CN115713434A - Cloud-edge-coordinated online calibration method and device for power load - Google Patents
Cloud-edge-coordinated online calibration method and device for power load Download PDFInfo
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Abstract
The invention discloses a cloud-edge cooperative power load online calibration method, which comprises the following steps: the cloud system automatically classifies and calibrates the existing power load sample library to obtain a calibration database; the cloud system issues the calibration database to edge side terminal equipment; the edge side terminal equipment carries out real-time classified calibration and cache on real-time samples in the monitored power utilization environment according to the calibration database, uniformly calibrates the real-time samples which do not belong to the known category in the calibration database into unknown samples, and uploads the unknown samples to the cloud system; and the cloud system stores and manages the real-time samples uploaded by the edge side terminal equipment according to classification calibration, and automatically classifies, calibrates and updates the unknown samples to the calibration database. The invention also discloses a cloud-edge cooperative power load online calibration device. The invention solves the technical problems of large workload of collecting the electrical characteristic samples of the existing electrical load and low efficiency of classification and calibration.
Description
Technical Field
The invention relates to the technical field of electrical engineering science, in particular to a cloud-edge collaborative power load online calibration method and device.
Background
The low-carbon technological innovation and the carbon peak reaching and carbon neutralizing targets set by the state are development trends of the energy industry. The carbon peak reaching and carbon neutralization targets promote the development, application and popularization of the electric load monitoring and identifying technology in the power industry. The non-intrusive load identification technology is known as a crown in the field of energy efficiency management, is the latest technical development direction and development hotspot of modern measurement technology and smart power grids, and the national and south network companies have smelled the strong technical advantages and potential commercial values of smart load identification. In the process of electric load monitoring and identification technology research and product incubation, massive electric load characteristic samples are needed as basic supports.
Patent document CN202210628178.5 discloses a side cloud collaborative intelligent terminal for electric power mass data, which includes a big data storage module, a central cloud module, an electric power internet of things data module, a GPS modeling module, a fully distributed collaborative algorithm module, a digital integration technology module, a wide area network management control central module, and an edge platform module, where the big data storage module is respectively electrically connected to the central cloud module, the fully distributed collaborative algorithm module, the digital integration technology module, the wide area network management control central module, and the edge platform module, the central cloud module is electrically connected to the electric power internet of things data module, the GPS modeling module, and the wide area network management control central module is electrically connected to a security control module. The terminal can not solve the technical problems that the collection workload of the existing electric load electrical characteristic samples is large, and the classification calibration efficiency is low. Therefore, a cloud-edge cooperative online calibration method and device for the power load are urgently needed to be provided, and the problems that the existing power load electrical characteristic sample collection workload is large and the classification calibration efficiency is low are solved.
Disclosure of Invention
The invention mainly aims to provide a cloud-edge cooperative power load online calibration method and device, and aims to solve the technical problems of large workload of collecting electrical characteristic samples of the existing power load and low classification calibration efficiency.
In order to achieve the above object, the present invention provides a cloud-edge collaborative online calibration method for electrical loads, wherein the method comprises the following steps:
s1, automatically classifying and calibrating an existing power load sample library by a cloud system to obtain a calibration database;
s2, the cloud system issues the calibration database to edge side terminal equipment;
s3, the edge side terminal device classifies, calibrates and caches real-time samples in the monitored power consumption environment in real time according to the calibration database, uniformly calibrates the real-time samples which do not belong to the known type in the calibration database into unknown type samples, and uploads the unknown type samples to a cloud end system;
and S4, the cloud system stores and manages the real-time samples uploaded by the edge side terminal equipment according to classification calibration, and automatically classifies, calibrates and updates the unknown samples to the calibration database.
In one preferred embodiment, the feature vector of the power load sample includes: voltage, voltage harmonic amplitude and angle, voltage harmonic content, current harmonic amplitude and angle, current harmonic content, full-wave active and/or reactive, fundamental active and/or reactive, and frequency.
In one preferred scheme, the cloud system in the step S1 automatically classifies and calibrates an existing electric load sample library to obtain a calibration database, and the method specifically includes the following steps:
s11, randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample, obtaining a spatial neighborhood of the central sample, and judging whether the central sample belongs to a sample to which a current cluster belongs;
s12, traversing all samples in the central sample field until the classification and calibration of all samples in the central sample field are completed;
and S13, repeating the steps S11-S12 until the classification calibration of all cluster samples is completed.
