CN116320824A - Thing allies oneself with electric energy meter of electrified energy quality module - Google Patents
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Abstract
The invention relates to the technical field of electric energy meters, in particular to an Internet of things electric energy meter with an electric energy quality module; according to the invention, through the acquired real-time power information, the real-time power information is processed by the power quality monitoring method, so that abnormal data, power quality information and early warning information are obtained. And the three types of information are sent to the user side, so that operation and maintenance personnel can process corresponding abnormal conditions and early warning information.
Description
Technical Field
The invention relates to the technical field of electric energy meters, in particular to an Internet of things electric energy meter with an electric energy quality module.
Background
The electric energy quality monitoring is used for representing the basic operation condition of the power grid according to the collected real-time monitoring data, further analyzing the problems existing in the power consumption of users, and providing helpful power grid monitoring tasks for the rapid overhaul and power failure recovery of the power grid. Along with the rapid development of new energy power stations such as wind energy, solar energy and the like, the grid-connected rate of the electric energy which is greatly influenced by natural factors and has lower stability is continuously improved, so that the cleanliness of a power grid is improved, and the electric energy quality in the power grid is greatly fluctuated. The monitoring of the power quality of the power grid becomes particularly important. The power quality function of the existing power grid is mainly realized by special power monitoring equipment; the installation and application costs of these devices are high.
The electric energy meter is an intelligent terminal of a power grid, and besides the metering function of the basic electric energy consumption of the traditional electric energy meter, under the development trend of the intelligent power grid and new energy, various functions such as multi-rate metering, user side control, electricity larceny prevention, topology identification and other intelligent functions are continuously sent out, and the intelligent electric meter represents the development direction of the intelligent terminal of the user of the energy-saving intelligent power grid in the future. With the rapid development of intelligent distribution networks, the number of sensors and power electronic devices is continuously increased. A large amount of network technologies (such as big data, cloud computing, etc.) are rushing into the power grid, so that the power grid company enters a new stage of technology reform. Therefore, how to realize the power quality monitoring of the power grid based on the intelligent electric energy meter is a new direction of the upgrading and upgrading of the power grid and the updating and development of the system.
However, new products available at present appear, and the quality monitoring of the electric energy still needs to rely on operation and maintenance personnel to periodically patrol each relevant node, discover problems in time and collect terminal data to upload to the system. The power quality detection of the power grid is time-consuming and labor-consuming, the efficiency is low, and the fault state of the power grid cannot be found in time.
Disclosure of Invention
In order to at least overcome the defects in the prior art, the invention aims to provide the internet of things electric energy meter with the electric energy quality module, which can obtain abnormal data of various electric information, abnormal information of an integral power grid and early warning information of the power grid through extracting real-time electric power information and processing the real-time electric power information, and can provide maintenance decision for remote maintenance personnel by sending the three information to a remote maintenance personnel user side.
In a first aspect, an internet of things electric energy meter with an electric energy quality module comprises an electric energy quality module, an internet of things communication module and a server, wherein the electric energy quality module establishes communication with the server through the internet of things communication module; the electric energy quality module is internally provided with a data acquisition unit, and the data acquisition unit is used for acquiring real-time electric power information of each item measured by a measuring unit in the electric energy meter; the server is internally provided with a storage module, the storage module comprises a program storage unit and a data storage unit, the program storage unit stores a power quality monitoring method, the power quality monitoring method determines power quality and power early warning information based on the real-time power information, and the data storage unit is used for storing the real-time power information and the power early warning information acquired by the data acquisition unit; the system also comprises an alarm unit, wherein the alarm unit sends the electric energy early warning information to a corresponding user side; the power quality monitoring method comprises the following steps: acquiring various real-time power information based on the metering unit; comparing each item of power information with a preset corresponding power information threshold value to obtain abnormal data; and sending the abnormal data to a corresponding user terminal.
