CN117874688A - Power digital anomaly identification method and system based on digital twin - Google Patents
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Abstract
The invention provides a digital twinning-based power digital anomaly identification method and system, and relates to the technical field of power. The method comprises the following steps: acquiring input voltage data, output voltage data and output power data of a transformer in a transformer area, and power consumption data and geographic position data of each ammeter; obtaining a power digital twin model of the transformer area according to the input voltage data, the output power data, the power consumption data and the geographic position data; determining a transformation anomaly score and a district electricity anomaly score; determining a plurality of ammeter clusters according to the geographic position data; determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data; and obtaining the power anomaly score according to the transformation anomaly score, the power utilization anomaly score of the transformer area and the cluster anomaly score. According to the invention, the abnormal conditions of multiple aspects of the transformer area can be observed in real time through the transformer area electric power digital twin model, so that the abnormality can be found in time, and the abnormality identification efficiency is improved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to an electric power digital abnormality identification method and system based on digital twinning.
Background
CN117273323a discloses and provides a digital twinning-based power equipment management method and system, which relate to the technical field of power equipment management, and the method comprises the following steps: acquiring equipment composition structure and equipment operation data of target ultra-high voltage power equipment in a target area; constructing a target digital twin model; obtaining vibration intensity data; obtaining temperature distribution data; obtaining corona discharge data; obtaining a seed point set; the method comprises the steps of obtaining an abnormal identification point, managing an insulating layer of a target ultra-high voltage power device, solving the technical problems that the management monitoring dimension of the insulating layer of the ultra-high voltage power device is single, and further, defect management accuracy and management efficiency of the insulating layer are poor, realizing defect identification of the insulating layer of the ultra-high voltage power device, and achieving the technical effects of improving the defect management efficiency of the insulating layer of the ultra-high voltage power device, and further guaranteeing safe operation of the ultra-high voltage power device.
CN116227647a discloses a novel digital twin deduction optimizing method and system for electric power system, comprising: generating a multi-distribution operation sample based on sample distribution contained in historical acquisition data of the power system, and acquiring the multi-distribution operation sample; constructing a digital twin hybrid model based on the multi-distributed operation samples, and acquiring an operable digital twin hybrid model; determining operational state data corresponding to the input operational data based on the operational digital twin hybrid model; setting an optimal result, and determining an association relationship between the running state data and the optimal result based on a preset digital twin dual model; based on the association relation, the digital twin body operation data in the optimized operation state is used as an optimization basis of the physical equipment of the power system, so that the power system equipment is optimized based on digital twin deduction.
According to the related art, a power system can be constructed based on a target digital twin model, thereby managing and optimizing power system equipment. However, when the power system works, abnormal conditions such as power consumption system faults, power distribution system faults, electricity larceny and the like can occur, and normal electricity consumption of a user is affected. However, according to the related art, the abnormal condition is identified, so that the power generation system, the power distribution system and the power utilization system in the power system need to be checked step by step, a large amount of manpower and material resources need to be consumed, repair time is difficult to ensure, and normal operation of the power system is affected.
The information disclosed in the background section of this application is only for enhancement of understanding of the general background of this application and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
The invention provides a digital twin power digital anomaly identification method, which can solve the technical problem that the power supply anomaly condition of a transformer area is difficult to identify in time in the related technology.
According to a first aspect of the present invention there is provided a method comprising:
acquiring input voltage data, output voltage data and output power data of a transformer in a transformer area at a plurality of moments in a current detection period, and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period;
Obtaining geographic position data of each ammeter;
obtaining a power digital twin model of the platform region according to the input voltage data, the output power data, the power consumption data and the geographic position data;
determining abnormal transformation scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
determining a power consumption abnormality score of the station area according to the output power data and the power consumption data;
determining a plurality of ammeter clusters in a power digital twin model of the transformer area according to the geographic position data;
determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
obtaining an electric power anomaly score according to the transformation anomaly score, the district electricity utilization anomaly score and the cluster anomaly score;
and determining the abnormal power supply condition of the platform area according to the abnormal power score.
According to a second aspect of the present invention, there is provided a digital twin-based power digitization anomaly identification system, comprising:
the first data acquisition module is used for acquiring input voltage data, output voltage data and output power data of the transformer in the transformer area at a plurality of moments in the current detection period and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period;
The second data acquisition module acquires geographic position data of each ammeter;
the digital twin model acquisition module is used for acquiring a power digital twin model of the platform region according to the input voltage data, the output power data, the power consumption data and the geographic position data;
the transformation anomaly score determining module is used for determining transformation anomaly scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
the power consumption abnormality score determining module is used for determining a power consumption abnormality score of the platform area according to the output power data and the power consumption power data;
the ammeter cluster determining module is used for determining a plurality of ammeter clusters in the electric power digital twin model of the station area according to the geographic position data;
the cluster anomaly score determining module is used for determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
the power anomaly score determining module is used for obtaining a power anomaly score according to the transformation anomaly score, the district power consumption anomaly score and the cluster anomaly score;
and the power supply abnormal condition determining module is used for determining the power supply abnormal condition of the platform area according to the power abnormality score.
