CN114184996A - Method, device and equipment for identifying metering abnormal behaviors of low-voltage transformer area intelligent electric meter and storage medium - Google Patents
Method, device and equipment for identifying metering abnormal behaviors of low-voltage transformer area intelligent electric meter and storage medium Download PDFInfo
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Abstract
The invention discloses a method, a device, equipment and a storage medium for identifying metering abnormal behaviors of a low-voltage transformer area intelligent electric meter. The method for identifying the metering abnormal behaviors of the intelligent electric meter in the low-voltage transformer area comprises the following steps: respectively obtaining the total quantity value of a general meter arranged on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter arranged on each user side; constructing an identification model based on the total quantity value and each partial quantity value; solving the identification model to obtain the operation error value of each sub-table; a sub-table of metering anomalies is determined based on the running error value. Only need gather the measurement value of integrating the table and branch table at the identification process, data processing work load is less relatively, and is relatively lower to the demand of calculation capacity, and solution efficiency and rate of accuracy are high, can distinguish the user that has the unusual action of measurement in the low pressure platform district more accurately fast, help measurement operation and maintenance personnel to accomplish measurement troubleshooting and handle and develop anti-electricity-stealing analysis.
Description
Technical Field
The embodiment of the invention relates to a power grid operation safety technology, in particular to a method, a device, equipment and a storage medium for identifying metering abnormal behaviors of a smart electric meter in a low-voltage transformer area.
Background
The illegal electricity stealing of the user and the faults of the metering device are main reasons causing the error of the electric energy meter to exceed the standard, and the analysis of the running error of the electric energy meter can help operation and maintenance personnel to identify the abnormal metering behavior so as to ensure the stable running of the power grid.
With the comprehensive popularization of the intelligent electric meter and the electricity utilization information acquisition system, the remote acquisition of mass data of the user electric meter becomes possible, the data are more comprehensively and intelligently mined and analyzed by relying on a big data processing technology, a suspected metering abnormal user is accurately identified, and reliable technical support is provided for accurate and efficient metering troubleshooting and processing, anti-electricity-stealing analysis and investigation work of first-line electricity utilization inspection and anti-electricity-stealing personnel.
However, the existing electric energy meter operation error calculation model requires more information, and generally requires at least power consumption data, topological structure parameters, line loss data and the like. The electric energy meter operation error calculation model needs to acquire and process a large amount of data in the operation process, and the calculation power requirement is relatively high.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for identifying metering abnormal behaviors of a smart electric meter in a low-voltage distribution area, and aims to realize identification of the metering abnormal behaviors of the smart electric meter in the low-voltage distribution area.
In a first aspect, an embodiment of the present invention provides a method for identifying metering abnormal behaviors of a smart meter in a low-voltage distribution area, including:
respectively obtaining the total quantity value of a general meter arranged on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter arranged on each user side;
constructing an identification model based on the total quantity value and each of the partial quantity values;
solving the identification model to obtain the operation error value of each sub-table;
determining the sub-table of metering anomalies based on the running error value.
Optionally, the obtaining the total quantity value of the summary table installed on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of the sub-table installed on each user side respectively includes:
acquiring the power consumption of each sub-table in the low-voltage station area within a preset time interval, wherein the power consumption is used as a sub-metering value;
and acquiring the power consumption of the summary table in the low-voltage station area in the time length at intervals of the time length as a total amount value.
Optionally, the constructing a recognition model based on the total measurement value and each of the fractional measurement values includes:
an optimization model that minimizes a difference between the total quantity value and a sum of the respective partial quantity values is constructed as an identification model based on the total quantity value and the respective partial quantity values.
Optionally, the identification model is represented as follows:
min f(x)
wherein min f (x) is a fitness evaluation function with minimal difference between the sum of the total quantity value and each of the partial quantity values, and n is a dimension of a variable representing the number of the partial tables of the low-pressure station area; m is the current number of time segments; and x (i, j) is the value of the ith dimension variable and represents the operation error value of the ith sub-table of the low-voltage station area in the jth time period.
Optionally, the following formula is adopted as the fitness evaluation function:
wherein n is the total number of the sub-tables in the low-voltage table area; x (i, j) represents the operation error value of the ith sub-table in the jth time period of the low-voltage station area; s (i, j) represents the component quantity value of the ith sub-table in the jth time period of the low-voltage station area; g (j) represents the total quantity value of the total table of the low-pressure station area in the j time period; l (j) represents the bus power loss of the low-voltage station area in the j time period.
