CN111650548A - Intelligent electric energy meter metering data remote online monitoring system and method - Google Patents
Intelligent electric energy meter metering data remote online monitoring system and method Download PDFInfo
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Abstract
A remote online monitoring system and method for metering data of an intelligent electric energy meter relate to the technical field of intelligent electric energy meters and solve the technical problems that data analysis is lacked in fault detection of conventional electric energy meters, and data fault information of the electric energy meters is difficult to obtain. The system comprises: the electric energy meter comprises an electric energy meter detection layer, an information transmission layer, a data processing layer, a data application layer and a remote monitoring layer, wherein the output end of the electric energy meter detection layer is connected with the input end of the information transmission layer, the output end of the information transmission layer is connected with the input end of the data processing layer, the output end of the data processing layer is connected with the input end of the data application layer, and the output end of the data application layer is connected with the input end of the remote monitoring layer. The invention can analyze the hidden data relation from the received macroscopic data of the electric energy meter, so that a user can remotely and online monitor the data of the electric energy meter at a remote monitoring center and a mobile monitoring terminal which are arranged at a remote monitoring layer.
Description
Technical Field
The invention relates to the technical field of intelligent electric energy meters, in particular to a remote online monitoring system and method for metering data of an intelligent electric energy meter.
Background
The intelligent electric energy meter is one of basic devices for data acquisition of an intelligent power grid, bears the tasks of original electric energy data acquisition, metering and transmission, and is the basis for realizing information inheritance, analysis optimization and information display. With the rapid development of the intelligent electric energy meter technology and the electric energy metering technology, the electric energy meter is more widely applied. Under the gradual advance of industrial technologies, the detection assembly line and the detection device of the large-scale electric energy meter are infinite, and the fault diagnosis of the metering data of the electric energy meter becomes a technical problem for users.
The patent number CN201410786851.3 discloses a remote online monitoring method for an electric energy metering device, in which a local electric energy meter monitoring unit is arranged at each station, a data server and an electric energy meter management monitoring terminal are arranged at a remote management master station, and the local electric energy meter monitoring unit monitors the metering status and the operating status of an electric energy meter in real time and sends the monitored data to the data server through an uplink remote communication network. Although the calculation realizes the online test, data recording, data analysis and fault judgment, fault alarm and the like of each electric energy meter to a certain extent, in a large-scale electric energy meter detection pipeline system, especially under the conditions of various devices, various models and various detection data, when the systematic detection of the electric energy meters is carried out, it is difficult to be accurate to which plant, or which device, even which intelligent electric energy meter has faults, and the relation between the electric energy meter data can not be analyzed through the detected data.
There is a need for a new monitoring method that overcomes the deficiencies of the above-mentioned techniques.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses a remote online monitoring system and a remote online monitoring method for metering data of an intelligent electric energy meter, which not only can realize online monitoring and real-time transmission of the intelligent electric energy meter, but also can mine microscopic reasons hidden in macroscopic data in the detection data of the electric energy meter, so that a user can realize fault analysis and monitoring of the electric energy meter through a data analysis technology.
The invention adopts the following technical scheme:
a remote online monitoring system for metering data of an intelligent electric energy meter comprises:
the electric energy meter detection layer is internally provided with electric energy meter calibrating devices or electric energy meter calibrating production lines which are arranged in different factories, and the electric energy meter calibrating devices or the production lines are used for detecting the electric energy meter to obtain data parameter information of the electric energy meter;
the system comprises an information transmission layer, a data communication module and a data communication module, wherein the information transmission layer is internally provided with a multi-channel transmission interface of the electric energy meter, and the multi-channel transmission interface of the electric energy meter is used for transmitting batch detected data of the electric energy meter;
the data processing layer is provided with a PC (personal computer), the PC is integrally provided with a big data processing unit and is used for calculating and processing the data information received by the information transmission layer, analyzing the relation between data from the received macroscopic data of the electric energy meter and revealing the microscopic data relation hidden in the macroscopic data;
the data application layer is provided with clients which are arranged in different information processing centers and used for receiving the data processed by the data processing layer and receiving, displaying, storing and managing the data through different clients;
the remote monitoring layer is provided with a remote monitoring center and a mobile monitoring terminal and is used for receiving the local data output by the data application layer so as to realize remote and online monitoring; wherein:
the output end of the electric energy meter detection layer is connected with the input end of the information transmission layer, the output end of the information transmission layer is connected with the input end of the data processing layer, the output end of the data processing layer is connected with the input end of the data application layer, and the output end of the data application layer is connected with the input end of the remote monitoring layer.
As a further technical scheme of the invention, the electric energy meter calibrating device or the assembly line is more than 10000 three-phase electric energy meter calibrating devices or assembly lines, and the daily detection electricity isEnergy meter data volume 1 x 105The above.
As a further technical solution of the present invention, the data communication module is provided with a communication control module, the communication control module includes a connection thread pool module, a connection chain table module, an analysis thread pool module, a transmission chain table module, a transmission thread pool module, a communication data calculation module, and a database unit, wherein an output end of the connection thread pool module is connected to an input end of the connection chain table module, an output end of the connection chain table module is connected to an input end of the analysis thread pool module, an output end of the analysis thread pool module is connected to input ends of the transmission chain table module and the communication data calculation module, an output end of the transmission chain table module is connected to an input end of the transmission thread pool module, and the communication data calculation module is connected to the database unit.