According to a third aspect of the present invention, there is provided a digital twin-based power digitization anomaly identification device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the digital twin-based power digitization anomaly identification method.
According to a fourth aspect of the present invention, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the digital twin based power digitisation anomaly identification method.
The technical effects are as follows: according to the invention, a digital twin model of the power of the transformer area can be obtained according to the input voltage data, the output power data, the power consumption data and the geographic position data of each electric meter of the transformer area, the abnormal grading of the power consumption of the transformer area and the abnormal grading of a plurality of electric meter clusters and clusters in the digital twin model of the power of the transformer area are determined, and the abnormal power supply condition of the transformer area is determined through the grading of three aspects. Therefore, the abnormal conditions of multiple aspects of the power system are observed in real time through the real-time observation digital twin model, the abnormality is found in time, the abnormality identification efficiency is improved, and the influence on the power system is reduced. When determining the transformation anomaly score of the district transformer, the transformation anomaly score of the district transformer can be obtained based on the stability of the output voltage data and the severity of deviation between the output voltage data and the theoretical output voltage data. The stability and the deviation severity can be comprehensively considered, and the condition of output voltage data can be determined in real time, so that the abnormal degree of the transformer in the transformer area is determined, and the comprehensiveness and the accuracy of monitoring the transformer in the transformer area are improved. When the abnormal electricity consumption score of the platform area is determined, the electricity consumption loss can be calculated through the electricity consumption power displayed by the electricity meter, and the electricity consumption loss is calculated through the electricity consumption power provided by a user of the electricity meter, so that the difference of the electricity consumption losses calculated in two modes is determined, whether the electricity consumption of the electricity meter is abnormal or not is determined based on the difference, the abnormal electricity consumption coefficient is obtained, the possibility of electricity stealing or electricity meter failure of the electricity meter can be accurately and objectively represented, and the monitoring accuracy is improved. Further, the abnormal power consumption scores of the platform regions can be determined according to the ratio of the number and the total number of the abnormal power meters, so that the duty ratio of the abnormal power consumption meters in the platform regions can be expressed, the abnormal power consumption conditions of the platform regions can be objectively expressed, the abnormal power consumption conditions can be monitored at the end of each detection period, and the timeliness and the accuracy of the monitoring are improved. When the cluster anomaly score is determined, the cluster anomaly score can be obtained through the relative value of the concentration degree of the target ammeter and the concentration degree in the target ammeter cluster, so that whether the target ammeter is concentrated in a certain cluster or not can be accurately expressed, and the concentration degree of the cluster target ammeter can be objectively judged. Further, the cluster anomaly density index may be determined based on the functional relationship determined by the non-target electric meters in the anomaly electric meter cluster and the functional relationship determined by the target electric meters, and the geographic scope of the anomaly cluster, so as to objectively express the average influence of the abnormal conditions such as electric meter faults or electricity larceny on the cluster anomaly score of the single electric meter. Based on the cluster anomaly density index and the cluster concentration anomaly score, the cluster anomaly score is obtained, so that the cluster anomaly score can accurately express the concentration and the anomaly degree of the ammeter in an anomaly state, a data basis is provided for checking the concentrated large-scale power utilization anomaly, and the efficiency of checking regional power supply anomaly is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed. Other features and aspects of the present invention will become apparent from the following detailed description of exemplary embodiments, which proceeds with reference to the accompanying drawings.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other embodiments may be obtained according to these drawings without inventive effort to a person skilled in the art;
FIG. 1 schematically illustrates a flow diagram of a digital twin-based power digitization anomaly identification method in accordance with an embodiment of the present invention;
FIG. 2 schematically illustrates a power-on digital anomaly identification system for digital twinning in accordance with an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The technical scheme of the invention is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
Fig. 1 exemplarily shows a flow diagram of a digital twin-based power digital anomaly identification method according to an embodiment of the present invention, the method including:
step S101, input voltage data, output voltage data and output power data of a transformer in a transformer area at a plurality of moments in a current detection period and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period are obtained;
step S102, geographical position data of each ammeter are obtained;
step S103, a digital twin model of the electric power of the transformer area is obtained according to the input voltage data, the output power data, the electric power data and the geographic position data;
step S104, determining abnormal transformation scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
step S105, determining a power utilization abnormality score of the station area according to the output power data and the power utilization data;
Step S106, determining a plurality of ammeter clusters in the electric power digital twin model of the platform area according to the geographic position data;
step S107, determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
step S108, obtaining an electric power anomaly score according to the transformation anomaly score, the district electricity anomaly score and the cluster anomaly score;
step S109, determining the abnormal power supply condition of the area according to the abnormal power score.
According to the digital twin-based power digital anomaly identification method, a power digital twin model of a platform can be obtained according to input voltage data, output power data, power consumption data and geographic position data of each electric meter of the transformer of the platform, transformation anomaly scores of the transformer of the platform, power consumption anomaly scores of the platform and a plurality of electric meter clusters and cluster anomaly scores in the power digital twin model of the platform are determined, and then power supply anomaly conditions of the platform are determined through the scores of three aspects. Therefore, the abnormal conditions of multiple aspects of the power system are observed in real time through the real-time observation digital twin model, the abnormality is found in time, the abnormality identification efficiency is improved, and the influence on the power system is reduced.
