CN110082579B - Intelligent platform area anti-electricity-stealing monitoring method, system, equipment and medium - Google Patents
Intelligent platform area anti-electricity-stealing monitoring method, system, equipment and medium Download PDFInfo
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Abstract
The invention discloses an intelligent platform area anti-electricity-stealing monitoring method, which comprises the following steps: extracting and analyzing the power utilization information characteristics of the users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area; after the electricity stealing loss characteristics are extracted, the electricity stealing loss characteristics are accurately positioned to the users by comprehensively comparing the dynamic loss with the metering information in combination with the electric energy information of the users in the transformer area; analyzing the occurrence reasons of the electricity stealing behaviors in the transformer area, summarizing and summarizing the occurrence reasons, and determining an electricity stealing behavior data model under various modes; and acquiring running state information of the monitoring platform area based on each electricity stealing behavior data model, and positioning the electricity stealing behavior. The invention also correspondingly discloses a monitoring system, mobile medium equipment and a medium corresponding to the method. The method, the system, the mobile medium equipment and the medium have the advantages of quickly and accurately monitoring the electricity stealing behavior and the like.
Description
Technical Field
The invention mainly relates to the technical field of electric power equipment electricity stealing detection, in particular to a platform area intelligent electricity anti-stealing monitoring method, system, equipment and medium.
Background
In daily production and life, the loss of a high-loss transformer area can be in three forms according to characteristics: variable losses, fixed losses, and other losses. The electric energy passes through a plurality of transformers in the process of transmission, in order to ensure the voltage quality, a plurality of reactive power regulators are needed, such as phase modulators, reactors, capacitors, power transformers, arc suppression coils and other devices, when the electric energy passes through the devices, certain loss is generated, namely copper loss, the loss is in direct proportion to the square of the magnitude of the current flowing on the devices, the magnitude of the current can be changed at any time, and therefore the loss is also variable, so that the loss is called variable loss. When voltage exists, certain loss, called iron loss or insulator loss, is generated in the equipment, namely, the equipment, such as a transformer, a mutual inductor, a capacitor, an electric reactor and an arc suppression coil, of the power grid, the loss is related to the size of the operating voltage, voltage fluctuation in the power grid is small, change can be ignored, and the loss which is fixed and unchanged is called fixed loss, also called no-load loss. And a part of loss is caused by loss in the aspect of enterprise management and consumption of the transformer substation, if the used metering device is not high in accuracy, metering errors are caused, meter reading time of meter reading personnel is not uniform, errors are caused by work, and loss is caused by electricity stealing behaviors of users, and the loss is collectively called as other loss.
The method comprises the steps that electricity stealing behaviors are used as a part of loss of a high-loss transformer area, in actual electricity stealing detection, a single detection mode cannot meet all detection requirements due to diversification of the electricity stealing behaviors and difference of performance characteristics of the electricity stealing behaviors, under the condition, an abnormal electricity utilization intelligent analysis model based on multi-mode data fusion is established, independent detection and evaluation are conducted according to different electrical characteristics and behavior characteristics possibly caused by different electricity stealing means, then independent evaluation results are fused by adopting a multi-mode data characteristic fusion algorithm to form a final evaluation result, and detection and positioning of the abnormal electricity utilization behaviors are conducted based on a comprehensive evaluation result.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the technical problems in the prior art, the invention provides an intelligent platform area anti-electricity-stealing monitoring method, system, equipment and medium for quickly and accurately monitoring electricity-stealing behaviors.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
an intelligent platform area electricity stealing prevention monitoring method comprises the following steps:
extracting and analyzing the power utilization information characteristics of the users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area;
after the electricity stealing loss characteristics are extracted, the electricity stealing loss characteristics are accurately positioned to the users by comprehensively comparing the dynamic loss with the metering information in combination with the electric energy information of the users in the transformer area;
analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons, and determining an electricity stealing behavior data model under various modes;
based on each electricity stealing behavior data model, the existing equipment in the transformer area is utilized to collect and monitor the running state information of the transformer area and position the electricity stealing behavior.
As a further improvement of the above technical solution:
the electricity utilization information characteristics comprise line impedance and are obtained by back calculation of actual measurement values, wherein the actual measurement values comprise voltage, active power and reactive power; the non-electrical information includes one or more of humidity, temperature, or season.
The method is based on comprehensive comparison of dynamic loss change and metering information, a high-accuracy loss dynamic real-time change characteristic model is established by adding a high-accuracy time tag to a dynamic loss process, a metering information and dynamic loss real-time change fusion model based on time matching is established, and an electricity stealing behavior positioning method based on comprehensive comparison of dynamic loss real-time change and metering information is established so as to accurately position electricity stealing loss characteristics to a user.
And establishing respective electricity stealing behavior data models based on the difference of electricity stealing behaviors of the transformer area check meter, the three-phase meter and the single-phase meter, and respectively identifying a data model, an electricity stealing behavior data model of the check meter, an electricity stealing behavior data model of the three-phase meter, an electricity stealing behavior data model of the single-phase meter and a wiring mode data model of all types of the three-phase meter for the user-to-user relationship.
The operation state information of the transformer area comprises one or more of live wire current, zero line current, meter cover opening event records, constant magnetic field event records, phase failure event records, voltage loss event records, current loss event records, historical electric quantity of the electric energy meter, freezing electric quantity, load records of the electric energy meter, current voltage, current, power and power of the data electric energy meter or current curves of external equipment.
And extracting and analyzing the power utilization information characteristics of the users in the transformer area by adopting a wavelet packet decomposition method in time-frequency analysis.
The electricity information characteristics include one or more of voltage, current, active power, reactive power, or power factor.