As a further technical scheme of the invention, the multichannel transmission interface of the electric energy meter at least comprises a GPRS wireless communication interface, a GSM wireless communication interface, an optical fiber communication interface, an RS 485 communication interface, an RS 232 communication interface or a low-voltage power line carrier communication interface.
As a further technical scheme of the invention, the big data processing unit comprises a big data processing unit based on a random matrix analysis algorithm model and a big data processing unit based on a BP neural network algorithm model.
The invention also adopts the following technical scheme:
a remote online monitoring method for metering data of an intelligent electric energy meter comprises the following steps:
(S1) detecting the electric energy meter by using an electric energy meter calibrating device or a production line to obtain data parameter information of the electric energy meter;
(S2) transmitting the batch detected electric energy meter data through the electric energy meter multi-channel transmission interface;
(S3) calculating and processing the data information received by the information transmission layer, analyzing the relation between the data from the received electric energy meter macro data, and revealing the micro data relation hidden in the macro data;
(S4) receiving the data processed by the data processing layer, and receiving, displaying, storing and managing through different clients;
(S5) receiving the local data output by the data application layer remotely to realize remote, on-line monitoring.
As a further technical solution of the present invention, the method for processing the electric energy meter information data in the step (S3) includes:
the method comprises the steps of inputting received electric energy meter data information into a random matrix analysis algorithm model, carrying out data classification on the received electric energy meter metering data according to classification attributes through the random matrix analysis algorithm model, carrying out fault diagnosis on the classified electric energy meter data through a BP neural network algorithm model, and finally outputting electric energy meter fault data information.
As a further technical scheme of the invention, the construction method of the random matrix analysis algorithm model comprises the following steps:
(1) data preprocessing, namely preprocessing the extracted data information of the electric energy meter, reserving effective data and filtering out useless data information, wherein the data types at least comprise output frequency, voltage, current, harmonic voltage, harmonic current, voltage unbalance parameter, current unbalance parameter, flicker, power or power factor of the electric energy meter; the extracted data is cleaned in different data preprocessing modes such as data cleaning, data integration, data specification or data transformation and the like;
(2) constructing a random matrix theoretical model, calculating an initial matrix, and substituting the electric energy meter information parameters into a random matrix formula model for calculation according to the constructed matrix model; the random matrix model formula is as follows:
wherein:
in the formula, M represents the types of parameters affecting the verification accuracy of the electric energy meter in the verification process of the electric energy meter, wherein the parameters at least comprise equipment factor parameters, human factor parameters, field environment factor parameters, operation factor parameters, data transmission factor parameters, magnetic fields, harmonic waves, loads or clutter, and the data set of the parameters is referred to as P { P ═1，P2，P3……PMN denotes electric energy meter calibration data including at least a kind of current, voltage or power, and the set of the calibration data is denoted as Q ═ Q1，Q2，Q3……QNT represents the time for detecting the electric energy meter by the electric energy meter calibrating device or the assembly line, D1Set, P, representing data affecting the accuracy of the verification of the electric energy meterijThe set data elements are expressed as measurement values measured by the electric energy meter at j time when the electric energy meter calibrating devices or the production lines of i factories detect;
(4) calculating related data according to the constructed random matrix model;
(5) analyzing data information of at least magnetic field, harmonic wave, load or clutter present in the detection system of the electric energy meter by using the random matrix model in the step (S3), and calculating a normalized matrix product D by using the following formulastdCharacteristic value of (d):
Dstd＝[D1,D2,D3,……DM+N]T(4)
when the normalized matrix product D is calculatedstdThe characteristic value of the electric energy meter is obtained, and the error influence quantity D of the magnetic field, the harmonic wave, the load or the clutter influencing the verification precision of the electric energy meter is obtainedstdThe larger the amount of influence.
As a further technical scheme of the invention, the BP neural network algorithm model consists of an input layer, an inclusion layer and an output layer, wherein the input layer comprises electric energy meter data information output by the random matrix analysis algorithm model; the BP neural network algorithm model gradually approaches to a required result by adjusting a weight and a threshold value, so that an output error is minimized, and when the BP neural network model is adjusted, the adjustment is carried out according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
wherein, η indicates that,
which is indicative of a desired output, is,
for practical output, η represents learning rate, k represents number of samples, p represents samples, 0 < η <1, and the formula for adjusting weight coefficient of hidden layer is:
the quadratic exact function model of the input mode pairs in each energy meter fault data sample is:
and (3) the total accurate function expression of the N electric energy meter fault information samples:
when calculation is started, when the fault type information of the complex electric energy meter is extracted, in order to improve the learning precision, standard processing is firstly carried out on sample data;
assuming that the types of the input electric energy meter fault information are m, the sample is N, and for input data xijThe normalization is performed according to the following formula:
wherein i is 1,2, …, N; j is 1,2 …, m, Z in the above formulaijThe normalized data is obtained.