According to the embodiment of the invention, in step S101, input voltage data, output voltage data and output power data of a transformer in a transformer area at a plurality of moments in a current detection period and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period are obtained, wherein the input voltage data and the input voltage data are used for judging whether the transformer works normally or not. The power consumption data are data of a plurality of moments in the current detection period of each ammeter, for example, in the current detection period, N ammeter exist, and N multiplied by M data are obtained in total when M detection moments exist.
According to an embodiment of the present invention, in step S102, geographical location data of each electric meter is acquired. For example, the geographic location data may be three-dimensional spatial coordinates.
According to an embodiment of the present invention, in step S103, a digital twin model of electric power of the district is obtained from the input voltage data, the output power data, the electric power data and the geographical position data. The power supply state of the platform area can be intuitively and real-time observed through the platform area power digital twin model, and whether the power supply state of the platform area is abnormal or not can be determined.
According to an embodiment of the present invention, in step S104, determining a transformation anomaly score of a transformer in a transformer area according to the input voltage data and the output voltage data, includes: obtaining the design transformation ratio of the transformer in the transformer area; determining abnormal transformation scores of transformer in transformer area according to formula，
（1）
Wherein,for the input voltage data at the i-th instant of the current detection period,/and the input voltage data at the i-th instant>For the output voltage data at the i-th instant of the current detection period,/and the output voltage data at the i-th instant>The transformer ratio is designed for the transformer in the transformer area, n is the time quantity in the current detection period, i is less than or equal to n, i and n are positive integers, and max is a maximum function.
According to the embodiment of the invention, the design transformation ratio is obtained, and the transformation anomaly score of the transformer in the transformer area can be determined according to the formula (1)Wherein->Which represents the theoretical output voltage data,/>maximum value representing the relative deviation of theoretical output voltage data and actual output voltage data, +.>An average value representing the relative deviation of the theoretical output voltage data and the actual output voltage data, and therefore,a difference representing the maximum value and the average value may represent a stability of the output voltage data, the greater the difference representing a poorer stability of the output voltage data during the current detection period. In an ideal state, the person is- >Therefore, the method can be used for manufacturing the optical fiber,representing the severity of the deviation between the output voltage data of the district transformer and the theoretical output voltage data. Therefore, according to the formula (1), the stability of the output voltage data and the deviation severity described above can be used to express the transformation anomaly score of the district transformer, the higher the transformation anomaly score is, the greater the possibility that the district transformer is abnormal.
In this way, the transformation anomaly score of the district transformer can be obtained based on the stability of the output voltage data, and the severity of the deviation between the output voltage data and the theoretical output voltage data. The stability and the deviation severity can be comprehensively considered, and the condition of output voltage data can be determined in real time, so that the abnormal degree of the transformer in the transformer area is determined, and the comprehensiveness and the accuracy of monitoring the transformer in the transformer area are improved.
According to an embodiment of the present invention, in step S105, determining a power consumption abnormality score of a station area according to the output power data and the power consumption data includes: determining power loss data of each moment according to the output power data of each moment and the power consumption data; determining a first loss relation function between the power loss data at each moment and the power consumption data; determining a second loss relation function between the power loss data and the output power data at each moment; according to the electricity consumption power data, the output power data, the first loss relation function and the second loss relation function of the jth ammeter at a plurality of moments, determining the electricity consumption abnormality coefficient of the jth ammeter, wherein j is a positive integer, j is less than or equal to m, and m is the number of the ammeter in the station area; and determining the power consumption anomaly scores of the areas according to the power consumption anomaly coefficients of the plurality of electric meters.
According to the embodiment of the invention, the power loss data of each moment is determined according to the output power data of each moment and the power consumption data, wherein the power loss data is the difference value of the output power data and the power consumption data at each moment. A first loss relation function between the power loss data at each time and the electricity consumption power data may be determined, and a second loss relation function between the power loss data at each time and the output power data may be determined, where the first loss relation function is a relation function calculated by data fitting from the power loss data at a plurality of times and the electricity consumption power data at a plurality of times (for example, a sum of the electricity consumption power data of a plurality of electricity meters), and similarly, the second loss relation function is a relation function calculated by data fitting from the power loss data at a plurality of times. And determining an electricity consumption abnormality coefficient according to the electricity consumption power data, the output power data, the first loss relation function and the second loss relation function of the ammeter, and determining an electricity consumption abnormality score according to the electricity consumption abnormality coefficient.
According to an embodiment of the present invention, determining an abnormal electricity consumption coefficient of a j-th electric meter according to electricity consumption power data of the j-th electric meter at a plurality of moments, the output power data, the first loss relation function and the second loss relation function includes:
determining the abnormal electricity consumption coefficient of the jth ammeter according to the formula，
（2）
Wherein,for the electricity consumption data of the jth ammeter at the ith moment,/for the jth ammeter>For the power data of the kth ammeter at the ith moment, k is not equal to j, k is not more than m, and k is a positive integer, < >>For the output power data at the i-th instant, < >>For said first loss relation function, +.>And n is the number of times in the current detection period, i is less than or equal to n, and i and n are positive integers.
