CN106443566A - Big data deduction based electric energy metering device error detection method and system - Google Patents

Abstract

The invention discloses a big data deduction based electric energy metering device error detection method and system. The method includes the steps: acquiring multiple sets of electric energy data of each circuit, calculating bus electricity quantity non-balance ratio, building a mapping model of electric energy and bus electricity quantity non-balance ratio, calculating close degree of electric energy data and a virtual load model, selecting a plurality of sets of electric energy data according to the close degree to form a training sample set, taking the bus electricity quantity non-balance ratio as a teacher sample set to train the artificial neural network, and performing deduction and reconfiguration to obtain whole error. The system comprises an electric energy data acquisition unit, a bus electricity quantity non-balance ratio calculation unit, a virtual load model building unit, a close degree calculation unit, a sample set generation unit, an artificial neural network training unit and a whole error calculation unit. The method and system can monitor whole metering error of electric energy metering devices in real time at the same time, and has the advantages of avoidance of field test, simplicity in realization method, real-time performance and quickness in detection, high efficiency and safety.

Description

The electric power meter error detection method deduced based on big data and system

Technical field

The present invention relates in power equipment electric power meter state estimation technology, specifically disclose a kind of based on several greatly According to the electric power meter error detection method deduced and system.

Background technology

The accurate metering of electric energy is to ensure that the fair basis of electricity clearing, causes because metering device error is overproof every year Wrong electric energy is considerable.On the one hand, presently, there are the technological means of multiple detection metering device errors.Traditional method has：Electricity The laboratory verification of energy table and Site Detection, the laboratory verification of transformer and Site Detection, voltage transformer secondary voltage drop are existing Field detection etc..Metering device is dismantled laboratory and is carried out by these job demand personnel, or need personnel by standard set-up with Testing equipment takes scene development to, and some experiments must also have a power failure and carry out, and convenience is not enough.Relatively new technological means have：Electricity Can the detection of Watch Error remote online, the detection of transformer error remote online, the detection of voltage transformer secondary voltage drop remote online etc.. These relatively new technology can remotely, the error of real-time detection metering device, but exist cost high, arrangement inconvenience, deposit It is also possible to affect the normal operation of power system the problems such as potential faults.

On the other hand, power information acquisition system has covered all responsible consumers, transformer station, has opened through building for many years Close station, be widely used in the sides such as automatic data logging management, ordered electric management, electricity consumption Statistic Analysis, line loss and loss on transmission analysis Face, and bring the electric energy data of magnanimity, provide and support for the analysis of each link of electric power enterprise operation and management, decision-making, for reality Now intelligent two-way interaction service provision information basis.

Electric energy data derives from metering device, had both reflected electric flux conveying situation, and had also contained the mistake of metering device Difference information.Because the error of metering device is relatively small, the control information amount containing in electric energy data also very faint, very in the past Hardly possible realizes error this defect state overproof by a small amount of electric energy data.But the electricity that power information acquisition system is provided that now Energy datum considerably beyond conventional, can be analysed in depth by suitable mathematical model, makes data by these data resources In the metering device control information that contains integrated, and clearly present, this is the brand-new way evaluating metering device error Footpath.

Content of the invention

The technical problem to be solved in the present invention is：For the problems referred to above of prior art, provide one kind simultaneously real-time The overall error in dipping of monitoring electric power meter, is not required to field test, implementation method is simple, detect real-time, efficient peace The electric power meter error detection method based on big data deduction of full advantage and system.

In order to solve above-mentioned technical problem, the technical solution used in the present invention is：

On the one hand, the present invention provides a kind of electric power meter error detection method based on big data deduction, step bag Include：

1) obtain multigroup electric energy data of each bar circuit in transformer station's given voltage region to be assessed；

2) corresponding bus Power unbalance rate is calculated according to every group of electric energy data, according to multigroup electric energy data And corresponding bus Power unbalance rate sets up the mapping model of electric flux and bus Power unbalance rate；

3) create N bar virtual circuit, N be transformer station's given voltage region to be assessed interior lines way, take any two virtual Railway superstructures circuit pair, by one of circuit to being set to current line pair, sets the metering electricity of a wherein virtual circuit Being worth for mete-wand, another virtual circuit is comparison other；The electric energy value of the virtual circuit of current line pair is set respectively For 1 and -1, the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, the electric energy value of the corresponding virtual circuit of comparison other For -1, the electric energy value of other virtual circuits is set to 0, forms virtual load model, N × (N-1) individual virtual load mould is obtained Type set；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, and this is sequentially that each bar virtual circuit is compiled The ascending order of count word, is defined as " line arrangement order "；

4) by all of electric energy data according to " line arrangement order " arrangement, and one by one with all virtual load model ratios Relatively, obtain the approach degree of electric energy data and virtual load model；

5) some groups of electric energy datas that approach degree exceedes specified threshold are selected to be replicated as typical sample, after duplication Sample and original sample form training sample set, corresponding bus Power unbalance rate is as teacher's sample set；

6) create artificial neural network, train artificial neural network, virtual load mould with training sample set, teacher's sample set Artificial neural network after type is trained is deduced and is obtained virtual bus Power unbalance rate；

7) arrange and deduce all virtual bus Power unbalance rate obtaining, form electric power meter entirety error in dipping Initial value collection, the error amount reconstruct that overall error in dipping initial value is concentrated, obtain the global error of corresponding electric power meter.

Preferably, step 1) detailed step include：

1.1) obtain in electric energy information acquisition system in transformer station's given voltage region to be assessed each bar circuit from when Between point t (1) moment start to be spaced the electric energy information of the Each point in time of specified time period Δ T；

1.2) the electric energy value Δ W of each bar circuit in the time period between adjacent moment point is calculated according to formula (1)_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m) N () represents the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit in time point t (m + 1) electric energy information in moment, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

1.3) by the electric energy value Δ W of each bar circuit in the time period between adjacent moment point_TmN () constitutes electric energy to be analyzed Amount data set W_TmAs shown in formula (2), by electric energy data group W_TmConstitute shown in electric energy data set W such as formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

Preferably, step 2) in be calculated shown in the function expression such as formula (4) of corresponding bus Power unbalance rate； Step 2) in set up electric flux and specifically refer to the mapping model of bus Power unbalance rate：Electric energy data set W is obtained Corresponding bus Power unbalance rate set ε=[ε_T1,ε_T2,...,ε_Tm]^TConstitute association, form electric flux with bus electricity not The mapping model of balanced ratio；

In formula (4), ε represents bus Power unbalance rate, and N represents the number of circuit in transformer station's given voltage region to be assessed Amount, W_nRepresent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus；e_nRepresent The global error of the corresponding electric quantity metering device of nth bar circuit；W_n' it is that electric quantity metering device shows the electric energy with control information Amount, W_n' derive from electric energy data group W_Tm.

Preferably, step 4) detailed step include：

4.1) by all electric energy data groups according to " line arrangement order " arrangement；

4.2) choose one of which in all virtual load models, represent the model of virtual load model in array mode Feature, in the array of the aspect of model, the electric energy value of the corresponding virtual circuit of mete-wand is 1, and comparison other corresponds to virtual circuit Electric energy value be -1, the electric energy value of other virtual circuits is set to 0；

4.3) in all groups of electric energy datas, traversal chooses one group of electric energy data as comparing main body W_real, electric flux The form of data set is W_real=[W₁,W₂,W₃,...,W_N], wherein W_iRepresent i-th line road transmission electricity, i ∈ [1, N], N are Transformer station's given voltage region to be assessed interior lines way；

4.4) virtual load model of selection and the electric energy data group chosen are compared, inclined according to distance as shown in formula (5) Difference proximity algorithm calculates its approach degree；

In formula (5), σ (T ', W) represents approach degree, and T ' represents one group of virtual load model array, and W represents that one group have passed through The electric energy data group of normalized, element W (n) in W=W_realN ()/max (W), n are the individual of this group electric energy data Number, the bigger typical characteristic illustrating that this group electric energy data more has virtual load model of approach degree；

4.5) judge whether that all electric energy datas have stepped through to finish, if having stepped through finishing, redirecting and executing step Rapid 5)；Otherwise, redirect execution step 4.3).

Preferably, step 5) detailed step include：

5.1) in the comparison that one group of virtual load model participates in, five groups of maximum electric energy datas of approach degree are selected, right This batch of electric energy data group and corresponding bus Power unbalance rate are replicated, and number of copy times is entered according to formula (6) with approach degree Row association；

In formula (6), l rounds as number of copy times, and σ is approach degree, and a, b, c are conversion coefficient；

5.2) the electric energy data group of duplication and corresponding bus Power unbalance rate are added to initial electric flux number According to group set and bus Power unbalance rate data acquisition system, the electric energy data that N × (N-1) individual virtual load model is replicated is then There is 5 × N × (N-1) group, electric energy data group set becomes training sample set, bus Power unbalance rate data acquisition system becomes religion Teacher's sample set.

