CN113884734B - Non-invasive electricity consumption abnormality diagnosis method and device - Google Patents
Non-invasive electricity consumption abnormality diagnosis method and device Download PDFInfo
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Abstract
The application discloses a non-invasive electricity consumption abnormality diagnosis method and device, wherein the method comprises the following steps: drawing a daily load characteristic curve according to the daily load data of each type of load of each user on the same day; 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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve; and carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result. The application can solve the technical problems that the existing detection technology has lower accuracy and cannot identify specific load types, so that the detection result lacks reliability.
Description
Technical Field
The application relates to the technical field of load identification, in particular to a non-invasive electricity consumption abnormality diagnosis method and device.
Background
For a long time, electric power is taken as a standardized product to carry out metering and charging, a bill provided by an electric company to a user can only reflect how much electricity is used, classification analysis of various equipment electricity consumption conditions of the user cannot be monitored timely, the user is actively reminded, and along with the higher and higher demands of the user, the electric company is urgent to grasp the electricity consumption conditions of the user and actively discover electricity consumption abnormality for the user timely.
The existing non-invasive electricity utilization abnormality detection technology can directly carry out general screening through electricity utilization amount and then carry out abnormality reminding, but the method is poor in reliability, low in screening precision and incapable of distinguishing specific abnormal load types; in addition, there is an intelligent socket which can find whether the inserted equipment is abnormal in electricity, such as an uninterruptable lamp, but the socket is high in cost, can only prompt, cannot give accurate load type identification, and cannot provide reliable theoretical support.
Disclosure of Invention
The application provides a non-invasive electricity consumption abnormality diagnosis method and device, which are used for solving the technical problems that the accuracy of the existing detection technology is low, a specific load type cannot be identified, and the reliability of a detection result is poor.
In view of the foregoing, 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 daily load data of each type of load of each user on the same day;
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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve;
And carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
Preferably, the drawing the 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 the users according to the user types and the user power 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 carrying out similarity calculation on the daily load characteristic curve and a historical month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve respectively to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
Preferably, the electrical anomaly diagnosis analysis is performed according to the similar result and a threshold value to obtain an anomaly diagnosis result, including:
Comparing the sub-similar results with corresponding sub-thresholds one by one respectively;
If two or more sub-similar results are smaller than the corresponding sub-thresholds, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the thresholds comprise a plurality of sub-thresholds.
Preferably, the electrical anomaly diagnosis analysis is performed according to the similar result and the threshold value to obtain an anomaly diagnosis result, and then the method further comprises the steps of:
When the abnormal diagnosis result is in an abnormal electricity utilization state, pushing an abnormal electricity utilization prompt to a user, wherein the pushing mode comprises APP, a small program and a short message.
A second aspect of the present application provides a non-invasive electricity usage abnormality diagnosis device 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 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;
And the diagnosis analysis module is used for carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
Preferably, the method further comprises:
and the user classification module is used for classifying all users according to the user types and the user power consumption to obtain a user classification distribution matrix.
Preferably, the similarity calculation module is specifically configured to:
And carrying out similarity calculation on the daily load characteristic curve and a historical month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve respectively 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 respectively;
If two or more sub-similar results are smaller than the corresponding sub-thresholds, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the thresholds comprise a plurality of sub-thresholds.
Preferably, the method further comprises:
the abnormal prompting module is used for pushing abnormal power utilization prompts to a user when the abnormal diagnosis result is in an abnormal power utilization state, and the pushing mode comprises APP, a small program and a short message.
From the above technical solutions, the embodiment of the present application has the following advantages:
The application provides a non-invasive electricity consumption abnormality diagnosis method, which comprises the following steps: drawing a daily load characteristic curve according to the daily load data of each type of load of each user on the same day; 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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve; and carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
According to the non-invasive electricity consumption abnormality diagnosis method provided by the application, abnormality detection analysis is carried out aiming at different types of loads, so that the electricity consumption condition of each type of load can be identified, and more targeted detection can be carried out; determining the power consumption condition of each type of load on the same day by comparing the load characteristic of the same day with the historical load characteristic; in order to ensure the accuracy of similar results, a plurality of different representative curves are selected from the preset historical load characteristic curves; according to the method, the similar result is used for carrying out power consumption abnormality analysis, so that the abnormality diagnosis result is more reliable. Therefore, the application can solve the technical problems that the existing detection technology has lower accuracy and cannot identify specific load types, so that the detection result lacks reliability.