In accordance with an embodiment of the present invention,and the electricity consumption of the user corresponding to the j-th ammeter calculated based on the electricity consumption power at the i-th moment displayed by the j-th ammeter is shown. />Representing the sum of the power consumption data and the power consumption loss of the m-1 meters except the jth meterRepresenting the difference between the output power data at the ith moment and the total power data and the total power loss of the m-1 electric meters except for the jth electric meter, namely the total power of the jth electric meter at the ith moment The rate data, in other words, the electric power supplied at the ith moment for the jth electricity meter, and therefore,and calculating the electricity consumption of the user corresponding to the obtained j-th ammeter based on the electric power provided by the j-th ammeter. If the electricity consumption difference of the user corresponding to the jth ammeter obtained in the two modes is larger, the situation that the electricity consumption displayed by the jth ammeter is inaccurate, electricity stealing is possible, or the ammeter fails and the like can be indicated.
In accordance with an embodiment of the present invention,representing the relative difference in power consumption of the jth meter at the ith moment obtained by the two modes.Average value representing relative difference of electricity consumption of jth meter at each moment for representing electricity consumption abnormality coefficient of jth meter>The larger the electricity consumption abnormality coefficient is, the more serious the electricity consumption abnormality degree of the platform area is.
According to the embodiment of the invention, according to the electricity consumption abnormality coefficients of a plurality of electric meters, the power consumption abnormality score of the platform area is determined, and the method comprises the following steps: determining a power consumption anomaly score for the region according to equation (3)，
（3）
Wherein,for the electricity consumption abnormality coefficient of the jth ammeter, < >>To preset abnormal systemThe number threshold, if, is a conditional function.
According to an embodiment of the present invention, the formula (3) includes a conditional function, Indicating +.>If the electricity consumption abnormality coefficient of the j-th ammeter is smaller than the preset abnormality coefficient threshold value, the electricity consumption abnormality coefficient of the j-th ammeter is equal to or greater than the preset abnormality coefficient threshold value>. Thus (S)>Indicating the number of meters in anomaly. According to the formula (3), the district electricity usage anomaly score +.>。
In this way, the electricity consumption can be calculated through the electricity consumption power displayed by the electric meter, and the electricity consumption is calculated through the electricity consumption power provided by the user of the electric meter, so that the difference of the electricity consumption losses calculated in two modes is determined, whether the electricity consumption of the electric meter is abnormal or not is determined based on the difference, the electricity consumption abnormality coefficient is obtained, the possibility of electricity stealing or failure of the electric meter can be accurately and objectively represented, and the monitoring accuracy is improved. Further, the abnormal power consumption scores of the platform regions can be determined according to the ratio of the number and the total number of the abnormal power meters, so that the duty ratio of the abnormal power consumption meters in the platform regions can be expressed, the abnormal power consumption conditions of the platform regions can be objectively expressed, the abnormal power consumption conditions can be monitored at the end of each detection period, and the timeliness and the accuracy of the monitoring are improved.
According to the embodiment of the invention, in step S106, a plurality of electric meter clusters in the digital twin model of the electric power of the area are determined according to the geographical position data, and a plurality of electric meter clusters in the digital twin model of the electric power of the area are determined according to the geographical position data through a clustering algorithm, wherein the positions of the electric meter clusters can be the positions of factory clusters or residential buildings.
According to an embodiment of the present invention, in step S107, determining a cluster anomaly score from the electric meter cluster, the electric power data, and the output power data, includes: determining a target ammeter with the electricity consumption anomaly coefficient larger than or equal to a preset anomaly coefficient threshold value from a plurality of ammeter; determining a target ammeter cluster to which each target ammeter belongs; determining the number of target electric meters in each target electric meter cluster and the number of electric meters in the target electric meter cluster; determining cluster concentration anomaly scores according to the number of target electric meters in each target electric meter cluster, the number of electric meters in the target electric meter clusters and the number of target electric meter clusters; obtaining the geographical range of the target ammeter cluster according to the geographical position data of each ammeter in the target ammeter cluster; determining a cluster abnormal density index according to the geographical range of the target electric meter cluster and the abnormal electricity consumption coefficient of the electric meters in the target electric meter cluster; and determining the cluster anomaly score according to the cluster anomaly density index and the cluster concentration anomaly score.
According to the embodiment of the invention, among the plurality of electric meters, a target electric meter with an abnormal electricity consumption coefficient greater than or equal to a preset abnormal coefficient threshold value, namely an abnormal electricity consumption electric meter, is determined, and a target electric meter cluster to which the target electric meter belongs is determined, for example, according to formula (3), the target electric meter can be determined among the plurality of electric meters, the number of target electric meters can be determined, and further the number of target electric meters in each target electric meter cluster and the total number of electric meters are determined. Determining cluster concentration anomaly scores according to the number of target electric meters in each target electric meter cluster, the number of electric meters and the number of target electric meter clusters, wherein the cluster concentration anomaly scores can represent the anomaly degree of cluster equipment, for example, the cluster power supply equipment is damaged, or the cluster concentration anomaly scores are increased due to the condition that the cluster power supply equipment steals electricity and the like. And obtaining a geographical range according to the geographical position information of the target ammeter cluster, wherein the geographical range can be a spatial range formed by a difference value between a maximum value and a minimum value of x, a difference value between a maximum value and a minimum value of y and a difference value between a maximum value and a minimum value of z in the geographical position data of all the ammeter in the target ammeter cluster. And determining a cluster abnormal density index according to the geographical range and the electricity consumption abnormal coefficient of the target ammeter cluster, wherein the cluster abnormal density index represents the degree of cluster electricity consumption abnormality. Further, the cluster anomaly score may be determined from the cluster anomaly density index and the cluster concentration anomaly score.
