CN115329565B - Comprehensive evaluation method and system for complex electromagnetic field environment - Google Patents
Comprehensive evaluation method and system for complex electromagnetic field environment Download PDFInfo
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- CN115329565B CN115329565B CN202210949666.6A CN202210949666A CN115329565B CN 115329565 B CN115329565 B CN 115329565B CN 202210949666 A CN202210949666 A CN 202210949666A CN 115329565 B CN115329565 B CN 115329565B
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Abstract
The invention discloses a comprehensive evaluation method and a comprehensive evaluation system for a complex electromagnetic field environment. Wherein, the method comprises the following steps: constructing complex known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics and known electric energy meter working state characteristics corresponding to the known electromagnetic field signals; determining a known coupling relation, and constructing a typical characteristic database according to the known coupling relation; when the interference of the complex electromagnetic field environment occurs, constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics; determining a complex coupling relation; and comparing and screening the complex coupling relation with a typical characteristic database, determining which electromagnetic field interference signal or signals of the complex electromagnetic field environment occur, and recording early warning information.
Description
Technical Field
The invention relates to the field of complex electromagnetic field monitoring, in particular to a comprehensive evaluation method and a comprehensive evaluation system for complex electromagnetic field environment.
Background
In recent years, china accelerates the construction of smart power grids, and smart electric energy meters are popularized in users comprehensively. Because the intelligent electric energy meter is easily subjected to electromagnetic interference in the operation process, the electric energy meter needs to be subjected to a performance test for resisting magnetic field interference before leaving a factory, so that the electric energy meter can still normally work in a constant magnetic field environment of 300mT, and the electric energy meter also has a constant magnetic field interference event record in the operation process.
However, the existing constant magnetic field interference cannot distinguish other complex electromagnetic field interference, the recording function of the constant magnetic field interference event is misreported after other electromagnetic field interference occurs, and the evidence obtaining and fixing effects cannot be achieved when some lawbreakers use strong electromagnetic fields to perform interference electricity stealing on the electric energy meter.
Disclosure of Invention
According to the invention, a comprehensive evaluation method and a comprehensive evaluation system for a complex electromagnetic field environment are provided, so as to solve the technical problems that the existing constant magnetic field interference cannot distinguish other complex electromagnetic field interference, the recording function of the constant magnetic field interference event is misreported after other electromagnetic field interference occurs, and the evidence obtaining and evidence fixing effects cannot be achieved when some lawbreakers use strong electromagnetic fields to perform interference electricity stealing on an electric energy meter.
According to a first aspect of the present invention, there is provided a comprehensive evaluation method for a complex electromagnetic field environment, comprising:
constructing known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics and known electric energy meter working state characteristics corresponding to the known electromagnetic field signals;
determining known coupling relations of the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and constructing a typical characteristic database according to the known coupling relations;
when the complex electromagnetic field environment interference occurs, constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals;
determining the complex coupling relation of the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics;
and comparing and screening the complex coupling relation with the typical characteristic database, determining which one or more electromagnetic field interference signals the complex electromagnetic field environment generates, and recording early warning information.
Optionally, the known electromagnetic field signal characteristics are constructed based on various known electromagnetic field signals, including:
decomposing the known electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signal;
and obtaining various different known electromagnetic field signal characteristics based on the time domain, frequency domain and energy domain information of the known electromagnetic field signals.
Optionally, constructing a known electric energy meter electricity consumption information characteristic and a known electric energy meter working state characteristic corresponding to the known electromagnetic field signal comprises:
analyzing the waveform difference characteristics of adjacent periods of current, voltage, daily freezing and electricity consumption information of the electric energy meter under various interference states of known electromagnetic field signals to obtain electricity consumption information characteristics of the known electric energy meter;
and extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
Optionally, when the interference of the complex electromagnetic field environment occurs, constructing a complex electromagnetic field signal characteristic based on the complex electromagnetic field signal, and constructing a complex electric energy meter electricity consumption information characteristic and a complex electric energy meter working state characteristic corresponding to the complex electromagnetic field signal, including:
when the environment interference of the complex electromagnetic field occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the characteristics of the complex electromagnetic field signal based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
and analyzing the waveform difference characteristics of adjacent periods of the current, voltage, daily freezing and electricity consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain the electricity consumption information characteristics of the complex electric energy meter, and extracting the metering error and fault information of the electric energy meter to obtain the working state characteristics of the complex electric energy meter.