Optionally, the solving the identification model to obtain the operation error value of each sub-table includes:
initializing a particle swarm algorithm, and generating a speed value and a position value corresponding to the number of the sub-tables of the low-voltage distribution area, wherein the position value is a predicted operation error value of each sub-table;
carrying out chaotic processing on the particle populations in the particle swarm algorithm by utilizing chaotic mapping;
updating the inertia weight factor of the particle swarm optimization by using a self-adaptive algorithm;
and solving the improved particle swarm algorithm to obtain an optimal value corresponding to the global optimal solution which meets the set optimal solution condition or reaches the maximum iteration times, and taking the optimal value as an operation error value of each sub-table.
Optionally, the determining the sub-table of the metering anomaly based on the running error value includes:
determining the running error value larger than a preset loss threshold value as a target object;
and determining the sub-table corresponding to the target object as the sub-table with abnormal metering.
In a second aspect, an embodiment of the present invention further provides a device for identifying a metering abnormal behavior of a smart meter in a low voltage distribution area, where the device includes:
the acquisition module is used for respectively acquiring the total quantity value of a master meter installed on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter installed on each user side;
a construction module for constructing an identification model based on the total amount value and each of the fractional amount values;
the solving module is used for solving the identification model to obtain the operation error value of each sub-table;
a determination module to determine the sub-table of metering anomalies based on the running error value.
In a third aspect, an embodiment of the present invention further provides a device for identifying metering abnormal behavior of a smart meter in a low-voltage distribution area, where the device includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the method for identifying metering abnormal behavior of the low-voltage station area smart meter according to the first aspect.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform the method for identifying metering abnormal behavior of a low-voltage station area smart meter according to the first aspect.
According to the technical scheme of the embodiment, the identification model is established by obtaining the total quantity value of the general meter arranged on the outgoing line side of the transformer in the low-voltage station area and the sub-metering value of the sub-meter arranged on each user side, the operation error value of each sub-meter is obtained by solving through the identification model, the sub-meters with the metering abnormality possibly are judged, only the metering values of the general meter and the sub-meters need to be collected in the identification process, the data processing workload is relatively small, the demand on the calculation capacity is relatively low, the solving efficiency and the accuracy are high, the users with the metering abnormality in the low-voltage station area can be accurately and quickly identified, and the metering operation and maintenance personnel are helped to complete the metering fault troubleshooting and the processing and develop the anti-electricity-stealing analysis.
Drawings
Fig. 1 is a flowchart of a method for identifying metering abnormal behavior of a smart meter in a low-voltage distribution area according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a metering abnormal behavior identification device of a low-voltage distribution area smart meter according to a second embodiment of the present invention;
fig. 3 is a schematic structural diagram of a metering abnormal behavior identification device of a low-voltage distribution area smart meter according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a flowchart of a method for identifying abnormal metering behavior of a low-voltage distribution area smart meter according to an embodiment of the present invention, where the method is applicable to a situation where abnormal metering behavior of a low-voltage distribution area smart meter is identified, the method may be executed by a device for identifying abnormal metering behavior of a low-voltage distribution area smart meter, the device for identifying abnormal metering behavior of a low-voltage distribution area smart meter may be implemented by software and/or hardware, and may be configured in a computer device, such as a server, a workstation, a personal computer, and the like, and the method specifically includes the following steps:
and 110, respectively obtaining the total quantity value of a master meter installed on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter installed on each user side.
In an electric power system, a transformer area refers to a power supply range or area of a transformer, that is, users connected to the same transformer belong to the same transformer area. The intelligent electric meter is one of basic devices for data acquisition of an intelligent power grid (particularly an intelligent power distribution network), bears the tasks of original electric energy data acquisition, metering and transmission, and is the basis for realizing information integration, analysis optimization and information display. The intelligent electric meter has the functions of metering basic electricity consumption of the traditional electric meter, and also has intelligent functions of bidirectional multi-rate metering, user side control, bidirectional data communication of various data transmission modes, electricity larceny prevention and the like in order to adapt to the use of an intelligent power grid and new energy.
In the embodiment of the invention, the abnormal metering condition of the intelligent electric meter in the low-voltage transformer area is mainly aimed at. And a transformer is arranged in the low-voltage platform area to step down and output the high-voltage power of the power grid so as to meet the household power demand of users. The outlet side of the transformer is usually provided with a general meter (intelligent electric meter) with higher metering precision grade, and the user side is provided with a branch meter (intelligent electric meter) with lower metering precision grade than the general meter before entering the house. The metering accuracy grade of the general meter is higher than that of the sub-meters, so that when the power consumption is metered by the power grid, the general meter is set as a standard meter, no operation error exists in the general meter, and the metering value of the general meter is the real power consumption of the whole low-voltage distribution room.