The following formula may be normalized:
yi'＝q(yi-ymin+b)/(ymax-ymin+b) (12)
wherein y isiOutputting a fault data sample of the electric energy meter;
y′ithe standardized fault data sample information of the electric energy meter is obtained;
ymax,yminthe maximum value and the minimum value in the fault information data sample of the output electric energy meter are obtained.
As a further aspect of the invention, wherein 1< q < 1.3; 1< b <1.6, the number of hidden layer nodes is determined to be between 7 and 9, the value from the input layer to the hidden layer is between 0.2 and 0.6, and the value from the hidden layer to the output layer can be between 0.2 and 0.6.
Has the positive and beneficial effects that:
the monitoring system can carry out remote and online monitoring, fault judgment and record analysis on batch electric energy metering equipment which is put into use, integrates signal acquisition, data transmission, data calculation, analysis, monitoring and diagnosis, and can realize online and real-time monitoring of data.
The invention adopts a big data analysis technical means to analyze the received electric energy meter data, analyzes the influence factors influencing the electric energy detection precision in the interference signals containing the complex electric energy detection network by constructing a random matrix theoretical model, so that the electric energy meter detection user can obtain more microscopic essential rules influencing the electric energy meter detection precision from macroscopic high-latitude output data, and can radically solve the problems existing in the intelligent electric energy meter detection process by the user, thereby realizing the remote monitoring of the electric energy meter.
The invention adopts a BP neural network algorithm model, gradually approaches the required result by adjusting the weight and the threshold value in the BP neural network, finally minimizes the accuracy of the output error, and performs complex nonlinear relation mapping on the output data in the steps of mapping by using the BP neural network algorithm model and processing the electric energy meter detection data information samples. More specifically, the factors causing the fault detection information of the electric energy meter include equipment factors, human factors, field environment factors, operation factors, data transmission factors and the like. When the BP network algorithm model is utilized, a nonlinear data relation can be formed by detecting output electric energy metering data and the factors, so that the fault learning efficiency is greatly improved, the diagnosis speed is increased, and the data accuracy is higher. The method combines the random matrix theoretical model, so that the fault diagnosis of the electric energy meter detection data is more accurate.
In the data communication, the communication control module is arranged on the data communication module and comprises a connection thread pool module, a connection chain table module, an analysis thread pool module, a sending chain table module, a sending thread pool module, a communication data calculation module and a database unit, and an IEC 62056 DLMS protocol is set to provide communication technical support for detecting data of the electric energy meter and enable a user to realize interconnection and interoperation of the electric energy meter data according to the IEC 62056 protocol.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without inventive exercise, wherein:
FIG. 1 is a schematic diagram of an architecture of a remote online monitoring system for metering data of an intelligent electric energy meter according to the present invention;
FIG. 2 is a schematic structural diagram of a communication control module in the remote online monitoring system for the metering data of the intelligent electric energy meter according to the present invention;
FIG. 3 is a schematic flow chart of a method for remotely monitoring metering data of an intelligent electric energy meter on line according to the present invention;
FIG. 4 is a schematic diagram of a big data analysis model in the remote online monitoring method for the metering data of the intelligent electric energy meter according to the invention;
FIG. 5 is a test data recording table in the method for remotely monitoring the metering data of the intelligent electric energy meter on line according to the invention;
FIG. 6 is another test data recording table in the method for remotely monitoring the metering data of the intelligent electric energy meter on line according to the invention;
FIG. 7 is a test data recording table in the method for remotely monitoring the metering data of the intelligent electric energy meter on line according to the invention;
fig. 8 is a schematic structural diagram of the influence of the clutter on the measurement in the method for remotely monitoring the metering data of the intelligent electric energy meter according to an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, and it should be understood that the embodiments described herein are merely for the purpose of illustrating and explaining the present invention and are not intended to limit the present invention.
Example 1 System
As shown in fig. 1, a remote online monitoring system for metering data of an intelligent electric energy meter includes:
the electric energy meter detection layer is internally provided with electric energy meter calibrating devices or electric energy meter calibrating production lines which are arranged in different factories, and the electric energy meter calibrating devices or the production lines are used for detecting the electric energy meter to obtain data parameter information of the electric energy meter;
the system comprises an information transmission layer, a data communication module and a data communication module, wherein the information transmission layer is internally provided with a multi-channel transmission interface of the electric energy meter, and the multi-channel transmission interface of the electric energy meter is used for transmitting batch detected data of the electric energy meter;
the data processing layer is provided with a PC (personal computer), the PC is integrally provided with a big data processing unit and is used for calculating and processing the data information received by the information transmission layer, analyzing the relation between data from the received macroscopic data of the electric energy meter and revealing the microscopic data relation hidden in the macroscopic data;
the data application layer is provided with clients which are arranged in different information processing centers and used for receiving the data processed by the data processing layer and receiving, displaying, storing and managing the data through different clients;
the remote monitoring layer is provided with a remote monitoring center and a mobile monitoring terminal and is used for receiving the local data output by the data application layer so as to realize remote and online monitoring; wherein:
the output end of the electric energy meter detection layer is connected with the input end of the information transmission layer, the output end of the information transmission layer is connected with the input end of the data processing layer, the output end of the data processing layer is connected with the input end of the data application layer, and the output end of the data application layer is connected with the input end of the remote monitoring layer. Through the connection mode, the data transmission of the electric energy meter is realized.