Optionally, when the interference of the complex electromagnetic field environment occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain, and an energy domain to obtain information of the time domain, the frequency domain, and the energy domain of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristics based on the information of the time domain, the frequency domain, and the energy domain of the complex electromagnetic field signal, including:
decomposing the data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot as N, the number of time snapshots as M, arranging the time-series data into N multiplied by M snapshots:
in the formula, ti is the starting time of the ith section, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, and performing singular value decomposition on X1M-1:
in the formula, K represents the rank of singular value decomposition of X1M-1;
calculate the K × K projection of a:
decomposing the A1 characteristics:
in the formula, the column of W is a characteristic vector, and Ʌ is a diagonal matrix containing corresponding characteristic values λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, and wk = ln (λ k)/Δ t initial coefficient value bk (0) is obtained by a pseudo-inverse (equivalent to least square) method:
the first round of decomposition is as follows:
where ψ k (1) denotes the first modality calculated from a complete M snapshots, and XM/2 is defined as:
XM/2 is decomposed into XM/2=X (1) M/2+X (2) M/2 by performing a second round of decomposition on the second half of the above equation, iterating until the multi-resolution decomposition is completed.
Optionally, the current, voltage, daily freezing and electric quantity power consumption information in the electric energy meter under the complex electromagnetic field signal interference state is subjected to adjacent period waveform difference characteristic analysis to obtain a complex electric energy meter power consumption information characteristic, and an electric meter metering error and fault information are extracted to obtain a complex electric energy meter working state characteristic, including:
calculating the difference value of the characteristic quantities of the adjacent periods, taking the absolute value of the difference value, and taking the absolute value average value as the measurement of the difference of the waveform of the characteristic quantities of the two adjacent periods;
according to the following formula, the current in the electric energy meter is subjected to adjacent period waveform difference characteristic analysis:
setting the sampling point number of the current in each period as N, the period number N, and the sampling values of the current in two adjacent periods as ik-1, ik:
the average of the absolute values of the current differences Δ ik (j) over one period is:
δ k reflects the magnitude of current difference between two adjacent periods, and is normalized to avoid the influence of current values on current deviation:
the waveform similarity is:
optionally, determining the complex coupling relationship of the complex electromagnetic field signal characteristic, the complex electric energy meter power consumption information characteristic and the complex electric energy meter working state characteristic comprises:
and determining a correlation coefficient calculation formula of the coupling relation of the complex electromagnetic field signal characteristic, the complex electric energy meter electricity utilization information characteristic and the complex electric energy meter working state characteristic as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
According to another aspect of the present invention, there is also provided a comprehensive evaluation system for a complex electromagnetic field environment, comprising:
the known characteristic constructing module is used for constructing known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics and known electric energy meter working state characteristics corresponding to the known electromagnetic field signals;
the typical database building module is used for determining the known coupling relations of the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and building a typical characteristic database according to the known coupling relations;
the complex characteristic constructing module is used for constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals when complex electromagnetic field environment interference occurs, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals;
the complex coupling relation determining module is used for determining complex coupling relations among the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics;
and the interference signal determining module is used for comparing and screening the complex coupling relation with the typical characteristic database, determining which electromagnetic field interference signal or electromagnetic field interference signals occur in the complex electromagnetic field environment, and recording early warning information.
Optionally, constructing a known feature module comprises:
the known electromagnetic field signal domain information obtaining submodule is used for decomposing the known electromagnetic field signals from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signals;
and the sub-module for obtaining the known electromagnetic field signal characteristics is used for obtaining various different known electromagnetic field signal characteristics based on the time domain, frequency domain and energy domain information of the known electromagnetic field signals.
Optionally, constructing a known feature module comprises:
the sub-module is used for analyzing the waveform difference characteristics of adjacent cycles of current, voltage, daily freezing and electricity consumption information in the electric energy meter under various different interference states of known electromagnetic field signals to obtain the electricity consumption information characteristics of the known electric energy meter;
and the submodule for obtaining the working state characteristics of the known electric energy meter is used for extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
Optionally, constructing a typical database module comprising:
the obtained complex electromagnetic field signal characteristic submodule is used for decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain when the interference of the complex electromagnetic field environment occurs, obtaining the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristic based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
and the obtained complex electric energy meter characteristic submodule is used for carrying out adjacent period waveform difference characteristic analysis on current, voltage, daily freezing and electric quantity power consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain complex electric energy meter power consumption information characteristics, and extracting electric meter metering errors and fault information to obtain complex electric energy meter working state characteristics.
Optionally, the obtaining a complex electromagnetic field signal feature submodule includes:
and obtaining a complex electromagnetic field signal characteristic unit, decomposing the data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot to be N, setting the number of time snapshots to be M, and arranging the time sequence data into N multiplied by M snapshots:
in the formula, ti is the starting time of the ith section, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, and performing singular value decomposition on X1M-1:
in the formula, K represents the rank of singular value decomposition of X1M-1;
calculate the K × K projection of a:
decomposing the characteristics of A1:
in the formula, the column of W is a eigenvector, and Ʌ is a diagonal matrix containing corresponding eigenvalues λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, and wk = ln (λ k)/Δ t initial coefficient value bk (0) is obtained by a pseudo-inverse (equivalent to least square) method:
the first round of decomposition is as follows:
where ψ k (1) denotes the first modality calculated from a complete M snapshots, and XM/2 is defined as:
XM/2 is decomposed into XM/2=X (1) M/2+X (2) M/2 by performing a second round of decomposition on the second half of the above equation, iterating until the multi-resolution decomposition is completed.
