CN113884734A - Non-invasive electricity utilization abnormity diagnosis method and device - Google Patents
Non-invasive electricity utilization abnormity diagnosis method and device Download PDFInfo
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Abstract
The application discloses a non-invasive electricity abnormity diagnosis method and a non-invasive electricity abnormity diagnosis device, wherein the method comprises the following steps: drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day; similarity calculation is carried out on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve; and carrying out power utilization abnormity diagnosis and analysis according to the similar result and the threshold value to obtain an abnormity diagnosis result. The method and the device can solve the technical problem that the existing detection technology is low in accuracy and cannot identify specific load types, so that the detection result is lack of reliability.
Description
Technical Field
The application relates to the technical field of load identification, in particular to a non-invasive power utilization abnormity diagnosis method and device.
Background
For a long time, electric power is measured and charged as a standardized product, bills provided for users by an electric power company only reflect the amount of used electric power, classification analysis of power utilization conditions of various devices of the users cannot be monitored in time, and the users can not be reminded actively.
The existing non-invasive electricity consumption abnormity detection technology can directly carry out general screening through electricity consumption and then carry out abnormity reminding, but the method has poor reliability and low screening precision and cannot judge the specific abnormal load type; in addition, a smart socket can find out whether the inserted equipment is abnormal in power utilization, such as a light which is not turned off, but the socket is high in cost, only can prompt, cannot provide accurate load type identification, and cannot provide reliable theoretical support.
Disclosure of Invention
The application provides a non-invasive power consumption abnormity diagnosis method and device, which are used for solving the technical problems that the existing detection technology is low in accuracy, and specific load types cannot be identified, so that the detection result is lack of reliability.
In view of the above, a first aspect of the present application provides a non-invasive electrical anomaly diagnosis method, including:
drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day;
similarity calculation is carried out on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve;
and carrying out power utilization abnormity diagnosis analysis according to the similar result and a threshold value to obtain an abnormity diagnosis result.
Preferably, the drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day further includes:
and classifying all users according to the user types and the user electricity consumption to obtain a user classification distribution matrix.
Preferably, the performing similarity calculation on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result includes:
and respectively carrying out similarity calculation on the daily load characteristic curve and a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
Preferably, the performing the power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result includes:
comparing the sub-similar results with corresponding sub-thresholds one by one;
if two or more than two sub-similar results are smaller than the corresponding sub-threshold, determining that the abnormal diagnosis result is an abnormal power utilization state, otherwise, determining that the abnormal diagnosis result is a normal power utilization state, wherein the threshold comprises a plurality of sub-thresholds.
Preferably, the power utilization abnormality diagnosis analysis is performed according to the similar result and a threshold value to obtain an abnormality diagnosis result, and then the method further includes:
and when the abnormal diagnosis result is an abnormal power utilization state, pushing an abnormal power utilization prompt to a user, wherein the pushing form comprises an APP, an applet and a short message.
A second aspect of the present application provides a non-invasive electrical abnormality diagnosis apparatus, including:
the curve drawing module is used for drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day;
the similarity calculation module is used for performing similarity calculation on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve;
and the diagnosis and analysis module is used for carrying out power utilization abnormality diagnosis and analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
Preferably, the method further comprises the following steps:
and the user classification module is used for classifying all users according to the user types and the user electricity consumption to obtain a user classification distribution matrix.
Preferably, the similarity calculation module is specifically configured to:
and respectively carrying out similarity calculation on the daily load characteristic curve and a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve to obtain a plurality of different sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
Preferably, the diagnostic analysis module is specifically configured to:
comparing the sub-similar results with corresponding sub-thresholds one by one;
if two or more than two sub-similar results are smaller than the corresponding sub-threshold, determining that the abnormal diagnosis result is an abnormal power utilization state, otherwise, determining that the abnormal diagnosis result is a normal power utilization state, wherein the threshold comprises a plurality of sub-thresholds.
Preferably, the method further comprises the following steps:
and the abnormal prompting module is used for pushing the abnormal power utilization prompt to the user when the abnormal diagnosis result is the abnormal power utilization state, and the pushing form comprises an APP, an applet and a short message.