Through the scheme, the system can be used for carrying out remote and online monitoring, fault judgment and record analysis on batch electric energy metering equipment which is put into use, is an integrated detection and analysis system integrating signal acquisition, data transmission, data calculation, analysis, monitoring and diagnosis, and can realize online and real-time monitoring of data. In the above embodiment, the electric energy meter calibration device or assembly line is 10000 or more three-phase electric energy meter calibration devices or assembly lines, and the daily detection data volume of the electric energy meter is 1 × 105Therefore, the detection data is very huge, and the conventional method is difficult to deal with in the process of calculating and processing the electric energy meter data. As will be described hereinafterAnd performing data analysis on the electric energy meter data with the scale.
In the above embodiment, as shown in fig. 2, the data communication module is provided with a communication control module, the communication control module includes a connection thread pool module, a connection chain table module, an analysis thread pool module, a transmission chain table module, a transmission thread pool module, a communication data calculation module and a database unit, wherein the output end of the connection thread pool module is connected with the input end of the connection chain table module, the output end of the connection chain table module is connected with the input end of the analysis thread pool module, the output end of the analysis thread pool module is respectively connected with the input ends of the transmission chain table module and the communication data calculation module, the output end of the transmission chain table module is connected with the input end of the transmission thread pool module, and the communication data calculation module is connected with the database unit. The communication control module of the invention effectively manages the thread in the server by adopting the thread pool technology, and ensures the stable and efficient operation of the communication network system. The connection thread pool is responsible for intercepting and responding to a connection request of the application terminal and managing each connection thread. For different communication modes, if a data network direct connection mode is adopted, a Socket structure body is constructed according to IP addresses and communication ports of two communication parties. If a dial-up connection mode is adopted, a corresponding dial-up server/client system needs to be configured, a corresponding Socket structural body is constructed by analyzing the IP address of the dial-up server end and combining a preset communication port, and a connection thread is put into a connection chain table. The analysis thread pool is responsible for reading the data packets in the connection threads from the connection chain table, analyzing and processing the communication data packets, and storing the electric energy meter data into the database through the data processing module. The analysis thread pool is also responsible for putting the data packets to be sent into the sending linked list. And the sending thread pool reads the data packet from the sending linked list, coordinates each sending thread and establishes communication connection with the corresponding power utilization end through the sending port. The design method enables the communication network system of the remote monitoring system to operate efficiently without instability.
In a further embodiment, in addition to the above control method, a Modbus-based communication protocol supporting a PLC-controlled communication system may be employed. In other embodiments, a dual-computer communication module design method based on an SPI (synchronous serial transmission specification) is adopted, and the module adopts two PICF877A single-chip microcomputers, so that a dual-computer master-slave serial communication function is realized. The EPON-based electric energy meter optical fiber communication module is adopted, and comprises a photoelectric conversion and control module, a storage unit, a power supply module, an MCU main controller, a power supply monitoring module, a clock module, a DBG module, an I2C-to-UART module and a level conversion module, which are not described in detail herein. Under the condition that the communication condition of the electric energy meter is relatively complex, the communication modes can be combined for use.
In a further embodiment, the multichannel transmission interface of the electric energy meter at least comprises a GPRS wireless communication interface, a GSM wireless communication interface, an optical fiber communication interface, an RS 485 communication interface, an RS 232 communication interface or a low-voltage power line carrier communication interface. By adopting a multi-communication technology, multi-channel transmission of the detection data of the electric energy meter is realized. Generally, in an electric energy meter detection system, communication modes which can be adopted by an automatic meter reading system in an uplink communication channel of the electric energy meter detection system mainly include a PSTN (public switched telephone network), GPRS (general packet radio service), GSM (global system for mobile communications) wireless, optical fibers and the like. A downlink communication channel in a power distribution station area of the electric energy meter detection system mainly comprises an RS-485 bus, a low-voltage power line carrier, a wireless mode, a mixed mode and the like. In other embodiments, low voltage carrier communication is also employed. Because the impedance and attenuation characteristics of the 220V/380V low-voltage distribution network are completely different from those of the high-voltage distribution network, the mature carrier technology is applied to the high-voltage distribution network and can hardly be used in the low-voltage distribution network. The frequency range of carrier signals of low-voltage power line carrier communication is 3-500 kHz, the frequency range used by power companies is 3-95 kHz, and 3-9 kHz is generally used for voice transmission. A micropower wireless network. The micro-power wireless communication refers to wireless radio frequency communication with the frequency of 433MHz/470MHz/780MHz/2.4GHz and the transmitting power of less than or equal to 50 mW. In the lower computer system of the electric energy meter detection system, a high-precision current-voltage transformer, an I/O board for executing multi-channel switching, a data acquisition card for alternating current analog signals, a power utilization end management mechanism for control, display and communication and the like can be arranged. After receiving various remote monitoring operation instructions of the server side, the power utilization side management machine selectively starts a corresponding terminal through a multi-way selector switch, the terminal collects pulse signals of the monitored electric energy meter by collecting voltage and current instantaneous values which are the same as those of the monitored electric energy meter, calculates various electric parameters and errors, transmits a processing result to the server side through a network according to a data frame format defined by IEC60870252104 protocol, and achieves the remote monitoring function of the metering device and the electric energy meter.