In one preferred embodiment, the step S11 randomly selects any sample in the existing electrical load sample library to obtain a spatial neighborhood of the sample, where the sample is a central sample, and determines whether the central sample belongs to a sample to which the current cluster belongs, and the specific steps are as follows:
randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample;
calculating the sample distance between the central sample and the rest samples to obtain a space field with the sample distance smaller than a first threshold value R;
judging whether the central sample belongs to a sample to which the current cluster belongs; if the number of the samples in the space domain is larger than a second threshold value M, marking the central sample as a sample to which the current cluster belongs; otherwise, the center sample is marked as noise.
In a preferred embodiment, the sample distance between the center sample and the rest of the samples is calculated as follows:
wherein d is the sample interval, A is the central sample, B is the rest samples, n is the feature quantity of the feature vector, and i is the ith feature in the feature vector.
In one preferred embodiment, in step S3, the real-time samples in the monitored power consumption environment are classified, calibrated and cached in real time, and the real-time samples that do not belong to the known class in the calibration database are calibrated as unknown samples in a unified manner, and the specific steps are as follows:
s31, calculating a matching coefficient between the real-time sample and the sample in the calibration database;
and S32, judging whether the real-time sample belongs to the class of the power load sample of the calibration database or not based on the matching coefficient.
In one preferred embodiment, the matching coefficient is:
wherein beta is i For matching coefficients, A is a real-time sample, B is a sample in the calibration database, n is the feature quantity of the feature vector, j is the jth class sample in the calibration database, and i is the ith feature of the feature vector.
In a preferred embodiment, the step S32 specifically includes:
taking the maximum value of the matching coefficient, and if the maximum value of the matching coefficient is larger than a third threshold value, calibrating the real-time sample into a corresponding sample type; and if the maximum value of the matching coefficient is smaller than a third threshold value, calibrating the real-time sample as an unknown sample.
A device comprising the cloud-edge cooperative online calibration method for the electrical load comprises a cloud end system and edge side terminal equipment;
the cloud system is in communication connection with the edge side terminal equipment;
the cloud system is used for automatically classifying and calibrating the existing power load sample library to obtain a calibration database and updating the calibration database in real time;
and the edge side terminal equipment is used for classifying and calibrating the real-time samples according to the calibration database and uniformly calibrating the real-time samples which do not belong to the known types in the calibration database into unknown samples.
In one preferred embodiment, the communication connection between the cloud system and the edge side terminal device includes: 3G, 4G, 5G, WIFI, bluetooth, 485 and HPLC.
In the technical scheme of the invention, the cloud-edge cooperative online calibration method for the power load comprises the following steps: the cloud system automatically classifies and calibrates the existing power load sample library to obtain a calibration database; the cloud system issues the calibration database to the edge side terminal equipment; the edge side terminal equipment classifies, calibrates and caches real-time samples in the monitored power utilization environment in real time according to the calibration database, uniformly calibrates the real-time samples which do not belong to the known class in the calibration database into unknown samples, and uploads the unknown samples to a cloud system; and the cloud system stores and manages the real-time samples uploaded by the edge side terminal equipment according to classification calibration, and automatically classifies, calibrates and updates the unknown samples to the calibration database. The invention solves the technical problems of large workload of collecting the electrical characteristic samples of the existing power load and low efficiency of classification and calibration.
In the invention, the processing complexity of the edge side terminal equipment and the requirement on hardware technology are reduced in a cloud edge cooperation mode, and the method can be conveniently applied in a large scale.
In the invention, the method of clustering of sample density and measuring the sample similarity is adopted, so that the method can be self-suitable for the classification of various complex samples, the influence of noise is weakened, and the classification and calibration of the samples are automatically realized.
Drawings
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 description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic flow chart of an online calibration method for cloud-edge collaborative power consumption loads according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of step S1 according to an embodiment of the present invention;
fig. 3 is a frame diagram of an online calibration apparatus for electrical loads with cloud-edge coordination according to an embodiment of the present invention.
The reference numbers illustrate:
1. a cloud system; 2. an edge side terminal device.
The implementation, functional features and advantages of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in 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, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive efforts based on the embodiments of the present invention, are within the scope of protection of the present invention.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature.
Moreover, the technical solutions in the embodiments of the present invention may be combined with each other, but it is necessary to be able to be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent, and is not within the protection scope of the present invention.
Example 1:
referring to fig. 1-2, according to an aspect of the present invention, the present invention provides a cloud-edge collaborative online calibration method for electrical loads, wherein the method includes the following steps:
s1, automatically classifying and calibrating an existing power load sample library by a cloud system to obtain a calibration database;
s2, the cloud system issues the calibration database to edge side terminal equipment;
s3, the edge side terminal device classifies, calibrates and caches real-time samples in the monitored power consumption environment in real time according to the calibration database, uniformly calibrates the real-time samples which do not belong to the known type in the calibration database into unknown type samples, and uploads the unknown type samples to a cloud end system;
and S4, the cloud system stores and manages the real-time samples uploaded by the edge side terminal equipment according to classification calibration, and automatically classifies, calibrates and updates the unknown samples to the calibration database.