In a first possible implementation manner of the first aspect, the preset corresponding power information threshold value determination includes the following method: acquiring sample data of each item of real-time power information, and determining a characteristic value of a target characteristic in the sample data; wherein the target characteristic is a characteristic of each item of real-time power information; transforming the characteristic values, wherein probability density distribution results corresponding to the transformed characteristic values meet the set distribution form requirements; determining a threshold value corresponding to the target feature according to the probability density distribution result corresponding to the converted feature value and the preset searching rate of each item of real-time power information; the threshold value corresponding to the target feature is used for detecting all real-time power information in all real-time power information, and the ratio of the number of the detected real-time power information to the number of all real-time power information is the detection rate.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the method further includes: probability density distribution is carried out on the converted characteristic values, and probability density distribution results corresponding to the converted characteristic values are obtained; verifying the probability density distribution result corresponding to the converted characteristic value by using a verification model to obtain the similarity between the probability density distribution form corresponding to the converted characteristic value and the set distribution form; if the similarity is larger than a similarity threshold, determining that the probability density distribution result corresponding to the transformed characteristic value meets the set distribution form requirement.
With reference to the second possible implementation manner of the first aspect, in a third possible implementation manner, the real-time power information includes any one of real-time voltage information, real-time frequency information, and real-time waveform information; the real-time voltage information includes a real-time voltage effective value, and the real-time waveform information includes a real-time waveform feature.
With reference to the third possible implementation manner of the first aspect, in a fourth possible implementation manner, acquiring the real-time waveform feature includes the following method: dividing the waveform according to a plurality of scales to obtain a waveform sequence corresponding to the scales, wherein the waveform sequence corresponding to any scale comprises a plurality of waveform fragments belonging to any scale; encoding a plurality of waveform fragments in a waveform sequence corresponding to each scale respectively to obtain a first characteristic sequence corresponding to each scale, wherein the first characteristic sequence corresponding to any scale comprises waveform characteristics obtained by encoding the plurality of waveform fragments belonging to any scale; respectively fusing a plurality of waveform features in the first feature sequence corresponding to each scale to obtain a second feature sequence corresponding to each scale; and splicing the second characteristic sequences corresponding to the scales to obtain waveform characteristic information corresponding to the waveform.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner, comparing each item of power information with a preset corresponding power information threshold to obtain abnormal data, including comparing the real-time voltage effective value with a preset corresponding voltage effective value threshold to obtain voltage abnormal data, comparing the real-time frequency information with a preset corresponding frequency threshold to obtain frequency abnormal data, and comparing the real-time waveform feature with a preset corresponding wave feature threshold to obtain waveform abnormal data.
In a sixth possible implementation manner of the first aspect, before the sending the abnormal data to the corresponding user side, the method further includes:
according to the electric power information and the corresponding preset weight vector, determining whether the electric energy quality meets the standard or not, wherein the method specifically comprises the following steps of: determining an efficiency evaluation index of the electric energy meter according to the electric power information and the corresponding preset weight vector; changing the value of the preset weight vector based on a preset efficiency evaluation index constraint condition until a set efficiency evaluation index optimization target is met, and determining an optimal weight vector meeting the set efficiency evaluation index optimization target; normalizing the optimal weight vector to determine the weight of each piece of electric power information; determining the score of each piece of electric power information according to the weight of each piece of electric power information and each piece of electric power information; substituting the scores of the electric power information into preset electric energy quality membership functions respectively, and determining membership coefficients corresponding to the electric power information; determining objective weights corresponding to the electric power information according to membership coefficients corresponding to the electric power information; and determining the electric energy quality score of the electric energy meter according to the objective weight and the score of each electric power information.
With reference to the sixth possible implementation manner of the first aspect, in a seventh possible implementation manner, comparing each item of power information with a preset corresponding power information threshold value to obtain abnormal data, further includes: judging that the electric energy quality of the electric energy meter meets the standard based on the electric energy quality score of the electric energy meter being greater than or equal to a preset threshold value; if the electric energy quality score of the electric network to be evaluated is smaller than a preset threshold value, judging that the electric energy quality of the electric energy meter does not reach the standard, and the data which does not reach the standard are abnormal data.
In an eighth possible implementation manner of the first aspect, before the sending the abnormal data to the corresponding user side, the method further includes: the early warning is carried out on all electric power information based on a preset early warning activity characteristic database, and the method specifically comprises the following steps: constructing an early warning activity characteristic database; extracting early warning state activity characteristics of early warning state activities related in the electric power information; comparing the early warning state activity characteristics with the early warning activity characteristic database to obtain early warning information; and sending the abnormal data to the corresponding user terminal, including sending the early warning information to the corresponding user terminal.