In a specific implementation, the total quantity value of the total table and the sub-quantity value of each sub-table need to be obtained. And, the total quantity value and the partial quantity value can be collected for a plurality of times at preset time intervals, and the total quantity value and the partial quantity value of the low-voltage station area in a period of time are collected and stored. The total amount value and the sub-amount value represent the amount of the power consumption measured by the total table and the sub-table respectively, and the power consumption can be a measured value in a single time period or the total amount value of the total table and the sub-table in the operation process. The obtained total and fractional quantities can be stored directly in the form of a data stream or in a specific format. For example, increments of the aggregate magnitude are sorted by acquisition time and stored as a set, and sub-meters are sorted by user and acquisition time and stored as one or more sets. Or the partial metering values of a single user are sorted according to time to form a set, or the partial metering values of a plurality of users in the same time period are used as a set, a plurality of data sets are generated according to the time sequence, or the electricity utilization data in a period of time is used as a set, and the like.
And 120, constructing an identification model based on the total quantity value and each partial quantity value.
In the embodiment of the invention, according to the law of conservation of energy, in any metering time period, the total quantity value (the total amount of power consumption in the corresponding time period) of the total table of the low-voltage station area is equal to the sum of the actual power consumption metered by each sub-table of each user in the station area plus the total loss of the line of the low-voltage station area in the time period. That is, the difference between the total magnitude and the sum of all sub-magnitudes should be equal to the total loss of the low-voltage distribution line during that time period, without electricity theft or meter failure. When electricity stealing or metering device faults occur, the total sum of the sub-metering values is reduced compared with the actual electricity consumption, so that the total loss obtained by calculation is increased, but in the actual operation process, the total loss of the low-voltage distribution area line is maintained in a certain range. Therefore, in the embodiment of the present invention, whether electricity stealing or metering failure occurs is judged based on the total quantity value and each of the sub-metering values, and an operation error of the actual electricity consumption from each of the sub-metering values is predicted.
In a specific implementation, the total measurement value, each partial measurement value, and the operation error value corresponding to each partial table may be constructed based on the above principle, and a reasonable fitness function, that is, an evaluation function, may be set to completely construct the identification model.
And step 130, solving the identification model to obtain the operation error value of each sub-table.
In the foregoing step, an identification model is constructed for the relationship between the total measurement value, each partial measurement value, and the operation error value corresponding to each partial table. In this step, the total quantity value and each partial quantity collected in step 110 may be used to perform a solution to obtain an operation error value corresponding to each partial table. Optionally, the particle swarm algorithm may be adopted to convert the identification model into an optimized model, and an optimal solution that minimizes the difference between the total power supply amount and the actual total power consumption amount of the distribution area in the current time period is found, that is, an operation error value that can truly reflect the sub-lists of all users in the low-voltage distribution area in the time period.
Step 140, determining a sub-table of metering anomalies based on the running error value.
And calculating to obtain the operation error value of each sub-meter in the steps, and when no electricity stealing or metering fault occurs, determining which users do not meter normally respectively by the operation error value within a normal range, and further arranging the staff to determine whether the metering fault or the electricity stealing occurs.
According to the technical scheme, the identification model is established by obtaining the total quantity value of the general meter installed on the outgoing line side of the transformer in the low-voltage station area and the sub-metering value of the sub-meter installed on each user side, then the identification model is used for solving to obtain the running error value of each sub-meter, so that the sub-meter with the possibility of abnormal metering is judged, only the metering values of the general meter and the sub-meters need to be adopted in the identification process, the data processing workload is relatively small, the demand on the calculation capacity is relatively low, the solving efficiency and the accuracy are high, users with abnormal metering behaviors in the low-voltage station area can be accurately and quickly identified, and the metering operation and maintenance personnel are helped to complete metering fault troubleshooting and processing and develop anti-electricity-stealing analysis.
In an embodiment of the present invention, step 110 may include:
step 111, acquiring the power consumption of each sub-table in the low-voltage area within a time span at intervals of a preset time span, and taking the power consumption as a sub-metering value;
and step 112, acquiring the power consumption of the summary table in the low-voltage station area in the time length as a total quantity value.
In a specific implementation, the total quantity value of the summary table and the sub-quantity value of the sub-table may be obtained by multiple acquisition at preset time intervals, that is, the total quantity value and the sub-quantity value used in each calculation are divided by the preset time length, so as to reduce the data volume in a single calculation process. And the total quantity value and the component measurement value are cut into a plurality of parts according to the time length, so that the power utilization condition of each time period of a user in a low-voltage distribution area can be calculated in a detailed mode, and the identification effect of electricity stealing or measurement faults only occurring in a single time period is improved.
