CN105913009A - Electric appliance type identifier - Google Patents

Abstract

The invention discloses an electric appliance type identifier, comprising an information collection module, an information processing module and a communication module; the electric appliance type identifier simultaneously adopts electric appliance start-up current characteristics including start-up surge current, start-up average current and start-up current impulse and a load current frequency spectrum characteristic as the identification characteristic of the electric appliance, and the characteristic information is abundant; a combined classifier comprising a support vector machine classifier and a Bayes classifier is adopted to perform identification classification; the comprehensive identification is performed by giving consideration to two classifiers and the identification accuracy is high; and the obtaining method for the start-up current characteristic and the load current spectrum characteristic is simple and reliable. The electric appliance load type intelligent identification device is applicable to some public places like the student dormitory, the large-scale pedlar market, etc, where need the electric appliance load management, and is also applicable to the occasions where need to perform statistic on the electric appliance load types and also need the electric equipment management.

Description

Appliance type evaluator

Technical field

The present invention relates to a kind of equipment identification and sorter, especially relate to a kind of appliance type evaluator.

Background technology

At present, electrical equipment character or the appliance type recognition methods of main flow includes based on bearing power coefficient of colligation algorithm Electrical appliance recognition, electrical appliance recognition based on electromagnetic induction, electrical appliance recognition based on neural network algorithm, based on week The electrical appliance recognition etc. of phase property discrete transform algorithm.Various methods all can be to a certain degree the knowledge realizing electrical equipment character Not, but owing to characteristic properties is single, means of identification is single, generally there is generalization ability not and can not the asking of entirely accurate identification Topic.

Summary of the invention

It is an object of the invention to, for the defect of present prior art, it is provided that a kind of electricity being capable of efficient identification Device type identifier.Described appliance type evaluator includes information acquisition module, message processing module, communication module.

Described information acquisition module is for gathering the load current of electrical equipment and being converted into current digital signal；Described electric current number Word signal is sent to message processing module；Described message processing module, according to the current digital signal of input, uses assembled classification Device carries out appliance type identification；Described communication module is for sending the appliance type recognition result of message processing module to upper Machine.

The input feature vector of described assembled classifier includes the starting current feature of electrical equipment and the load current frequency spectrum spy of electrical equipment Levy；Described assembled classifier includes support vector machine classifier and Bayes classifier；Described starting current feature includes starting Current rush, startup average current, starting current momentum.

Described information acquisition module includes current sensor, preamplifier, wave filter, A/D converter；At described information The core of reason module is DSP, or is ARM, or is single-chip microcomputer, or is FPGA.

Described A/D converter can use the A/D converter that the core of message processing module includes.

Described information acquisition module, message processing module, all or part of function of communication module are integrated in a piece of SoC On.

Described communication module also receives the related work instruction of host computer；Communication between described communication module and host computer Mode includes communication and wire communication mode；Described communication include ZigBee, bluetooth, WiFi, 433MHz number passes mode；Described wire communication mode includes 485 buses, CAN, the Internet, power carrier mode.

Described load current spectrum signature is prepared by the following:

Step one, the steady state current signals of acquisition electrical equipment, and it is converted into the steady-state current digital signal of correspondence；

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic；

Step 3, using odd harmonic signal relative magnitude that overtone order in load current spectral characteristic is n time as negative Load current spectrum feature, n=1,3 ..., M；Described M represents that harmonic wave high reps and M are more than or equal to 3.

In described assembled classifier, support vector machine classifier is Main classification device, and Bayes classifier is subsidiary classification device.

Described assembled classifier carries out appliance type knowledge method for distinguishing: when Main classification device successfully realizes appliance type identification Time, the recognition result that appliance type recognition result is assembled classifier of Main classification device；When Main classification device fails to realize electric type Type identification, and the recognition result of Main classification device is 2 kinds or two or more appliance type, 2 kinds or 2 exported by Main classification device Planting in above appliance type recognition result, the appliance type that in the output of subsidiary classification device, probability is the highest is as the electricity of assembled classifier Device type identification result；When Main classification device fails to realize failing to be given in appliance type identification, and the recognition result of Main classification device During the appliance type identified, the appliance type that in being exported by subsidiary classification device, probability is the highest is as the appliance type of assembled classifier Recognition result.