Preferably, step 6) detailed step include：

6.1) initialize BP artificial neural network, give weights and the threshold values of each layer of network by randomly assigne；

6.2) concentrate from training sample and take one group of electric energy data, take corresponding bus electricity uneven from teacher's sample set Weighing apparatus rate sends into BP artificial neural network as input signal；

6.3) electric energy data positive calculation in BP artificial neural network obtains result, is compared with teacher's sample, Calculate neutral net output layer output signal error according to formula (7)；

δ_l=-(T_l-Y_l)Y_l(1-Y_l) (7)

In formula (7), δ_lFor the node error of output layer, T_lFor teacher's sample data of output layer, Y_lNode for output layer Output signal；

6.4) the node error of each hidden layer is calculated according to formula (8)；

In formula (8),For the node error of n-th neuron in kth layer hidden layer,For n-th in kth layer hidden layer The output signal node of neuron,For the node error of a-th neuron in l layer, l layer is hidden layer or output layer, p For the neuron population of l layer,Weights for each neuron in a-th connection l layer in kth layer hidden layer；

6.5) adopt newton-Gauss innovatory algorithm BP ANN, update hidden layer and output according to formula (9) The weights of interlayer, are calculated the node error of each hidden layer by formula (11), update the threshold values of output layer according to formula (12)；

In formula (9), ω (k+1) is new weights, and ω (k) is former weights, and Δ ω (k) is right value update amount, H_kAnd g_kFor centre Matrix of variables, J^T(ω) it is Jacobian matrix, e (ω) is the vector set of single neuron node error composition, single neuron The set value of node error composition is output layer output signal error Y_l, μ is controlling elements, and I is unit matrix；Wherein refined can Compare matrix J^T(ω) shown in function expression such as formula (10)；

In formula (10), J represents Jacobian matrix J^T(ω), e_iRepresent the node error of i-th neuron in this hidden layer, i ∈ [1, M], M are the quantity of this layer of hidden layer neuron, ω_ijRepresent that i-th neuron of this hidden layer connects next layer of output layer The weights of j-th neuron, j ∈ [1, O], O represent the neuronal quantity of next layer of output layer；The node of neuron in hidden layer Error is represented with formula (11) with the weights of corresponding output layer neuron：

In formula (11), e_iFor the node error of i-th neuron in this hidden layer, YⁱFor i-th neuron in this hidden layer Output signal node δ_jFor the node error of j-th neuron in next layer of output layer, ω_ijFor i-th in this hidden layer even Connect the weights of j-th neuron of next layer of output layer；

In formula (12),For m-th neuron of output layer the t+1 moment threshold values,For m-th god of output layer Through unit t threshold values, β be correction factor,Node error for m-th neuron in l layer hidden layer；

6.6) judge whether all of training sample set and teacher's sample set is all trained finishes, if not yet trained Finish, then redirect execution step 6.2), otherwise redirect execution step 6.7)；

6.7) error and the study number of times of BP artificial neural network are checked, if the error of artificial neural network is more than or equal to Setting value and when learning number of times and being not reaching to pre-determined number, redirects execution step 6.2)；Otherwise, terminate to train and store currently The network parameter of BP artificial neural network；

6.8) N × (N-1) individual virtual load model is sent into the BP artificial neural network training to be deduced, obtain void Intend the individual virtual bus Power unbalance rate of the corresponding N of load model × (N-1)；By individual for N × (N-1) virtual bus Power unbalance Rate is sorted out, and sorting out principle is that corresponding N × (N-1) individual virtual load model has N number of mete-wand, each mete-wand pair Should there is N-1 comparison other, corresponding for the model of identical mete-wand bus Power unbalance rate is included into same set, Form N number of set, N-1 element of each set, the element in each set is according to " line arrangement order " arrangement, mete-wand Corresponding position is empty, using the set obtaining as the overall error in dipping initial value collection of the electric power meter of corresponding mete-wand.

Preferably, step 7) detailed step include：

7.1) concentrate in N number of electric power meter entirety error in dipping initial value and all add zero error, fill up mete-wand The corresponding evolution initial value collection being empty position, forming as shown in formula (13)；

In formula (13), ε represents evolution initial value collection, ε_i,jRepresent on the basis of i-th set of metering device, jth set metering dress The Initial value put, ε_i,xRepresent on the basis of i-th set of metering device, the Initial value of all metering devices (includes first set meter Amount device) vector, i ∈ [1, N], j ∈ [1, N-1], N represent metering device quantity；

7.2) traversal chooses one of evolution initial value collection, takes the median of each element numerical value in its set；

7.3) by this evolution initial value integrate according to median as translation distance carry out translation obtain shift evolution initial value collection, according to " line arrangement order " obtains the data that displacement evolution initial value concentration is associated with each metering device, as overall error in dipping；

7.4) judge whether that all evolution initial value collection have stepped through selection and finish, if not yet traversal selection finishes, jump Turn execution step 7.2)；Otherwise, it is determined that all evolution initial value collection are all translated, and often set electric quantity metering device all obtains N Shown in individual overall error in dipping formula (14)；

In formula (14), ε ' represents error reconstruct collection, median (ε_i,x) represent ε_i,xMedian, ε_i′_,jRepresent at the beginning of error Value, ε_i,jReconstruction value after translation, translational movement is median (ε_i,x), i ∈ [1, N], j ∈ [1, N], N represents metering device Quantity；

7.5) make according to the meansigma methodss that function expression shown in formula (15) asks for every set N number of overall error in dipping of metering device Finally deduce the overall error in dipping obtaining for this set metering device；

ε_n=[avg (ε '_x,1),avg(ε′_x,2),…avg(ε′_x,N)] (15)

In formula (14), ε_nRepresent that n-th set of metering device finally deduces the overall error in dipping obtaining, avg (ε '_x,i) represent Set ε '_x,iThe meansigma methodss of middle all elements.

On the other hand, the present invention also provides a kind of electric power meter error detecting system based on big data deduction, bag Include：

Electric energy data data capture unit, for obtaining the many of each bar circuit in transformer station's given voltage region to be assessed Group electric energy data；

Bus Power unbalance rate computing unit, for being calculated corresponding bus electricity according to every group of electric energy data Unbalance factor, sets up electric flux according to multigroup electric energy data and corresponding bus Power unbalance rate uneven with bus electricity The mapping model of weighing apparatus rate；

Virtual load model sets up unit, and for creating N bar virtual circuit, N is transformer station's given voltage region to be assessed Interior lines way, takes any two virtual circuits to constitute circuit pair, by one of circuit to being set to current line pair, sets wherein Article one, the metering charge value of virtual circuit be mete-wand, another virtual circuit be comparison other；Void by current line pair The electric energy value intending circuit is set to 1 and -1, and the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, comparison other The electric energy value of corresponding virtual circuit is -1, and the electric energy value of other virtual circuits is set to 0, forms virtual load model, there are To N × (N-1) individual virtual load model set；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, This is sequentially the ascending order of each bar virtual circuit numerical digit, is defined as " line arrangement order "；

Approach degree computing unit, for by all of electric energy data according to " line arrangement order " arrangement, and one by one with All virtual load models compare, and obtain the approach degree of electric energy data and virtual load model；

Sample set signal generating unit, for selecting approach degree to exceed some groups of electric energy datas of specified threshold as typical sample Originally replicated, the sample after duplication forms training sample set with original sample, and corresponding bus Power unbalance rate is as religion Teacher's sample set；

Artificial neural network training unit, for creating artificial neural network, with training sample set, the training of teacher's sample set Artificial neural network, the artificial neural network after virtual load model is trained is deduced and is obtained virtual bus Power unbalance rate；

Global error computing unit, deduces, for arranging, all virtual bus Power unbalance rate obtaining, and forms electric energy Metering device entirety error in dipping initial value collection, the initial value collection reconstruct that overall error in dipping initial value is concentrated, obtain corresponding electric energy meter The global error of amount device.

Preferably, described electric energy data data capture unit includes：

Electric energy information acquisition module, for obtaining transformer station to be assessed given voltage in electric energy information acquisition system In region, each bar circuit starts to be spaced the electric energy information of the Each point in time of specified time period Δ T from time point t (1) moment；

Electric energy magnitude calculation acquisition module, for calculating each bar circuit in the time period between adjacent moment point according to formula (1) Electric energy value Δ W_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m) N () represents the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit in time point t (m + 1) electric energy information in moment, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

Electric energy data group builds module, for by the electric energy value Δ of each bar circuit in the time period between adjacent moment point W_TmN () constitutes electric energy data group W to be analyzed_TmAs shown in formula (2), by electric energy data group W_TmConstitute electric energy data collection Close shown in W such as formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

Preferably, described bus Power unbalance rate computing unit is calculated the letter of corresponding bus Power unbalance rate Shown in number expression formula such as formula (4)；Described bus Power unbalance rate computing unit sets up electric flux and bus Power unbalance rate Mapping model specifically refer to：Electric energy data set W is obtained corresponding bus Power unbalance rate set ε=[ε_T1, ε_T2,...,ε_Tm]^TConstitute association, form the mapping model of electric flux and bus Power unbalance rate；

In formula (4), ε represents bus Power unbalance rate, and N represents the number of circuit in transformer station's given voltage region to be assessed Amount, W_nRepresent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus；e_nRepresent The global error of the corresponding electric quantity metering device of nth bar circuit；W_n' it is that electric quantity metering device shows the electric energy with control information Amount, W_n' derive from electric energy data group W_Tm.