According to an embodiment of the present invention, determining a cluster concentration anomaly score according to a number of target electric meters in each target electric meter cluster, a number of electric meters in the target electric meter cluster, and a number of target electric meter clusters includes: determining cluster anomaly scores according to equation (4)，
（4）
Wherein,for the number of target meters in the s-th target meter cluster,/for the number of target meters in the s-th target meter cluster>For the number of meters in the s-th target meter cluster,/->S is less than or equal to ∈18 for the number of target ammeter clusters>And s and->All are positive integers, and max is a maximum function.
In accordance with an embodiment of the present invention,representing the abnormal proportions of the target electric meters in the s-th target electric meter cluster,mean value representing the abnormal proportions of the target electric meters in the respective target electric meter clusters, and therefore +.>The ratio between the abnormal proportion of the target electric meters in the s-th target electric meter cluster and the average value of the abnormal proportion is the relative value of the concentration degree of the abnormal electric meters in the s-th target electric meter cluster, and the greater the ratio is, the greater the abnormal proportion of the s-th target electric meter cluster is, in other words, the greater the probability of finding the target electric meters in the s-th target electric meter cluster is, the greater the probability of the target electric meters in the s-th target electric meter cluster is. / >Mean value representing the number of target meters in the respective target meter cluster, thus +>The quantity ratio of the target electric meters in the s-th target electric meter cluster to the target electric meters in each cluster is represented, and the concentration degree of the target electric meters in the s-th target electric meter cluster is represented. The formula (4) further comprises a maximum value taking function, wherein the maximum value taking function represents that clusters with highest concentration degree of abnormal electric meters are taken from all target electric meter clusters, and cluster concentration degree abnormal scores are obtained. The above solving process comprehensively considers the relative value of the concentration degree of the target ammeter and the concentration degree in the target ammeter cluster to obtain cluster anomaly score +.>The larger the cluster anomaly score, the more concentrated the anomaly ammeter.
According to the embodiment of the invention, according to the geographical range of the target ammeter cluster and the electricity utilization abnormality of the ammeter in the target ammeter clusterA coefficient, determining a cluster anomaly density index, comprising: determining an abnormal ammeter cluster which enables the abnormal score of the cluster to be maximum among a plurality of target ammeter clusters; determining the geographical range of the abnormal ammeter cluster and the electricity consumption abnormal coefficient of the ammeter in the abnormal ammeter cluster; acquiring the electricity consumption abnormal coefficient of the non-target ammeter with the electricity abnormal coefficient smaller than a preset abnormal coefficient threshold value in the abnormal ammeter cluster and the geographic position information of the non-target ammeter; determining a first coefficient relation function between the electricity consumption abnormal coefficient of the non-target ammeter and geographic position information; acquiring an electricity consumption anomaly coefficient of a target ammeter in the anomaly ammeter cluster and geographic position information of the target ammeter; determining a second coefficient relation function between the electricity consumption abnormal coefficient of the target ammeter and geographic position information; determining a cluster anomaly density index according to equation (5) ，
（5）
Wherein,for the geographical range of the abnormal electricity meter cluster, < >>For the volume of the geographical range of said cluster of abnormal electric meters,/->For said first coefficient relation function, +.>As a function of the relationship of the second coefficients,and the three-dimensional coordinates of any position in the geographical range of the abnormal ammeter cluster.
According to the embodiment of the invention, according to the formula (4), an abnormal ammeter cluster enabling the cluster abnormal score to be the maximum value can be determined, and the geographical range of the abnormal ammeter cluster and the electricity consumption abnormal coefficient of the ammeter in the abnormal ammeter cluster are determined. And further, acquiring the electricity consumption abnormal coefficient of the non-target ammeter with the electricity abnormal coefficient smaller than the preset abnormal coefficient threshold value in the abnormal ammeter cluster and the geographic position information of the non-target ammeter, namely the electricity consumption abnormal coefficient and the geographic position information of the ammeter working in a normal state. And obtaining a first coefficient relation function between the electricity consumption abnormal coefficient of the non-target ammeter and the geographic position information through fitting calculation. Further, the electricity consumption abnormal coefficient of the target ammeter in the abnormal ammeter cluster and the geographic position information of the target ammeter can be obtained, and similarly, a second coefficient relation function between the electricity consumption abnormal coefficient of the target ammeter and the geographic position information can be obtained through fitting calculation.