The preset time length may be divided in units of hours or days, that is, the total quantity value and the partial quantity value may be collected at intervals of 1, 2, 3, … … or 24 hours, or the total quantity value and the partial quantity value may be collected at intervals of 1, 2, 3, … … or 30 days, and the specific time interval may be adjusted according to actual conditions. For example, when the intelligent electric meter runs at ordinary times, the time length is set to be 30 days, the total quantity value and the sub-metering value are collected once every 30 days, and the electricity stealing or metering fault judgment is carried out on the total quantity value and the sub-metering value within 30 days by adopting the metering abnormal behavior identification method of the intelligent electric meter in the low-voltage distribution area provided by the embodiment of the invention; when the existence of electricity stealing or metering faults is judged, the time length is set to be 2 hours, the total quantity value and the sub-metering value are collected once, the specific time period of the electricity stealing or metering faults is judged in a subdivision mode, and further workers are assisted to efficiently check electricity stealing behaviors or metering faults, potential safety hazards of power grid operation are reduced, the line loss rate of the power grid is reduced, and the safe operation and the power supply service quality of the power grid are guaranteed.
Step 120 may include:
an optimization model that minimizes a difference between the total quantity value and the sum of the individual partial quantity values is constructed as an identification model based on the total quantity value and the individual partial quantity values.
In the embodiment of the invention, the outgoing line side of the transformer of the power grid system is usually provided with the master meter, and the branch meters are arranged before the user side enters the house. The total quantity value of the summary table is the real electricity consumption of the whole low-voltage distribution area. According to the law of conservation of energy, in any metering time period, the total quantity value of the general table of the low-voltage station area is equal to the sum of the actual electricity consumption metered by the sub-tables of each user in the station area and the total loss of the line of the low-voltage station area in the time period. That is, the difference between the total magnitude and the sum of all sub-magnitudes should be equal to the total loss of the low-voltage distribution line during that time period, without electricity theft or meter failure.
When electricity stealing or metering device faults occur, the total sum of the sub-metering values is reduced compared with the actual electricity consumption, so that the total loss obtained by calculation is increased, but in the actual operation process, the total loss of the low-voltage distribution area line is maintained in a certain range. Therefore, in the embodiment of the present invention, whether electricity stealing or metering failure occurs is judged based on the total quantity value and each of the sub-metering values, and an operation error of the actual electricity consumption from each of the sub-metering values is predicted.
In specific implementation, an optimization model is constructed based on the relation of the total quantity value, the partial quantity value and the operation error, and a fitness evaluation function is set. The optimization model (optimization model) applies a model which represents an optimal scheme and is determined by linear programming, nonlinear programming, dynamic programming, integer programming and a system science method in economic management work. It can reflect the problem of extreme condition in economic activities, i.e. how to utilize various resources most effectively under a given target, or how to achieve the best effect under the condition of limited resources. In the present embodiment, an optimization model is obtained that minimizes the difference between the total quantity value and the sum of the individual partial quantity values.
In a particular implementation, the recognition model may be represented as follows:
min f(x)
wherein min f (x) is a fitness evaluation function with the minimum difference between the sum of the total quantity value and each partial quantity value, and n is the dimension of a variable and represents the number of the partial tables of the low-voltage station area; m is the current number of time segments; and x (i, j) is the value of the ith dimension variable and represents the operation error value of the ith sub-table of the low-voltage station area in the jth time period.
Further, the following formula is adopted as the fitness evaluation function:
wherein n is the total number of the sub-tables in the low-voltage table area; x (i, j) represents the operation error value of the ith sub-table in the jth time period of the low-voltage station area; s (i, j) represents the component quantity value of the ith sub-table in the jth time period of the low-voltage station area; g (j) represents the total quantity value of the total table of the low-voltage table area in the j time period; l (j) represents the bus power loss of the low-voltage station area in the j time period.
Step 130 may include:
step 131, initializing a particle swarm algorithm, and generating a speed value and a position value corresponding to the number of the sub-tables of the low-voltage station area, wherein the position value is a predicted operation error value of each sub-table.
In the embodiment of the present invention, an improved particle swarm algorithm may be adopted to solve the identification model constructed in the foregoing steps.
The Particle Swarm Optimization (PSO) is a random search algorithm based on Swarm cooperation developed by simulating the foraging behavior of a bird Swarm. The main principle is that the randomly distributed particles determine the moving direction and distance of the particles through different speeds, search in a solution space continuously following the current optimal particles, compare the current position state with the optimal position, move the position if the particles are still at the position of disadvantage, and continue searching until all the particles are at the position close to the optimal solution, namely the optimal position.