Optionally, obtaining a complex electric energy meter feature sub-module, including:
the characteristic quantity waveform difference determining unit is used for solving the difference of the characteristic quantities of the adjacent periods, taking the absolute value of the difference, and taking the average value of the absolute values as the measurement of the difference of the characteristic quantity waveforms of the adjacent two periods;
the current waveform analyzing unit is used for analyzing the waveform difference characteristics of adjacent cycles of the current in the electric energy meter according to the following formula:
and setting the number of sampling points of current in each period as N, the number of periods N, and the sampling values of current in two adjacent periods as ik-1, ik:
the average of the absolute values of the current differences Δ ik (j) over a period is:
δ k reflects the magnitude of current difference between two adjacent periods, and is normalized to avoid the influence of current values on current deviation:
the waveform similarity is as follows:
optionally, the determining a complex coupling relationship module includes:
and the correlation coefficient calculation formula of the coupling relation of the complex coupling relation used for determining the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics is as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
Therefore, the signal characteristics of the complex electromagnetic environment are extracted rapidly and comprehensively, the signal characteristics are analyzed, the categories of the complex electromagnetic field environment are divided according to the characteristics, useful interference events are screened and reported, the problem of false alarm of the events is solved, the effectiveness of event recording is ensured, evidence can be solidified in time when artificial abnormal electromagnetic field impact electricity stealing behaviors occur, and the adverse effect of magnetic field interference on the metering performance of the electric energy meter is reduced or avoided.
Drawings
Exemplary embodiments of the invention may be more completely understood in consideration of the following drawings:
fig. 1 is a schematic flow chart of a comprehensive evaluation method for a complex electromagnetic field environment according to the present embodiment;
FIG. 2 is a schematic diagram of the coupling relationship between the features according to the present embodiment;
FIG. 3 is a schematic diagram of the comprehensive diagnosis, screening and evaluation of complex electromagnetic field environment by analyzing three types of characteristic data according to the embodiment;
fig. 4 is a schematic diagram of a comprehensive evaluation system for a complex electromagnetic field environment according to the embodiment.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the drawings are not intended to limit the present invention. In the drawings, the same unit/element is denoted by the same reference numeral.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
According to a first aspect of the present invention, there is provided a comprehensive evaluation method 100 for a complex electromagnetic field environment, as shown with reference to fig. 1, the method 100 comprising:
s101, constructing complex known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics corresponding to the known electromagnetic field signals, electric energy meter metering data characteristics and known electric energy meter working state characteristics; (ii) a
S102, determining known coupling relations among the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and constructing a typical characteristic database according to the known coupling relations;
s103, when the complex electromagnetic field environment interference occurs, constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals; s104, determining the complex coupling relation of the complex electromagnetic field signal characteristics, the complex electric energy meter electricity consumption information characteristics and the complex electric energy meter working state characteristics;
and S105, comparing and screening the complex coupling relation with the typical characteristic database, determining which electromagnetic field interference signal or signals of the generated complex electromagnetic field environment exist, and recording early warning information.
Specifically, a complex electromagnetic field environment signal is decomposed, the signal is decomposed from a time domain, a frequency domain and an energy domain, the time domain, the frequency domain and the energy domain information of the complex electromagnetic field environment are obtained, and complex electromagnetic field signal characteristics are constructed;
analyzing waveform difference characteristics of adjacent cycles of power consumption information such as current, voltage, daily freezing and electric quantity in the electric energy meter, extracting metering errors, fault information and the like of the electric energy meter, and constructing working state characteristics of the electric energy meter;
extracting different electromagnetic field generation signals such as a constant magnetic field, a power frequency magnetic field, a high-frequency electric field and the like, and constructing various electromagnetic field signal characteristics.
The method comprises the steps of analyzing the coupling relationship among the electricity consumption information characteristics, the work state characteristics of the electric energy meter, the electromagnetic field signal characteristics and the complex electromagnetic field signal characteristics, revealing the linear and nonlinear association characteristics among the electricity consumption information characteristics, the work state characteristics of the electric energy meter, the electromagnetic field signal characteristics and the complex electromagnetic field signals, eliminating redundant characteristics between the characteristics and the complex electromagnetic field signals (shown in a reference figure 2), and carrying out comprehensive diagnosis, screening and evaluation on the complex electromagnetic field environment by analyzing three types of characteristic data aiming at three types of characteristic data including electricity consumption information (current, voltage, daily freezing, electric quantity and the like), meter work states (metering errors and the like) and typical characteristic quantities of the complex electromagnetic field (shown in a reference figure 3). The specific method comprises the following steps:
(1) the complex electromagnetic field signals are separated into modal components with different space-time scales according to different resolutions, and the instantaneous frequency and energy characteristics of each mode are collected by utilizing time-frequency distribution to obtain the characteristics of a time domain, a frequency domain and an energy domain.