The present invention is had the advantage that based on the electric power meter error detection method tool that big data is deduced：

1) present invention by on-line monitoring obtain electric energy information, by electric energy data calculate bus electric quantity balancing rate and by Substantial amounts of electric energy data, bus Power unbalance rate set up mapping model, thus assessing the entirety metering of electric power meter Error condition is it is not necessary to carry out on-site proving or detection to metering device it is not required that detection that equipment is had a power failure, easy to use, high Effect and safety；Electric energy data with collection is different, and error judgment result also will change accordingly, thus reflecting electric energy in real time The change of metering device entirety error in dipping, with the timely development trend finding metering device hidden failure or deterioration, Neng Gouyou Effect ensures the health operation of metering device.

2) present invention adopts newton Gauss innovatory algorithm to train artificial neural network, and newton Gauss innovatory algorithm has Taking into account global property, training convergence block, avoiding training process to be easily absorbed in the problem jumping out local to a certain extent so that deducing knot Fruit precision is higher, and the metering device global error of reaction is more accurate.

3) electric energy data and virtual load model are compared by the present invention, using proximity algorithm and fuzzy membership letter Number realizes the fuzzy strengthening of typical characteristic electric energy data, increases frequency of training under this feature for the artificial neural network, thus Make artificial neural network have higher diagnostic accuracy and generalization near deduction domain, make deduction result more accurate.

4) the result addition zero error obtaining deduced by artificial neural network by the present invention, forms evolution initial value collection, utilizes Metering device this feature of synthetic error Normal Distribution, evolution initial value collection is reconstructed, discloses bus Power unbalance rate With the mapping relations of metering device global error it is achieved that assessment to metering device global error.

The present invention is based on big data based on the electric power meter error detecting system that big data is deduced for the present invention and deduces The completely corresponding device of electric power meter error detection method, therefore equally also have what the present invention was deduced based on big data The aforementioned advantages of electric power meter error detection method, therefore will not be described here.

Brief description

Fig. 1 is the basic procedure schematic diagram of present invention method.

Fig. 2 be embodiment of the present invention step 4) schematic flow sheet.

Fig. 3 is the deduction of BP artificial neural network and training principle schematic in the present invention.

Fig. 4 is the present embodiment step 6) newton Gauss innovatory algorithm training schematic flow sheet.

Fig. 5 is the present embodiment step 7) in error reconstruct schematic flow sheet.

Specific embodiment

As shown in figure 1, the step of electric power meter error detection method that the present embodiment is deduced based on big data includes：

1) obtain multigroup electric energy data of each bar circuit in transformer station's given voltage region to be assessed；

2) corresponding bus Power unbalance rate is calculated according to every group of electric energy data, according to multigroup electric energy data And corresponding bus Power unbalance rate sets up the mapping model of electric flux and bus Power unbalance rate；

3) create N bar virtual circuit, N be transformer station's given voltage region to be assessed interior lines way, take any two virtual Railway superstructures circuit pair, by one of circuit to being set to current line pair, sets the metering electricity of a wherein virtual circuit Being worth for mete-wand, another virtual circuit is comparison other；The electric energy value of the virtual circuit of current line pair is set respectively For 1 and -1, the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, the electric energy value of the corresponding virtual circuit of comparison other For -1, the electric energy value of other virtual circuits is set to 0, forms virtual load model, N × (N-1) individual virtual load mould is obtained Type set；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, and this is sequentially that each bar virtual circuit is compiled The ascending order of count word, is defined as " line arrangement order "；

4) by all of electric energy data according to " line arrangement order " arrangement, and one by one with all virtual load model ratios Relatively, obtain the approach degree of electric energy data and virtual load model；

5) some groups of electric energy datas that approach degree exceedes specified threshold are selected to be replicated as typical sample, after duplication Sample and original sample form training sample set, corresponding bus Power unbalance rate is as teacher's sample set；

6) create artificial neural network, train artificial neural network, virtual load mould with training sample set, teacher's sample set Artificial neural network after type is trained is deduced and is obtained virtual bus Power unbalance rate；

7) arrange and deduce all virtual bus Power unbalance rate obtaining, form electric power meter entirety error in dipping Initial value collection, the error amount reconstruct that overall error in dipping initial value is concentrated, obtain the global error of corresponding electric power meter.

In the present embodiment, step 1) detailed step include：

1.1) obtain in electric energy information acquisition system in transformer station's given voltage region to be assessed each bar circuit from when Between point t (1) moment start to be spaced the electric energy information of the Each point in time of specified time period Δ T；

1.2) the electric energy value Δ W of each bar circuit in the time period between adjacent moment point is calculated according to formula (1)_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m) N () represents the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit in time point t (m + 1) electric energy information in moment, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

1.3) by the electric energy value Δ W of each bar circuit in the time period between adjacent moment point_TmN () constitutes electric energy to be analyzed Amount data set W_TmAs shown in formula (2), by electric energy data group W_TmConstitute shown in electric energy data set W such as formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

The electric energy information W in time point t (1) moment for each bar circuit is obtained in electric energy information acquisition system_t(1) (n)；Obtain the electric energy information W in time point t (2) moment_t(2)N (), t (1) arrives the electricity of line related between t (2) time period T1 Energy value is Δ W_T1(n)=W_t(2)(n)-W_t(1)(n).Further, extend to relation line from t (m) to t (m+1) time period Tm The electric energy value on road is Δ W_Tm(n)=W_t(m+1)(n)-W_t(m)(n), wherein T1=T2=...=Tm=Δ T.ΔW_TmN () composition is treated Electric energy data group W of analysis_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(n)] as shown in formula (2), electric energy data group W_TmConstitute electric energy data set W=[W_T1,W_T2,...,W_Tm]^TAs shown in formula (3), thus obtain transformer station to be assessed and specify electricity Multigroup electric energy data of each bar circuit in intermediate pressure section.

In the present embodiment, step 2) in be calculated the function expression such as formula (4) of corresponding bus Power unbalance rate Shown；Step 2) in set up electric flux and specifically refer to the mapping model of bus Power unbalance rate：By electric energy data set W Obtain corresponding bus Power unbalance rate set ε=[ε_T1,ε_T2,...,ε_Tm]^TConstitute association, form electric flux and bus electricity The mapping model of amount unbalance factor；

In formula (4), ε represents bus Power unbalance rate, and N represents the number of circuit in transformer station's given voltage region to be assessed Amount, W_nRepresent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus (W_nDuring ＞ 0 Represent that primary side electric flux sends bus, W_nRepresent during ＜ 0 that primary side electric flux sends into bus)；e_nRepresent that nth bar circuit corresponds to Electric quantity metering device global error；W_n' it is that electric quantity metering device shows the electric flux with control information, W_n' derive from electric energy Amount data set W_Tm.For transformer station's identical electric pressure region, same time window enters, sends electricity and the bus electricity of bus The association of amount unbalance factor can be represented with function expression as shown in formula (4), W_n' it is the electric flux that metering device shows, band There is the control information of metering device；Derive from electric energy data group W_Tm；By electric energy data set W=[W_T1,W_T2,..., W_Tm]^TObtain corresponding bus Power unbalance rate set ε=[ε further_T1,ε_T2,...,ε_Tm]^T, and constitute association, form W_Tm Mapping model with ε.

In the present embodiment, first confirm that the circuit ingredient in transformer station to be assessed a certain electric pressure region, i.e. this electricity The N bar circuit that in intermediate pressure section, bus is connected, is then obtained in electric energy information data base by electric energy information acquisition platform Take electric energy information W in the time periods such as N bar circuit_n', built by every group of electric energy data and corresponding bus Power unbalance rate Vertical metering electric flux W_n' with the mapping model of bus Power unbalance rate ε.The present embodiment takes the electric energy data of nearest 30 days simultaneously Take the period to be 3 hours, that is, acquire 240 groups of data, every group of data all includes the electric energy data of all N bar circuits, 240 groups Electric energy data correspondence is calculated 240 bus Power unbalance rates ε.