For the objective function min f (X), the objective variable is X ═ (X1, X2, … xd)TThe basic implementation process of the PSO algorithm is described as follows:
step 1: population initialization, i.e., the diffusion of particles in a random fashion in a D-dimensional space distributes an initial set of solutions. The method specifically comprises the following steps: the number of population individuals (i.e. the number of particles) N is initialized, the spatial dimension (i.e. the number of independent variables) D of the particle search, the velocity range [ vmin, vmax ], the displacement range [ xmin, xmax ], the search precision ∈ or the maximum number of iterations Tmax.
Step 2: velocity v of randomly initialized particlesiAnd position xiAnd according to the quality of the fitness value, the current self-optimal position P is foundbestAnd global optimum position Gbest。
Step3 the velocity and position of the particle are updated as shown in the following equation:
in the formula: omega is an inertia weight factor, reflects the influence of individual particle historical performance on the current situation, and is generally 0.5-1; c1 and c2 are learning factors, and are generally 0-4; r is1,r2∈[0,1]Is a uniformly distributed random number;
respectively representing the speed of the particle i at the t-th iteration moment and the t-1 iteration moment;
respectively representing the position of the particle i at the time of the t-th and t-1-th iteration, PbestAnd GbestRespectively representing the optimal position and the global optimal position of the current particle.
Step 4: and (4) according to the set speed range and displacement range, carrying out constraint condition judgment on the updated particle speed and position, and directly taking boundary values of boundary crossing if the boundary crossing.
Step 5: and sequencing the fitness values of all the particles to find out the current optimal solution and optimal value.
Step 6: and repeating the steps 2-5 until the set optimal solution condition is met or the maximum iteration number is reached.
Step 7: and outputting the global optimal value and the optimal solution.
And 132, performing chaotic processing on the particle populations in the particle swarm algorithm by using chaotic mapping.
In the PSO algorithm, the defects of easy falling into local optimization and low optimization precision exist, so in order to increase the population diversity in the initialization and iteration later stages of the PSO algorithm, a chaotic mapping principle is introduced in the particle initialization and updating process, the ergodicity of the algorithm is increased, the uniformity degree of the particles is improved, and the algorithm is prevented from falling into the local optimization.
Chaotic mapping is used to generate chaotic sequences, characterized by ergodicity and randomness. In the field of optimization, chaotic maps can be used instead of pseudo random number generators, generating chaotic numbers between 0 and 1. Research shows that the whole process of the algorithm is influenced by performing population initialization, selection, crossing, variation and other operations by using the chaotic sequence, and the effect better than that of a pseudo-random number can be obtained frequently. Therefore, in order to increase the population diversity in the later stage of PSO algorithm initialization and iteration, a chaotic mapping principle is introduced in the particle initialization and updating process, the ergodicity of the algorithm is increased, and the uniformity of the particles is improved, so that the algorithm is prevented from falling into local optimization. A typical Cubic mapping equation is introduced in the updating process:
wherein x isn+1，xnThe state values of the particles at the time and the last time; rho is a control parameter when rho epsilon (3.3, 4)]At this point, the Cubic map is in a completely chaotic state.
And step 133, updating the inertia weight factor of the particle swarm algorithm by using a self-adaptive algorithm.
The adaptive theory refers to a process of automatically adjusting a processing method, a processing sequence, processing parameters, boundary conditions or constraint conditions according to data characteristics of processing data in the processing and analyzing processes so as to adapt to statistical distribution characteristics and structural characteristics of the processing data to obtain the optimal processing effect. A new inertia weight function is constructed by using a self-adaptive theory, a speed updating formula is improved, and dynamic adjustment can be performed according to fitness evaluation, so that the convergence accuracy and the convergence speed are balanced while the global search capability is improved.
After the inertia weight factor is adaptively improved, the following steps are performed:
wherein the content of the first and second substances,
the inertia weight factor corresponding to the t-th iteration moment of the particle i is obtained; omegamaxAnd ωminMaximum and minimum inertial weight factors, respectively; t and TmaxThe t-th iteration and the maximum number of iterations.
The moving speed of the particle population is adjusted by introducing a self-adaptive inertia weight factor, so that the algorithm focuses on global search in the early stage and local search in the later stage, and the performance requirement in the whole optimization process is better met.
And 134, solving the improved particle swarm algorithm to obtain an optimal value corresponding to the global optimal solution meeting the set optimal solution condition or reaching the maximum iteration number, and taking the optimal value as an operation error value of each sub-table.