Specifically, in this embodiment, the feature vector of the power load sample includes, but is not limited to: voltage, voltage harmonic amplitude and angle, voltage harmonic content, current harmonic amplitude and angle, current harmonic content, full-wave active and/or reactive power, fundamental wave active and/or reactive power, frequency and the like; the characteristic vector of the electric load sample is not specifically limited, and can be specifically set according to needs.
Specifically, in this embodiment, the cloud system in step S1 automatically classifies and calibrates an existing power consumption load sample library to obtain a calibration database, and the specific steps are as follows:
s11, randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample, obtaining a spatial neighborhood of the central sample, and judging whether the central sample belongs to a sample to which a current cluster belongs; wherein, for step S11, any sample in the existing power load sample library is randomly selected to obtain a spatial neighborhood of the sample, the sample is a central sample, and whether the central sample belongs to a sample to which the current cluster belongs is determined, and the specific steps are as follows: randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample; calculating the sample distance between the central sample and the rest samples to obtain a space field with the sample distance smaller than a first threshold value R; judging whether the central sample belongs to a sample to which the current cluster belongs; if the number of the samples in the space field is larger than a second threshold value M, marking the central sample as a sample to which the current cluster belongs; otherwise, the central sample is marked as noise; wherein the state of a sample is updated to be "processed" whether it is designated as the current cluster of samples or noise.
S12, traversing all samples in the central sample field until the classification and calibration of all samples in the central sample field are completed;
and S13, repeating the steps S11-S12 until the classification calibration of all the cluster samples is completed, and completing the classification calibration until all the electric load samples in the electric load sample library are in a processed state to obtain a calibration database.
Specifically, in this embodiment, the sample distance is calculated by using a euclidean distance, and the sample distances between the center sample and the remaining samples are calculated as follows:
wherein d is the sample interval, A is the central sample, B is the rest samples, n is the feature quantity of the feature vector, and i is the ith feature in the feature vector.
Specifically, in this embodiment, in step S3, the real-time samples in the monitored power consumption environment are classified, calibrated and cached in real time, and the real-time samples that do not belong to the known class in the calibration database are calibrated as unknown samples in a unified manner, which specifically includes:
s31, calculating a matching coefficient between the real-time sample and the sample in the calibration database; the matching coefficient is as follows:
wherein beta is i For matching coefficients, A is a real-time sample, B is a sample in a calibration database, n is the feature quantity of the feature vector, j is the jth category sample in the calibration database, and i is the ith feature of the feature vector;
and S32, judging whether the real-time sample belongs to the class of the power load sample of the calibration database or not based on the matching coefficient. The step S32 is specifically: taking the maximum value of the matching coefficient, and if the maximum value of the matching coefficient is larger than a third threshold value, calibrating the real-time sample into a corresponding sample type; and if the maximum value of the matching coefficient is smaller than a third threshold value, calibrating the real-time sample as an unknown sample, wherein the third threshold value is the standard similarity of the characteristics of all samples.
Specifically, in this embodiment, before uploading to the cloud system in step S3, the method further includes: and uploading the real-time sample cache quantity to the cloud system after the real-time sample cache quantity reaches a preset first sample capacity threshold value.
Specifically, in this embodiment, in the step S4, the cloud system performs storage management on the real-time samples uploaded by the edge side terminal device according to classification calibration, wherein after the number of unknown samples reaches a preset second sample capacity threshold, the unknown samples are automatically classified and calibrated according to the step S1, and are updated to the calibration database and sent to the edge side terminal device.
Example 2:
referring to fig. 3, a device including the cloud-edge collaborative online calibration method for electrical loads includes a cloud end system and edge side terminal equipment;
the cloud system is in communication connection with the edge side terminal equipment;
the cloud system is used for automatically classifying and calibrating the existing power load sample library to obtain a calibration database and updating the calibration database in real time;
and the edge side terminal equipment is used for classifying and calibrating the real-time samples according to the calibration database and uniformly calibrating the real-time samples which do not belong to the known class in the calibration database into unknown samples.
