coefficient, the third proportional coefficient, the fluctuation proportional coefficient and the topological structure judgment coefficient under normal operation of the power bus. When there is a coefficient that exceeds the deviation range of 10% compared with the average value of its corresponding historical data, the location of the corresponding data is regarded as a risk point.

[0062] In one embodiment of the present invention, the fault location module is used to identify risk points, determine the logical associations and fault levels between risk points, and obtain correlation coefficients between logical associations in risk points and actually running nodes;

[0063] The logical association at this time is used to identify whether there is a logical association between risk points, as well as the association of the current risk point with the identified risk problem, and to quantify these risk problems to express the degree of failure of the risk point; the node association of the risk point is quantified through logical association and failure degree, so as to obtain a comprehensive node association coefficient to express the process of monitoring the power system using the power bus.

[0064] When locating a fault, you can first obtain the logical association between risk points and set a logical association matrix between risk points. The logical association matrix is ​​represented as an n×n matrix, where n represents the number of risk points. Each element in the logical association matrix indicates whether there is a logical association between risk points. If there is a logical association, it is set to 1, and if there is no logical association, it is set to 0.

[0065] The node association coefficient can be expressed as the product of the logical association matrix and the fault degree to represent the corresponding node association coefficient between the current risk points, thereby obtaining whether the nodes between the risk points are associated.

[0066] The degree of failure can be expressed as the product of the topological structure judgment coefficient and the fluctuation proportion coefficient of the corresponding risk point.

[0067] Therefore, the node association coefficient is expressed as The logical association matrix corresponding to the logical association, the fluctuation proportion coefficient corresponding to the fault degree, and the topological structure judgment coefficient are obtained to calculate the node association coefficient.

[0068] ;in, represents the node association coefficient, Indicates the number of risk points. The value range of i and j is 1 to n. Represents the index value of the i-th risk point and the j-th risk point in the logical association matrix, represents the volatility coefficient of the i-th risk point, represents the topological structure judgment coefficient of the i-th risk point, 、 、 represents the weight coefficient; , , .

[0069] At this time, the degree of possible failure can be quantified by the fluctuation proportion coefficient and the topological structure judgment coefficient, and the relationship between these risk points can be further described according to the relevant logical relationship matrix, so that when verifying the node association, it is more inclined to consider the fluctuations and deviations in the current operating data; making the overall consideration more complete, and being able to improve the early warning and monitoring effect of the system.

[0070] In one embodiment of the present invention, the positioning compensation module determines the positioning error generated by the risk point during positioning based on the logical association between the risk points, and obtains a positioning compensation coefficient related to the positioning error;

[0071] The positioning compensation coefficient is used to adjust the positioning error caused by the risk point. The risk point is obtained through the data measured on the power bus. If the currently measured risk point is easily affected by the surrounding power bus, it will cause the risk point to be unable to directly locate the problem location when identifying the risk point. In this case, it is necessary to verify the probability of the corresponding risk classification under the corresponding logical relationship of the current risk point to identify the positioning error, so as to obtain the positioning compensation coefficient.

[0072] Identification of positioning errors can be done by clustering, where the risk points are classified into clusters. After the classification is completed, the positioning error of the risk point is calculated for each cluster. The positioning error measures the difference between the position where the risk point is located and its actual position, so as to more accurately identify the current error and improve the accuracy of identifying risk points.

[0073] like Figure 4As shown in the figure, the positioning compensation coefficient is obtained by obtaining the positioning error of the risk point, determining the average recognition period, maximum positioning error and average positioning error ratio of the risk point under the corresponding positioning error; based on the average recognition period, maximum positioning error and average positioning error ratio of the risk point, the risk points are decomposed and sorted, and the distribution degree of the risk points is calculated; according to the distribution degree of the risk points, the risk classification probability of the current risk point is output, and the positioning compensation coefficient is calculated according to the risk classification probability and the positioning error.

[0074] The positioning error can be calculated by combining the position deviation calculated when screening risk points with the actual position of the risk point. The average recognition period represents the average time from the appearance to the recognition of each risk point, which is used to describe the number of risk points existing in the corresponding time period. The average positioning error ratio represents the ratio of the average value of the current positioning error to the average value of the positioning error in the historical data. At this time, the risk points can be divided into multiple areas, and the multiple areas can be sorted and continuously iterated to calculate the positioning compensation coefficient.