After the particle swarm optimization is improved, the actual solving process is as follows:
step 1: population initialization, i.e., the diffusion of particles in a random fashion in a D-dimensional space distributes an initial set of solutions.
The method specifically comprises the following steps: setting the number of initialized population individuals (namely the number of particles) N, the spatial dimension (namely the number of independent variables) D of particle search, a speed range [ vmin, vmax ], a displacement range [ xmin, xmax ], a search precision epsilon or a maximum iteration time Tmax.
Step 2: velocity v of randomly initialized particlesiAnd position xiAnd carrying out chaotic treatment on the initialized particle population according to a Cubic mapping equation.
Step3: calculating the fitness value of the chaotically processed particles, and searching the current self optimal position P according to the advantages and disadvantagesbestAnd global optimum position Gbest。
Step 4: and updating the self-adaptive inertia weight factor by a self-adaptive formula according to the inertia weight factor, wherein the inertia weight factor is increased in the early stage and focuses on global search, and the inertia weight factor is decreased in the later stage and focuses on local search. The velocity and position of the particles are updated.
Step 5: and (4) according to the set speed range and displacement range, carrying out constraint condition judgment on the updated particle speed and position, and directly taking boundary values of boundary crossing if the boundary crossing.
Step 6: and judging whether the set optimal solution condition is met or the maximum iteration number is reached, if so, exiting the program and outputting a global optimal solution and an optimal value, otherwise, turning to Step 3.
In a specific example, for example, there are 100 low-voltage users under a certain low-voltage platform area, there are 100 sub-tables correspondingly, each sub-table has its own operation error, and then the number of the algorithm arguments is 100. Taking the topological structure of the low-voltage distribution network area as an example, a PC with a processor of 2.10GHz and an internal memory of 8GB is adopted to perform simulation in the simulation environment of MatlabR 2010B. In the step of population initialization, the adopted parameter values are as follows: the number of population individuals (i.e., the number of particles) N is initialized to 200, the spatial dimension of the particle search (i.e., the number of arguments) D is 100, and the velocity range [ vmin，vmax]＝[-2,2]Range of displacement [ x ]min，xmax]＝[-0.99,0.99]Range of inertial weight factor [ omega ]min，ωmax]＝[0.5,1]Maximum number of iterations Tmax＝50。
Step 140 may include:
step 141, determining a running error value greater than a preset loss threshold as a target object.
In the foregoing step, the operation error values of the respective sub-tables are predicted by using the identification model, and in this step, the sub-table in which the operation error value is greater than the preset loss threshold is determined as the target object based on the prediction result.
For example, for an electric energy meter with an accuracy rating of 1.0 used in a low-voltage power station area, its normal operation error value should be within ± 1%, assuming that the 1 st to 99 th values in the optimal solution are all within 0.01, and the 100 th value is-0.7628, that is, the operation error value of the 100 th electric energy meter arranged in the meter reading sequence in the low-voltage power station area is-76.28%, which exceeds the normal value, there is a metering abnormal behavior, which may be an artificial damage to the lead seal of the electric energy meter to steal electricity, or a metering failure occurs. At the moment, the user number and the asset number of the electric meter corresponding to the electric energy meter can be output, and metering personnel can conveniently check the site according to the metering abnormal list.
And 142, determining the sub-table corresponding to the target object as the sub-table with abnormal metering.
Example two
Fig. 2 is a structural diagram of a metering abnormal behavior identification device of a smart meter in a low-voltage distribution area according to a second embodiment of the present invention. The device includes: an acquisition module 21, a construction module 22, a solving module 23 and a determination module 24. Wherein:
the acquisition module 21 is configured to acquire a total quantity value of a summary table installed on an outgoing line side of a transformer in the low-voltage transformer area and a sub-quantity value of a sub-table installed on each user side;
a construction module 22 for constructing an identification model based on the total measurement values and the respective partial measurement values;
the solving module 23 is used for solving the identification model to obtain the operation error value of each sub-table;
a determination module 24 determines a sub-table of metering anomalies based on the running error value.
The acquisition module 21 includes:
acquiring the power consumption of each sub-table in the low-voltage station area within a time span at intervals of a preset time span, and taking the power consumption as a sub-metering value;
the interval time length obtains the power consumption of the summary table in the low-voltage station area in the time length as a total amount value.
The building block 22 comprises:
and a construction unit for constructing an optimized model that minimizes a difference between the total quantity value and the sum of the respective partial quantity values as an identification model based on the total quantity value and the respective partial quantity values.