With reference to the eighth possible implementation manner of the first aspect, in a ninth possible implementation manner, the early warning state activity feature includes a behavior feature and a vector feature corresponding to the behavior feature; the early warning activity feature database is internally configured with behavior features and vector features corresponding to the behavior features, wherein the vector features comprise threshold tags; comparing the early warning state activity features with the early warning activity feature library comprises the steps of comparing the behavior features of the early warning state activity features with the behavior features in the early warning activity feature library to obtain a first comparison result, and determining the vector features of the early warning state activity features based on the first comparison result to compare with the corresponding vector feature threshold values in the early warning activity feature library to obtain a second comparison result.
According to any one of the realizable modes, the acquired real-time power information is used for processing the real-time power information by a power quality monitoring method to obtain abnormal data, power quality information and early warning information. And the three types of information are sent to the user side, so that operation and maintenance personnel can process corresponding abnormal conditions and early warning information.
Drawings
For a clearer description of the technical solutions of the embodiments of the present invention, reference will be made to the accompanying drawings, which are needed to be activated in the embodiments, and it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and other related drawings can be extracted from these drawings without the need of inventive effort for a person skilled in the art.
Fig. 1 is a schematic diagram of an internet of things electric energy meter with an electric energy quality module according to an embodiment of the present invention;
fig. 2 is a flowchart of a method for implementing the monitoring and early warning method of the internet of things electric energy meter with the electric energy quality module according to the embodiment of the invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a particular application and its requirements. It will be apparent to those having ordinary skill in the art that various changes can be made to the disclosed embodiments and that the general principles defined herein may be applied to other embodiments and applications without departing from the principles and scope of the invention. Therefore, the present invention is not limited to the described embodiments, but is to be accorded the widest scope consistent with the claims.
The terminology used in the present invention is for the purpose of describing particular exemplary embodiments only and is not intended to be limiting of the scope of the present invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
These and other features, characteristics of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the drawings, all of which form a part of this specification. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the invention. It should be understood that the figures are not drawn to scale.
A flowchart is used in the present invention to describe the operations performed by a system according to some embodiments of the present invention. It should be understood that the operations in the flow diagrams may be performed out of order. Rather, the various steps may be processed in reverse order or simultaneously. Further, one or more other operations may be added to the flowchart. One or more operations may also be deleted from the flowchart.
The following description of the invention refers to the accompanying drawings, and the specific operation method in the method embodiment may also be applied to the device embodiment or the system embodiment.
Fig. 1 is an internet of things electric energy meter with an electric energy quality module according to an embodiment of the present invention, including an electric energy quality module, an internet of things communication module, and a server, wherein the electric energy quality module establishes communication with the server through the internet of things communication module.
In this embodiment, a data acquisition unit is disposed in the power quality module, where the data acquisition unit is configured to acquire real-time power information of each item measured by a measurement unit in the electric energy meter. The server is internally provided with a storage module, the storage module comprises a program storage unit and a data storage unit, the program storage unit is stored with an electric energy quality monitoring method, the electric energy quality monitoring method determines abnormal data, electric energy quality through an electric energy meter and electric energy early warning information based on real-time electric power information, and the data storage unit is used for storing the real-time electric power information and the electric energy early warning information acquired by the data acquisition unit.
The Internet of things electric energy meter with the electric energy quality module further comprises an alarm unit, and abnormal data, electric energy quality information and electric energy early warning information are sent to the corresponding user side.
The abnormal data, the power quality information and the power early warning information are obtained through a storage module arranged in the server, and in the embodiment, the storage module comprises a program storage unit and a data storage unit, wherein a power quality monitoring method is stored in the program storage unit, and the power quality monitoring method determines the abnormal data, the power quality and the power early warning information based on the real-time power information. The data storage unit is used for storing the real-time power information and the electric energy early warning information acquired by the data acquisition unit.
In addition, the embodiment also comprises an alarm unit, the obtained information is sent to the corresponding user end through the communication module, and a corresponding operation and maintenance decision scheme is issued to operation and maintenance management personnel.
In this embodiment, the abnormal data, the power quality information and the power early warning information are respectively three different data types. The abnormal data are real-time abnormal data obtained by processing various real-time power information obtained by metering through the metering unit through the power quality monitoring method, and are used for representing abnormal conditions of the real-time data. The electric energy quality information is information related to electric energy quality obtained by various real-time electric energy information obtained by metering through the metering unit through an electric energy quality monitoring method, and is used for representing the quality of the electric energy information and judging whether the quality meets the standard and the requirement. The electric energy early warning information is information which can play a role in early warning in abnormal data obtained by the electric energy quality monitoring method of various real-time electric power information obtained by metering through the metering unit and normal data except unqualified electric energy quality information, and is used for representing possible abnormal situations in the future.