Decomposing data into a set of dynamic modes, setting the number of spatial measurements of each time snapshot to be N, and the number of time snapshots to be M, firstly arranging time series data into N multiplied by M snapshots:
where ti is the start time of the ith segment and the vector x is the N-dimensional column vector.
Then intercepting a1 xM-1 partial matrix in X, and carrying out singular value decomposition on X1M-1:
where, denotes the conjugate transpose, K is the rank of the singular value decomposition of X1M-1.
Calculate the K × K projection of a:
decomposing the A1 characteristics:
where the columns of W are eigenvectors, Ʌ is a diagonal matrix containing the corresponding eigenvalues λ k.
The approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, and wk = ln (λ k)/Δ t initial coefficient value bk (0) is obtained by a pseudo-inverse (equivalent to least square) method.
The first round of decomposition is as follows:
where ψ k (1) indicates that the first modality is calculated from the full M snapshots. XM/2 is defined as:
XM/2 is decomposed into XM/2=X (1) M/2+X (2) M/2 by performing a second round of decomposition on the second half of the above equation, iterating until the multi-resolution decomposition is completed.
(2) The waveform difference characteristics of adjacent periods of each characteristic quantity of the electric energy meter are analyzed, and the identification of a complex electromagnetic field environment is assisted by analyzing the electricity consumption information such as current, voltage, daily freezing and electric quantity in the electric energy meter.
The difference value of the characteristic quantities of the adjacent periods is firstly solved, then the absolute value of the difference value is obtained, and finally the average value of the absolute value is used for measuring the difference of the characteristic quantity waveforms of the two adjacent periods. The current is taken as an example as follows:
and respectively setting the sampling point number of the current in each period as N, the period number N and the sampling values of the current in two adjacent periods as ik-1,ik.
The average of the absolute values of the current differences Δ ik (j) over a period is:
δ k reflects the magnitude of the current difference between two adjacent periods, and δ k is normalized to avoid the influence of current values on current deviation.
The waveform similarity is:
the change degree of adjacent periods of each characteristic quantity of the electric energy meter can be seen through the waveform similarity, and further the influence of the complex electromagnetic field pulse on the change degree can be reflected.
(3) Even if an abnormal value occurs in the data, because the order of the abnormal value usually does not change obviously, the influence on the correlation coefficient is small, the coupling relation among the electricity consumption information characteristic, the electric energy meter working state characteristic, the electromagnetic field signal characteristic and the complex electromagnetic field signal characteristic of the electric energy meter is obtained, and the correlation coefficient calculation formula of the coupling relation is as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
By calculating the electricity consumption information characteristics of the electric energy meter, the working state characteristics of the electric energy meter and the correlation coefficient between the electromagnetic field signal characteristics and each complex electromagnetic field signal, the redundant characteristics between each characteristic and the complex electromagnetic field impact scene can be effectively eliminated.
(4) Aiming at three types of characteristic data of electric energy meter electricity consumption information (current, voltage, daily freezing, electric quantity and the like), meter working state (metering error and the like) and typical characteristic quantity of a complex electromagnetic field, fusion characteristics capable of comprehensively representing the three types of data are extracted, a fusion layer of deep characteristics of three types of complex electromagnetic field environments is constructed, the fusion layer can fully retain original three types of complex electromagnetic field environment characteristic information, and comprehensive diagnosis, screening and evaluation of the complex electromagnetic field environments are carried out by analyzing the three types of characteristic data.
Therefore, the signal characteristics of the complex electromagnetic environment are extracted rapidly and comprehensively, the signal characteristics are analyzed, the categories of the complex electromagnetic field environment are divided according to the characteristics, useful interference events are screened and reported, the problem of false alarm of the events is solved, the effectiveness of event recording is ensured, evidence can be solidified in time when artificial abnormal electromagnetic field impact electricity stealing behaviors occur, and the adverse influence of magnetic field interference on the metering performance of the electric energy meter is reduced or avoided.
Optionally, the known electromagnetic field signal characteristics are constructed based on various known electromagnetic field signals, including:
decomposing the known electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signal;
and obtaining various different known electromagnetic field signal characteristics based on the time domain, frequency domain and energy domain information of the known electromagnetic field signals.