The recognition model is represented as follows:
min f(x)
wherein min f (x) is a fitness evaluation function with the minimum difference between the sum of the total quantity value and each partial quantity value, and n is the dimension of a variable and represents the number of the partial tables of the low-voltage station area; m is the current number of time segments; and x (i, j) is the value of the ith dimension variable and represents the operation error value of the ith sub-table of the low-voltage station area in the jth time period.
The following formula is adopted as the fitness evaluation function:
wherein n is the total number of the sub-tables in the low-voltage table area; x (i, j) represents the operation error value of the ith sub-table in the jth time period of the low-voltage station area; s (i, j) represents the component quantity value of the ith sub-table in the jth time period of the low-voltage station area; g (j) represents the total quantity value of the total table of the low-voltage table area in the j time period; l (j) represents the bus power loss of the low-voltage station area in the j time period.
The solving module 23 includes:
the initialization unit is used for initializing a particle swarm algorithm and generating speed values and position values corresponding to the number of the sub-tables of the low-voltage station area, wherein the position values are predicted operation error values of each sub-table;
the chaotic unit is used for performing chaotic processing on the particle population in the particle swarm algorithm by utilizing chaotic mapping;
the self-adaptive unit is used for updating the inertia weight factors of the particle swarm algorithm by utilizing the self-adaptive algorithm;
and the solving unit is used for solving the improved particle swarm algorithm to obtain an optimal value corresponding to the global optimal solution meeting the set optimal solution condition or reaching the maximum iteration times, and the optimal value is used as an operation error value of each sub-table.
The determination module 24 includes:
the object determination unit is used for determining a running error value larger than a preset loss threshold value as a target object;
and the respective determining unit is used for determining the sub-table corresponding to the target object as the sub-table with abnormal metering.
The device for identifying the metering abnormal behavior of the intelligent electric meter in the low-voltage transformer area, provided by the embodiment of the invention, can execute the method for identifying the metering abnormal behavior of the intelligent electric meter in the low-voltage transformer area, provided by any embodiment of the invention, and has the corresponding functional module and the beneficial effects of the execution method.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a metering abnormal behavior identification device of a smart meter in a low-voltage distribution area according to a third embodiment of the present invention. As shown in fig. 3, the electronic apparatus includes a processor 30, a memory 31, a communication module 32, an input device 33, and an output device 34; the number of the processors 30 in the electronic device may be one or more, and one processor 30 is taken as an example in fig. 3; the processor 30, the memory 31, the communication module 32, the input device 33 and the output device 34 in the electronic device may be connected by a bus or other means, and the bus connection is taken as an example in fig. 3.
The memory 31 serves as a computer-readable storage medium, and can be used for storing software programs, computer-executable programs, and modules, such as the modules corresponding to the method for identifying the metering abnormal behavior of the low-voltage station area smart meter in the embodiment (for example, the obtaining module 21, the constructing module 22, the solving module 23, and the determining module 24 in the identification of the metering abnormal behavior of the low-voltage station area smart meter). The processor 30 executes various functional applications and data processing of the electronic device by running software programs, instructions and modules stored in the memory 31, that is, the method for identifying metering abnormal behaviors of the low-voltage distribution area smart meter is implemented.
The memory 31 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device, and the like. Further, the memory 31 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 31 may further include memory located remotely from the processor 30, which may be connected to the electronic device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And the communication module 32 is used for establishing connection with the display screen and realizing data interaction with the display screen. The input device 33 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus.
The device for identifying the metering abnormal behavior of the low-voltage distribution area intelligent electric meter, provided by the embodiment of the invention, can execute the method for identifying the metering abnormal behavior of the low-voltage distribution area intelligent electric meter, provided by any embodiment of the invention, and has the corresponding functions and beneficial effects.
Example four
The fourth embodiment of the present invention further provides a storage medium containing computer executable instructions, where the computer executable instructions are executed by a computer processor to perform a method for identifying metering abnormal behaviors of a smart meter in a low voltage transformer area, where the method includes:
respectively obtaining the total quantity value of a general meter arranged on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter arranged on each user side;
constructing an identification model based on the total quantity value and each partial quantity value;
solving the identification model to obtain the operation error value of each sub-table;
a sub-table of metering anomalies is determined based on the running error value.
Of course, the storage medium containing the computer-executable instructions provided by the embodiments of the present invention is not limited to the method operations described above, and may also perform related operations in a method for identifying metering abnormal behavior of a low-voltage distribution area smart meter provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes instructions for enabling a computer electronic device (which may be a personal computer, a server, or a network electronic device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the device for identifying metering abnormal behavior of the smart meter in the low-voltage distribution area, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as corresponding functions can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A method for identifying metering abnormal behaviors of a smart electric meter in a low-voltage transformer area is characterized by comprising the following steps:
respectively obtaining the total quantity value of a general meter arranged on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter arranged on each user side;
constructing an identification model based on the total quantity value and each of the partial quantity values;
solving the identification model to obtain the operation error value of each sub-table;
determining the sub-table of metering anomalies based on the running error value.