As a further technical scheme of the invention, the big data processing unit comprises a big data processing unit based on a random matrix analysis algorithm model and a big data processing unit based on a BP neural network algorithm model.
EXAMPLE 2 method
A remote online monitoring method for metering data of an intelligent electric energy meter is shown in FIG. 3, and comprises the following steps:
(S1) detecting the electric energy meter by using an electric energy meter calibrating device or a production line to obtain data parameter information of the electric energy meter;
(S2) transmitting the batch detected electric energy meter data through the electric energy meter multi-channel transmission interface;
(S3) calculating and processing the data information received by the information transmission layer, analyzing the relation between the data from the received electric energy meter macro data, and revealing the micro data relation hidden in the macro data;
(S4) receiving the data processed by the data processing layer, and receiving, displaying, storing and managing through different clients;
(S5) receiving the local data output by the data application layer remotely to realize remote, on-line monitoring.
In step (S1), the electric energy meter detection belongs to a large-scale production and detection system, and is influenced by a plurality of factors such as a detection environment, the obtained data is detected under the influence of a plurality of kinds of information, and the obtained data is a raw database of user analysis data. In this step, the data may be acquired in various ways, not limited to the above-described electric energy meter calibrating apparatus and assembly line, but also by using other detection devices such as sensors and sensor circuits.
Aiming at the step (S2), data transmission is mainly realized, communication of electric energy meters of different communication types can be realized through the multi-channel transmission interface of the electric energy meter, different manufacturers of the electric energy meters have different sizes and communication modes, the multi-channel transmission interface can be compatible with data communication of the electric energy meters of various communication modes, in the step, different communication modes can be set through programs, and in the working process, the communication modes are identified through the programs. Such as PLC auto-id programs, etc.
In step (S3), the method for processing the electric energy meter information data is as follows:
inputting the received electric energy meter data information into a random matrix analysis algorithm model, performing data classification on the received electric energy meter measurement data according to classification attributes through the random matrix analysis algorithm model, performing fault diagnosis on the classified electric energy meter data through a BP neural network algorithm model, and finally outputting electric energy meter fault data information, wherein the specific information is shown in FIG. 4:
the construction method of the random matrix analysis algorithm model comprises the following steps:
(1) data preprocessing, namely preprocessing the extracted data information of the electric energy meter, reserving effective data and filtering out useless data information, wherein the data types at least comprise output frequency, voltage, current, harmonic voltage, harmonic current, voltage unbalance parameter, current unbalance parameter, flicker, power or power factor of the electric energy meter; the extracted data is cleaned in different data preprocessing modes such as data cleaning, data integration, data specification or data transformation and the like. When the data preprocessing is adopted, besides the filtering method, a standardized processing method, vector normalization, a transformation method of interval type attributes, a linear transformation method, data integration, data conversion, and the like may be adopted, and a detailed description thereof is omitted.
(2) Constructing a random matrix theoretical model, calculating an initial matrix, and substituting the electric energy meter information parameters into a random matrix formula model for calculation according to the constructed matrix model; the random matrix model formula is as follows:
wherein:
in the formula, M represents the types of parameters affecting the verification accuracy of the electric energy meter in the verification process of the electric energy meter, wherein the parameters at least comprise equipment factor parameters, human factor parameters, field environment factor parameters, operation factor parameters, data transmission factor parameters, magnetic fields, harmonic waves, loads or clutter, and the data set of the parameters is referred to as P { P ═1，P2，P3……PMN denotes electric energy meter calibration data including at least a kind of current, voltage or power, and the set of the calibration data is denoted as Q ═ Q1，Q2，Q3……QNT represents the time for detecting the electric energy meter by the electric energy meter calibrating device or the assembly line, D1Set, P, representing data affecting the accuracy of the verification of the electric energy meterijThe set data elements are expressed as measurement values measured by the electric energy meter at j time when the electric energy meter calibrating devices or the production lines of i factories detect; d2Indicating the measurement accuracy of the electric energy meter
(4) Calculating related data according to the constructed random matrix model;
(5) analyzing data information of at least magnetic field, harmonic wave, load or clutter present in the detection system of the electric energy meter by using the random matrix model in the step (S3), and calculating a normalized matrix product D by using the following formulastdCharacteristic value of (d):
Dstd＝[D1,D2,D3,……DM+N]T(4)
when the normalized matrix product D is calculatedstdThe characteristic value of the electric energy meter is obtained, and the error influence quantity D of the magnetic field, the harmonic wave, the load or the clutter influencing the verification precision of the electric energy meter is obtainedstdThe larger the amount of influence.
For example, as shown in fig. 5, 6 and 7, assuming that the matrix D1 is 100 × 200 and D2 is 400 × 500, the calculation can be performed by substituting the following equations:
d1 is the factors that influence the detection accuracy of the electric energy meter, such as equipment factors, human factors, field environment factors, operation factors, data transmission factors, and the like, and can also be a matrix formed by shadow data of humidity, harmonic waves, temperature, load, magnetic fields, and the like. In a specific embodiment, the power grid influence factor is used as an evaluation, and other factors are similar methods. The clutter existing in the power grid is assumed to be used as the detection factor of the influence electric energy meter for analysis and judgment, and after calculation by the method, the influence curve shown in fig. 8 is obtained. As shown in fig. 8, the influence of clutter on the measurement accuracy can be visually observed within 2000 min.