As shown in Fig. 2 the present embodiment step 4) detailed step include：

4.1) by all electric energy data groups according to " line arrangement order " arrangement；

4.2) choose one of which in all virtual load models, represent the model of virtual load model in array mode Feature, in the array of the aspect of model, the electric energy value of the corresponding virtual circuit of mete-wand is 1, and comparison other corresponds to virtual circuit Electric energy value be -1, the electric energy value of other virtual circuits is set to 0, such as one group model feature example be Μ=[1, -1, 0,...,0]；

4.3) in all groups of electric energy datas, traversal chooses one group of electric energy data as comparing main body W_real, electric flux The form of data set is W_real=[W₁,W₂,W₃,...,W_N], wherein W_iRepresent i-th line road transmission electricity, i ∈ [1, N], N are Transformer station's given voltage region to be assessed interior lines way；

4.4) virtual load model of selection and the electric energy data group chosen are compared, inclined according to distance as shown in formula (5) Difference proximity algorithm calculates its approach degree；

In formula (5), σ (T ', W) represents approach degree, and T ' represents one group of virtual load model array, and W represents that one group have passed through The electric energy data group of normalized, element W (n) in W=W_realN ()/max (W), n are the individual of this group electric energy data Number, the bigger typical characteristic illustrating that this group electric energy data more has virtual load model of approach degree；

4.5) judge whether that all electric energy datas have stepped through to finish, if having stepped through finishing, redirecting and executing step Rapid 5)；Otherwise, redirect execution step 4.3).

In the present embodiment, any two lines road is taken to constitute circuit pair, by one of circuit to being set to current line pair, if The metering charge value of a fixed wherein circuit is mete-wand, and another is comparison other；Electric energy value by current line pair It is set to 1 and -1, wherein mete-wand corresponding electric energy value is 1, and comparison other is -1, and the electric energy value of All other routes sets For 0, form virtual load model；Similar virtual load model composition model set, contains N × (N- in this model set 1) individual virtual load model, N is number of, lines；In each model, same sequence is pressed in the arrangement of the virtual electric energy value of each bar circuit, This is sequentially defined as " line arrangement order "；By step 1) in multigroup electric energy data of obtaining according to " line arrangement order " row Row, are compared with virtual load model, calculate its approach degree according to range deviation proximity algorithm as shown in formula (5).

In the present embodiment, step 5) detailed step include：

5.1) in the comparison that one group of virtual load model participates in, five groups of maximum electric energy datas of approach degree are selected, right This batch of electric energy data group and corresponding bus Power unbalance rate are replicated, and number of copy times is entered according to formula (6) with approach degree Row association；

In formula (6), l rounds as number of copy times, and σ is approach degree, and a, b, c are conversion coefficient；In the present embodiment, by approach degree Some groups of larger electric energy datas are replicated as typical sample, and sample replicates follows more former of approach degree more higher duplication Then, it is distributed the transform of fuzzy membership functions using Cauchy shown in formula (6), the approach degree after processing is associated with number of copy times, Realize the fuzzy strengthening of typical sample：

5.2) the electric energy data group of duplication and corresponding bus Power unbalance rate are added to initial electric flux number According to group set and bus Power unbalance rate data acquisition system, the electric energy data that N × (N-1) individual virtual load model is replicated is then There is 5 × N × (N-1) group, electric energy data group set becomes training sample set, bus Power unbalance rate data acquisition system becomes religion Teacher's sample set.

In the present embodiment, example takes 5 groups of maximum data of approach degree；This batch of electric energy data group is replicated, number of copy times It is associated according to formula (6) with approach degree：Virtual load model has N × (N-1) individual, and the electric energy data being replicated then has 5 × N × (N-1) group, 5 × N × (N-1) organizes the possibility that there is repeat replication in electric energy data.

Using characteristic strengthening type electric energy data group as training sample, corresponding bus Power unbalance rate group conduct Teacher's sample, described training sample and teacher's sample is inputted to default BP artificial neural network, using newton Gauss Improved algorithm trains weights and the threshold values of each layer of described BP artificial neural network, obtains the electric energy based on BP artificial neural network Amount and the mapping model of bus Power unbalance rate.

As shown in figure 3, in the present embodiment the training of BP artificial neural network and deduce when, from virtual load model extract allusion quotation Type feature, electric energy data is recognized with this typical characteristic, obtains identification result.According to identification result by each group electric energy data It is divided into typical sample and common sample two class, wherein typical sample is some groups of electric fluxs that feature is pressed close to virtual load model Data, it is typical sample that this example takes 5 groups of maximum electric energy datas of approach degree, and remaining is common sample.Replicate typical sample, abide by Follow the more principles of approach degree more higher duplication, be distributed the transform of fuzzy membership functions by approach degree and number of copy times using Cauchy Association.Sample and the original sample replicating forms training sample set, corresponding bus Power unbalance rate as teacher's sample set, Obtain training sample and teacher's sample set, training sample and teacher's sample set are sent into BP artificial neural network and is trained.Will The BP artificial neural network that virtual load model input trains is deduced, and obtains deducing result.In the present embodiment, specifically Refer to by N × (N-1) individual virtual load model send in the BP artificial neural network completing to train and deduce, obtain N × (N-1) individual Virtual bus Power unbalance rate, is converted into electric power meter entirety error in dipping initial value.In the present embodiment, N × (N-1) is individual Virtual bus Power unbalance rate conversion electric power meter entirety error in dipping initial value is processed, the specific implementation method of conversion For：The individual virtual bus Power unbalance rate of N × (N-1) has corresponded to N × (N-1) individual condition；This condition is N bar line for evaluation object Road, every circuit all occurs in that N-1 time as mete-wand and comparison other；Selecting a circuit is mete-wand, will meet N-1 virtual bus Power unbalance rate of this condition is arranged, and carries out according to " line arrangement order ", mete-wand corresponds to Position be to be empty, obtain electric power meter entirety error in dipping initial value collection, similar initial value collection has N number of.

As shown in figure 4, the present embodiment step 6) detailed step include：

6.1) initialize BP artificial neural network, give weights and the threshold values of each layer of network by randomly assigne；

6.2) concentrate from training sample and take one group of electric energy data, take corresponding bus electricity uneven from teacher's sample set Weighing apparatus rate sends into BP artificial neural network as input signal；

6.3) electric energy data positive calculation in BP artificial neural network obtains result, is compared with teacher's sample, Calculate neutral net output layer output signal error according to formula (7)；

δ_l=-(T_l-Y_l)Y_l(1-Y_l) (7)

In formula (7), δ_lFor the node error of output layer, T_lFor teacher's sample data of output layer, Y_lNode for output layer Output signal；

6.4) the node error of each hidden layer is calculated according to formula (8)；

In formula (8),For the node error of n-th neuron in kth layer hidden layer,For n-th in kth layer hidden layer The output signal node of neuron,For the node error of a-th neuron in l layer, l layer is hidden layer or output layer, p For the neuron population of l layer,Weights for each neuron in a-th connection l layer in kth layer hidden layer；

6.5) adopt newton-Gauss innovatory algorithm BP ANN, update hidden layer and output according to formula (9) The weights of interlayer, are calculated the node error of each hidden layer by formula (11), update the threshold values of output layer according to formula (12)；

In formula (9), ω (k+1) is new weights, and ω (k) is former weights, and Δ ω (k) is right value update amount, H_kAnd g_kFor centre Matrix of variables, J^T(ω) it is Jacobian matrix, e (ω) is the vector set of single neuron node error composition, single neuron The set value of node error composition is output layer output signal error Y_l, μ is controlling elements, and I is unit matrix；Wherein refined can Compare matrix J^T(ω) shown in function expression such as formula (10)；

In formula (10), J represents Jacobian matrix J^T(ω), e_iRepresent the node error of i-th neuron in this hidden layer, i ∈ [1, M], M are the quantity of this layer of hidden layer neuron, ω_ijRepresent that i-th neuron of this hidden layer connects next layer of output layer The weights of j-th neuron, j ∈ [1, O], O represent the neuronal quantity of next layer of output layer；The node of neuron in hidden layer Error is represented with formula (11) with the weights of corresponding output layer neuron：

In formula (11), e_iFor the node error of i-th neuron in this hidden layer, YⁱFor i-th neuron in this hidden layer Output signal node δ_jFor the node error of j-th neuron in next layer of output layer, ω_ijFor i-th in this hidden layer even Connect the weights of j-th neuron of next layer of output layer；

In formula (12),For m-th neuron of output layer the t+1 moment threshold values,For m-th god of output layer Through unit t threshold values, β be correction factor,Node error for m-th neuron in l layer hidden layer；

6.6) judge whether all of training sample set and teacher's sample set is all trained finishes, if not yet trained Finish, then redirect execution step 6.2), otherwise redirect execution step 6.7)；

6.7) error and the study number of times of BP artificial neural network are checked, if the error of artificial neural network is more than or equal to Setting value and when learning number of times and being not reaching to pre-determined number, redirects execution step 6.2)；Otherwise, terminate to train and store currently The network parameter of BP artificial neural network；

6.8) N × (N-1) individual virtual load model is sent into the BP artificial neural network training to be deduced, obtain void Intend the individual virtual bus Power unbalance rate of the corresponding N of load model × (N-1)；By individual for N × (N-1) virtual bus Power unbalance Rate is sorted out, and sorting out principle is that corresponding N × (N-1) individual virtual load model has N number of mete-wand, each mete-wand pair Should there is N-1 comparison other, corresponding for the model of identical mete-wand bus Power unbalance rate is included into same set, Form N number of set, N-1 element of each set, the element in each set is according to " line arrangement order " arrangement, mete-wand Corresponding position is empty, using the set obtaining as the overall error in dipping initial value collection of the electric power meter of corresponding mete-wand.