According to the technical scheme, the embodiment of the application has the following advantages:
the application provides a non-invasive electrical anomaly diagnosis method, which comprises the following steps: drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day; similarity calculation is carried out on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve; and carrying out power utilization abnormity diagnosis and analysis according to the similar result and the threshold value to obtain an abnormity diagnosis result.
According to the non-invasive power consumption abnormity diagnosis method, abnormity detection analysis is carried out on different types of loads, the power consumption condition of each type of load can be identified, and more targeted detection is carried out; determining the current electricity utilization condition of each type of load in a mode of comparing the current load characteristic with the historical load characteristic; in order to ensure the accuracy of similar results, a plurality of different representative curves are selected from a preset historical load characteristic curve; the power utilization abnormity analysis is carried out according to the similar results obtained by the method, so that the abnormity diagnosis result is more reliable. Therefore, the method and the device can solve the technical problems that the existing detection technology is low in accuracy and cannot identify specific load types, and the detection result is lack of reliability.
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Fig. 1 is a schematic flowchart of a non-invasive electrical anomaly diagnosis method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a non-invasive electrical anomaly diagnosis apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a non-invasive electrical anomaly diagnosis system according to an application example of the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The non-intrusive load identification technology belongs to one of important technologies of a ubiquitous power internet of things at a client side, acquires load data (voltage and current) at a power load input line end, adopts a pattern identification algorithm, and decomposes user power load components and identifies the power utilization condition at the tail end of a power grid by analyzing load characteristic quantities under steady state and transient state, so that type identification and energy utilization decomposition of the power load at the client side are realized.
For ease of understanding, referring to fig. 1, the present application provides an embodiment of a method for non-invasive electrical anomaly diagnosis, comprising:
The load of each category of each user, namely the electric appliance, can acquire corresponding daily load data, and a curve is drawn by a plurality of data points, so that data acquisition can be carried out at one point every 15 minutes every day, and a daily load characteristic curve is drawn according to the data points; other time intervals can be set reasonably. The general load data refers to data such as current, voltage or power; the load data of different electrical appliances when power is used shows different changes.
Further, step 101, before, further includes:
and classifying all users according to the user types and the user electricity consumption to obtain a user classification distribution matrix.
By classifying users, it is possible to grasp the type of the load characteristic curve and manage load data of each user. The user types can generally comprise office users, home users, vacation users and the like, and the electricity consumption can be directly obtained through meter statistics. The occupancy of each user can be determined in the user classification distribution matrix.
And 102, carrying out similarity calculation on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve.
Further, step 102 includes:
and respectively carrying out similarity calculation on the daily load characteristic curve and the historical monthly average load characteristic curve, the historical annual average load characteristic curve and the historical typical daily load characteristic curve to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
If the daily load characteristic curve passes (x)1,x2,......,xi) Data points are shown and other predetermined historical load characteristics are taken as (theta)1,θ2,......,θi) Data points represent, then the similarity calculation can use the following formula:
where ε is a similar result. The daily load characteristic curve and each characteristic curve can be calculated to obtain a similar value, namely a sub-similar result, and the similar result is formed together.
And 103, carrying out power utilization abnormity diagnosis analysis according to the similar result and a threshold value to obtain an abnormity diagnosis result.
Further, step 103 includes:
comparing the sub-similar results with corresponding sub-threshold values one by one;
if two or more sub-similar results are smaller than the corresponding sub-threshold, the abnormal diagnosis result is judged to be the abnormal power utilization state, otherwise, the abnormal diagnosis result is the normal power utilization state, and the threshold comprises a plurality of sub-thresholds.
Because a plurality of sub-similar results exist, a plurality of comparison results exist after the similar values are compared with the corresponding sub-threshold values, a comparison mechanism is arranged, and as long as two or more than two conditions smaller than the sub-threshold values exist, the daily load characteristic curve on the same day is judged to have large variation, which indicates that the possibility of power utilization abnormity is high, and a corresponding abnormity diagnosis result is obtained; if only one comparison result is lower than the sub-threshold, the fluctuation of normal power utilization is possible, and the power utilization abnormality is not included in order to avoid misjudgment. The sub-threshold may be set according to different curve properties, which are to be in accordance with practical situations.