Drawings
FIG. 1 is a schematic flow chart of a non-invasive electrical anomaly diagnosis method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a non-invasive electrical anomaly diagnosis device according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a non-invasive electrical anomaly diagnosis system according to an embodiment of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The non-invasive load identification technology belongs to one of the important technologies of the ubiquitous power Internet of things of a client side, and is characterized in that load data (voltage and current) are acquired at an electric load input line end, a pattern identification algorithm is adopted, and the type identification and energy utilization decomposition of the electric load of the client side are realized by analyzing load characteristic quantities in steady state and transient state, decomposing the electric load components of a user and identifying the electric condition of the tail end of a power grid.
For ease of understanding, referring to fig. 1, an embodiment of a non-invasive electrical anomaly diagnosis method provided by the present application includes:
and 101, 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 electric appliance can acquire corresponding daily load data, and a plurality of data points are needed for drawing a curve, so that data acquisition can be carried out according to one point every 15 minutes every day, and further a daily load characteristic curve is drawn according to the data points; other time intervals can be set, and the method is reasonable. The general load data refers to data such as current, voltage or power; the load data of different electric appliances when power is used presents different changes.
Further, step 101, before further includes:
And classifying all the users according to the user types and the user power consumption to obtain a user classification distribution matrix.
Classifying the users can grasp the type of the load characteristic curve, and manage the 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 may be determined in a user classification distribution matrix.
Step 102, 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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve.
Further, step 102 includes:
And carrying out similarity calculation on the daily load characteristic curve and the historical month average load characteristic curve, the historical year average load characteristic curve and the historical typical daily load characteristic curve respectively to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results.
If the daily load characteristic is represented by (x 1,x2,......,xi) data points and the other preset historical load characteristic is represented by (θ 1,θ2,......,θi) data points, then the similarity calculation can be as follows:
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 results are formed together.
And 103, carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result.
Further, step 103 includes:
comparing the sub-similar results with the corresponding sub-threshold values one by one respectively;
If two or more sub-similar results are smaller than the corresponding sub-threshold values, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the threshold values comprise a plurality of sub-threshold values.
Because a plurality of sub-similar results exist, a plurality of comparison results exist after the similar values are compared with the corresponding sub-thresholds, a comparison mechanism is arranged, and as long as two or more conditions smaller than the sub-thresholds exist, the daily load characteristic curve on the same day is judged to have larger fluctuation, the possibility of power utilization abnormality is higher, and the corresponding abnormality diagnosis result is obtained; if only one comparison result is lower than the sub-threshold value, the comparison result is possibly the fluctuation of normal electricity, and in order to avoid misjudgment, the comparison result is not included in the electricity utilization abnormality. The sub-threshold can be set according to different curve properties, so as to meet the practical situation.
Further, step 103 further comprises:
When the abnormality diagnosis result is in an abnormal electricity utilization state, an abnormal electricity utilization prompt is pushed to a user, wherein the pushing mode comprises APP, a small program and a short message.
The power consumption abnormality is notified to the user, the user can be helped to monitor the household appliances, and the situation that the large property personal loss is caused by the power consumption abnormality is avoided, and the power consumption of the high-power electric appliances is not timely powered off or other types of loads are added to influence the normal power consumption is avoided. The pushed message may be specifically customized by the user through APP, applet and sms notification, or may be pushed in other forms, which is not limited herein.
In order to facilitate understanding, the application provides an application example of a non-invasive electricity consumption abnormality diagnosis system, which comprises 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, and please refer to fig. 3. The intelligent ammeter with the non-invasive load identification function can identify the types of the electric appliances through the load data such as harmonic current, voltage, power and the like of different electric appliances, namely the load identification, and the electric appliance identification types are household appliances which are mainly used for air conditioning, short-time electric heating, electric water heater, microwave ovens, washing machines, refrigerators and the like. The metering terminal with the edge computing function collects power consumption data collected by each intelligent ammeter with the non-invasive load recognition function, and uploads the power consumption data to the metering automation master station after converting the data form according to a data protocol. The metering automation master station refers to a large data platform for data acquisition and processing of the intelligent ammeter, and is characterized by storing and processing large data of 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 forms such as a mobile phone special APP, a small program, a short message and the like. The user interaction terminal can also send the feedback of the user on inaccurate load data back to the metering automation master station, so that the metering automation master station can update the adjustment data conveniently. In addition, the metering function terminal with edge calculation is also responsible for training maintenance work of the load identification framework, receiving the load identification framework issued by the metering automation master station, storing and issuing the load identification framework to the intelligent ammeter with the non-invasive load identification function, and generally, at most 2000 metering terminals with edge calculation can be accessed.