The invention also discloses an intelligent platform area anti-electricity-stealing monitoring system, which comprises
The first module is used for extracting and analyzing the power utilization information characteristics of users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area;
the second module is used for extracting the electricity stealing loss characteristics, combining the electric energy information of users in the distribution room and accurately positioning the electricity stealing loss characteristics to the users through comprehensive comparison of dynamic loss and metering information;
the third module is used for analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons and determining an electricity stealing behavior data model under various modes;
and the fourth module is used for acquiring and monitoring the running state information of the transformer area by utilizing the existing equipment of the transformer area based on each electricity stealing behavior data model and positioning the electricity stealing behavior.
The invention further discloses a mobile media device, comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the intelligent platform district anti-electricity-stealing monitoring method when the computer program is executed.
The invention also discloses a computer readable storage medium, wherein a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the intelligent platform district anti-electricity-stealing monitoring method are realized.
Compared with the prior art, the invention has the advantages that:
the intelligent platform area anti-electricity-stealing monitoring method of the invention collects and calculates the electricity utilization information of the platform area, automatically adjusts, parameters and judges the accurate side according to the data characteristics of the processed data in the processing and analyzing process, and judges the abnormal electricity utilization condition of the user by combining the past electricity utilization behavior characteristics of the user and the field data analyzing condition, thereby achieving the purpose of electricity-stealing monitoring; in the aspect of data processing, in order to better realize real-time processing of data, a data processing module is optimized by combining an edge calculation method, so that monitoring of power utilization information of a platform area and anti-electricity-stealing analysis are better supported; through realizing the hierarchical monitoring system to the platform district, reduce personnel's operation and tour, reduce the operation cost, in time discover to steal the electric action, reduce the loss that steal the electric and bring.
The intelligent platform area anti-electricity-stealing monitoring system, the mobile medium equipment and the medium have the advantages of the method.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a flow chart of a time-frequency analysis method according to the present invention.
FIG. 3 is a flow chart of a method for locating electricity stealing behavior in the present invention.
Fig. 4 is a flow chart of the analysis of electricity stealing behavior in the present invention.
Fig. 5 is a block diagram of the system of the present invention.
Fig. 6 is a block diagram of a monitoring system according to the present invention.
Fig. 7 is a topological diagram of a monitoring system according to the present invention.
Detailed Description
The invention is further described below with reference to the figures and the specific embodiments of the description.
As shown in fig. 1, the method for monitoring the intelligent anti-electricity-stealing capacity of the distribution room of the present embodiment includes the steps of:
extracting and analyzing the power utilization information characteristics of users in the high-loss transformer area, combining multi-mode information fusion of non-electrical information in the transformer area, realizing extraction of the electricity stealing loss characteristics, and forming a monitoring record of electricity stealing behavior in the transformer area;
after the electricity stealing loss characteristics are extracted, the electricity stealing loss characteristics are accurately positioned to the users by comprehensively comparing the dynamic loss with the metering information in combination with the electric energy information of the users in the transformer area;
analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons, and determining an electricity stealing behavior data model under various modes;
based on each electricity stealing behavior data model, the existing equipment in the transformer area is utilized to collect and monitor the running state information of the transformer area and position the electricity stealing behavior.
In this embodiment, the power consumption information features include line impedance, which is obtained by back calculation of actual measurement values, including voltage, active power, and reactive power; the non-electrical information includes one or more of humidity, temperature, or season.
In the embodiment, the comprehensive comparison of the dynamic loss change and the metering information is used as a basis, a high-accuracy loss dynamic real-time change characteristic model is established by adding a high-accuracy time tag to the dynamic loss process, a metering information and dynamic loss real-time change fusion model based on time matching is established, and an electricity stealing behavior positioning method based on the comprehensive comparison of the dynamic loss real-time change and the metering information is established, so that the electricity stealing loss characteristic is accurately positioned to a user.
In this embodiment, respective electricity stealing behavior data models are established based on the difference of electricity stealing behaviors of the district examination and verification meter, the three-phase meter and the single-phase meter, and a data model for identifying the household-to-variable relationship, a data model for electricity stealing behaviors of the examination and verification meter, a data model for electricity stealing behaviors of the three-phase meter, a data model for electricity stealing behaviors of the single-phase meter and a data model for all types of wiring modes of the three-phase meter are respectively established.
In this embodiment, the operation state information of the transformer area includes one or more of a live wire current, a zero line current, an event record of meter cover opening, an event record of a constant magnetic field, an event record of phase interruption, an event record of voltage loss, an event record of current loss, a historical electric quantity of the electric energy meter, a frozen electric quantity, a load record of the electric energy meter, a current voltage, a current, a power and a power of the data electric energy meter, or a current curve of an external device.
In the embodiment, a wavelet packet decomposition method in time-frequency analysis is adopted to extract and analyze the characteristics of the power utilization information of the users in the transformer area; wherein the electricity usage information characteristics include one or more of voltage, current, active power, reactive power, or power factor.