In this embodiment, the storage module is divided into two storage areas, where one storage area is a program storage unit and the other storage area is a data storage unit. The program storage unit is equivalent to a firmware area, the read-write authority of the area is set to be in a read-only mode, and the data stored in the area can not be erased and changed. And the data in the data storage unit can be erased or read and written, and when the capacity of the data storage area is full, the newly written data can cover the earliest historical data.
The embodiment provides the Internet of things electric energy meter with the electric energy quality module, and the acquisition of abnormal conditions and the judgment of future states of the electric energy meter are realized through the acquisition of the three different types of information. The method for monitoring the power quality according to the present embodiment will be described in detail with reference to fig. 2, with respect to the method for monitoring the power quality based on the information of the type in the third embodiment and the determination of the status.
Referring to fig. 2, the power quality monitoring method provided in this embodiment includes the following steps:
and S200, acquiring various real-time power information based on the metering unit.
And S300, comparing each piece of power information with a preset corresponding power information threshold value to obtain abnormal information.
And S400, transmitting the abnormal information to a corresponding user terminal.
In this embodiment, since the monitoring of the power quality involves more data, the data that can be used to characterize the power quality is mainly voltage information, frequency information, and waveform information, which can reflect the power quality via the power meter.
With respect to the above information acquisition, the acquisition can be performed by each corresponding module provided in the metering unit, because the acquisition of the above data is not described in the present embodiment as a matter of course in the prior art. In addition, since the characteristic values of the power information are mainly compared when the power information is compared, it is necessary to extract the characteristics of the power information. Since the voltage information and the frequency information are specific values, the characteristics for the voltage information and the frequency information are specific values obtained via the metering unit, wherein the voltage information is characterized by a voltage effective value and the frequency information is a specific frequency value. The method for acquiring the effective voltage value comprises the following steps of:
and taking the value of each phase voltage effective value register in the electric energy meter metering unit, wherein the voltage metering parameter is a parameter for determining the value of the corresponding phase voltage effective value register.
And calculating the corresponding phase voltage effective value obtained by the electric energy meter measuring unit according to the value of each phase voltage effective value register, and particularly multiplying the value of the voltage effective value register by a set voltage conversion coefficient to obtain the corresponding phase voltage effective value.
The method aims at the specific numerical value of the voltage information and the frequency information, and the real-time wave property information is a section of wave shape, and is characterized in that the characteristic of the wave shape information is extracted by a targeted setting extraction method, and the specific method for extracting the characteristic of the real-time wave shape information comprises the following steps:
s210, respectively dividing the waveform according to a plurality of scales to obtain a waveform sequence corresponding to the scales, wherein the waveform sequence corresponding to any scale comprises a plurality of waveform fragments belonging to any scale.
S220, respectively encoding a plurality of waveform fragments in the waveform sequence corresponding to each scale to obtain a first characteristic sequence corresponding to each scale, wherein the first characteristic sequence corresponding to any scale comprises waveform characteristics obtained by encoding the plurality of waveform fragments belonging to any scale.
In this embodiment, the first feature sequence corresponding to any scale includes waveform features obtained by encoding a plurality of waveform segments belonging to any scale, that is, the first feature sequence corresponding to any scale includes a plurality of waveform features, and the plurality of waveform features are in one-to-one correspondence with the plurality of waveform segments corresponding to the scale.
And S230, respectively fusing a plurality of waveform features in the first feature sequence corresponding to each scale to obtain a second feature sequence corresponding to each scale.
And S240, splicing the second characteristic sequences corresponding to the scales to obtain waveform characteristics corresponding to the waveforms.
In the step, the second characteristic sequences corresponding to the scales are spliced, so that the obtained waveform characteristic information contains waveform characteristics corresponding to the scales, and the characteristics contained in the waveform characteristic information are enriched, thereby improving the accuracy of the waveform characteristic information.