[0075] The method of decomposing and sorting risk points is to calculate the assessment value of the risk point according to the average recognition period, maximum positioning error and average positioning error ratio of the risk point, sort the risk points according to the assessment value, and obtain the distribution degree of the risk point based on the cumulative distribution value of the risk point.

[0076] The assessment value of the risk point can be expressed as, ;in, Indicates the assessment value of the risk point, represents the average recognition period, Indicates the maximum value of the average recognition period, represents the weight coefficient of the average recognition period, represents the maximum positioning error, Indicates the standard value of the maximum positioning error. The standard value of the maximum positioning error is expressed by the average value of the maximum positioning error in the historical data; represents the weight coefficient of the maximum positioning error, represents the average positioning error ratio, The weight coefficient representing the average positioning error ratio.

[0077] At this time, the risk point can be evaluated by its average recognition period, maximum positioning error, and average positioning error ratio. At the same time, the weight coefficients of the average recognition period, maximum positioning error, and average positioning error ratio can be set to 0.3, 0.4, and 0.3 respectively to describe the positioning error of the current risk point.

[0078] The distribution degree of risk points is expressed as the ratio of the number of risk points determined according to the evaluation value of the risk point to the total number of risk points under the corresponding positioning error value. The obtained distribution degree value is used to represent the risk classification probability of the current risk point; the evaluation value of the risk point can represent the risk classification when classification is performed.

[0079] The positioning compensation coefficient is expressed as follows: based on the distribution degree of risk points, the risk classification probability of the risk points is obtained, and the positioning compensation coefficient is calculated.

[0080] ;in, Indicates the positioning compensation coefficient, Represents the risk classification, represents the positioning error, represents the risk classification probability of the i-th risk point under the corresponding positioning error, represents the positioning error of the i-th risk point, Indicates the basic value of positioning compensation. Indicates the number of risk points, and the value of i ranges from 1 to n.

[0081] The positioning compensation coefficient obtained at this time can represent the corresponding distance value that should be compensated when performing positioning analysis on the risk point, so as to reduce the error generated when locating the risk point.

[0082] In one embodiment of the present invention, the loss assessment module is used to obtain the spatial position and damage data of the power bus corresponding to the risk point when locating the risk point, determine the relative loss degree of the risk point; and identify the warning loss degree of the risk point based on the relative loss degree of the risk point.

[0083] The spatial position selected at this time represents the distribution of risk points according to their actual positions, and this actual position is combined with the damage data to verify the specific situation of the damage data at different spatial positions.

[0084] The damage data is expressed as conductor damage and damage radius generated on the power bus.

[0085] Wire damage: Wires can become damaged during use due to various factors, such as mechanical stress, environmental corrosion, high temperatures, and current surges. This damage can manifest as surface wear, internal wire breakage, or insulation damage. Wire damage indicates the type of damage currently occurring.

[0086] Damage Radius: The damage radius is the extent or impact of the damaged area on the conductor. This range can be linear (along the conductor length) or radial (along the conductor diameter). In this case, the damage radius includes the measurement method and length of the loss radius to indicate the physical damage to the power busbar at the time of the risk point.

[0087] Damage radius can be measured in the following ways: Linear damage: Damage along the length of the conductor, usually expressed as damage length. For example, the surface wear length of a certain section of the conductor is 1 meter. Radial damage: Damage along the diameter of the conductor, usually expressed as damage depth or width. For example, the surface wear depth at a certain point on the conductor is 1 mm, or the width of the insulation damage is 2 mm.

[0088] The damage radius monitoring method can use multiple sensors for measurement, such as strain sensors: monitoring the strain of the wire and detecting the tensile or compressive deformation of the wire; corrosion sensors: monitoring the corrosion of the wire and detecting chemical changes on the surface of the wire; visual inspection: using cameras or drones for visual inspection to observe the surface damage of the wire; ultrasonic inspection: using ultrasonic flaw detectors to detect damage inside the wire.