Optionally, constructing a known electric energy meter electricity consumption information characteristic and a known electric energy meter working state characteristic corresponding to the known electromagnetic field signal comprises:
analyzing the waveform difference characteristics of adjacent periods of current, voltage, daily freezing and electricity consumption information of the electric energy meter under various interference states of known electromagnetic field signals to obtain electricity consumption information characteristics of the known electric energy meter;
and extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
Optionally, when the interference of the complex electromagnetic field environment occurs, constructing a complex electromagnetic field signal characteristic based on the complex electromagnetic field signal, and constructing a complex electric energy meter electricity consumption information characteristic and a complex electric energy meter working state characteristic corresponding to the complex electromagnetic field signal, including:
when the environment interference of the complex electromagnetic field occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the characteristics of the complex electromagnetic field signal based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
and analyzing the waveform difference characteristics of adjacent periods of the current, voltage, daily freezing and electricity consumption information of the electric energy meter under the complex electromagnetic field signal interference state to obtain the electricity consumption information characteristics of the complex electric energy meter, and extracting the metering error and fault information of the electric energy meter to obtain the working state characteristics of the complex electric energy meter.
Optionally, when the complex electromagnetic field environment interference occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristics based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, including:
decomposing the data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot as N, the number of time snapshots as M, arranging the time-series data into N multiplied by M snapshots:
in the formula, ti is the starting time of the ith section, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, and performing singular value decomposition on X1M-1:
wherein, K represents the rank of singular value decomposition of X1M-1;
calculate the K × K projection of a:
decomposing the A1 characteristics:
in the formula, the column of W is a eigenvector, and Ʌ is a diagonal matrix containing corresponding eigenvalues λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, and wk = ln (λ k)/Δ t initial coefficient value bk (0) is obtained by a pseudo-inverse (equivalent to least square) method:
the first round of decomposition is as follows:
where ψ k (1) denotes the first modality calculated from a complete M snapshots, and XM/2 is defined as:
XM/2 is decomposed into XM/2=X (1) M/2+X (2) M/2 by performing a second round decomposition on the second half of the above equation, iterating until the multi-resolution decomposition is completed.
Optionally, analyzing the waveform difference characteristics of adjacent periods of the current, voltage, daily freezing and electricity consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain the electricity consumption information characteristics of the complex electric energy meter, and extracting the metering error and fault information of the electric energy meter to obtain the working state characteristics of the complex electric energy meter, including:
calculating the difference value of the characteristic quantities of the adjacent periods, taking the absolute value of the difference value, and taking the average value of the absolute value as the measurement of the difference of the waveform of the characteristic quantities of the adjacent two periods;
according to the following formula, the current in the electric energy meter is subjected to adjacent period waveform difference characteristic analysis:
setting the sampling point number of the current in each period as N, the period number N, and the sampling values of the current in two adjacent periods as ik-1, ik:
the average of the absolute values of the current differences Δ ik (j) over a period is:
δ k reflects the magnitude of current difference between two adjacent periods, and is normalized to avoid the influence of current values on current deviation:
the waveform similarity is as follows:
optionally, determining the complex coupling relationship of the complex electromagnetic field signal characteristic, the complex electric energy meter electricity consumption information characteristic and the complex electric energy meter working state characteristic includes:
and determining a correlation coefficient calculation formula of the coupling relation of the complex electromagnetic field signal characteristic, the complex electric energy meter electricity utilization information characteristic and the complex electric energy meter working state characteristic as follows:
in the formula, n is sample energy, rho is a correlation coefficient, and x and y are corresponding elements in two variables.
Therefore, the signal characteristics of the complex electromagnetic environment are extracted rapidly and comprehensively, the signal characteristics are analyzed, the categories of the complex electromagnetic field environment are divided according to the characteristics, useful interference events are screened and reported, the problem of false alarm of the events is solved, the effectiveness of event recording is ensured, evidence can be solidified in time when artificial abnormal electromagnetic field impact electricity stealing behaviors occur, and the adverse influence of magnetic field interference on the metering performance of the electric energy meter is reduced or avoided.
According to another aspect of the present invention, there is also provided a comprehensive evaluation system 400 for a complex electromagnetic field environment, as shown in fig. 4, the system 400 comprising:
a known characteristic constructing module 410, configured to construct known electromagnetic field signal characteristics based on various known electromagnetic field signals, and construct known electric energy meter electricity consumption information characteristics and known electric energy meter operating state characteristics corresponding to the known electromagnetic field signals;
a typical database constructing module 420, which is used for determining the known coupling relations of the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and constructing a typical characteristic database according to the known coupling relations;
the complex characteristic constructing module 430 is used for constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals when complex electromagnetic field environment interference occurs, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals;
the complex coupling relation determining module 440 is used for determining complex coupling relations among the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics;
and an interference signal determining module 450, configured to compare and screen the complex coupling relationship with the typical characteristic database, determine which electromagnetic field interference signal or electromagnetic field interference signals occur in the complex electromagnetic field environment, and record early warning information.