As shown in figure 5, the present embodiment step 7) detailed step include：

7.1) concentrate in N number of electric power meter entirety error in dipping initial value and all add zero error, fill up mete-wand The corresponding evolution initial value collection being empty position, forming as shown in formula (13)；

In formula (13), ε represents evolution initial value collection, ε_i,jRepresent on the basis of i-th set of metering device, jth set metering dress The Initial value put, ε_i,xRepresent on the basis of i-th set of metering device, the Initial value of all metering devices (includes first set meter Amount device) vector, i ∈ [1, N], j ∈ [1, N-1], N represent metering device quantity；

7.2) traversal chooses one of evolution initial value collection, takes the median of each element numerical value in its set；

7.3) by this evolution initial value integrate according to median as translation distance carry out translation obtain shift evolution initial value collection, according to " line arrangement order " obtains the data that displacement evolution initial value concentration is associated with each metering device, as overall error in dipping；

7.4) judge whether that all evolution initial value collection have stepped through selection and finish, if not yet traversal selection finishes, jump Turn execution step 7.2)；Otherwise, it is determined that all evolution initial value collection are all translated, and often set electric quantity metering device all obtains N Shown in individual overall error in dipping formula (14)；

In formula (14), ε ' represents error reconstruct collection, median (ε_i,x) represent ε_i,xMedian, ε '_i,jRepresent at the beginning of error Value, ε_i,jReconstruction value after translation, translational movement is median (ε_i,x), i ∈ [1, N], j ∈ [1, N], N represents metering device Quantity；

7.5) make according to the meansigma methodss that function expression shown in formula (15) asks for every set N number of overall error in dipping of metering device Finally deduce the overall error in dipping obtaining for this set metering device；

ε_n=[avg (ε '_x,1),avg(ε′_x,2),…avg(ε′_x,N)] (15)

In formula (14), ε_nRepresent that n-th set of metering device finally deduces the overall error in dipping obtaining, avg (ε '_x,i) represent Set ε '_x,iThe meansigma methodss of middle all elements.

Because the present embodiment Chinese style (4) considers particular case, taking the 1st article of circuit and the 2nd article of circuit as a example, if only two Circuit sends into bus and send bus in transmission load, respectively electric flux, then the difference of the error of this two sets of metering devices is For Power unbalance rate, as shown in formula (16).

In formula (16), ε_1,2Represent the Power unbalance rate between the 1st article of circuit and the 2nd article of circuit, W₁Represent the 1st bar of line Electricity, W are transmitted in road₂Represent the 2nd article of line transmission electricity, e₁Represent the entirety of the 1st article of corresponding electric quantity metering device of circuit by mistake Difference, e₂Represent the global error of the 2nd article of corresponding electric quantity metering device of circuit.Formula (4-1) is by the bus of single input output state Power unbalance rate is directly connected with metering device error.Artificially a benchmark is set, even e₂For 0, then e₁= ε_1,2, further genralrlization to the metering device error obtaining bus All other routes, formation error collection is it is possible to obtain all to be evaluated The global error of estimator device.But the method can be in the face of following problem：1. it is difficult to occur only two during transformer station's actual motion The situation of bar circuit on-load is it is impossible to directly set up the corresponding model of formula (2) using electric quantity data collection.2. metering device error is subject to The impact of the random factors such as environment, load is continually changing, and the boundary condition of Power unbalance rate model is unstable.3. it is manually set A set of metering device error is 0, and practical situation is certainly different, so that whole error collection is offset toward a direction, and side-play amount is not Fixed.

For solve problem 1., the present embodiment step 3) in adopt with the following method：Create N bar virtual circuit, N is change to be assessed Power station given voltage region interior lines way, takes any two virtual circuits to constitute circuit pair, one of circuit is worked as to being set to Front circuit pair, the metering charge value setting a wherein virtual circuit is as mete-wand, another virtual circuit as comparison other； The electric energy value of the virtual circuit of current line pair is set to 1 and -1, the wherein electric energy of the corresponding virtual circuit of mete-wand Amount T ' is worth for 1, and the electric energy value of the corresponding virtual circuit of comparison other is -1, and the electric energy value of other virtual circuits is set to 0, is formed Virtual load model, is obtained N × (N-1) individual virtual load model set；Virtual electric energy value in each virtual load model Arrangement press same sequence, this is sequentially ascending order of each bar virtual circuit numerical digit, is defined as " line arrangement order ".

For solve problem 2., the present embodiment step 6) in adopt with the following method：Create BP artificial neural network, using nerve The compatibility of network and error correction solve boundary condition shakiness problem；Using step 5) in the training sample set that obtains and teacher's sample This collection trains neutral net by newton Gauss innovatory algorithm.

For solve problem 3., the present embodiment step 7) in deduce all virtual bus Power unbalance that obtains by arranging Rate, forms electric power meter entirety error in dipping initial value collection, and the error amount reconstruct that overall error in dipping initial value is concentrated obtains The global error of corresponding electric power meter.Each electric power meter entirety error in dipping initial value collection and a zero error knot Close, form evolution initial value collection, what zero error filled up mete-wand is empty position.Take the median median of evolution initial value collection [ε], the actual global error of the metering device corresponding to this median is most likely to be close to zero.This is because the synthesis of metering device Error Normal Distribution ε～N (μ, σ), that is, error be distributed centered on expecting μ, on the other hand, the same voltage regime of transformer station The configuration consistency of metering device, that is, often set metering device has consistent expectation, and the corresponding actual error of this expectation is in close proximity to Zero, can be considered zero error.The median of evolution initial value collection is closest with expectation on probability, even if accidental offset, its skew Amount is also less, it is taken as that the corresponding actual error of median of evolution initial value collection is zero.With median as distance, according to formula (17) evolution initial value collection is translated, obtain the global error of metering device；

In formula (17), ε represents evolution initial value collection, and ε ' represents error reconstruct collection, and each element in error reconstruct collection ε ' is exactly The entirety of corresponding metering device deduces error.Due to there being N number of evolution initial value collection, thus every suit metering device all can obtain N number of Overall deduction error, N number of result equalization is obtained final overall error in dipping and deduces result.

The electric power meter error detection method that the present embodiment is deduced based on big data is particular by computer program Come to realize, realize the electric power meter error detecting system bag deduced based on big data by this computer program Include：

Electric energy data data capture unit, for obtaining the many of each bar circuit in transformer station's given voltage region to be assessed Group electric energy data；

Bus Power unbalance rate computing unit, for being calculated corresponding bus electricity according to every group of electric energy data Unbalance factor, sets up electric flux according to multigroup electric energy data and corresponding bus Power unbalance rate uneven with bus electricity The mapping model of weighing apparatus rate；

Virtual load model sets up unit, and for creating N bar virtual circuit, N is transformer station's given voltage region to be assessed Interior lines way, takes any two virtual circuits to constitute circuit pair, by one of circuit to being set to current line pair, sets wherein Article one, the metering charge value of virtual circuit be mete-wand, another virtual circuit be comparison other；Void by current line pair The electric energy value intending circuit is set to 1 and -1, and the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, comparison other The electric energy value of corresponding virtual circuit is -1, and the electric energy value of other virtual circuits is set to 0, forms virtual load model, there are To N × (N-1) individual virtual load model set；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, This is sequentially the ascending order of each bar virtual circuit numerical digit, is defined as " line arrangement order "；

Approach degree computing unit, for by all of electric energy data according to " line arrangement order " arrangement, and one by one with All virtual load models compare, and obtain the approach degree of electric energy data and virtual load model；

Sample set signal generating unit, for selecting approach degree to exceed some groups of electric energy datas of specified threshold as typical sample Originally replicated, the sample after duplication forms training sample set with original sample, and corresponding bus Power unbalance rate is as religion Teacher's sample set；

Artificial neural network training unit, for creating artificial neural network, with training sample set, the training of teacher's sample set Artificial neural network, the artificial neural network after virtual load model is trained is deduced and is obtained virtual bus Power unbalance rate；

Global error computing unit, deduces, for arranging, all virtual bus Power unbalance rate obtaining, and forms electric energy Metering device entirety error in dipping initial value collection, the initial value collection reconstruct that overall error in dipping initial value is concentrated, obtain corresponding electric energy meter The global error of amount device.