According to the embodiment of the invention, since the second relation function is determined by the electricity consumption abnormality coefficient of the target electricity meter and the geographical position information, the first relation function is determined by the electricity consumption abnormality coefficient of the non-target electricity meter and the geographical position information, and the electricity consumption abnormality coefficient of the target electricity meter is larger than the electricity consumption abnormality coefficient of the non-target electricity meter, therefore,then->And (3) indicating abnormal conditions such as ammeter faults or electricity larceny and the like, and having total influence on the abnormal ammeter cluster area. According to formula (5), cluster abnormality density index +.>The average influence of abnormal conditions such as ammeter faults or electricity larceny on the cluster abnormal scores of the single ammeter can be represented, and the average influence can be used for representing the abnormal degree in the abnormal ammeter cluster, wherein the higher the cluster abnormal density index is, the higher the abnormal degree in the abnormal ammeter cluster is. The cluster anomaly density index and the cluster concentration anomaly score can be multiplied to obtain cluster anomalyAnd (3) the normal score enables the cluster anomaly score to accurately express the concentration and the anomaly degree of the ammeter in an anomaly state.
By the method, the cluster anomaly score can be obtained through the relative value of the concentration degree of the target electric meter and the concentration degree of the target electric meter cluster, so that whether the target electric meter is concentrated in a certain cluster or not can be accurately expressed, and the concentration degree of the cluster target electric meter can be objectively judged. Further, the cluster anomaly density index may be determined based on the functional relationship determined by the non-target electric meters in the anomaly electric meter cluster and the functional relationship determined by the target electric meters, and the geographic scope of the anomaly cluster, so as to objectively express the average influence of the abnormal conditions such as electric meter faults or electricity larceny on the cluster anomaly score of the single electric meter. Based on the cluster anomaly density index and the cluster concentration anomaly score, the cluster anomaly score is obtained, so that the cluster anomaly score can accurately express the concentration and the anomaly degree of the ammeter in an anomaly state, a data basis is provided for checking the concentrated large-scale power utilization anomaly, and the efficiency of checking regional power supply anomaly is improved.
According to an embodiment of the present invention, in step S108, the foregoing transformation anomaly score, the district electricity anomaly score, and the cluster anomaly score may be weighted and summed to obtain an electricity anomaly score.
According to an embodiment of the present invention, in step S109, a power supply abnormality condition of the station area is determined according to the power abnormality score. For example, the electric power anomaly score may be displayed by the electric power digital twin model of the platform, so that a worker may obtain the electric power anomaly score in real time, the electric power digital twin model of the platform may determine an abnormal condition of power supply of the platform based on the electric power anomaly score, generate alarm information when the abnormal condition of power supply of the platform exists (for example, when the electric power anomaly score is higher than a preset score threshold), and display the alarm information in a display interface of the electric power digital twin model of the platform, so as to prompt the worker to check the abnormal condition.
According to the digital twin-based power digital anomaly identification method provided by the embodiment of the invention, a power digital twin model of a platform region can be obtained according to input voltage data, output power data, power consumption data and geographic position data of each electric meter of the transformer of the platform region, and the transformation anomaly score, the power consumption anomaly score of the platform region and a plurality of electric meter clusters and cluster anomaly scores in the power digital twin model of the platform region are determined, so that the power supply anomaly condition of the platform region is determined through the scores of three aspects. Therefore, the abnormal conditions of multiple aspects of the power system are observed in real time through the real-time observation digital twin model, the abnormality is found in time, the abnormality identification efficiency is improved, and the influence on the power system is reduced. When determining the transformation anomaly score of the district transformer, the transformation anomaly score of the district transformer can be obtained based on the stability of the output voltage data and the severity of deviation between the output voltage data and the theoretical output voltage data. The stability and the deviation severity can be comprehensively considered, and the condition of output voltage data can be determined in real time, so that the abnormal degree of the transformer in the transformer area is determined, and the comprehensiveness and the accuracy of monitoring the transformer in the transformer area are improved. When the abnormal electricity consumption score of the platform area is determined, the electricity consumption loss can be calculated through the electricity consumption power displayed by the electricity meter, and the electricity consumption loss is calculated through the electricity consumption power provided by a user of the electricity meter, so that the difference of the electricity consumption losses calculated in two modes is determined, whether the electricity consumption of the electricity meter is abnormal or not is determined based on the difference, the abnormal electricity consumption coefficient is obtained, the possibility of electricity stealing or electricity meter failure of the electricity meter can be accurately and objectively represented, and the monitoring accuracy is improved. Further, the abnormal power consumption scores of the platform regions can be determined according to the ratio of the number and the total number of the abnormal power meters, so that the duty ratio of the abnormal power consumption meters in the platform regions can be expressed, the abnormal power consumption conditions of the platform regions can be objectively expressed, the abnormal power consumption conditions can be monitored at the end of each detection period, and the timeliness and the accuracy of the monitoring are improved. When the cluster anomaly score is determined, the cluster anomaly score can be obtained through the relative value of the concentration degree of the target ammeter and the concentration degree in the target ammeter cluster, so that whether the target ammeter is concentrated in a certain cluster or not can be accurately expressed, and the concentration degree of the cluster target ammeter can be objectively judged. Further, the cluster anomaly density index may be determined based on the functional relationship determined by the non-target electric meters in the anomaly electric meter cluster and the functional relationship determined by the target electric meters, and the geographic scope of the anomaly cluster, so as to objectively express the average influence of the abnormal conditions such as electric meter faults or electricity larceny on the cluster anomaly score of the single electric meter. Based on the cluster anomaly density index and the cluster concentration anomaly score, the cluster anomaly score is obtained, so that the cluster anomaly score can accurately express the concentration and the anomaly degree of the ammeter in an anomaly state, a data basis is provided for checking the concentrated large-scale power utilization anomaly, and the efficiency of checking regional power supply anomaly is improved.