Optionally, constructing a known feature module comprises:
the known electromagnetic field signal domain information obtaining submodule is used for decomposing the known electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signal;
and the sub-module for obtaining the known electromagnetic field signal characteristics is used for obtaining various different known electromagnetic field signal characteristics based on the time domain, frequency domain and energy domain information of the known electromagnetic field signals.
Optionally, constructing a known feature module comprises:
the sub-module is used for carrying out adjacent period waveform difference characteristic analysis on current, voltage, daily freezing and electricity consumption information of the electric energy meter under various different interference states of known electromagnetic field signals to obtain electricity consumption information characteristics of the known electric energy meter;
and the sub-module for obtaining the working state characteristics of the known electric energy meter is used for extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
Optionally, constructing a typical database module comprising:
the obtained complex electromagnetic field signal characteristic submodule is used for decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain when the interference of the complex electromagnetic field environment occurs, obtaining the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristic based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
and the obtained complex electric energy meter characteristic submodule is used for carrying out adjacent period waveform difference characteristic analysis on current, voltage, daily freezing and electric quantity power consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain complex electric energy meter power consumption information characteristics, and extracting electric meter metering errors and fault information to obtain complex electric energy meter working state characteristics. Optionally, obtaining a complex electromagnetic field signal feature submodule includes:
obtaining a complex electromagnetic field signal characteristic unit, which is used for decomposing data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot as N, the number of time snapshots as M, and arranging time sequence data into N multiplied by M snapshots:
in the formula, ti is the starting time of the ith section, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, and performing singular value decomposition on X1M-1:
in the formula, K represents the rank of singular value decomposition of X1M-1;
calculate the K × K projection of a:
decomposing the A1 characteristics:
in the formula, the column of W is a characteristic vector, and Ʌ is a diagonal matrix containing corresponding characteristic values λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, and wk = ln (λ k)/Δ t initial coefficient value bk (0) is obtained by a pseudo-inverse (equivalent to least square) method:
the first round of decomposition is as follows:
where ψ k (1) represents the first modality calculated from a full M snapshots, and XM/2 is defined as:
XM/2 is decomposed into XM/2=X (1) M/2+X (2) M/2 by performing a second round decomposition on the second half of the above equation, iterating until the multi-resolution decomposition is completed.
Optionally, obtaining a complex electric energy meter feature submodule, including:
the waveform difference determining unit is used for solving the difference of the characteristic quantities of the adjacent periods, taking the absolute value of the difference, and taking the average value of the absolute values as the difference for measuring the waveform of the characteristic quantities of the adjacent two periods;
the current waveform analyzing unit is used for analyzing the waveform difference characteristics of adjacent cycles of the current in the electric energy meter according to the following formula:
setting the sampling point number of the current in each period as N, the period number N, and the sampling values of the current in two adjacent periods as ik-1, ik:
the average of the absolute values of the current differences Δ ik (j) over a period is:
δ k reflects the magnitude of current difference between two adjacent periods, and is normalized to avoid the influence of current values on current deviation:
the waveform similarity is:
optionally, the determining a complex coupling relationship module includes:
and the correlation coefficient calculation formula of the coupling relation of the complex coupling relation used for determining the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics is as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
The comprehensive evaluation system 400 for a complex electromagnetic field environment according to an embodiment of the present invention corresponds to the comprehensive evaluation method 100 for a complex electromagnetic field environment according to another embodiment of the present invention, and will not be described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solution in the embodiment of the present application may be implemented by using various computer languages, for example, object-oriented programming language Java and transliteration scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (9)
1. A comprehensive evaluation method for a complex electromagnetic field environment is characterized by comprising the following steps:
constructing known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics and known electric energy meter working state characteristics corresponding to the known electromagnetic field signals;
determining known coupling relations of the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and constructing a typical characteristic database according to the known coupling relations;
when the interference of the complex electromagnetic field environment occurs, constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals;
determining the complex coupling relation of the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics;
comparing and screening the complex coupling relation with the typical characteristic database, determining which one or more electromagnetic field interference signals are in the complex electromagnetic field environment, and recording early warning information;
when the interference of the complex electromagnetic field environment occurs, the complex electromagnetic field signal characteristics are constructed based on the complex electromagnetic field signals, and the electricity utilization information characteristics of the complex electric energy meter and the working state characteristics of the complex electric energy meter corresponding to the complex electromagnetic field signals are constructed, and the method comprises the following steps:
when the environment interference of the complex electromagnetic field occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the characteristics of the complex electromagnetic field signal based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
analyzing the waveform difference characteristics of adjacent periods of current, voltage, daily freezing and electricity consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain electricity consumption information characteristics of the complex electric energy meter, and extracting metering errors and fault information of the electric energy meter to obtain working state characteristics of the complex electric energy meter;
when the complex electromagnetic field environment interference occurs, decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the time domain, the frequency domain and the energy domain information of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristics based on the time domain, the frequency domain and the energy domain information of the complex electromagnetic field signal, wherein the method comprises the following steps:
decomposing the data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot as N, the number of time snapshots as M, arranging the time-series data into N multiplied by M snapshots:
X＝[x(t 1 ),x(t 2 ),...,x(t M )]
in the formula, ti is the starting time of the ith section, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, for X 1 M-1 Singular value decomposition is carried out:
wherein, represents a conjugate transpose, and K is X 1 M-1 Rank of singular value decomposition of;
calculate the K × K projection of a:
decomposing the A1 characteristics:
A1W＝WΛ
in the formula, the column of W is a eigenvector, and Λ is a diagonal matrix containing a corresponding eigenvalue λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is the spatial coordinate, w k Initial coefficient value b of = ln (λ k)/Δ t k( 0) The following were determined by the pseudo-inverse method:
the first round decomposition process is as follows:
in the formula, # k (1) Representing a first modality computed from a complete M snapshots, definition X M/2 Comprises the following steps:
by subjecting the latter half of the above formula to a second round of decomposition, X is M/2 Decomposition into X M/2 ＝X(1) M/2 +X(2) M/2 Iterating until the multi-resolution decomposition is completed;
and carrying out adjacent period waveform difference characteristic analysis on current, voltage, daily freezing and electric quantity power consumption information in the electric energy meter under the complex electromagnetic field signal interference state to obtain the power consumption information characteristics of the complex electric energy meter, extracting the metering error and fault information of the electric energy meter, and obtaining the working state characteristics of the complex electric energy meter, wherein the method comprises the following steps:
calculating the difference value of the characteristic quantities of the adjacent periods, taking the absolute value of the difference value, and taking the absolute value average value as the measurement of the difference of the waveform of the characteristic quantities of the two adjacent periods;
according to the following formula, the current in the electric energy meter is subjected to adjacent period waveform difference characteristic analysis:
the sampling point number of the current in each period is N, the period number N, and the sampling values of the current in two adjacent periods are i k-1 ，i k ：
Δi k (j)＝i k (j)-i k-1 (j),j＝1,2,...,N；k＝2,3,...,n
Difference in current Δ i k (j) The average of the absolute values of (a) over a period is:
δ k reflects the current difference between two adjacent periods, and avoids the influence of current value on current deviation k And (4) normalization is carried out:
the waveform similarity is as follows:
2. the method of claim 1, wherein constructing the known electromagnetic field signal signature based on various known electromagnetic field signals comprises:
decomposing the known electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signal;
and obtaining various different known electromagnetic field signal characteristics based on the time domain, frequency domain and energy domain information of the known electromagnetic field signals.
3. The method of claim 1, wherein constructing a known power meter power consumption information signature and a known power meter operating state signature corresponding to the known electromagnetic field signal comprises:
analyzing the waveform difference characteristics of adjacent periods of current, voltage, daily freezing and electricity consumption information of the electric energy meter under various interference states of known electromagnetic field signals to obtain electricity consumption information characteristics of the known electric energy meter;
and extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
4. The method of claim 1, wherein determining the complex coupling relationship of the complex electromagnetic field signal characteristic, the complex electric energy meter power consumption information characteristic and the complex electric energy meter operating state characteristic comprises:
and determining a correlation coefficient calculation formula of the complex coupling relation of the complex electromagnetic field signal characteristics, the complex electric energy meter electricity consumption information characteristics and the complex electric energy meter working state characteristics as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
5. A comprehensive evaluation system for complex electromagnetic field environments, comprising:
the known characteristic constructing module is used for constructing known electromagnetic field signal characteristics based on various known electromagnetic field signals, and constructing known electric energy meter electricity utilization information characteristics and known electric energy meter working state characteristics corresponding to the known electromagnetic field signals;
the typical database building module is used for determining the known coupling relations of the known electromagnetic field signal characteristics, the known electric energy meter electricity utilization information characteristics and the known electric energy meter working state characteristics, and building a typical characteristic database according to the known coupling relations;
the complex characteristic constructing module is used for constructing complex electromagnetic field signal characteristics based on complex electromagnetic field signals when complex electromagnetic field environment interference occurs, and constructing complex electric energy meter electricity utilization information characteristics and complex electric energy meter working state characteristics corresponding to the complex electromagnetic field signals;