For step S300, the real-time information is compared with the power information threshold, and for the characteristics of the power grid, because of the influence of external power devices and environment, each power information will fluctuate to some extent during the power transmission process, and for this fluctuation, the threshold cannot be set according to the conventional empirical algorithm, so that a more accurate threshold setting method needs to be established for more accurate threshold acquisition in the electric energy meter. The construction of the threshold comprises the following processes:
and S310, acquiring sample data of each item of real-time power information, and determining the characteristic value of the target characteristic in the sample data.
In the present embodiment, the target feature is a feature possessed by each item of real-time power information, and this feature can be obtained through steps S210 to S240.
S320, converting the characteristic values, wherein probability density distribution results corresponding to the converted characteristic values meet the set distribution form requirements.
S330, determining a threshold value corresponding to the target feature according to the probability density distribution result corresponding to the converted feature value and the preset searching rate of each item of real-time power information.
In this embodiment, for step S320, the following process is specifically included:
s321, probability density distribution is carried out on the converted characteristic values, and probability density distribution results corresponding to the converted characteristic values are obtained.
S322, verifying probability density distribution results corresponding to the transformed feature values by using a verification model to obtain the similarity between the probability density distribution forms corresponding to the transformed feature values and the set distribution forms.
S333, if the similarity is larger than a similarity threshold, determining that probability density distribution results corresponding to the transformed characteristic values meet the set distribution form requirements.
The threshold is obtained for the step S330, and the threshold is compared with each item of power information to obtain abnormal data, and the specific process is as follows: and comparing the various electric power information with an electric power information threshold value, and determining various electric power information exceeding the threshold value range as abnormal data.
For step S400, when the user side interacts with the electric energy meter, the corresponding label of the electric energy meter is sent to the user side along with the abnormal data, the label is a position label and a unique code of the electric energy meter, and the corresponding electric energy meter information is determined through the unique code and the position label and is used for solving the abnormal condition of the electric energy meter by maintenance personnel.
In the power quality monitoring method provided by the embodiment, the identification of abnormal data, power quality information and early warning information can be realized. In the above steps, it is mainly described that the obtaining of the abnormal data is aimed at, and the obtaining of the power quality information is aimed at based on each item of power information obtained in step S200, and whether the power quality meets the standard or not and whether the abnormal power quality data exists is determined by each item of power information and a corresponding preset weight vector, which comprises the following steps:
and S310, determining an efficiency evaluation index of the electric energy meter according to each piece of electric power information and the corresponding preset weight vector.
S320, changing the value of a preset weight vector based on a preset efficiency evaluation index constraint condition until a set efficiency evaluation index optimization target is met, and determining an optimal weight vector meeting the set efficiency evaluation index optimization target.
And S330, carrying out normalization processing on the optimal weight vector, and determining the weight of each item of power information.
And S340, determining the score of each piece of electric power information according to the weight of each piece of electric power information and each piece of electric power information.
And S350, respectively substituting the scores of the electric power information into preset electric energy quality membership functions to determine membership coefficients corresponding to the electric power information.
And S360, determining objective weights corresponding to the electric power information according to membership coefficients corresponding to the electric power information.
And S370, determining the electric energy quality score of the electric energy meter according to the objective weight and the scores of all electric power information.
S380, judging that the electric energy quality of the electric energy meter meets the standard based on the electric energy quality score of the electric energy meter being greater than or equal to a preset threshold value; if the electric energy quality score of the electric network to be evaluated is smaller than a preset threshold value, judging that the electric energy quality of the electric energy meter does not reach the standard, and the data which does not reach the standard are abnormal data.