FIG. 2 schematically illustrates a power-on-digital-twinned anomaly-recognition system, according to an embodiment of the present invention, the system including:
the first data acquisition module is used for acquiring input voltage data, output voltage data and output power data of the transformer in the transformer area at a plurality of moments in the current detection period and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period;
the second data acquisition module acquires geographic position data of each ammeter;
the digital twin model acquisition module is used for acquiring a power digital twin model of the platform region according to the input voltage data, the output power data, the power consumption data and the geographic position data;
the transformation anomaly score determining module is used for determining transformation anomaly scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
the power consumption abnormality score determining module is used for determining a power consumption abnormality score of the platform area according to the output power data and the power consumption power data;
the ammeter cluster determining module is used for determining a plurality of ammeter clusters in the electric power digital twin model of the station area according to the geographic position data;
The cluster anomaly score determining module is used for determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
the power anomaly score determining module is used for obtaining a power anomaly score according to the transformation anomaly score, the district power consumption anomaly score and the cluster anomaly score;
and the power supply abnormal condition determining module is used for determining the power supply abnormal condition of the platform area according to the power abnormality score.
According to an embodiment of the present invention, there is provided a digital twin-based power digital anomaly identification device including: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the memory-stored instructions to perform the digital twin-based power digitization anomaly identification method.
According to one embodiment of the present invention, a computer-readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the digital twin-based power digitization anomaly identification method.
The present invention may be a method, apparatus, system, and/or computer program product. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing various aspects of the present invention.
It will be appreciated by persons skilled in the art that the embodiments of the invention described above and shown in the drawings are by way of example only and are not limiting. The objects of the present invention have been fully and effectively achieved. The functional and structural principles of the present invention have been shown and described in the examples and embodiments of the invention may be modified or practiced without departing from the principles described.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. The digital twinning-based power digital anomaly identification method is characterized by comprising the following steps of:
acquiring input voltage data, output voltage data and output power data of a transformer in a transformer area at a plurality of moments in a current detection period, and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period;
Obtaining geographic position data of each ammeter;
obtaining a power digital twin model of the platform region according to the input voltage data, the output power data, the power consumption data and the geographic position data;
determining abnormal transformation scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
determining a power consumption abnormality score of the station area according to the output power data and the power consumption data;
determining a plurality of ammeter clusters in a power digital twin model of the transformer area according to the geographic position data;
determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
obtaining an electric power anomaly score according to the transformation anomaly score, the district electricity utilization anomaly score and the cluster anomaly score;
and determining the abnormal power supply condition of the platform area according to the abnormal power score.
2. The digital twin based power digitization anomaly identification method of claim 1, wherein determining a transformation anomaly score for a district transformer from the input voltage data and the output voltage data comprises:
Obtaining the design transformation ratio of the transformer in the transformer area;
according to the formula
Determining transformer anomaly scores for a transformer in a bayWherein->For the input voltage data at the i-th instant of the current detection period,/and the input voltage data at the i-th instant>For the output voltage data at the i-th instant of the current detection period,/and the output voltage data at the i-th instant>The transformer ratio is designed for the transformer in the transformer area, n is the time quantity in the current detection period, i is less than or equal to n, i and n are positive integers, and max is a maximum function.
3. The digital twin-based power digitization anomaly identification method of claim 1, wherein determining a zone power usage anomaly score from the output power data and the power usage data comprises:
determining power loss data of each moment according to the output power data of each moment and the power consumption data;
determining a first loss relation function between the power loss data at each moment and the power consumption data;
determining a second loss relation function between the power loss data and the output power data at each moment;
according to the electricity consumption power data, the output power data, the first loss relation function and the second loss relation function of the jth ammeter at a plurality of moments, determining the electricity consumption abnormality coefficient of the jth ammeter, wherein j is a positive integer, j is less than or equal to m, and m is the number of the ammeter in the station area;
And determining the power consumption anomaly scores of the areas according to the power consumption anomaly coefficients of the plurality of electric meters.
4. The digital twin-based power digitization anomaly identification method of claim 3, wherein determining the power consumption anomaly coefficient of the j-th meter from the power consumption data, the output power data, the first loss relation function, and the second loss relation function of the j-th meter at a plurality of times comprises:
according to the formula
Determining the electricity consumption abnormality coefficient of the jth ammeterWherein->For the electricity consumption data of the jth ammeter at the ith moment,/for the jth ammeter>For the power data of the kth ammeter at the ith moment, k is not equal to j, k is not more than m, and k is a positive integer, < >>For the output power data at the i-th instant, < >>For said first loss relation function, +.>And n is the number of times in the current detection period, i is less than or equal to n, and i and n are positive integers.