The above method is described in additional detail below with reference to a specific example:
firstly, analyzing an intelligent monitoring and processing method of electricity stealing information of a high-loss transformer area based on multi-mode information fusion, and establishing a transformer area grading monitoring system; on the basis, an intelligent electricity-stealing-prevention monitoring device which is provided with edge calculation and hardware modularization on a physical architecture and has the functions of monitoring the operation of a transformer area, acquiring information of the transformer area, adaptively intelligently processing the information and the like is developed, and as shown in fig. 1, the intelligent electricity-stealing-prevention monitoring device specifically comprises the following contents:
(1) the multimode electricity stealing information intelligent monitoring and processing method comprises the steps that multimode electricity stealing information in a high-loss transformer area is intelligently monitored and processed, and a hierarchical monitoring system is established;
analyzing the loss characteristics of the high-loss transformer area, researching a multi-mode information fusion method of the power utilization information of the transformer area by combining non-electrical information such as environment information of the transformer area, and establishing a transformer area loss characteristic information base. In addition, electricity stealing identification and accurate positioning based on the station area electricity parameter time-frequency characteristics are realized through an electricity stealing characteristic extraction and positioning algorithm.
a) Power utilization characteristic extraction algorithm for distribution room based on time-frequency analysis
When electricity stealing happens, the line impedance obtained by reverse calculation of actual measurement values (voltage, active power and reactive power) changes and deviates from the real operation parameters of the line. The dynamic change of the line impedance can be quickly calculated and analyzed by a high-resolution time-frequency extraction method, so that a basis is provided for identifying the electricity stealing loss characteristics.
The method comprises the steps of extracting and analyzing power utilization information characteristics of users in a transformer area by adopting an improved time-frequency analysis method, namely a wavelet packet decomposition algorithm (WPD), combining multi-mode information fusion of non-electrical information such as temperature, humidity, seasons and the like in the transformer area, realizing extraction of power stealing loss characteristics, forming monitoring records (shown in figure 2) of power stealing behaviors in the transformer area, and laying a foundation for positioning the power stealing behaviors in the transformer area subsequently.
b) Electricity stealing behavior positioning algorithm based on power utilization characteristics of transformer area
In the monitoring process of the transformer area, after the electricity stealing loss characteristics are successfully extracted, the electric energy information of users in the transformer area is combined, and the electricity stealing behavior characteristics are accurately positioned to the users by screening the electricity utilization characteristics of the users through comprehensive comparison of dynamic loss and metering information. The method is based on comprehensive comparison of loss dynamic change characteristics and electric quantity data, a high-accuracy loss dynamic real-time change characteristic model is established by adding a high-accuracy time tag in the loss dynamic change process, a metering information (port electric energy metering value, loop line loss, line position and other information) and loss dynamic real-time change fusion model based on time matching is established, and an electricity stealing behavior positioning method based on comprehensive comparison of loss dynamic real-time change and metering information is established, wherein the algorithm flow is shown in fig. 3.
c) Grading method for monitoring anti-electricity-stealing performance of transformer area
And analyzing the occurrence reasons of various high-loss transformer areas, summarizing and summarizing the occurrence reasons, and determining a typical data model of the electricity stealing information under various modes. The method is used for researching, searching and processing the condition that a data source is incorrect due to incorrect user transformation relation or overlarge clock error, establishing corresponding self-adaptive data model analysis aiming at a public transformation user, a single-phase table and a three-phase table respectively, establishing a hierarchical monitoring system depending on data source analysis, electricity stealing behavior analysis and wiring fault analysis, and obtaining an analysis flow chart shown in fig. 4.
(2) The intelligent electricity stealing prevention monitoring device has the advantages of realizing edge calculation and hardware modularization on a physical framework, and realizing the functions of operation monitoring, transformer area information acquisition, information self-adaptive intelligent processing and the like.
Analyzing the running state monitoring mode of the intelligent anti-electricity-stealing monitoring device on the transformer area, continuously accumulating the collected transformer area information, carrying out curve analysis with the embedded data model, carrying out key monitoring or subsequent data processing by a self-adaptive correlation algorithm, and positioning the electricity-stealing behavior.
And (3) obtaining the anti-electricity-stealing monitoring device by combining the practical application scene on the basis of the step (1). Problems that may occur in practice are solved such as: when the line loss is calculated, the user variable relation is incorrect, which directly causes the error of line loss calculation, and the clock error is too large, which causes the problem of large data deviation.
The method comprises the steps of establishing respective data model systems based on different electricity stealing modes of a district examination and verification meter, a three-phase meter and a single-phase meter under the multi-level mode, collecting and monitoring running state information of the district by using the existing terminal, an electric energy meter and external wireless current sampling equipment in the district, comparing the similarity of the data models with the electricity stealing behavior, positioning the electricity stealing behavior and recording the occurrence time, wherein the data analysis type is shown in a table 1.
Table 1: data analysis type table
In addition, the intelligent electricity stealing prevention monitoring device for the localized transformer area with the edge computing capability carries out long-term monitoring and information processing on the transformer area, and can timely discover and position electricity stealing behaviors of the transformer area by combining the above contents.
The intelligent anti-electricity-stealing monitoring device mainly comprises a multi-stage data acquisition part, an external sampling device access part, a user-to-user relationship analysis part, a clock error correction part, an electricity-stealing behavior analysis part, a wiring fault analysis part, a data storage part, a safety encryption part, a data analysis output part and the like, as shown in figure 5.
The multistage data acquisition function needs to automatically judge the types of the lower distribution area and the electric energy meter, corresponding associated data acquisition is carried out according to different types, and data support is provided for the multistage data analysis function. The external sampling equipment access function is used for accessing various wireless communication sampling equipment and acquiring data which cannot be provided by the terminal and the electric energy meter. The user-variant relation analysis (or identification) is used for discovering and processing line loss calculation errors caused by the station area attribution problems; clock error correction the user finds and handles the problem of excessive line loss calculation errors caused by clock asynchronization. The electricity stealing behavior analysis is characterized in that different electricity stealing models under various modes such as a check meter, a single-phase meter and a three-phase meter are embedded in the device, the running state information of the distribution room obtained by data is compared, and the type and the occurrence time of the electricity stealing behavior are located. And the wiring fault analysis is implemented by embedding all three-phase meter wiring mode models and determining a real wiring mode through a three-phase meter load curve, so as to assist in analysis of electricity stealing behaviors. The data storage function is to record all original data and study and judge the result, can be by intelligent anti-electricity-stealing monitoring devices superior system or APP analysis software unified acquisition, carries out secondary analysis. The safety encryption module is used for encrypting data of each communication interface to prevent information leakage. And the data analysis output module is used for connecting external safety equipment and displaying an analysis result.