Electric energy data data capture unit described in the present embodiment includes：

Electric energy information acquisition module, for obtaining transformer station to be assessed given voltage in electric energy information acquisition system In region, each bar circuit starts to be spaced the electric energy information of the Each point in time of specified time period Δ T from time point t (1) moment；

Electric energy magnitude calculation acquisition module, for calculating each bar circuit in the time period between adjacent moment point according to formula (1) Electric energy value Δ W_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m) N () represents the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit in time point t (m + 1) electric energy information in moment, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

Electric energy data group builds module, for by the electric energy value Δ of each bar circuit in the time period between adjacent moment point W_TmN () constitutes electric energy data group W to be analyzed_TmAs shown in formula (2), by electric energy data group W_TmConstitute electric energy data collection Close shown in W such as formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

In the present embodiment, described bus Power unbalance rate computing unit is calculated corresponding bus Power unbalance rate Function expression such as formula (4) shown in；It is uneven with bus electricity that described bus Power unbalance rate computing unit sets up electric flux The mapping model of weighing apparatus rate specifically refers to：Electric energy data set W is obtained corresponding bus Power unbalance rate set ε=[ε_T1, ε_T2,...,ε_Tm]^TConstitute association, form the mapping model of electric flux and bus Power unbalance rate；

In formula (4), ε represents bus Power unbalance rate, and N represents the number of circuit in transformer station's given voltage region to be assessed Amount, W_nRepresent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus；e_nRepresent The global error of the corresponding electric quantity metering device of nth bar circuit；W_n' it is that electric quantity metering device shows the electric energy with control information Amount, W_n' derive from electric energy data group W_Tm.In sum, the electric power meter error that the present embodiment is deduced based on big data Detection method and system are capable of the overall error in dipping of simultaneous real-time monitoring electric power meter, have and are not required to field test, reality Existing method is simple, detection is real-time, the advantage of highly effective and safe.

The above is only the preferred embodiment of the present invention, and protection scope of the present invention is not limited merely to above-mentioned enforcement Example, all technical schemes belonging under thinking of the present invention belong to protection scope of the present invention.It should be pointed out that for the art Those of ordinary skill for, some improvements and modifications without departing from the principles of the present invention, these improvements and modifications Should be regarded as protection scope of the present invention.

Claims (10)

1. a kind of electric power meter error detection method based on big data deduction is it is characterised in that step includes：

1) obtain multigroup electric energy data of each bar circuit in transformer station's given voltage region to be assessed；

2) corresponding bus Power unbalance rate is calculated according to every group of electric energy data, according to multigroup electric energy data and Corresponding bus Power unbalance rate sets up the mapping model of electric flux and bus Power unbalance rate；

3) create N bar virtual circuit, N is transformer station's given voltage region to be assessed interior lines way, takes any two virtual circuits Constitute circuit pair, by one of circuit to being set to current line pair, set the metering charge value of a wherein virtual circuit as Mete-wand, another virtual circuit are comparison other；The electric energy value of the virtual circuit of current line pair is set to 1 With -1, the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, the electric energy value of the corresponding virtual circuit of comparison other is - 1, the electric energy value of other virtual circuits is set to 0, forms virtual load model, N × (N-1) individual virtual load model collection is obtained Close；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, and this is sequentially each bar virtual circuit numbering number The ascending order of word, is defined as " line arrangement order "；

4) by all of electric energy data according to " line arrangement order " arrangement, and compare with all virtual load models one by one, Obtain the approach degree of electric energy data and virtual load model；

5) some groups of electric energy datas that approach degree exceedes specified threshold are selected to be replicated as typical sample, the sample after duplication This forms training sample set with original sample, and corresponding bus Power unbalance rate is as teacher's sample set；

6) create artificial neural network, train artificial neural network, virtual load model warp with training sample set, teacher's sample set Artificial neural network after training is deduced and is obtained virtual bus Power unbalance rate；

7) arrange and deduce all virtual bus Power unbalance rate obtaining, form electric power meter entirety error in dipping initial value Collection, the error amount reconstruct that overall error in dipping initial value is concentrated, obtain the global error of corresponding electric power meter.

2. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 1) detailed step include：

1.1) obtain in transformer station's given voltage region to be assessed each bar circuit in electric energy information acquisition system from time point t (1) moment starts to be spaced the electric energy information of the Each point in time of specified time period Δ T；

1.2) the electric energy value Δ W of each bar circuit in the time period between adjacent moment point is calculated according to formula (1)_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m)(n) table Show the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit when time point t (m+1) The electric energy information carved, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

1.3) by the electric energy value Δ W of each bar circuit in the time period between adjacent moment point_TmN () constitutes electric flux number to be analyzed According to group W_TmAs shown in formula (2), by electric energy data group W_TmConstitute shown in electric energy data set W such as formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

3. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 2) in be calculated shown in the function expression such as formula (4) of corresponding bus Power unbalance rate；Step 2) middle foundation electricity Energy is specifically referred to the mapping model of bus Power unbalance rate：Electric energy data set W is obtained corresponding bus electricity Unbalance factor set ε=[ε_T1,ε_T2,...,ε_Tm]^TConstitute association, form the mapping mould of electric flux and bus Power unbalance rate Type；

$\mathrm{\ϵ}=\frac{2\·\underset{n=1}{\overset{N}{\Σ}}{W}_n\·(1+e_n)}{\underset{n=1}{\overset{N}{\Σ}}|W_n\·(1+e_n)|}=\frac{2\·\underset{n=1}{\overset{N}{\Σ}}{W}_n^{\′}}{\underset{n=1}{\overset{N}{\Σ}}\left|W_n^{\′}\right|}---\left(4\right)$

In formula (4), ε represents bus Power unbalance rate, and N represents the quantity of circuit in transformer station's given voltage region to be assessed, W_n Represent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus；e_nRepresent nth bar The global error of the corresponding electric quantity metering device of circuit；W′_nIt is that electric quantity metering device shows the electric flux with control information, W '_nCome Come from electric energy data group W_Tm.

4. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 4) detailed step include：

4.1) by all electric energy data groups according to " line arrangement order " arrangement；

4.2) choose one of which in all virtual load models, represent that in array mode the model of virtual load model is special Levy, in the array of the aspect of model, the electric energy value of the corresponding virtual circuit of mete-wand is 1, the corresponding virtual circuit of comparison other Electric energy value is -1, and the electric energy value of other virtual circuits is set to 0；

4.3) in all groups of electric energy datas, traversal chooses one group of electric energy data as comparing main body W_real, electric energy data The form of group is W_real=[W₁,W₂,W₃,...,W_N], wherein W_iRepresent i-th line road transmission electricity, i ∈ [1, N], N are to be evaluated Estimate transformer station's given voltage region interior lines way；

4.4) compare the virtual load model of selection and the electric energy data group chosen, paste according to range deviation as shown in formula (5) Recency algorithm calculates its approach degree；

$\mathrm{\σ}(T^{\′},W)=1-\sqrt{\frac{\left|\right|T^{\′}-W|{|}^2}{n}}---\left(5\right)$

In formula (5), σ (T ', W) represents approach degree, and T ' represents one group of virtual load model array, and W represents that have passed through a normalizing Change the electric energy data group processing, element W (n) in W=W_realN ()/max (W), n are the number of this group electric energy data, patch The bigger typical characteristic illustrating that this group electric energy data more has virtual load model of recency；

4.5) judging whether that all electric energy datas have stepped through to finish, if having stepped through finished, redirecting execution step 5)； Otherwise, redirect execution step 4.3).

5. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 5) detailed step include：

5.1) in the comparison that one group of virtual load model participates in, five groups of maximum electric energy datas of approach degree are selected, to this batch Electric energy data group and corresponding bus Power unbalance rate are replicated, and number of copy times is closed according to formula (6) with approach degree Connection；

$l\left(\mathrm{\σ}\right)={\left(\frac{a}{1+{(b\·(\mathrm{\σ}-1\left)\right)}^2}\right)}^C---\left(6\right)$

In formula (6), l rounds as number of copy times, and σ is approach degree, and a, b, c are conversion coefficient；

5.2) the electric energy data group of duplication and corresponding bus Power unbalance rate are added to initial electric energy data group Set and bus Power unbalance rate data acquisition system, the electric energy data that N × (N-1) individual virtual load model is replicated then has 5 × N × (N-1) group, electric energy data group set becomes training sample set, and bus Power unbalance rate data acquisition system becomes teacher Sample set.

6. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 6) detailed step include：

6.1) initialize BP artificial neural network, give weights and the threshold values of each layer of network by randomly assigne；

6.2) concentrate from training sample and take one group of electric energy data, take corresponding bus Power unbalance rate from teacher's sample set Send into BP artificial neural network as input signal；

6.3) electric energy data positive calculation in BP artificial neural network obtains result, is compared with teacher's sample, according to Formula (7) calculates neutral net output layer output signal error；

δ_l=-(T_l-Y_l)Y_l(1-Y_l) (7)

In formula (7), δ_lFor the node error of output layer, T_lFor teacher's sample data of output layer, Y_lNode output for output layer Signal；

6.4) the node error of each hidden layer is calculated according to formula (8)；

${\mathrm{\δ}}_k^n=Y_k^n(1-Y_k^n)\underset{a=1}{\overset{p}{\Σ}}{\mathrm{\δ}}_l^a{W}_{kl}^a---\left(8\right)$

In formula (8),For the node error of n-th neuron in kth layer hidden layer,For n-th nerve in kth layer hidden layer The output signal node of unit,For the node error of a-th neuron in l layer, l layer is hidden layer or output layer, and p is the The neuron population of l layer,Weights for each neuron in a-th connection l layer in kth layer hidden layer；

6.5) adopt newton-Gauss innovatory algorithm BP ANN, update hidden layer and output interlayer according to formula (9) Weights, by formula (11) calculate each hidden layer node error, according to formula (12) update output layer threshold values；

$\left\{\begin{array}{c}\mathrm{\ω}(k+1)=\mathrm{\ω}\left(k\right)+\mathrm{\Δ}\mathrm{\ω}\left(k\right)=\mathrm{\ω}\left(k\right)-H_k^{-1}{g}_k\\ g_k=J^T\left(\mathrm{\ω}\right)e\left(\mathrm{\ω}\right)\\ H_k==J^T\left(\mathrm{\ω}\right)J\left(\mathrm{\ω}\right)+\mathrm{\μ}I\end{array}\right.---\left(9\right)$

In formula (9), ω (k+1) is new weights, and ω (k) is former weights, and Δ ω (k) is right value update amount, H_kAnd g_kFor intermediate variable Matrix, J^T(ω) it is Jacobian matrix, e (ω) is the vector set of single neuron node error composition, single neuron node The set value of error composition is output layer output signal error Y_l, μ is controlling elements, and I is unit matrix；Wherein Jacobean matrix Battle array J^T(ω) shown in function expression such as formula (10)；

$J=\left[\begin{array}{cccc}\frac{\∂e_1}{\∂{\mathrm{\ω}}_{11}}& \frac{\∂e_1}{\∂{\mathrm{\ω}}_{12}}& \mathrm{...}& \frac{\∂e_1}{\∂{\mathrm{\ω}}_{1O}}\\ \frac{\∂e_2}{\∂{\mathrm{\ω}}_{21}}& \frac{\∂e_2}{\∂{\mathrm{\ω}}_{22}}& \mathrm{...}& \frac{\∂e_2}{\∂{\mathrm{\ω}}_{2O}}\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ \frac{\∂e_M}{\∂{\mathrm{\ω}}_{M1}}& \frac{\∂e_M}{\∂{\mathrm{\ω}}_{M2}}& \mathrm{...}& \frac{\∂e_M}{\∂{\mathrm{\ω}}_{MO}}\end{array}\right]---\left(10\right)$

In formula (10), J represents Jacobian matrix J^T(ω), e_iRepresent the node error of i-th neuron in this hidden layer, i ∈ [1, M], M is the quantity of this layer of hidden layer neuron, ω_ijRepresent that i-th neuron of this hidden layer connects next layer of output layer jth The weights of individual neuron, j ∈ [1, O], O represent the neuronal quantity of next layer of output layer；In hidden layer, the node of neuron misses Difference is represented with formula (11) with the weights of corresponding output layer neuron：

$e_i=Y^i(1-Y^i)\underset{i=1}{\overset{M}{\Σ}}\underset{j=1}{\overset{O}{\Σ}}{\mathrm{\δ}}_j{\mathrm{\ω}}_{ij}---\left(11\right)$

In formula (11), e_iFor the node error of i-th neuron in this hidden layer, YⁱSection for i-th neuron in this hidden layer Point output signal δ_jFor the node error of j-th neuron in next layer of output layer, ω_ijUnder connecting for i-th in this hidden layer The weights of j-th neuron of one layer of output layer；

${\mathrm{\θ}}_l^m(t+1)={\mathrm{\θ}}_l^m\left(t\right)+{\mathrm{\β\δ}}_l^m---\left(12\right)$

In formula (12),For m-th neuron of output layer the t+1 moment threshold values,For m-th neuron of output layer In the threshold values of t, β is correction factor,Node error for m-th neuron in l layer hidden layer；

6.6) judge whether all of training sample set and teacher's sample set is all trained finishes, if not yet training finishes, Then redirect execution step 6.2), otherwise redirect execution step 6.7)；

6.7) error and the study number of times of BP artificial neural network are checked, if the error of artificial neural network is more than or equal to set When being worth and learning number of times and be not reaching to pre-determined number, redirect execution step 6.2)；Otherwise, terminate to train and store current BP people The network parameter of artificial neural networks；

6.8) N × (N-1) individual virtual load model is sent into the BP artificial neural network training to be deduced, obtain virtual negative The individual virtual bus Power unbalance rate of the corresponding N of lotus model × (N-1)；Individual for N × (N-1) virtual bus Power unbalance rate is entered Row is sorted out, and sorting out principle is that corresponding N × (N-1) individual virtual load model has N number of mete-wand, and each mete-wand is to should have There is N-1 comparison other, corresponding for the model of identical mete-wand bus Power unbalance rate is included into same set, formed N number of set, N-1 element of each set, the element in each set corresponds to according to " line arrangement order " arrangement, mete-wand Position be empty, using the set obtaining as the overall error in dipping initial value collection of the electric power meter of corresponding mete-wand.

7. the electric power meter error detection method based on big data deduction according to claim 1 it is characterised in that Step 7) detailed step include：

7.1) concentrate in N number of electric power meter entirety error in dipping initial value and all add zero error, fill up mete-wand and correspond to The evolution initial value collection being empty position, forming as shown in formula (13)；

$\mathrm{\ϵ}=\left[\begin{array}{c}{\mathrm{\ϵ}}_{1,x}\\ {\mathrm{\ϵ}}_{2,x}\\ {\mathrm{\ϵ}}_{3,x}\\ \mathrm{...}\\ {\mathrm{\ϵ}}_{N-1,x}\\ {\mathrm{\ϵ}}_{N,x}\end{array}\right]=\left[\begin{array}{ccccc}0& {\mathrm{\ϵ}}_{1,2}& {\mathrm{\ϵ}}_{1,3}& \mathrm{...}& {\mathrm{\ϵ}}_{1,N}\\ {\mathrm{\ϵ}}_{2,1}& 0& {\mathrm{\ϵ}}_{2,3}& \mathrm{...}& {\mathrm{\ϵ}}_{2,N}\\ {\mathrm{\ϵ}}_{3,1}& {\mathrm{\ϵ}}_{3,2}& 0& \mathrm{...}& {\mathrm{\ϵ}}_{3,N}\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ {\mathrm{\ϵ}}_{N-1,1}& {\mathrm{\ϵ}}_{N-1,2}& \mathrm{...}& 0& {\mathrm{\ϵ}}_{N-1,N}\\ {\mathrm{\ϵ}}_{N,1}& {\mathrm{\ϵ}}_{N,2}& \mathrm{...}& {\mathrm{\ϵ}}_{N,N-1}& 0\end{array}\right]---\left(13\right)$

In formula (13), ε represents evolution initial value collection, ε_i,jRepresent that, on the basis of i-th set of metering device, jth covers metering device Initial value, ε_i,xRepresent on the basis of i-th set of metering device, the Initial value of all metering devices (includes first set metering dress Put) vector, i ∈ [1, N], j ∈ [1, N-1], N represent metering device quantity；

7.2) traversal chooses one of evolution initial value collection, takes the median of each element numerical value in its set；

7.3) by this evolution initial value integrate according to median as translation distance carry out translation obtain shift evolution initial value collection, according to " line Road puts in order " obtain the data that displacement evolution initial value concentration is associated with each metering device, as overall error in dipping；

7.4) judge whether that all evolution initial value collection have stepped through selection and finish, if not yet traversal selection finishes, redirect and hold Row step 7.2)；Otherwise, it is determined that all evolution initial value collection are all translated, and often set electric quantity metering device all obtain N number of whole Shown in body error in dipping formula (14)；