According to the embodiment, the electric energy parameters of the electric energy meter in actual operation can be obtained by obtaining the electric energy quality monitoring data of the electric energy meter. According to the power quality monitoring data and the preset weight vector, the efficiency evaluation index of the electric energy meter, namely the ratio of input to output, can be determined. The method comprises the steps of setting constraint conditions of an efficiency evaluation index, changing the value of a preset weight vector, and adjusting the value of the efficiency evaluation index to enable the efficiency evaluation index to meet a set optimization target, so that the weight vector at the moment is obtained and is used as an optimal weight vector. And carrying out normalization processing on the optimal weight vector to obtain the weight of each power quality monitoring data. And determining the score of each power quality monitoring data, namely scoring the single power quality, according to the power quality monitoring data and the weight of each power quality monitoring data. And then substituting the scores of the electric energy quality monitoring data into a preset membership function for mapping, and determining the corresponding membership coefficient, so that the scores corresponding to different electric energy quality indexes in the electric energy quality monitoring data can be conveniently converted into the same evaluation standard. And determining objective weights corresponding to the power quality indexes according to the membership coefficient, namely the weights occupied by different power quality indexes when the power grid to be evaluated is scored. And finally, determining the electric energy quality score of the electric network to be evaluated according to the objective weight and the score of each electric energy quality monitoring data, namely integrating the scores of different electric energy quality indexes in the electric energy quality monitoring data, thereby determining the comprehensive score of the electric energy quality of the electric energy meter. And judging whether the electric energy quality of the electric energy meter meets the standard according to whether the comprehensive score of the electric energy quality of the electric energy meter is larger than a preset scoring threshold value. By the method, the electric energy quality of the electric energy meter is evaluated by combining various electric energy quality indexes of the electric energy meter, and the comprehensive electric energy quality of the electric network to be evaluated can be comprehensively reflected, so that a worker can accurately judge the electric energy quality condition of the electric energy meter, and the electric energy meter is convenient to maintain or optimize. And compared with the acquisition of abnormal data, the judgment is carried out, and comprehensive information acquisition is realized through comprehensive acquisition of electric energy quality.
The method for acquiring the abnormal data and the electric energy quality and evaluating the working state of the electric energy meter is mainly used for evaluating the abnormal working state of the electric energy meter under the condition that the abnormal working state is not met, and when the abnormal working state is met, in order to achieve the integrity of maintenance of the electric energy meter, avoid corresponding electric energy meter accidents and need to pre-warn the normal data, so the method for pre-warning the non-abnormal data is also provided in the embodiment, and pre-warn various electric power information through a pre-set pre-warning activity characteristic database, and specifically comprises the following steps:
and S310, constructing an early warning activity characteristic database.
And S320, extracting early warning state activity characteristics of early warning state activities related in various electric power information.
S330, comparing the early warning state activity characteristics with an early warning activity characteristic database to obtain early warning information.
In this embodiment, the construction of the early warning activity feature database in step S310 is mainly based on the early warning activity feature data extracted from the historical abnormal data in the electric energy meter and the corresponding derivative electric energy meter, where the derivative electric energy meter is a plurality of electric energy meters with the same configuration and the same installation environment as the electric energy meter in this embodiment. Wherein the obtaining of the historical abnormal data can be obtained through the step S300 without setting a corresponding method.
In this embodiment, the activity features for the early warning state include a behavior feature and a vector feature corresponding to the behavior feature. For example, the activity characteristic for the voltage alert state includes a voltage and a voltage valid value corresponding to the voltage. The early warning activity feature database is internally provided with behavior features and vector features corresponding to the behavior features, wherein the vector features comprise threshold tags, and the acquisition of the threshold tags can be obtained through steps S310-S330 in abnormal data judgment. Comparing the early warning state activity features with the early warning activity feature library comprises the steps of comparing the behavior features of the early warning state activity features with the behavior features in the early warning activity feature library to obtain a first comparison result, and determining the vector features of the early warning state activity features based on the first comparison result to compare with the threshold values of the corresponding vector features in the early warning activity feature library to obtain a second comparison result.
The early warning information provided by the embodiment can be obtained based on the abnormal state used for representing the abnormal condition in the normal data so as to determine whether the electric energy meter is in the abnormal state, and the early warning information is sent to the user side, so that decision support is provided for operation and maintenance personnel.
In addition, the embodiment of the invention also provides a readable storage medium, wherein computer executable instructions are preset in the readable storage medium, and when a processor executes the computer executable instructions, the electric energy meter monitoring data processing method based on the 5G technology is realized.
It is to be understood that the above description is intended to be illustrative only and is not intended to limit the scope of the present invention. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the present description. However, such modifications and variations do not depart from the scope of the present invention.
While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art after reading this application that the above disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements and adaptations of the invention may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within the present disclosure, and therefore, such modifications, improvements, and adaptations are intended to be within the spirit and scope of the exemplary embodiments of the present disclosure.
Meanwhile, the present invention uses specific words to describe embodiments of the present invention. For example, "one embodiment," "an embodiment," and/or "some embodiments" means a particular feature, structure, or characteristic in connection with at least one embodiment of the invention. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the invention may be combined as suitable.