Further, step 103, thereafter, further includes:
and when the abnormal diagnosis result is the abnormal power utilization state, pushing an abnormal power utilization prompt to a user, wherein the pushing form comprises an APP, an applet and a short message.
The power utilization abnormity is informed to the user, the user can be helped to monitor the household appliance, and the phenomenon that the normal power utilization is influenced because the large property and personal loss is caused due to the power utilization abnormity is avoided, generally, the high-power electric appliance is not powered off in time, or other types of loads are added. The pushed message may be notified by APP, applet, and short message, specifically customized by the user, or may be pushed in other forms, which is not limited herein.
For the convenience of understanding, the present application provides an application example of a non-invasive power consumption anomaly diagnosis system, which includes a smart meter with a non-invasive load identification function, a metering terminal with an edge calculation function, a metering automation system and a user interaction terminal, please refer to fig. 3. The intelligent electric meter with the non-invasive load identification function can identify the types of electric appliances through load data such as harmonic current, voltage, power and the like of different electric appliances, namely load identification, wherein the types of the electric appliances are main household appliances such as air conditioners, short-time electric heating, electric water heaters, microwave ovens, washing machines, refrigerators and the like. The metering terminal with the edge calculation function collects the electricity utilization data collected by the intelligent electric meters with the non-invasive load identification function, converts the data form according to the data protocol and uploads the data to the metering automation master station. The metering automation master station refers to a big data platform for data acquisition and processing of the intelligent electric meter, and is characterized in that the big data platform is used for storing and processing user electricity consumption data. The user interaction terminal directly carries out information interaction to the user by acquiring the data of the metering automation system, and the interaction mode mainly comprises the modes of a special APP for a mobile phone, a small program, short messages and the like. And the user interaction terminal can also send the feedback of the user to the inaccurate load data back to the metering automation master station, so that the metering automation master station can update and adjust the data conveniently. In addition, the metering function terminal with the edge calculation is also responsible for training the maintenance work of the load identification frame, receiving the load identification frame issued by the metering automation master station, and storing and issuing the load identification frame to the smart electric meter with the non-invasive load identification function, and generally, at most 2000 metering terminals with the edge calculation can be accessed.
According to the non-invasive power consumption abnormity diagnosis method provided by the embodiment of the application, abnormity detection analysis is carried out aiming at different types of loads, the power consumption condition of each type of load can be identified, and more targeted detection is carried out; determining the current electricity utilization condition of each type of load in a mode of comparing the current load characteristic with the historical load characteristic; in order to ensure the accuracy of similar results, a plurality of different representative curves are selected from a preset historical load characteristic curve; the power utilization abnormity analysis is carried out according to the similar results obtained by the method, so that the abnormity diagnosis result is more reliable. Therefore, the technical problems that the existing detection technology is low in accuracy and cannot identify specific load types, and the detection result is lack of reliability can be solved.
For ease of understanding, referring to fig. 2, the present application provides an embodiment of a non-invasive electrical anomaly diagnosis apparatus, comprising:
a curve drawing module 201, configured to draw a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day;
the similarity calculation module 202 is configured to perform similarity calculation on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, where the preset historical load characteristic curve includes a historical monthly average load characteristic curve, a historical yearly average load characteristic curve, and a historical typical daily load characteristic curve;
and the diagnosis and analysis module 203 is used for performing power utilization abnormality diagnosis and analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
Further, still include:
and the user classification module 204 is configured to classify all users according to the user types and the power consumption of the users to obtain a user classification distribution matrix.
Further, the similarity calculation module 202 is specifically configured to:
and respectively carrying out similarity calculation on the daily load characteristic curve and the historical monthly average load characteristic curve, the historical annual average load characteristic curve and the historical typical daily load characteristic curve to obtain a plurality of different sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
Further, the diagnostic analysis module 203 is specifically configured to:
comparing the sub-similar results with corresponding sub-threshold values one by one;
if two or more sub-similar results are smaller than the corresponding sub-threshold, the abnormal diagnosis result is judged to be the abnormal power utilization state, otherwise, the abnormal diagnosis result is the normal power utilization state, and the threshold comprises a plurality of sub-thresholds.