5. The digital twin-based power digital anomaly identification method of claim 3, wherein determining a district power anomaly score based on power anomaly coefficients of a plurality of electric meters comprises:
according to the formula
Determining a power usage anomaly score for a bay Wherein->For the electricity consumption abnormality coefficient of the jth ammeter, < >>For presetting an abnormality coefficient threshold, if is a conditional function,/->Indicating +.>If the electricity consumption abnormality coefficient of the j-th ammeter is smaller than the preset abnormality coefficient threshold value, the electricity consumption abnormality coefficient of the j-th ammeter is equal to or greater than the preset abnormality coefficient threshold value>。
6. A digital twin based power digitization anomaly identification method according to claim 3, wherein determining cluster anomaly scores from the electricity meter clusters, the electricity power data, and the output power data comprises:
determining a target ammeter with the electricity consumption anomaly coefficient larger than or equal to a preset anomaly coefficient threshold value from a plurality of ammeter;
determining a target ammeter cluster to which each target ammeter belongs;
determining the number of target electric meters in each target electric meter cluster and the number of electric meters in the target electric meter cluster;
determining cluster concentration anomaly scores according to the number of target electric meters in each target electric meter cluster, the number of electric meters in the target electric meter clusters and the number of target electric meter clusters;
obtaining the geographical range of the target ammeter cluster according to the geographical position data of each ammeter in the target ammeter cluster;
Determining a cluster abnormal density index according to the geographical range of the target electric meter cluster and the abnormal electricity consumption coefficient of the electric meters in the target electric meter cluster;
and determining the cluster anomaly score according to the cluster anomaly density index and the cluster concentration anomaly score.
7. The digital twin based power digitization anomaly identification method of claim 6, wherein determining cluster concentration anomaly scores based on a number of target meters in each target meter cluster, a number of meters in the target meter cluster, and a number of target meter clusters comprises:
according to the formula
Determining cluster anomaly scoresWherein->For the number of target meters in the s-th target meter cluster,/for the number of target meters in the s-th target meter cluster>For the number of meters in the s-th target meter cluster,/->S is less than or equal to ∈18 for the number of target ammeter clusters>And s and->All are positive integers, and max is a maximum function.
8. The digital twin-based power digitization anomaly identification method of claim 7, wherein determining a cluster anomaly density index based on a geographic extent of the target electric meter cluster, an electric anomaly coefficient of electric meters in the target electric meter cluster, comprises:
Determining an abnormal ammeter cluster which enables the abnormal score of the cluster to be maximum among a plurality of target ammeter clusters;
determining the geographical range of the abnormal ammeter cluster and the electricity consumption abnormal coefficient of the ammeter in the abnormal ammeter cluster;
acquiring the electricity consumption abnormal coefficient of the non-target ammeter with the electricity abnormal coefficient smaller than a preset abnormal coefficient threshold value in the abnormal ammeter cluster and the geographic position information of the non-target ammeter;
determining a first coefficient relation function between the electricity consumption abnormal coefficient of the non-target ammeter and geographic position information;
acquiring an electricity consumption anomaly coefficient of a target ammeter in the anomaly ammeter cluster and geographic position information of the target ammeter;
determining a second coefficient relation function between the electricity consumption abnormal coefficient of the target ammeter and geographic position information;
according to the formula
Determining cluster anomaly density indexWherein->For the geographical range of the abnormal electricity meter cluster, < >>For the volume of the geographical range of said cluster of abnormal electric meters,/->For said first coefficient relation function, +.>For the second coefficient relation function, +.>And the three-dimensional coordinates of any position in the geographical range of the abnormal ammeter cluster.
9. A digital twinning-based power digital anomaly identification system, comprising:
the first data acquisition module is used for acquiring input voltage data, output voltage data and output power data of the transformer in the transformer area at a plurality of moments in the current detection period and power consumption data of each ammeter in the transformer area at a plurality of moments in the current detection period;
the second data acquisition module acquires geographic position data of each ammeter;
the digital twin model acquisition module is used for acquiring a power digital twin model of the platform region according to the input voltage data, the output power data, the power consumption data and the geographic position data;
the transformation anomaly score determining module is used for determining transformation anomaly scores of the transformer in the transformer area according to the input voltage data and the output voltage data;
the power consumption abnormality score determining module is used for determining a power consumption abnormality score of the platform area according to the output power data and the power consumption power data;
the ammeter cluster determining module is used for determining a plurality of ammeter clusters in the electric power digital twin model of the station area according to the geographic position data;
the cluster anomaly score determining module is used for determining cluster anomaly scores according to the ammeter clusters, the electricity power data and the output power data;
The power anomaly score determining module is used for obtaining a power anomaly score according to the transformation anomaly score, the district power consumption anomaly score and the cluster anomaly score;
and the power supply abnormal condition determining module is used for determining the power supply abnormal condition of the platform area according to the power abnormality score.
10. A digital twin-based power digitization anomaly identification device, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method of any of claims 1-8.
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| US20190081476A1 (en) * | 2017-09-12 | 2019-03-14 | Sas Institute Inc. | Electric power grid supply and load prediction |
| CN113221931A (en) * | 2020-12-23 | 2021-08-06 | 国网吉林省电力有限公司电力科学研究院 | Electricity stealing prevention intelligent identification method based on electricity utilization information acquisition big data analysis |
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