In a further embodiment, the BP neural network algorithm model is composed of three layers, namely an input layer, an implication layer and an output layer, wherein the input layer comprises electric energy meter data information output by the stochastic matrix analysis algorithm model; the BP neural network algorithm model gradually approaches to a required result by adjusting a weight and a threshold value, so that an output error is minimized, and when the BP neural network model is adjusted, the adjustment is carried out according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
wherein, η indicates that,
which is indicative of a desired output, is,
for actual output, η represents learning rate, k represents number of samples, p represents samples, 0 < η < 1;
the formula for adjusting the weight coefficient of the hidden layer is as follows:
the quadratic exact function model of the input mode pairs in each energy meter fault data sample is:
and (3) the total accurate function expression of the N electric energy meter fault information samples:
when calculation is started, when the fault type information of the complex electric energy meter is extracted, in order to improve the learning precision, standard processing is firstly carried out on sample data;
assuming that the types of the input electric energy meter fault information are m, the sample is N, and for input data xijThe normalization is performed according to the following formula:
wherein i is 1,2, …, N; j is 1,2 …, m, Z in the above formulaijThe normalized data is obtained.
The following formula may be normalized:
yi'＝q(yi-ymin+b)/(ymax-ymin+b) (12)
wherein y isiOutputting a fault data sample of the electric energy meter;
y′ithe standardized fault data sample information of the electric energy meter is obtained;
ymax,yminthe maximum value and the minimum value in the fault information data sample of the output electric energy meter are obtained.
As a further aspect of the invention, wherein 1< q < 1.3; 1< b <1.6, the number of hidden layer nodes is determined to be between 7 and 9, the value from the input layer to the hidden layer is between 0.2 and 0.6, and the value from the hidden layer to the output layer can be between 0.2 and 0.6.
Through the BP neural network model, the data fault type of the electric energy meter can be further rapidly diagnosed, and the precision is higher. The required result is gradually approached by adjusting the weight and the threshold value in the BP neural network, the accuracy of the output error is finally minimized, and the output data is subjected to complex nonlinear relation mapping in the steps of utilizing a BP network algorithm model to map and process the electric energy meter detection data information samples. More specifically, the factors causing the fault detection information of the electric energy meter include equipment factors, human factors, field environment factors, operation factors, data transmission factors and the like. When the BP network algorithm model is utilized, a nonlinear data relation can be formed by detecting output electric energy metering data and the factors, so that the fault learning efficiency is greatly improved, the diagnosis speed is increased, and the data accuracy is higher. The method combines the random matrix theoretical model, so that the fault diagnosis of the electric energy meter detection data is more accurate.
Although specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are merely illustrative and that various omissions, substitutions and changes in the form of the detail of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the steps of the above-described methods to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is to be limited only by the following claims.
Claims (10)
1. The utility model provides an intelligence electric energy meter measurement data remote on-line monitoring system which characterized in that: the method comprises the following steps:
the electric energy meter detection layer is internally provided with electric energy meter calibrating devices or electric energy meter calibrating production lines which are arranged in different factories, and the electric energy meter calibrating devices or the production lines are used for detecting the electric energy meter to obtain data parameter information of the electric energy meter;
the system comprises an information transmission layer, a data communication module and a data communication module, wherein the information transmission layer is internally provided with a multi-channel transmission interface of the electric energy meter, and the multi-channel transmission interface of the electric energy meter is used for transmitting batch detected data of the electric energy meter;
the data processing layer is provided with a PC (personal computer), the PC is integrally provided with a big data processing unit and is used for calculating and processing the data information received by the information transmission layer, analyzing the relation between data from the received macroscopic data of the electric energy meter and revealing the microscopic data relation hidden in the macroscopic data;
the data application layer is provided with clients which are arranged in different information processing centers and used for receiving the data processed by the data processing layer and receiving, displaying, storing and managing the data through different clients;
the remote monitoring layer is provided with a remote monitoring center and a mobile monitoring terminal and is used for receiving the local data output by the data application layer so as to realize remote and online monitoring; wherein:
the output end of the electric energy meter detection layer is connected with the input end of the information transmission layer, the output end of the information transmission layer is connected with the input end of the data processing layer, the output end of the data processing layer is connected with the input end of the data application layer, and the output end of the data application layer is connected with the input end of the remote monitoring layer.
2. The system for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 1, characterized in that: the electric energy meter calibrating device or the assembly line is more than 10000 three-phase electric energy meter calibrating devices or assembly lines, and the daily detection data volume of the electric energy meter is 1 x 105The above.
3. The system for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 1, characterized in that: the data communication module is provided with communication control module, communication control module is including connecting thread pond module, connecting the chain table module, analyzing thread pond module, sending the chain table module, sending thread pond module, communication data calculation module and database unit, wherein the output of connecting thread pond module is connected with the input of connecting the chain table module, the output of connecting the chain table module with the input of analyzing thread pond module is connected, the output of analyzing thread pond module respectively with the input of sending chain table module and communication data calculation module is connected, the output of sending the chain table module with the input of sending thread pond module is connected, communication data calculation module is connected with the database unit.