[0089] Wire damage can affect its electrical performance, such as increasing resistance, reducing conductivity, and causing local overheating. Wire damage can reduce its mechanical strength, potentially causing the wire to break or fall off, affecting the physical structure of the power system. Severe wire damage can lead to safety accidents such as short circuits and fires, threatening the safety of personnel and equipment.

[0090] By comprehensively processing the physical damage to the power bus and the risk points identified from the operating data, the specific types of risk points currently identified can be further shortened, making the risk point description more specific. At the same time, the effect of risk point positioning and early warning can be improved, risk points can be discovered in a timely manner, and the service life of the equipment can be extended.

[0091] The acquired damage data can be used to quantify the damage caused to the current power bus based on the identified conductor damage and damage radius. For example, the damage degree value can be obtained from the database according to the conductor damage at that time, and the damage difference between the corresponding damage radius and the average damage radius in the historical data can be calculated. Finally, the node association coefficient and the positioning compensation coefficient are combined to obtain the warning degree of the risk point.

[0092] The damage degree value at this time can be obtained by image recognition to obtain the damage type of the corresponding risk point of the current power bus during monitoring, and a score is set for each damage type.

[0093] Therefore, the early warning loss degree is expressed as follows: obtain the damage degree value and damage difference corresponding to the damage data, take the damage degree value, damage difference, node correlation coefficient and positioning compensation coefficient as input, and calculate the early warning loss degree.

[0094] ;in, Indicates the warning loss degree, represents the node association coefficient, Indicates the positioning compensation coefficient, Indicates the damage degree value, represents the damage difference, It represents the standard value of the damage difference. At this time, the standard value of the damage difference is the average value of the damage difference in the historical data. At this time, the damage difference is equivalent to a distance value, and the positioning compensation coefficient is also equivalent to the distance value. The purpose of combining the damage difference with the positioning compensation coefficient is to determine whether the size of the damage radius can affect the compensation value when locating the risk point at the corresponding position. The node association coefficient is simply used to describe the overall value of the current risk point when the corresponding association coefficient exists. The damage degree value supplements the final calculated value to make the warning degree more in line with the overall requirements.

[0095] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0096] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0097] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0099] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0100] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A power operation fault monitoring and early warning system based on digital twins, characterized by: include: Positioning acquisition module, used to obtain the operating data of the power bus, including current, voltage, temperature, and vibration; Generate a positioning data set based on the topological structure of the power bus according to the operating data; the positioning data set includes the operating data, the topological structure of the power bus, and the actual connection relationship; The risk point positioning module is used to analyze the current state of the power bus based on the positioning data set, obtain the fluctuation ratio coefficient and topology structure judgment coefficient of the power bus, and identify the risk points during the operation of the power bus based on the fluctuation ratio coefficient and topology structure judgment coefficient; The fault location module is used to identify risk points, determine the logical associations and fault levels between risk points, and obtain the correlation coefficients between the logical associations in the risk points and the actual operating nodes; The positioning compensation module is used to determine the positioning error generated by the risk point during positioning based on the logical association between the risk points, and obtain the positioning compensation coefficient related to the positioning error; The loss assessment module is used to obtain the spatial position and damage data of the power bus corresponding to the risk point when locating the risk point, and identify the early warning loss degree of the risk point; The implementation methods for analyzing the status of the power bus include: Based on the operating data, obtain the fluctuation proportional coefficient of the power bus; Based on the topological structure of the power bus, a topological structure judgment coefficient of the power bus is obtained; Based on the fluctuation ratio coefficient and topology structure judgment coefficient, the risk points of the output power bus are determined; The fluctuation proportional coefficient is expressed as follows: a first proportional coefficient related to current and voltage, a second proportional coefficient related to temperature, and a third proportional coefficient related to vibration are sequentially obtained, and the first proportional coefficient, the second proportional coefficient, and the third proportional coefficient are combined into the fluctuation proportional coefficient; The first proportional coefficient related to the current and voltage in the fluctuation proportional coefficient is processed as follows: the standard deviation, average value, and fluctuation range of the current and voltage are obtained, and the first proportional coefficient is calculated; The second proportional coefficient related to temperature in the fluctuation proportional coefficient is expressed as follows: the maximum value and the average value of the temperature are obtained to calculate the second proportional coefficient; The third proportional coefficient related to vibration in the fluctuation proportional coefficient is expressed as follows: the occurrence frequency and frequency interval of the vibration are obtained to obtain the third proportional coefficient; Fluctuation ratio coefficient It can be expressed as: ； in, represents the first proportionality coefficient, represents the second proportionality coefficient, represents the third proportional coefficient; The topology structure judgment coefficient is expressed as follows: Based on the actual connection relationship of the power bus, a topology diagram is constructed; Calculate the position deviation between the actual position and the expected position of the nodes in the topology graph, as well as the load deviation between the actual load and the expected load of the nodes in the topology; Obtaining the Pearson correlation coefficient corresponding to the load deviation and the position deviation, and combining the Pearson correlation coefficient corresponding to the load deviation and the position deviation, the load deviation, and the position deviation to obtain a topology structure judgment coefficient; ； in, represents the topological structure judgment coefficient, represents the Pearson correlation coefficient corresponding to load deviation and position deviation, represents the weight coefficient of the Pearson correlation coefficient, Indicates load deviation, Indicates the maximum value of load deviation, represents the weight of the load deviation, Indicates position deviation, Indicates the maximum value of position deviation, represents the weight of the position deviation; The positioning compensation coefficient is obtained by obtaining the positioning error of the risk point and determining the average recognition period, maximum positioning error, and average positioning error ratio of the risk point under the corresponding positioning error. Based on the average recognition period, maximum positioning error, and average positioning error ratio of the risk point, the risk points are decomposed and sorted, and the distribution of the risk points is calculated. Based on the distribution of the risk points, the risk classification probability of the current risk point is output, and the positioning compensation coefficient is calculated based on the risk classification probability and the positioning error. The positioning compensation coefficient is expressed as follows: Based on the distribution of risk points, the risk classification probability of the risk points is obtained, and the positioning compensation coefficient is calculated: ； in, Indicates the positioning compensation coefficient, Represents the risk classification, represents the positioning error, represents the risk classification probability of the i-th risk point under the corresponding positioning error, represents the positioning error of the i-th risk point, Indicates the basic value of positioning compensation. Indicates the number of risk points, and the value of i ranges from 1 to n.