In order to construct a data model base embedded in the intelligent anti-electricity-stealing monitoring device of the transformer area, and by combining the algorithm for extracting the electricity utilization characteristics of the transformer area in the point (1) and the method for monitoring the transformer area in a grading manner, the data model required to be constructed is as follows:
a) data model for distinguishing user-variant relationship (or data model for identifying user-variant relationship or data model for analyzing user-variant relationship)
The user variable relationship is analyzed through the power failure time, one or more groups of definite power failure time of the transformer area are needed, and the time can be recorded through external data and analyzed through historical power failure data. The standard value of the clock error is obtained by calculating the clock error of the electric energy meter for many times, and meanwhile, the electric energy meter with the clock jumping caused by power failure is screened out, and the deviation value of the power failure time is compensated. After the factors are determined, data analysis is carried out through the relevance of the power failure deviation of the electric energy meter, whether the electric energy meter belongs to the local distribution area or not is distinguished, and a specific data model is shown in table 2.
Table 2: data model table for distinguishing user-variant relation
b) Platform district electricity stealing behavior data model
Most of the power stealing behaviors of the transformer area have states of power utilization characteristics, the characteristic states are collected and a data model is established, the similarity between the power utilization information of the transformer area and the data model is monitored and analyzed, and the power stealing behaviors can be effectively positioned and found. The types of the electric energy metering equipment are different, corresponding data characteristics are different, and data models are divided into three types according to metering types: an examination and check meter, a user three-phase meter and a user single-phase meter.
The metering error of the examination table is usually caused by damage or artificial replacement of the mutual inductor, and the mutual inductor cannot acquire a real state without a data interface. The current sampling device with wireless communication is used for collecting the current and apparent power curves of the high-voltage side, the metering error of the mutual inductor or the electric energy meter is found by comparing the current and apparent power of the checking meter, and the data model is applied as the following table 3.
Table 3: examination table data model table
The single-phase meter has many types of electricity stealing, and the current and voltage metering error is usually caused by people for wiring. The electricity stealing data model aiming at the voltage and current characteristic state of the single-phase meter comprises data analysis of a zero line live wire with the highest utilization rate, data analysis of voltage loss and phase loss and data analysis of an electric energy meter cover opening event, and the specific data model is as shown in the following table 4.
Table 4: user single-phase meter data model table
The electricity stealing behavior of the three-phase meter is very similar to the characteristic state of the wiring fault, and the three-phase meter is analyzed and distinguished by combining a data model of the wiring fault. The electricity stealing behavior of the three-phase meter needs to be researched by combining the event occurrence state and the electric energy meter running state and positioning the electricity stealing behavior through the correlation program of a plurality of groups of data models, wherein the common data model is shown in the following table 5.
Table 5: user three-phase table data model table
c) Wiring fault analysis data model
The three-phase electric energy meter has a plurality of (such as 288) wiring types, wherein the correct wiring mode is only 3, and the wiring fault of the three-phase meter itself must be determined when a data model of electricity stealing behaviors is analyzed. The wiring analysis is based on the wiring abnormal events recorded by the electric energy meter, combined with 288 wiring mode data models, the voltage and current included angles are obtained through load recording, the distribution situation of the voltage and current included angles is drawn, and the real three-phase meter wiring mode is determined, wherein the specific data calculation model is as shown in the following table 6.
Table 6: data model table for analyzing wiring fault
The platform district power consumption information monitoring system is a comprehensive system for monitoring and managing power loads by applying a communication technology, a computer technology and an automatic control technology, acquires, processes and monitors power consumption information of power consumers in real time, and realizes the functions of automatic acquisition of power consumption information, abnormal measurement monitoring, power quality monitoring, power consumption analysis and management, related information release, distributed energy monitoring, information interaction of intelligent power consumption equipment and the like, and the system structure is shown in fig. 6.
The electricity utilization state of a specific user can be represented by the variation characteristics of parameters such as voltage, current, active power, reactive power and power factors, therefore, based on the information of the electric energy meter read by the electricity utilization information acquisition system of the platform area, the analysis of the electricity utilization state of the user can be realized by establishing a mathematical model, whether the user is open-phase, overvoltage, voltage loss, power failure, power on and the like can be judged through voltage data, whether the user is open-phase, reverse-phase sequence, overload, three-phase current unbalance, primary short circuit of a current transformer, secondary short circuit of the current transformer and the like and the occurrence time of the phenomena can be judged through current data, and the load variation condition and the like of the user can be found in time by comparing an uploaded power curve with a historical power curve.
In actual production and life, the electricity utilization characteristics in different transformer area ranges are different, for example, the electricity consumption of an industrial area is larger than that of a residential area, the load characteristics are different, and the region information is obtained; the power utilization characteristics of the same platform area range are different in different seasons and weather, which is hydrological environment information; the temporary construction of a certain area in the area range, such as subway construction, can also change the electricity utilization characteristics of the area, which is city construction information, and the temporary construction can influence the accurate identification of electricity stealing prevention. Therefore, in addition to the electricity consumption information, the electricity metering unit should integrate information of a plurality of data sources, such as location information, hydrological and climate information, city construction information and the like, so as to eliminate interference and improve the accuracy of electricity stealing prevention.