4. The system for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 1, characterized in that: the multichannel transmission interface of the electric energy meter at least comprises a GPRS wireless communication interface, a GSM wireless communication interface, an optical fiber communication interface, an RS 485 communication interface, an RS 232 communication interface or a low-voltage power line carrier communication interface.
5. The system for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 1, characterized in that: the big data processing unit comprises a big data processing unit based on a random matrix analysis algorithm model and a big data processing unit based on a BP neural network algorithm model.
6. A method for remote monitoring by using the intelligent electric energy meter metering data remote online monitoring system of any one of claims 1-5, is characterized in that: the method comprises the following steps:
(S1) detecting the electric energy meter by using an electric energy meter calibrating device or a production line to obtain data parameter information of the electric energy meter;
(S2) transmitting the batch detected electric energy meter data through the electric energy meter multi-channel transmission interface;
(S3) calculating and processing the data information received by the information transmission layer, analyzing the relation between the data from the received electric energy meter macro data, and revealing the micro data relation hidden in the macro data;
(S4) receiving the data processed by the data processing layer, and receiving, displaying, storing and managing through different clients;
(S5) receiving the local data output by the data application layer remotely to realize remote, on-line monitoring.
7. The method for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 6, characterized by comprising the following steps: the method for processing the information data of the electric energy meter in the step (S3) is as follows:
the method comprises the steps of inputting received electric energy meter data information into a random matrix analysis algorithm model, carrying out data classification on the received electric energy meter metering data according to classification attributes through the random matrix analysis algorithm model, carrying out fault diagnosis on the classified electric energy meter data through a BP neural network algorithm model, and finally outputting electric energy meter fault data information.
8. The method for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 7, characterized by comprising the following steps: the construction method of the random matrix analysis algorithm model comprises the following steps:
(1) data preprocessing, namely preprocessing the extracted data information of the electric energy meter, reserving effective data and filtering out useless data information, wherein the data types at least comprise output frequency, voltage, current, harmonic voltage, harmonic current, voltage unbalance parameter, current unbalance parameter, flicker, power or power factor of the electric energy meter; the extracted data is cleaned in different data preprocessing modes such as data cleaning, data integration, data specification or data transformation and the like;
(2) constructing a random matrix theoretical model, calculating an initial matrix, and substituting the electric energy meter information parameters into a random matrix formula model for calculation according to the constructed matrix model; the random matrix model formula is as follows:
wherein:
in the formula, M represents the types of parameters affecting the verification accuracy of the electric energy meter in the verification process of the electric energy meter, wherein the parameters at least comprise equipment factor parameters, human factor parameters, field environment factor parameters, operation factor parameters, data transmission factor parameters, magnetic fields, harmonic waves, loads or clutter, and the data set of the parameters is referred to as P { P ═1，P2，P3……PMN denotes electric energy meter calibration data including at least a kind of current, voltage or power, and the set of the calibration data is denoted as Q ═ Q1，Q2，Q3……QNT represents the time for detecting the electric energy meter by the electric energy meter calibrating device or the assembly line, D1Set, P, representing data affecting the accuracy of the verification of the electric energy meterijThe aggregate data elements are expressed as electric energy meter calibrating devices or assembly lines in i factoriesDuring line detection, measuring values of the electric energy meter are measured at j time;
(4) calculating related data according to the constructed random matrix model;
(5) analyzing data information of at least magnetic field, harmonic wave, load or clutter present in the detection system of the electric energy meter by using the random matrix model in the step (S3), and calculating a normalized matrix product D by using the following formulastdCharacteristic value of (d):
Dstd＝[D1,D2,D3,……DM+N]T(4)
when the normalized matrix product D is calculatedstdThe characteristic value of the electric energy meter is obtained, and the error influence quantity D of the magnetic field, the harmonic wave, the load or the clutter influencing the verification precision of the electric energy meter is obtainedstdThe larger the amount of influence.
9. The method for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 7, characterized by comprising the following steps: the BP neural network algorithm model consists of an input layer, an inclusion layer and an output layer, wherein the input layer comprises electric energy meter data information output by the random matrix analysis algorithm model; the BP neural network algorithm model gradually approaches to a required result by adjusting a weight and a threshold value, so that an output error is minimized, and when the BP neural network model is adjusted, the adjustment is carried out according to the following formula:
the formula for adjusting the weight coefficient of the output layer is as follows:
wherein, η indicates that,
which is indicative of a desired output, is,
for actual output, η represents learning rate, k represents number of samples, and p represents samples0 < η < 1;
the formula for adjusting the weight coefficient of the hidden layer is as follows:
the quadratic exact function model of the input mode pairs in each energy meter fault data sample is:
and (3) the total accurate function expression of the N electric energy meter fault information samples:
when calculation is started, when the fault type information of the complex electric energy meter is extracted, in order to improve the learning precision, standard processing is firstly carried out on sample data;
assuming that the types of the input electric energy meter fault information are m, the sample is N, and for input data xijThe normalization is performed according to the following formula:
wherein i is 1,2, …, N; j is 1,2 …, m, Z in the above formulaijThe normalized data is obtained.