Furthermore, those of ordinary skill in the art will appreciate that aspects of the invention are capable of being illustrated and described in terms of several patentable categories or circumstances, including any novel and useful process, machine, product, or combination of materials, or any novel and useful modifications thereof. Accordingly, aspects of the present invention may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.) or by a combination of hardware and software. The above hardware or software may be referred to as a "unit," module, "or" system. Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media, wherein the computer-readable program code is embodied therein.
The computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, etc., or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer readable signal medium may be propagated in accordance with any suitable medium including radio, cable, fiber optic cable, RF, or the like, or any combination thereof.
The computer program code necessary for operation of portions of the present invention may be written in any one or more programming languages, including a host-oriented programming language such as Java, scala, smalltalk, eiffel, JADE, emerald, C ++, c#, vb net, python, etc., a conventional programming language such as C language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, an active programming language such as Python, ruby and Groovy, or other programming languages, etc. The program code may run entirely on the power-division computer, or as a stand-alone software package, or partly on the power-division computer and partly on a remote computer or entirely on the remote computer.
Furthermore, the order in which the elements and sequences are presented, the use of numerical letters, or other designations are used in the invention is not intended to limit the sequence of the processes and methods unless specifically recited in the claims. While in the foregoing disclosure there has been discussed in terms of various examples of what is presently considered to be a useful invention, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the invention. For example, while the system components described above may be implemented in terms of hardware devices, they may also be implemented in terms of software only solutions, such as installing the described system on an existing 5G technology based power meter monitoring data processing system or mobile device.
Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof. Similarly, it should be noted that in order to simplify the description of the present disclosure and thereby aid in understanding one or more embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof.
Claims (10)
1. The internet of things electric energy meter with the electric energy quality module is characterized by comprising the electric energy quality module, an internet of things communication module and a server, wherein the electric energy quality module is communicated with the server through the internet of things communication module; the electric energy quality module is internally provided with a data acquisition unit, and the data acquisition unit is used for acquiring various real-time electric power information metered by a metering unit in the electric energy meter; the server is internally provided with a storage module, the storage module comprises a program storage unit and a data storage unit, the program storage unit stores a power quality monitoring method, the power quality monitoring method determines power quality and power early warning information based on the real-time power information, and the data storage unit is used for storing the real-time power information and the power early warning information acquired by the data acquisition unit; the system also comprises an alarm unit, wherein the alarm unit sends the electric energy early warning information to a corresponding user side;
the power quality monitoring method comprises the following steps:
acquiring various real-time power information based on the metering unit;
comparing each item of power information with a preset corresponding power information threshold value to obtain abnormal data;
and sending the abnormal data to a corresponding user terminal.
2. The internet of things electric energy meter with electric energy quality module according to claim 1, wherein the preset corresponding power information threshold value determination comprises the following steps:
acquiring sample data of each item of real-time power information, and determining a characteristic value of a target characteristic in the sample data; wherein the target characteristic is a characteristic of each item of real-time power information;
transforming the characteristic values, wherein probability density distribution results corresponding to the transformed characteristic values meet the set distribution form requirements;
determining a threshold value corresponding to the target feature according to the probability density distribution result corresponding to the converted feature value and the preset searching rate of each item of real-time power information;
the threshold value corresponding to the target feature is used for detecting all real-time power information in all real-time power information, and the ratio of the number of the detected real-time power information to the number of all real-time power information is the detection rate.
3. The internet of things electric energy meter with electric energy quality module of claim 2, wherein the method further comprises:
probability density distribution is carried out on the converted characteristic values, and probability density distribution results corresponding to the converted characteristic values are obtained;
verifying the probability density distribution result corresponding to the converted characteristic value by using a verification model to obtain the similarity between the probability density distribution form corresponding to the converted characteristic value and the set distribution form;
if the similarity is larger than a similarity threshold, determining that the probability density distribution result corresponding to the transformed characteristic value meets the set distribution form requirement.
4. The internet of things electric energy meter with electric energy quality module according to claim 3, wherein each item of real-time electric power information includes any one of real-time voltage information, real-time frequency information and real-time waveform information; the real-time voltage information includes a real-time voltage effective value, and the real-time waveform information includes a real-time waveform feature.