The existing transformer area monitoring system aims to solve the problem of one-stop monitoring operation condition of power grid maintenance workers. The intelligent low-voltage transformer area system needs to monitor physical quantities such as voltage, current, active power, reactive power, three-phase unbalance rate and load rate, wherein the statistical parameters of the three-phase unbalance rate and the load rate are focused. The low-voltage intelligent platform area monitoring system needs to complete three parts of work, wherein one part is to complete data acquisition, transmission, analysis and storage of an intelligent terminal, the other part is to manage collected data, the third part is load prediction, but the limited condition reason is to mainly research a prediction algorithm in a software system, and the load prediction function is simply realized. Fig. 7 shows a topology diagram of a detection system for a station area.
The intelligent low-voltage distribution room monitoring system is established by combining the development level of current electric power hardware equipment and the development of the Internet of things level technology, and realizes real-time monitoring and remote control of the operation condition of the low-voltage distribution room through a series of processes of intelligent terminal data acquisition, data transmission, data storage, data analysis and calculation, remote pushing and the like. The intelligent monitoring system for the low-voltage transformer area is mainly designed for finishing two aspects of real-time monitoring of an intelligent terminal and collection and storage of a real-time data server, the intelligent terminal is responsible for monitoring real-time working data of the low-voltage side of a distribution transformer and uploading the real-time data to the server for storage through a communication network at regular time, and finally, management of the data is realized through WEB.
In this embodiment, an improved time-frequency analysis method, Wavelet Packet Decomposition (WPD), is to be used to extract and analyze the characteristics of the power consumption information of the distribution room. The wavelet packet analysis can provide a more refined analysis method for the signal (belongs to a mature analysis method, and is applied to the extraction and analysis of the power utilization information characteristics of the transformer area, so that the accurate reliability of the extraction of the signal is improved). Wavelet (wavelet), i.e. a small region of waves. The exact definition of the wavelet function is: let ψ (t) be a square integrable function, i.e. ψ (t) ∈ L2(R) if its fourier transform Ψ (ω) satisfies the condition:
let ψ (t) be a basic wavelet or wavelet mother function and be the admissible condition for the wavelet function.
The continuous wavelet basis function psi is introduced belowα，τ(t) definition.
The wavelet mother function psi (t) is subjected to stretching and translation, the stretching factor (also called scale factor) is set as a, the translation factor is set as tau, and the function after translation and stretching is set as psia，τ(t) then there are
Title psia，τ(t) is the wavelet basis function dependent on the parameter a, τ. Since the scale factor a and the panning factor τ are continuously changing values, they are referred to as ψa，τAnd (t) is a continuous wavelet basis function.
The concepts of scale space and wavelet space are introduced below:
let ek(t) is a sequence of functions, X represents ek(t) all possibilitiesAre formed by linear combinations of (i.e. are)
X＝{∑k akek(t)；t，ak∈R，k∈Z} (3)
Called X by the sequence ek(t) a linear space of (t) tension, denoted as
X＝span{ek} (4)
That is, for any g (t) e X, there is
g(t)＝∑k akek(t) (5)
Defining a function phi (t) epsilon L2(R) is a scale function if it is shifted by an integer of the series phik(t) satisfies phi (t-k)
<φk(t)，φk′(t)>＝δkk′ (6)
Definition of phik(t) at L2The closed subspace spanned by the (R) space is V0Referred to as zero scale space:
for any f (t) e V0Is provided with
f(t)＝∑k akφk(t) (8)
Similar to the wavelet function, assuming that the scale function phi (t) is subjected to scale expansion while being translated, a function set with variable scale and displacement is obtained:
then the translation series phi on each fixed scale j is calledk(2-jt) a space VjScale space for scale j:
for any f (t) e VjIs provided with
Thus, the translation series of the scale function phi (t) under different scales is expanded into a series of scale spaces Vj}j∈Z。
Now define a multi-resolution analysis, referred to as a series of closed subspace V satisfying the following propertiesj}，j∈Z：
1) Consistent monotonicity:
2) progressive completeness:
3) the telescoping regularity is as follows:
4) translation without deformation:
5) existence of orthogonal group: presence of phi e V0So that { phi (t-n) }n∈ZIs V0Orthogonal basis of, i.e.
As can be seen from the above analysis, the series scale space of the multiresolution analysis is formed by the same scale function under different scales, i.e. a multiresolution analysis { V }j}j∈ZCorresponding to a scaling function. Now define the scale space Vj}j∈ZThe complement space of (1). Let WmIs a VmAt Vm-1Complementary space of, i.e.
Obviously, an arbitrary subspace WmAnd WnAre mutually orthogonal (space does not want to intersect), and Wm⊥WnWhen m ≠ n and m, n ∈ Z, as given by formula (14) and formula (15):
thus, { Wj}j∈ZForm L2A series of orthogonal subspaces of (R) to obtain
W0＝V_1-V0
And is
Wj＝Vj-1-Vj (19)
If f (t) e W0Then f (t) e V-1-V0Represented by the formula (13), f (2)-jt)∈Vj-1-VjThat is to say
Let { psi0，k(ii) a k ∈ Z } is space W0By formula (18) for all scales
k ∈ Z must be the orthogonal basis of the space Wj. Whereby the entire set of ψ j, k according to equation (17); j belongs to Z, and j belongs to Z to form L2(R) a set of orthogonal bases of space. Here psij，k(t) is just the orthogonal wavelet basis resulting from the same mother function telescopic translation. Thus, psi can be referred to as a wavelet function, and W accordinglyjIs a wavelet space of dimension j.