2. The digital twin-based power operation fault monitoring and early warning system according to claim 1 is characterized in that: The node correlation coefficient is expressed as follows: the logical correlation matrix corresponding to the logical correlation, the fluctuation ratio coefficient corresponding to the fault degree and the topological structure judgment coefficient are obtained, and the node correlation coefficient is calculated; ； in, represents the node association coefficient, Indicates the number of risk points. The value range of i and j is 1 to n. Represents the index value of the i-th risk point and the j-th risk point in the logical association matrix, represents the volatility coefficient of the i-th risk point, represents the topological structure judgment coefficient of the i-th risk point, 、 、 Represents the weight coefficient.

3. The power operation fault monitoring and early warning system based on digital twin according to claim 1 is characterized in that: The method of decomposing and sorting risk points is to calculate the assessment value of the risk point according to the average recognition period, maximum positioning error and average positioning error ratio of the risk point, sort the risk points according to the assessment value, and obtain the distribution degree of the risk point based on the cumulative distribution value of the risk point: ； in, Indicates the assessment value of the risk point, represents the average recognition period, Indicates the maximum value of the average recognition period, represents the weight coefficient of the average recognition period, represents the maximum positioning error, Indicates the standard value of the maximum positioning error; represents the weight coefficient of the maximum positioning error, represents the average positioning error ratio, The weight coefficient representing the average positioning error ratio; The distribution degree of risk points is expressed as the ratio of the number of risk points determined according to the evaluation value of the risk points to the total number of risk points under the corresponding positioning error value.

4. The power operation fault monitoring and early warning system based on digital twin according to claim 1 is characterized in that: The early warning loss degree is expressed as follows: the damage degree value and damage difference corresponding to the damage data are obtained, and the damage degree value, damage difference, node correlation coefficient and positioning compensation coefficient are used as input to calculate the early warning loss degree; ； in, Indicates the warning loss degree, represents the node association coefficient, Indicates the positioning compensation coefficient, Indicates the damage degree value, represents the damage difference, Indicates the standard value of the damage difference amount.

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