Further, still include:
and the abnormal prompting module 205 is configured to, when the abnormal diagnosis result is the abnormal power utilization state, push an abnormal power utilization prompt to the user, where the push form includes APP, an applet, and a short message.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for executing all or part of the steps of the method described in the embodiments of the present application through a computer device (which may be a personal computer, a server, or a network device). And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.
Claims (10)
1. A method for non-invasive diagnosis of electrical anomalies, comprising:
drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day;
similarity calculation is carried out on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve;
and carrying out power utilization abnormity diagnosis analysis according to the similar result and a threshold value to obtain an abnormity diagnosis result.
2. The method according to claim 1, wherein the step of drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day further comprises:
and classifying all users according to the user types and the user electricity consumption to obtain a user classification distribution matrix.
3. The method for non-invasive electrical anomaly diagnosis according to claim 1, wherein the similarity calculation of the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result comprises:
and respectively carrying out similarity calculation on the daily load characteristic curve and a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
4. The method according to claim 3, wherein the performing the power abnormality diagnosis analysis based on the similarity result and the threshold value to obtain an abnormality diagnosis result comprises:
comparing the sub-similar results with corresponding sub-thresholds one by one;
if two or more than two sub-similar results are smaller than the corresponding sub-threshold, determining that the abnormal diagnosis result is an abnormal power utilization state, otherwise, determining that the abnormal diagnosis result is a normal power utilization state, wherein the threshold comprises a plurality of sub-thresholds.
5. The method according to claim 1, wherein the analyzing of the power abnormality diagnosis is performed according to the similarity result and a threshold value to obtain an abnormality diagnosis result, and then further comprising:
and when the abnormal diagnosis result is an abnormal power utilization state, pushing an abnormal power utilization prompt to a user, wherein the pushing form comprises an APP, an applet and a short message.
6. A non-invasive electrical anomaly diagnostic apparatus, comprising:
the curve drawing module is used for drawing a daily load characteristic curve according to the acquired daily load data of each type of load of each user on the same day;
the similarity calculation module is used for performing similarity calculation on the daily load characteristic curve and a preset historical load characteristic curve to obtain a similar result, wherein the preset historical load characteristic curve comprises a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve;
and the diagnosis and analysis module is used for carrying out power utilization abnormality diagnosis and analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
7. The non-invasive electrical anomaly diagnostic apparatus according to claim 6, further comprising:
and the user classification module is used for classifying all users according to the user types and the user electricity consumption to obtain a user classification distribution matrix.
8. The non-invasive electrical anomaly diagnosis device according to claim 6, wherein said similarity calculation module is specifically configured to:
and respectively carrying out similarity calculation on the daily load characteristic curve and a historical monthly average load characteristic curve, a historical annual average load characteristic curve and a historical typical daily load characteristic curve to obtain a plurality of different sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
9. The non-invasive electrical anomaly diagnosis device according to claim 8, wherein said diagnostic analysis module is specifically configured to:
comparing the sub-similar results with corresponding sub-thresholds one by one;
if two or more than two sub-similar results are smaller than the corresponding sub-threshold, determining that the abnormal diagnosis result is an abnormal power utilization state, otherwise, determining that the abnormal diagnosis result is a normal power utilization state, wherein the threshold comprises a plurality of sub-thresholds.
10. The non-invasive electrical anomaly diagnostic apparatus according to claim 6, further comprising:
and the abnormal prompting module is used for pushing the abnormal power utilization prompt to the user when the abnormal diagnosis result is the abnormal power utilization state, and the pushing form comprises an APP, an applet and a short message.
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| CN114118302A (en) * | 2022-01-25 | 2022-03-01 | 全球能源互联网研究院有限公司 | Electric energy meter serial identification method and device and electronic equipment |
| CN116660621A (en) * | 2023-07-27 | 2023-08-29 | 江西琰圭技术服务有限公司 | Electricity larceny prevention intelligent management system for local sampling analysis |
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