The two-scale equation and the multi-resolution filter bank are defined as follows:
the two-dimensional equation is the most basic characteristic of multi-dimensional analysis given to a scale function phi (t) and a wavelet function phi (t), and describes two adjacent scale spaces Vj-1And VjOr adjacent scale spaces Vj-1Sum wavelet space WjOf the basis function phij-1，h(t)，φj，h(t) and phij-1，k(t)，φj，kIntrinsic physical connection between (t).
From the multi-resolution analysis concept, phi (t), psi (t) are the scale space V respectively0And wavelet space W0An orthonormal basis function of (1). Due to V0∈V-1，W0∈V-1Therefore, phi (t), psi (t) must also belong to V-1Space, i.e. phi (t), psi (t) can be used for V-1Orthogonal basis of space phij-1，h(t) linear expansion:
wherein the expansion coefficient h0(n)，h1(n) is
h0(n)＝<φ，φ-1，n>h1(n)＝<ψ，ψ-1，n> (23)
Equations (14) and (15) describe the relationship between adjacent two-dimensional spatial basis functions, so the two equations are called two-dimensional equations. Filter bank coefficient h0(n)，h1(n) describes the intrinsic relationship between the two-scale spatial functions and uniquely corresponds to phi (t), psi (t).
The definition of the wavelet packet is described below, and the wavelet packet analysis divides the time-frequency plane more finely, and has a higher resolution for the high-frequency part of the signal than the binary wavelet. Moreover, the method introduces the concept of optimal basis selection on the basis of wavelet analysis theory. After the frequency band is divided into multiple layers, the optimal base function is selected adaptively according to the characteristics of the analyzed signal, so as to match the optimal base function with the signal, thereby improving the analysis capability of the signal.
In this embodiment, the fusion analysis method of multi-modal data learns more accurate complex data characteristics by complementation of information between modalities, and supports subsequent decision, prediction, and classification. Multimodal data refers to data acquired through different fields or perspectives for the same descriptive object, and each field or perspective describing the data is called a modality. In multi-modal data, each modality can provide certain information for the rest of modalities, i.e. there is a certain correlation between modalities. The user data in the transformer district is processed by the multi-mode data fusion algorithm, and the electricity utilization characteristics of the user can be distinguished, so that a more effective way is provided for capturing the electricity stealing behavior characteristics.
The fusion analysis algorithm for multimodal data is now set forth as follows:
given a continuously collected multimodal dataset
Which contains V modal feature sets of n data instances.
Is used to represent that d is contained in the set of the v-th modal featurevN data instances of the dimensional feature vector.
In a multimodal dataset, some of the set of data features only contains [0, 1 ]]Sparsification represents an example. Based on this, a modal-like cluster structure description is constructed using the existing probability distribution. Hypothetical cluster RkContaining nkAn instance of data
We define xj(j＝1，2，...，nk) Of the sparse mode v is a meta-information feature vector
Wherein
Corresponding to the mth tag t in the data tag tablem。
The values of (A) are as follows:
thus, the cluster RkCan be represented by its neutral point as
Wherein
In cluster class R for mth tagkThe probability of occurrence in (1) is specifically defined as follows:
when newly added data instance
Is divided into cluster RkIn (m) th label in cluster RkThe probability of occurrence in (1) is updated as follows:
thus, a cluster R can be obtainedkProbability of the m-th label being present
The general expression of the learning strategy, namely:
due to the fact that
Equal to 0 or 1, the formula can be further simplified as:
in the formula
In addition, edge computing has become popular with the development of the internet and mobile industries, which describes an architecture in which network data is prefetched and cached close to user nodes, improving customer experience through ultra-low latency. In the context of the internet of things, edge computing means that most device computing tasks are performed in the field, and these tasks can be performed in a terminal node or gateway (as a bridge connecting a simple terminal node and the internet). Compared with remote computing of cloud computing, edge computing is local computing close to a physical environment or a data source, and aims to realize local real-time acquisition, instant computing, online diagnosis, timely response and accurate control. A large-scale information physical system (CPS) unit with calculation and communication functions can solve data on the edge side through edge calculation of the CPS, and is more suitable for real-time data analysis and intelligent processing. The edge calculation focuses on the analysis of real-time and short-period data, has the advantages of safety, rapidness, easiness in management and the like, can better support the real-time intelligent processing and execution of the CPS unit, meets the real-time requirement of a network, and accordingly enables the calculation resources to be more effectively utilized. In a distribution room monitoring system, a large amount of environmental information needs to be collected, and in order to improve the real-time monitoring efficiency of the system, a part of data can be subjected to feature extraction operation at a modularized collection terminal.
The invention also discloses an intelligent platform area anti-electricity-stealing monitoring system, which comprises
The first module is used for extracting and analyzing the power utilization information characteristics of users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area;
the second module is used for extracting the electricity stealing loss characteristics, combining the electric energy information of users in the distribution room and accurately positioning the electricity stealing loss characteristics to the users through comprehensive comparison of dynamic loss and metering information;
the third module is used for analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons and determining an electricity stealing behavior data model under various modes;
and the fourth module is used for acquiring and monitoring the running state information of the transformer area by utilizing the existing equipment of the transformer area based on each electricity stealing behavior data model and positioning the electricity stealing behavior.
Since the embodiment of the system part corresponds to the embodiment of the method part, the embodiment of the system part is described with reference to the embodiment of the method part, and is not described herein again.
The invention further discloses a mobile media device, comprising:
a memory for storing a computer program;
and the processor is used for realizing the steps of the intelligent platform district anti-electricity-stealing monitoring method when executing the computer program.
Since the embodiment of the mobile media device portion corresponds to the embodiment of the method portion, please refer to the description of the embodiment of the method portion for the embodiment of the mobile media device portion, which is not described herein again.