5. The internet of things electric energy meter with electric energy quality module of claim 4, wherein the acquiring the real-time waveform characteristics comprises the following method:
dividing the waveform according to a plurality of scales to obtain a waveform sequence corresponding to the scales, wherein the waveform sequence corresponding to any scale comprises a plurality of waveform fragments belonging to any scale;
encoding a plurality of waveform fragments in a waveform sequence corresponding to each scale respectively to obtain a first characteristic sequence corresponding to each scale, wherein the first characteristic sequence corresponding to any scale comprises waveform characteristics obtained by encoding the plurality of waveform fragments belonging to any scale;
respectively fusing a plurality of waveform features in the first feature sequence corresponding to each scale to obtain a second feature sequence corresponding to each scale;
and splicing the second characteristic sequences corresponding to the scales to obtain waveform characteristic information corresponding to the waveform.
6. The internet of things electric energy meter with electric energy quality module according to claim 5, wherein comparing each electric power information with a preset corresponding electric power information threshold to obtain abnormal data includes comparing the real-time voltage effective value with a preset corresponding voltage effective value threshold to obtain voltage abnormal data, comparing the real-time frequency information with a preset corresponding frequency threshold to obtain frequency abnormal data, and comparing the real-time waveform characteristic with a preset corresponding waveform characteristic threshold to obtain waveform abnormal data.
7. The internet of things electric energy meter with electric energy quality module of claim 1, further comprising the following method before issuing the abnormal data to the corresponding user terminal:
according to the electric power information and the corresponding preset weight vector, determining whether the electric energy quality meets the standard or not, wherein the method specifically comprises the following steps of:
determining an efficiency evaluation index of the electric energy meter according to the electric power information and the corresponding preset weight vector;
changing the value of the preset weight vector based on a preset efficiency evaluation index constraint condition until a set efficiency evaluation index optimization target is met, and determining an optimal weight vector meeting the set efficiency evaluation index optimization target;
normalizing the optimal weight vector to determine the weight of each piece of electric power information;
determining the score of each piece of electric power information according to the weight of each piece of electric power information and each piece of electric power information;
substituting the scores of the electric power information into preset electric energy quality membership functions respectively, and determining membership coefficients corresponding to the electric power information;
determining objective weights corresponding to the electric power information according to membership coefficients corresponding to the electric power information;
and determining the electric energy quality score of the electric energy meter according to the objective weight and the score of each electric power information.
8. The internet of things electric energy meter with electric energy quality module of claim 7, wherein comparing each electric power information with a preset corresponding electric power information threshold to obtain abnormal data, further comprising:
judging that the electric energy quality of the electric energy meter meets the standard based on the electric energy quality score of the electric energy meter being greater than or equal to a preset threshold value; if the electric energy quality score of the electric network to be evaluated is smaller than a preset threshold value, judging that the electric energy quality of the electric energy meter does not reach the standard, and the data which does not reach the standard are abnormal data.
9. The internet of things electric energy meter with electric energy quality module of claim 1, further comprising the following method before issuing the abnormal data to the corresponding user terminal:
the early warning is carried out on all electric power information based on a preset early warning activity characteristic database, and the method specifically comprises the following steps:
constructing an early warning activity characteristic database; extracting early warning state activity characteristics of early warning state activities related in the electric power information; comparing the early warning state activity characteristics with the early warning activity characteristic database to obtain early warning information;
and sending the abnormal data to the corresponding user terminal, including sending the early warning information to the corresponding user terminal.
10. The internet of things electric energy meter with the electric energy quality module according to claim 9, wherein the early warning state activity feature comprises a behavior feature and a vector feature corresponding to the behavior feature; the early warning activity feature database is internally configured with behavior features and vector features corresponding to the behavior features, wherein the vector features comprise threshold tags; comparing the early warning state activity features with the early warning activity feature library comprises the steps of comparing the behavior features of the early warning state activity features with the behavior features in the early warning activity feature library to obtain a first comparison result, and determining the vector features of the early warning state activity features based on the first comparison result to compare with the corresponding vector feature threshold values in the early warning activity feature library to obtain a second comparison result.
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| CN117783995A (en) * | 2024-02-28 | 2024-03-29 | 深圳市江机实业有限公司 | Online calibration method and system for electric energy meter |
| CN117783995B (en) * | 2024-02-28 | 2024-05-10 | 深圳市江机实业有限公司 | Online calibration method and system for electric energy meter |
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