the complex coupling relation determining module is used for determining complex coupling relations among the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics;
the interference signal determining module is used for comparing and screening the complex coupling relation with the typical characteristic database, determining which electromagnetic field interference signal or electromagnetic field interference signals occur in the complex electromagnetic field environment, and recording early warning information;
constructing a complex feature module comprising:
the obtained complex electromagnetic field signal characteristic submodule is used for decomposing the complex electromagnetic field signal from a time domain, a frequency domain and an energy domain when the interference of the complex electromagnetic field environment occurs, obtaining the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal, and obtaining the complex electromagnetic field signal characteristic based on the information of the time domain, the frequency domain and the energy domain of the complex electromagnetic field signal;
the obtained complex electric energy meter characteristic submodule is used for analyzing the waveform difference characteristics of adjacent periods of current, voltage, daily freezing and electricity consumption information in the electric energy meter under the state of the interference of the complex electromagnetic field signals to obtain the electricity consumption information characteristics of the complex electric energy meter, and extracting the metering error and fault information of the electric energy meter to obtain the working state characteristics of the complex electric energy meter;
obtaining a complex electromagnetic field signal characteristic submodule, which comprises:
and obtaining a complex electromagnetic field signal characteristic unit, decomposing the data into a group of dynamic modes, setting the number of spatial measurements of each time snapshot to be N, setting the number of time snapshots to be M, and arranging the time sequence data into N multiplied by M snapshots:
X＝[x(t 1 ),x(t 2 ),...,x(t M )]
in the formula, ti is the starting time of the ith segment, and the vector x is an N-dimensional column vector;
intercepting a1 xM-1 partial matrix in X, and aiming at X 1 M-1 Singular value decomposition is carried out:
in the formula, X represents conjugate transpose, and K is X 1 M-1 Rank of singular value decomposition of;
calculate the K × K projection of a:
A1＝U * A U＝U * X 2 M VΣ -1
decomposing the A1 characteristics:
A1W＝WΛ
in the formula, the column of W is a eigenvector, and Λ is a diagonal matrix containing a corresponding eigenvalue λ k;
the approximate decomposition for all future times can be expressed as:
where ζ is a spatial coordinate, w k Initial coefficient value b of = ln (λ k)/Δ t k (0) The following were found by the pseudo-inverse (equivalent to least squares) method:
the first round decomposition process is as follows:
in the formula, # k (1) Representing the first modality computed from the full M snapshots, definition X M/2 Comprises the following steps:
by subjecting the latter half of the above formula to a second round of decomposition, X is M/2 Decomposition into X M/2 ＝X(1) M/2 +X(2) M/2 Iterate until completeDecomposing into multi-resolution;
obtaining a characteristic submodule of the complex electric energy meter, comprising:
the characteristic quantity waveform difference determining unit is used for solving the difference of the characteristic quantities of the adjacent periods, taking the absolute value of the difference, and taking the average value of the absolute values as the measurement of the difference of the characteristic quantity waveforms of the adjacent two periods;
the current waveform analyzing unit is used for analyzing the waveform difference characteristics of adjacent cycles of the current in the electric energy meter according to the following formula:
the sampling point number of the current in each period is N, the period number N, and the sampling values of the current in two adjacent periods are i k-1 ，i k ：
Δi k (j)＝i k (j)-i k-1 (j),j＝1,2,...,N；k＝2,3,...,n
Difference in current Δ i k (j) The average of the absolute values of (a) over a period is:
δ k reflects the current difference of two adjacent periods, and avoids the influence of current value on current deviation, namely delta k And (3) carrying out normalization:
the waveform similarity is:
6. the system of claim 5, wherein constructing a known feature module comprises:
the known electromagnetic field signal domain information obtaining submodule is used for decomposing the known electromagnetic field signal from a time domain, a frequency domain and an energy domain to obtain the information of the time domain, the frequency domain and the energy domain of the known electromagnetic field signal;
and the known electromagnetic field signal characteristic submodule is used for obtaining various different known electromagnetic field signal characteristics based on the time domain, the frequency domain and the energy domain information of the known electromagnetic field signals.
7. The system of claim 5, wherein building a known feature module comprises:
the sub-module is used for carrying out adjacent period waveform difference characteristic analysis on current, voltage, daily freezing and electricity consumption information of the electric energy meter under various different interference states of known electromagnetic field signals to obtain electricity consumption information characteristics of the known electric energy meter;
and the sub-module for obtaining the working state characteristics of the known electric energy meter is used for extracting the metering error and the fault information of the electric energy meter to obtain the working state characteristics of the known electric energy meter.
8. The system of claim 5, wherein the determine complex coupling relationship module comprises:
and the correlation coefficient calculation formula of the coupling relation of the complex coupling relation used for determining the complex electromagnetic field signal characteristics, the complex electric energy meter electricity utilization information characteristics and the complex electric energy meter working state characteristics is as follows:
in the formula, n is sample energy, ρ is a correlation coefficient, and x and y are corresponding elements in two variables.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program for performing the method of any of the preceding claims 1-4.
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