According to the non-invasive electricity consumption abnormality diagnosis method provided by the embodiment of the application, abnormality detection analysis is carried out on different types of loads, so that the electricity consumption condition of each type of load can be identified, and more targeted detection can be carried out; determining the power consumption condition of each type of load on the same day by comparing the load characteristic of the same day with the historical load characteristic; in order to ensure the accuracy of similar results, a plurality of different representative curves are selected from the preset historical load characteristic curves; according to the method, the similar result is used for carrying out power consumption abnormality analysis, so that the abnormality diagnosis result is more reliable. Therefore, the embodiment of the application can solve the technical problems that the existing detection technology is low in accuracy and cannot identify specific load types, so that the detection result lacks reliability.
For ease of understanding, referring to fig. 2, the present application provides an embodiment of a non-invasive electrical anomaly diagnosis device, comprising:
a curve drawing module 201, configured to draw a daily load characteristic curve according to the obtained 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 a 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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve;
The diagnostic analysis module 203 is configured to perform power consumption abnormality diagnostic analysis according to the similar result and the threshold value, and obtain an abnormality diagnostic result.
Further, the method further comprises the following steps:
the user classification module 204 is configured to classify all users according to the user type and the user power consumption, and obtain a user classification distribution matrix.
Further, the similarity calculation module 202 is specifically configured to:
and carrying out similarity calculation on the daily load characteristic curve and the historical month average load characteristic curve, the historical year average load characteristic curve and the historical typical daily load characteristic curve respectively 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 the corresponding sub-threshold values one by one respectively;
If two or more sub-similar results are smaller than the corresponding sub-threshold values, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the threshold values comprise a plurality of sub-threshold values.
Further, the method further comprises the following steps:
the abnormality prompting module 205 is configured to push an abnormality power consumption prompt to a user when the abnormality diagnosis result is in an abnormality power consumption state, where the pushing form includes APP, applet and sms.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for executing all or part of the steps of the method according to the embodiments of the present application by means of a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-only memory (ROM), random access memory (RandomAccess Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (6)
1. A method for non-invasive electrical anomaly diagnosis, comprising:
Drawing a daily load characteristic curve according to the daily load data of each type of load of each user on the same day;
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 month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve, and the similarity calculation process comprises the following steps of:
Carrying out similarity calculation on the daily load characteristic curve and a historical month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve respectively to obtain a plurality of sub-similar results, wherein the similar results comprise a plurality of sub-similar results;
and carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result, wherein the analysis process specifically comprises the following steps of:
Comparing the sub-similar results with corresponding sub-thresholds one by one respectively;
If two or more sub-similar results are smaller than the corresponding sub-thresholds, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the thresholds comprise a plurality of sub-thresholds.
2. The non-invasive electricity usage abnormality diagnosis method according to claim 1, wherein the plotting of the daily load characteristic curve from the daily load data of each type of load of each user on the same day, further comprises, before:
And classifying all the users according to the user types and the user power consumption to obtain a user classification distribution matrix.
3. The method of claim 1, wherein the performing a power consumption abnormality diagnosis analysis according to the similar result and a threshold value, to obtain an abnormality diagnosis result, further comprises:
When the abnormal diagnosis result is in an abnormal electricity utilization state, pushing an abnormal electricity utilization prompt to a user, wherein the pushing mode comprises APP, a small program and a short message.
4. A non-invasive electrical anomaly diagnostic device, 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 similarity calculation module is specifically used for:
Carrying out similarity calculation on the daily load characteristic curve and a historical month average load characteristic curve, a historical year average load characteristic curve and a historical typical daily load characteristic curve respectively to obtain a plurality of different sub-similar results, wherein the similar results comprise a plurality of sub-similar results;
The diagnosis analysis module is used for carrying out power consumption abnormality diagnosis analysis according to the similar result and the threshold value to obtain an abnormality diagnosis result, and is specifically used for:
Comparing the sub-similar results with corresponding sub-thresholds one by one respectively;
If two or more sub-similar results are smaller than the corresponding sub-thresholds, judging that the abnormal diagnosis result is in an abnormal power utilization state, otherwise, judging that the abnormal diagnosis result is in a normal power utilization state, wherein the thresholds comprise a plurality of sub-thresholds.
5. The non-invasive electrical anomaly diagnostic device according to claim 4, further comprising:
and the user classification module is used for classifying all users according to the user types and the user power consumption to obtain a user classification distribution matrix.
6. The non-invasive electrical anomaly diagnostic device according to claim 4, further comprising:
the abnormal prompting module is used for pushing abnormal power utilization prompts to a user when the abnormal diagnosis result is in an abnormal power utilization state, and the pushing mode comprises APP, a small program and a short message.
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