Specifically, in this embodiment, the cloud system and the edge side terminal device are connected in a communication manner, including: one of 3G, 4G, 5G, WIFI, bluetooth, 485 and HPLC; the present invention is not particularly limited, and a suitable communication connection mode may be selected as needed.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A cloud-edge cooperative power load online calibration method is characterized by comprising the following steps:
s1, automatically classifying and calibrating an existing power load sample library by a cloud system to obtain a calibration database;
s2, the cloud system issues the calibration database to edge side terminal equipment;
s3, the edge side terminal equipment carries out real-time classified calibration and cache on real-time samples in the monitored power utilization environment according to the calibration database, and uniformly calibrates the real-time samples which do not belong to the known types in the calibration database into unknown samples and uploads the unknown samples to a cloud system;
and S4, the cloud system stores and manages the real-time samples uploaded by the edge side terminal equipment according to classification calibration, and automatically classifies, calibrates and updates the unknown samples to the calibration database.
2. The cloud-edge collaborative online calibration method for the electrical load according to claim 1, wherein the feature vector of the electrical load sample includes: voltage, voltage harmonic amplitude and angle, voltage harmonic content, current harmonic amplitude and angle, current harmonic content, full wave active and/or reactive, fundamental active and/or reactive, and frequency.
3. The cloud-edge collaborative online calibration method for the electric load according to claim 1, characterized in that the cloud system in the step S1 automatically classifies and calibrates an existing electric load sample library to obtain a calibration database, and the specific steps are as follows:
s11, randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample, obtaining a spatial neighborhood of the central sample, and judging whether the central sample belongs to a sample to which a current cluster belongs;
s12, traversing all samples in the central sample field until the classification and calibration of all samples in the central sample field are completed;
and S13, repeating the steps S11-S12 until the classification calibration of all cluster samples is completed.
4. The cloud-edge-coordinated online calibration method for the electrical load according to claim 3, wherein in the step S11, any sample in an existing electrical load sample library is randomly selected to obtain a spatial neighborhood of the sample, the sample is a central sample, and whether the central sample belongs to a sample to which a current cluster belongs is judged, and the specific steps are as follows:
randomly selecting any sample in an existing power load sample library, wherein the sample is a central sample;
calculating the sample distance between the central sample and the rest samples to obtain a space field with the sample distance smaller than a first threshold value R;
judging whether the central sample belongs to a sample to which the current cluster belongs; if the number of the samples in the space field is larger than a second threshold value M, marking the central sample as a sample to which the current cluster belongs; otherwise, the center sample is marked as noise.
5. The cloud-edge collaborative online calibration method for the power load according to claim 4, wherein the sample distances between the calculation center sample and the other samples are as follows:
wherein d is the sample interval, A is the central sample, B is the rest samples, n is the feature quantity of the feature vector, and i is the ith feature in the feature vector.
6. The method according to claim 1, wherein in step S3, real-time samples in the monitored power consumption environment are classified, calibrated and cached in real time, and real-time samples that do not belong to known classes in the calibration database are uniformly calibrated as unknown samples, and the specific steps are as follows:
s31, calculating a matching coefficient between the real-time sample and the sample in the calibration database;
and S32, judging whether the real-time sample belongs to the class of the power load sample of the calibration database or not based on the matching coefficient.
7. The cloud-edge-coordinated online calibration method for the electrical load according to claim 6, wherein the matching coefficient is as follows:
wherein beta is i For matching coefficients, A is a real-time sample, B is a sample in the calibration database, n is the feature quantity of the feature vector, j is the jth class sample in the calibration database, and i is the ith feature of the feature vector.
8. The cloud-edge-coordinated online calibration method for the electrical load according to claim 6, wherein the step S32 specifically comprises:
taking the maximum value of the matching coefficient, and if the maximum value of the matching coefficient is larger than a third threshold value, calibrating the real-time sample into a corresponding sample type; and if the maximum value of the matching coefficient is smaller than a third threshold value, calibrating the real-time sample as an unknown sample.
9. The device comprising the cloud-edge coordinated online calibration method for the electrical load according to any one of claims 1 to 8, wherein the device comprises a cloud end system and edge side terminal equipment;
the cloud system is in communication connection with the edge side terminal equipment;
the cloud system is used for automatically classifying and calibrating the existing power load sample library to obtain a calibration database and updating the calibration database in real time;
and the edge side terminal equipment is used for classifying and calibrating the real-time samples according to the calibration database and uniformly calibrating the real-time samples which do not belong to the known types in the calibration database into unknown samples.
10. The device according to claim 9, wherein the cloud system and the edge-side terminal device are connected in a communication manner in a manner that: 3G, 4G, 5G, WIFI, bluetooth, 485 and HPLC.
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| CN117389971A (en) * | 2023-12-11 | 2024-01-12 | 吉林大学 | Cloud-edge real-time cooperative non-intervention type load monitoring system and method |
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| CN117389971A (en) * | 2023-12-11 | 2024-01-12 | 吉林大学 | Cloud-edge real-time cooperative non-intervention type load monitoring system and method |
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