The invention also provides a computer readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the above-mentioned intelligent platform area anti-electricity-stealing monitoring method.
Since the embodiment of the medium portion and the embodiment of the method portion correspond to each other, please refer to the description of the embodiment of the method portion for the embodiment of the medium portion, which is not described herein again.
Although the present invention has been described with reference to the preferred embodiments, it is not intended to be limited thereto. Those skilled in the art can make numerous possible variations and modifications to the present invention, or modify equivalent embodiments to equivalent variations, without departing from the scope of the invention, using the teachings disclosed above. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical spirit of the present invention should fall within the protection scope of the technical scheme of the present invention, unless the technical spirit of the present invention departs from the content of the technical scheme of the present invention.
Claims (9)
1. An intelligent platform area electricity stealing prevention monitoring method is characterized by comprising the following steps:
extracting and analyzing the power utilization information characteristics of the users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area;
after the electricity stealing loss characteristics are extracted, the electricity stealing loss characteristics are accurately positioned to the users by comprehensively comparing the dynamic loss with the metering information in combination with the electric energy information of the users in the transformer area;
analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons, and determining an electricity stealing behavior data model under various modes;
based on each electricity stealing behavior data model, acquiring and monitoring the running state information of the transformer area by using the existing terminal of the transformer area, and positioning the electricity stealing behavior;
specifically, firstly, analyzing an intelligent monitoring and processing method of electricity stealing information of a high-loss transformer area based on multi-mode information fusion, and establishing a transformer area grading monitoring system; on the basis, the intelligent electricity-stealing-prevention monitoring device which is provided with edge calculation and hardware modularization on a physical architecture and has the functions of monitoring the operation of a transformer area, acquiring information of the transformer area and adaptively intelligently processing the information specifically comprises the following contents:
(1) the multimode electricity stealing information intelligent monitoring and processing method comprises the steps that multimode electricity stealing information in a high-loss transformer area is intelligently monitored and processed, and a hierarchical monitoring system is established;
analyzing the loss characteristics of the high-loss transformer area, researching a multi-mode information fusion method of the power utilization information of the transformer area by combining the environmental information and non-electrical information of the transformer area, and establishing a transformer area loss characteristic information base; in addition, electricity stealing identification and accurate positioning based on the time-frequency characteristics of the electricity parameters of the transformer area are realized through an electricity stealing characteristic extraction and positioning algorithm;
a) a power utilization characteristic extraction algorithm of the distribution room based on time-frequency analysis;
when electricity stealing happens, the line impedance obtained by reverse calculation of the actual measurement value changes and deviates from the real operation parameters of the line; the dynamic change of the line impedance can be quickly calculated and analyzed by a high-resolution time-frequency extraction method, so that a basis is provided for identifying the electricity stealing loss characteristics;
b) an electricity stealing behavior positioning algorithm based on the power utilization characteristics of the transformer area;
in the monitoring process of the transformer area, after the electricity stealing loss characteristics are successfully extracted, the electricity stealing behavior characteristics are accurately positioned to the user by screening the electricity utilization characteristics of the user through comprehensive comparison of dynamic loss and metering information by combining the electric energy information of the user in the transformer area; based on the comprehensive comparison of the loss dynamic change characteristics and the electric quantity data, a high-accuracy loss dynamic real-time change characteristic model is established by adding a high-accuracy time tag in the loss dynamic change process, a metering information and loss dynamic real-time change fusion model based on time matching is established, and an electricity stealing behavior positioning method based on the comprehensive comparison of the loss dynamic real-time change and the metering information is established;
c) a classification method for monitoring the anti-electricity-stealing performance of the transformer area;
analyzing the occurrence reasons of various high-loss transformer areas, summarizing and summarizing the occurrence reasons, and determining a typical data model of electricity stealing information under various modes; the method comprises the steps of researching, searching and processing the condition that a data source is incorrect due to incorrect user transformation relation or overlarge clock error, respectively establishing corresponding self-adaptive data model analysis aiming at a public transformation user, a single-phase table and a three-phase table, and establishing a hierarchical monitoring system depending on data source analysis, electricity stealing behavior analysis and wiring fault analysis;
(2) the intelligent anti-electricity-stealing monitoring device has the advantages that the edge calculation and hardware modularization on a physical framework are realized, and the functions of operation monitoring, transformer area information acquisition and information self-adaptive intelligent processing are realized;
analyzing the running state monitoring mode of the intelligent anti-electricity-stealing monitoring device on the transformer area, continuously accumulating the collected transformer area information, carrying out curve analysis with the embedded data model, carrying out key monitoring or subsequent data processing by a self-adaptive correlation algorithm, and positioning the electricity-stealing behavior.
2. The method of claim 1, wherein the electricity consumption information characteristics comprise line impedance, obtained by back-calculating actual measurement values comprising voltage, active power and reactive power; the non-electrical information includes one or more of humidity, temperature, or season.
3. The intelligent transformer district anti-electricity-stealing monitoring method according to claim 1 or 2, characterized in that respective electricity-stealing behavior data models are established based on the difference of electricity-stealing behaviors of a transformer district examination and check meter, a three-phase meter and a single-phase meter, and a data model for identifying the data model, the electricity-stealing behavior data model of the examination and check meter, the electricity-stealing behavior data model of the three-phase meter, the electricity-stealing behavior data model of the single-phase meter and the wiring mode data model of all types of the three-phase meter are respectively provided for the user-variable relationship.