$\begin{array}{c}{\mathrm{\ϵ}}^{\′}=\left[\begin{array}{ccccc}0& {\mathrm{\ϵ}}_{1,2}& {\mathrm{\ϵ}}_{1,3}& \mathrm{...}& {\mathrm{\ϵ}}_{1,N}\\ {\mathrm{\ϵ}}_{2,1}& 0& {\mathrm{\ϵ}}_{2,3}& \mathrm{...}& {\mathrm{\ϵ}}_{2,N}\\ {\mathrm{\ϵ}}_{3,1}& {\mathrm{\ϵ}}_{3,2}& 0& \mathrm{...}& {\mathrm{\ϵ}}_{3,N}\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ {\mathrm{\ϵ}}_{N-1,1}& {\mathrm{\ϵ}}_{N-1,2}& \mathrm{...}& 0& {\mathrm{\ϵ}}_{N-1,N}\\ {\mathrm{\ϵ}}_{N,1}& {\mathrm{\ϵ}}_{N,2}& \mathrm{...}& {\mathrm{\ϵ}}_{N,N-1}& 0\end{array}\right]-\left[\begin{array}{c}median\left({\mathrm{\ϵ}}_{1,x}\right)\\ median\left({\mathrm{\ϵ}}_{2,x}\right)\\ median\left({\mathrm{\ϵ}}_{3,x}\right)\\ \mathrm{...}\\ median\left({\mathrm{\ϵ}}_{N-1,x}\right)\\ median\left({\mathrm{\ϵ}}_{N,x}\right)\end{array}\right]\\ =\left[\begin{array}{ccccc}{\mathrm{\ϵ}}_{1,1}^{\′}& {\mathrm{\ϵ}}_{1,2}^{\′}& {\mathrm{\ϵ}}_{1,3}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{1,N}^{\′}\\ {\mathrm{\ϵ}}_{2,1}^{\′}& {\mathrm{\ϵ}}_{2,2}^{\′}& {\mathrm{\ϵ}}_{2,3}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{2,N}^{\′}\\ {\mathrm{\ϵ}}_{3,1}^{\′}& {\mathrm{\ϵ}}_{3,2}^{\′}& {\mathrm{\ϵ}}_{3,3}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{3,N}^{\′}\\ \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}& \mathrm{...}\\ {\mathrm{\ϵ}}_{N-1,1}^{\′}& {\mathrm{\ϵ}}_{N-1,2}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{N-1,N-1}^{\′}& {\mathrm{\ϵ}}_{N-1,N}^{\′}\\ {\mathrm{\ϵ}}_{N,1}^{\′}& {\mathrm{\ϵ}}_{N,2}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{N,N-1}^{\′}& {\mathrm{\ϵ}}_{N,N}^{\′}\end{array}\right]\\ =\left[\begin{array}{cccc}{\mathrm{\ϵ}}_{x,1}^{\′}& {\mathrm{\ϵ}}_{x,2}^{\′}& \mathrm{...}& {\mathrm{\ϵ}}_{x,N}^{\′}\end{array}\right]\end{array}---\left(14\right)$

In formula (14), ε ' represents error reconstruct collection, median (ε_i,x) represent ε_i,xMedian, ε '_i,jRepresent Initial value, ε_i,j Reconstruction value after translation, translational movement is median (ε_i,x), i ∈ [1, N], j ∈ [1, N], N represent metering device quantity；

7.5) meansigma methodss of every set N number of overall error in dipping of metering device are asked for as this according to function expression shown in formula (15) The overall error in dipping obtaining finally deduced by set metering device；

ε_n=[avg (ε '_x,1),avg(ε′_x,2),…avg(ε′_x,N)] (15)

In formula (14), ε_nRepresent that n-th set of metering device finally deduces the overall error in dipping obtaining, avg (ε '_x,i) represent set ε′_x,iThe meansigma methodss of middle all elements.

8. a kind of electric power meter error detecting system based on big data deduction is it is characterised in that include：

Electric energy data data capture unit, for obtaining multigroup electricity of each bar circuit in transformer station's given voltage region to be assessed Energy datum；

Bus Power unbalance rate computing unit is uneven for being calculated corresponding bus electricity according to every group of electric energy data Weighing apparatus rate, sets up electric flux and bus Power unbalance rate according to multigroup electric energy data and corresponding bus Power unbalance rate Mapping model；

Virtual load model sets up unit, and for creating N bar virtual circuit, N is transformer station's given voltage region to be assessed interior lines Way, takes any two virtual circuits to constitute circuit pair, by one of circuit to being set to current line pair, sets wherein one The metering charge value of virtual circuit is mete-wand, another virtual circuit is comparison other；Dummy line by current line pair The electric energy value on road is set to 1 and -1, and the electric energy value of the wherein corresponding virtual circuit of mete-wand is 1, and comparison other corresponds to The electric energy value of virtual circuit is -1, and the electric energy value of other virtual circuits is set to 0, forms virtual load model, be obtained N × (N-1) individual virtual load model set；In each virtual load model, same sequence is pressed in the arrangement of virtual electric energy value, this order For the ascending order of each bar virtual circuit numerical digit, it is defined as " line arrangement order "；

Approach degree computing unit, for by all of electric energy data according to " line arrangement order " arrangement, and one by one with all Virtual load model compares, and obtains the approach degree of electric energy data and virtual load model；

Sample set signal generating unit, some groups of electric energy datas for selecting approach degree to exceed specified threshold enter as typical sample Row replicates, and the sample after duplication forms training sample set with original sample, and corresponding bus Power unbalance rate is as teacher's sample This collection；

Artificial neural network training unit, for creating artificial neural network, is trained artificial with training sample set, teacher's sample set Neutral net, the artificial neural network after virtual load model is trained is deduced and is obtained virtual bus Power unbalance rate；

Global error computing unit, deduces, for arranging, all virtual bus Power unbalance rate obtaining, and forms electric energy metrical Device entirety error in dipping initial value collection, the initial value collection reconstruct that overall error in dipping initial value is concentrated, obtain corresponding electric energy metrical dress The global error put.

9. the electric power meter error detecting system based on big data deduction according to claim 8 it is characterised in that Described electric energy data data capture unit includes：

Electric energy information acquisition module, for obtaining transformer station's given voltage region to be assessed in electric energy information acquisition system Interior each bar circuit starts to be spaced the electric energy information of the Each point in time of specified time period Δ T from time point t (1) moment；

Electric energy magnitude calculation acquisition module, for calculating the electricity of each bar circuit in the time period between adjacent moment point according to formula (1) Energy value Δ W_Tm(n)；

ΔW_Tm(n)=W_t(m+1)(n)-W_t(m)(n) (1)

In formula (1), Δ W_TmN () represents that nth bar circuit arrives the electric energy value between t (m+1) time period Tm, W in t (m)_t(m)(n) table Show the electric energy information in time point t (m) moment for the nth bar circuit, W_t(m+1)N () represents nth bar circuit when time point t (m+1) The electric energy information carved, Δ W_TmN () is just to represent that electric flux is sent bus, sent into bus for negative indication electric flux；

Electric energy data group builds module, for by the electric energy value Δ W of each bar circuit in the time period between adjacent moment point_Tm N () constitutes electric energy data group W to be analyzed_TmAs shown in formula (2), by electric energy data group W_TmConstitute electric energy data set W As shown in formula (3)；

W_Tm=[Δ W_Tm(1),ΔW_Tm(2),...,ΔW_Tm(N)] (2)

W=[W_T1,W_T2,...,W_Tm]^T(3).

10. the electric power meter error detecting system based on big data deduction according to claim 8, its feature exists In described bus Power unbalance rate computing unit is calculated the function expression such as formula of corresponding bus Power unbalance rate (4) shown in；Described bus Power unbalance rate computing unit sets up the mapping model tool of electric flux and bus Power unbalance rate Body refers to：Electric energy data set W is obtained corresponding bus Power unbalance rate set ε=[ε_T1,ε_T2,...,ε_Tm]^TConstitute Association, forms the mapping model of electric flux and bus Power unbalance rate；

$\mathrm{\ϵ}=\frac{2\·\underset{n=1}{\overset{N}{\Σ}}{W}_n\·(1+e_n)}{\underset{n=1}{\overset{N}{\Σ}}|W_n\·(1+e_n)|}=\frac{2\·\underset{n=1}{\overset{N}{\Σ}}{W}_n^{\′}}{\underset{n=1}{\overset{N}{\Σ}}\left|W_n^{\′}\right|}---\left(4\right)$

In formula (4), ε represents bus Power unbalance rate, and N represents the quantity of circuit in transformer station's given voltage region to be assessed, W_n Represent nth bar line transmission electricity, W_nIt is worth for just representing that electricity sends bus, negative indication electricity sends into bus；e_nRepresent nth bar The global error of the corresponding electric quantity metering device of circuit；W_n' it is that electric quantity metering device shows the electric flux with control information, W '_nCome Come from electric energy data group W_Tm.