2. The method for identifying the metering abnormal behavior of the low-voltage transformer area intelligent ammeter according to claim 1, wherein the step of respectively acquiring the total measurement value of the total meter installed on the outgoing line side of the transformer in the low-voltage transformer area and the sub-measurement value of the sub-meter installed on each user side comprises the following steps:
acquiring the power consumption of each sub-table in the low-voltage station area within a preset time interval, wherein the power consumption is used as a sub-metering value;
and acquiring the power consumption of the summary table in the low-voltage station area in the time length at intervals of the time length as a total amount value.
3. The method for identifying metering abnormal behaviors of the low-voltage transformer area intelligent ammeter according to claim 1, wherein the constructing of the identification model based on the total measurement value and each of the sub-measurement values comprises:
an optimization model that minimizes a difference between the total quantity value and a sum of the respective partial quantity values is constructed as an identification model based on the total quantity value and the respective partial quantity values.
4. The method for identifying metering abnormal behaviors of the low-voltage transformer area intelligent ammeter according to claim 3, wherein the identification model is represented as follows:
min f(x)
wherein min f (x) is a fitness evaluation function with minimal difference between the sum of the total quantity value and each of the partial quantity values, and n is a dimension of a variable representing the number of the partial tables of the low-pressure station area; m is the current number of time segments; and x (i, j) is the value of the ith dimension variable and represents the operation error value of the ith sub-table of the low-voltage station area in the jth time period.
5. The method for identifying the metering abnormal behavior of the low-voltage transformer area intelligent ammeter according to claim 4, wherein the following formula is adopted as a fitness evaluation function:
wherein n is the total number of the sub-tables in the low-voltage table area; x (i, j) represents the operation error value of the ith sub-table in the jth time period of the low-voltage station area; s (i, j) represents the component quantity value of the ith sub-table in the jth time period of the low-voltage station area; g (j) represents the total quantity value of the total table of the low-pressure station area in the j time period; l (j) represents the bus power loss of the low-voltage station area in the j time period.
6. The method for identifying metering abnormal behaviors of the low-voltage transformer area smart meter according to claim 1, wherein solving the identification model to obtain the operation error value of each sub-meter comprises:
initializing a particle swarm algorithm, and generating a speed value and a position value corresponding to the number of the sub-tables of the low-voltage distribution area, wherein the position value is a predicted operation error value of each sub-table;
carrying out chaotic processing on the particle populations in the particle swarm algorithm by utilizing chaotic mapping;
updating the inertia weight factor of the particle swarm optimization by using a self-adaptive algorithm;
and solving the improved particle swarm algorithm to obtain an optimal value corresponding to the global optimal solution which meets the set optimal solution condition or reaches the maximum iteration times, and taking the optimal value as an operation error value of each sub-table.
7. The method for identifying metering anomaly behavior of the low-voltage transformer area smart meter according to claim 1, wherein the step of determining the sub-meter of the metering anomaly based on the operation error value comprises the following steps:
determining the running error value larger than a preset loss threshold value as a target object;
and determining the sub-table corresponding to the target object as the sub-table with abnormal metering.
8. The utility model provides a device is discerned to unusual action of measurement of low pressure platform district smart electric meter which characterized in that includes:
the acquisition module is used for respectively acquiring the total quantity value of a master meter installed on the outgoing line side of the transformer in the low-voltage transformer area and the sub-quantity value of a sub-meter installed on each user side;
a construction module for constructing an identification model based on the total amount value and each of the fractional amount values;
the solving module is used for solving the identification model to obtain the operation error value of each sub-table;
a determination module to determine the sub-table of metering anomalies based on the running error value.
9. The utility model provides an equipment is discerned to unusual action of measurement of low pressure platform district smart electric meter which characterized in that, the equipment includes:
one or more processors;
storage means for storing one or more programs;
when the one or more programs are executed by the one or more processors, the one or more processors implement the metering abnormal behavior recognition method of the low-voltage station area smart meter according to any one of claims 1 to 7.
10. A storage medium containing computer-executable instructions, wherein the computer-executable instructions, when executed by a computer processor, are configured to perform the method for identifying metering abnormal behavior of a low-voltage station area smart meter according to any one of claims 1 to 7.
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