The following formula may be normalized:
y′i＝q(yi-ymin+b)/(ymax-ymin+b) (12)
wherein y isiOutputting a fault data sample of the electric energy meter;
y′ithe standardized fault data sample information of the electric energy meter is obtained;
ymax,yminthe maximum value and the minimum value in the fault information data sample of the output electric energy meter are obtained.
10. The method for remotely and online monitoring the metering data of the intelligent electric energy meter according to claim 9, characterized by comprising the following steps: wherein 1< q < 1.3; 1< b <1.6, the number of hidden layer nodes is determined to be between 7 and 9, the value from the input layer to the hidden layer is between 0.2 and 0.6, and the value from the hidden layer to the output layer can be between 0.2 and 0.6.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113655432A (en) * | 2021-09-09 | 2021-11-16 | 国网上海市电力公司 | Automatic identification and comparison method and system for intelligent metering electric energy meter |
| CN114114130A (en) * | 2021-09-18 | 2022-03-01 | 宁夏隆基宁光仪表股份有限公司 | Electric energy meter testing method, system and device based on actual load on simulation site |
| CN114200372A (en) * | 2021-11-30 | 2022-03-18 | 深圳市国电科技通信有限公司 | Remote detection method, system and storage medium for metering device |
| CN116449284A (en) * | 2023-03-30 | 2023-07-18 | 宁夏隆基宁光仪表股份有限公司 | Household electricity anomaly monitoring method and intelligent ammeter thereof |
| CN117008043A (en) * | 2023-09-27 | 2023-11-07 | 青岛鼎信通讯股份有限公司 | Electric energy meter fault simulation detection system and method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104597426A (en) * | 2014-12-17 | 2015-05-06 | 浙江涵普电力科技有限公司 | Remote online monitoring method of energy measurement device |
| CN106597351A (en) * | 2016-11-18 | 2017-04-26 | 云南电网有限责任公司电力科学研究院 | Electric energy meter verification device online real-time monitoring realization method |
| CN107229602A (en) * | 2017-05-22 | 2017-10-03 | 湘潭大学 | A kind of recognition methods of intelligent building microgrid electricity consumption behavior |
| CN107831465A (en) * | 2017-10-31 | 2018-03-23 | 国网黑龙江省电力有限公司电力科学研究院 | A kind of intelligent electric energy meter fault judgment method based on BP neural network |
| CN107863824A (en) * | 2017-12-10 | 2018-03-30 | 国家电网公司 | Distribute-electricity transformer district intelligence managing and control system based on the optimization of load Intelligent cluster |
| CN110019098A (en) * | 2017-12-30 | 2019-07-16 | 广州明领基因科技有限公司 | Electrical energy measurement big data analysis system based on Hadoop frame |
-
2020
- 2020-04-23 CN CN202010324553.8A patent/CN111650548A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104597426A (en) * | 2014-12-17 | 2015-05-06 | 浙江涵普电力科技有限公司 | Remote online monitoring method of energy measurement device |
| CN106597351A (en) * | 2016-11-18 | 2017-04-26 | 云南电网有限责任公司电力科学研究院 | Electric energy meter verification device online real-time monitoring realization method |
| CN107229602A (en) * | 2017-05-22 | 2017-10-03 | 湘潭大学 | A kind of recognition methods of intelligent building microgrid electricity consumption behavior |
| CN107831465A (en) * | 2017-10-31 | 2018-03-23 | 国网黑龙江省电力有限公司电力科学研究院 | A kind of intelligent electric energy meter fault judgment method based on BP neural network |
| CN107863824A (en) * | 2017-12-10 | 2018-03-30 | 国家电网公司 | Distribute-electricity transformer district intelligence managing and control system based on the optimization of load Intelligent cluster |
| CN110019098A (en) * | 2017-12-30 | 2019-07-16 | 广州明领基因科技有限公司 | Electrical energy measurement big data analysis system based on Hadoop frame |
Non-Patent Citations (2)
| Title |
|---|
| 陈家璘: ""基于数据挖掘算法的通信领域故障信号识别性能分析"", 《电子测量技术》 * |
| 黄丹: ""电力系统中网络质量问题分析方法及实现"", 《专题研究与综述》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113655432A (en) * | 2021-09-09 | 2021-11-16 | 国网上海市电力公司 | Automatic identification and comparison method and system for intelligent metering electric energy meter |
| CN114114130A (en) * | 2021-09-18 | 2022-03-01 | 宁夏隆基宁光仪表股份有限公司 | Electric energy meter testing method, system and device based on actual load on simulation site |
| CN114114130B (en) * | 2021-09-18 | 2024-05-14 | 宁夏隆基宁光仪表股份有限公司 | Electric energy meter testing method, system and device based on simulation site actual load |
| CN114200372A (en) * | 2021-11-30 | 2022-03-18 | 深圳市国电科技通信有限公司 | Remote detection method, system and storage medium for metering device |
| CN116449284A (en) * | 2023-03-30 | 2023-07-18 | 宁夏隆基宁光仪表股份有限公司 | Household electricity anomaly monitoring method and intelligent ammeter thereof |
| CN117008043A (en) * | 2023-09-27 | 2023-11-07 | 青岛鼎信通讯股份有限公司 | Electric energy meter fault simulation detection system and method |
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