4. The method for monitoring the intelligent anti-electricity-stealing behavior of the transformer district according to claim 1 or 2, wherein the operation state information of the transformer district includes one or more of a live wire current, a zero wire current, a meter cover opening event record, a constant magnetic field event record, a phase failure event record, a voltage loss event record, a current loss event record, a historical electric quantity of the electric energy meter, a frozen electric quantity, a load record of the electric energy meter, a current, a power and a power of the data electric energy meter or a current curve of an external terminal.
5. The method for monitoring the intelligent anti-electricity-stealing behavior of the transformer district as claimed in claim 1 or 2, characterized in that the extraction and analysis of the characteristics of the electricity information of the transformer district users are performed by a wavelet packet decomposition method in time-frequency analysis.
6. The method of claim 5, wherein the power information characteristics comprise one or more of voltage, current, active power, reactive power, or power factor.
7. An intelligent platform district anti-electricity-stealing monitoring system is characterized by comprising
The first module is used for extracting and analyzing the power utilization information characteristics of users in the transformer area, and combining multi-mode information fusion of non-electrical information in the transformer area to realize extraction of the electricity stealing loss characteristics and form a monitoring record of electricity stealing behavior in the transformer area;
the second module is used for extracting the electricity stealing loss characteristics, combining the electric energy information of users in the distribution room and accurately positioning the electricity stealing loss characteristics to the users through comprehensive comparison of dynamic loss and metering information;
the third module is used for analyzing the occurrence reasons of the electricity stealing behaviors of various transformer substations, summarizing and summarizing the occurrence reasons and determining an electricity stealing behavior data model under various modes;
the fourth module is used for collecting and monitoring the operation state information of the transformer area by utilizing the existing terminal of the transformer area based on each electricity stealing behavior data model and positioning the electricity stealing behavior;
specifically, firstly, analyzing an intelligent monitoring and processing method of electricity stealing information of a high-loss transformer area based on multi-mode information fusion, and establishing a transformer area grading monitoring system; on the basis, the intelligent electricity-stealing-prevention monitoring device which is provided with edge calculation and hardware modularization on a physical architecture and has the functions of monitoring the operation of a transformer area, acquiring information of the transformer area and adaptively intelligently processing the information specifically comprises the following contents:
(1) the multimode electricity stealing information intelligent monitoring and processing method comprises the steps that multimode electricity stealing information in a high-loss transformer area is intelligently monitored and processed, and a hierarchical monitoring system is established;
analyzing the loss characteristics of the high-loss transformer area, researching a multi-mode information fusion method of the power utilization information of the transformer area by combining the environmental information and non-electrical information of the transformer area, and establishing a transformer area loss characteristic information base; in addition, electricity stealing identification and accurate positioning based on the time-frequency characteristics of the electricity parameters of the transformer area are realized through an electricity stealing characteristic extraction and positioning algorithm;
a) a power utilization characteristic extraction algorithm of the distribution room based on time-frequency analysis;
when electricity stealing happens, the line impedance obtained by reverse calculation of the actual measurement value changes and deviates from the real operation parameters of the line; the dynamic change of the line impedance can be quickly calculated and analyzed by a high-resolution time-frequency extraction method, so that a basis is provided for identifying the electricity stealing loss characteristics;
b) an electricity stealing behavior positioning algorithm based on the power utilization characteristics of the transformer area;
in the monitoring process of the transformer area, after the electricity stealing loss characteristics are successfully extracted, the electricity stealing behavior characteristics are accurately positioned to the user by screening the electricity utilization characteristics of the user through comprehensive comparison of dynamic loss and metering information by combining the electric energy information of the user in the transformer area; based on the comprehensive comparison of the loss dynamic change characteristics and the electric quantity data, a high-accuracy loss dynamic real-time change characteristic model is established by adding a high-accuracy time tag in the loss dynamic change process, a metering information and loss dynamic real-time change fusion model based on time matching is established, and an electricity stealing behavior positioning method based on the comprehensive comparison of the loss dynamic real-time change and the metering information is established;
c) a classification method for monitoring the anti-electricity-stealing performance of the transformer area;
analyzing the occurrence reasons of various high-loss transformer areas, summarizing and summarizing the occurrence reasons, and determining a typical data model of electricity stealing information under various modes; the method comprises the steps of researching, searching and processing the condition that a data source is incorrect due to incorrect user transformation relation or overlarge clock error, respectively establishing corresponding self-adaptive data model analysis aiming at a public transformation user, a single-phase table and a three-phase table, and establishing a hierarchical monitoring system depending on data source analysis, electricity stealing behavior analysis and wiring fault analysis;
(2) the intelligent anti-electricity-stealing monitoring device has the advantages that the edge calculation and hardware modularization on a physical framework are realized, and the functions of operation monitoring, transformer area information acquisition and information self-adaptive intelligent processing are realized;
analyzing the running state monitoring mode of the intelligent anti-electricity-stealing monitoring device on the transformer area, continuously accumulating the collected transformer area information, carrying out curve analysis with the embedded data model, carrying out key monitoring or subsequent data processing by a self-adaptive correlation algorithm, and positioning the electricity-stealing behavior.
8. A mobile media device, comprising:
a memory for storing a computer program;
a processor for implementing the steps of the intelligent platform area anti-electricity-stealing monitoring method of any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, implements the steps of the intelligent platform electricity larceny monitoring method according to any one of claims 1 to 6.
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| CN109615004A (en) * | 2018-12-07 | 2019-04-12 | 江苏瑞中数据股份有限公司 | A kind of anti-electricity-theft method for early warning of multisource data fusion |
| CN109633321B (en) * | 2018-12-24 | 2021-09-07 | 国网山东省电力公司潍坊供电公司 | Transformer area household variable relation distinguishing system and method and transformer area high loss monitoring method |
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