Described starting current feature is prepared by the following by message processing module:

Before step 1, appliance starting, start the load current continuous sampling to electrical equipment and load current size is sentenced Disconnected；When load current virtual value is more than ε, it is determined that electrical equipment starts start and turn to step 2；Described ε is the numerical value more than 0；

Step 2, load current to electrical equipment carry out continuous sampling, with power frequency period for unit computational load current effective value And preserve；Calculate the meansigma methods of the load current virtual value of nearest N number of power frequency period；Every within nearest N number of power frequency period The load current virtual value of individual power frequency period, compared with the meansigma methods of the load current virtual value of this N number of power frequency period, fluctuates When amplitude is respectively less than the relative error range E set, it is determined that electrical equipment enters steady statue, turns to step 3；The value model of described N Enclose for 50-500；The span of described E is 2%-20%；

Step 3, using the meansigma methods of the load current virtual value within nearest N number of power frequency period as electric appliance load stable state electricity Stream；Electrical equipment is started Startup time to the time between nearest N number of power frequency period initial time as the start-up course time；Calculate Electrical equipment start to start after the meansigma methods of electric appliance load current effective value within L power frequency period and electric appliance load steady-state current it Between ratio, using this ratio as the startup current rush of electrical equipment；Calculate the electric appliance load within the start-up course time of electrical equipment Ratio between meansigma methods and the electric appliance load steady-state current of current effective value, this ratio is the most electric as the startup of electrical equipment Stream；Calculate electrical equipment starts average current and the product between the start-up course time, using this product as the starting current of electrical equipment Momentum；The span of described L is 1-5.

The input feature vector of described assembled classifier also includes electrical equipment steady-state current.

The invention has the beneficial effects as follows: use the load current spectrum signature of the starting current feature of electrical equipment, electrical equipment simultaneously As the identification feature of described appliance type evaluator, characteristic information enriches；Employing includes support vector machine classifier and pattra leaves The assembled classifier of this grader is identified classification, and the feature taking into account support vector machine classifier and Bayes classifier is carried out Comprehensive identification, generalization ability is high with recognition accuracy；There is provided includes startup current rush, startup average current, starting current Momentum is at interior starting current characteristic-acquisition method, and load current spectrum signature acquisition methods is simple, reliable.

Accompanying drawing explanation

Fig. 1 is the structural representation of the embodiment of the present invention；

Fig. 2 is the start-up course current waveform of electric filament lamp desk lamp；

Fig. 3 is the start-up course current waveform of the resistive loads such as resistance furnace；

Fig. 4 is the start-up course current waveform of monophase machine class load；

Fig. 5 is computer and the start-up course current waveform of Switching Power Supply class load；

Fig. 6 is evaluator identification appliance type method flow diagram.

Detailed description of the invention

Below in conjunction with accompanying drawing, the invention will be further described.

Fig. 1 is the structural representation of the embodiment of the present invention, including information acquisition module 101, message processing module 102, leads to Letter module 103.

Information acquisition module 102 is used for gathering the load current of electrical equipment and load current being converted into current digital signal, Current digital signal is sent to message processing module 102.Information acquisition module includes current sensor, preamplifier, filter The ingredients such as ripple device, A/D converter, are respectively completed the sensing of load current signal, amplify, filter and analog-digital conversion function. When load current range is bigger, the preamplifier with programmable function can be selected, or increase again before A/D converter Add an independent programmable amplifier, the load current that scope is bigger is carried out Discrete control and amplifies, make input to A/D converter Voltage signal range be maintained at rational interval, it is ensured that conversion accuracy.Wave filter is used for filtering high fdrequency component, it is to avoid frequency spectrum mixes Folded.

Message processing module 102, according to the current digital signal of input, uses and includes support vector machine classifier and pattra leaves The assembled classifier of this grader realizes appliance type identification.The input feature vector of assembled classifier includes that the starting current of electrical equipment is special Seek peace the load current spectrum signature of electrical equipment.The core of message processing module 102 is DSP, ARM, single-chip microcomputer, or is FPGA. When the core of message processing module includes A/D converter and this A/D converter meet require time, information acquisition module 101 In A/D converter can use the A/D converter that the core of message processing module 102 includes.

Recognition result, for realizing the communication between host computer, is sent to host computer by communication module 103.Communication module Communication mode between 102 and host computer includes communication and wire communication mode, the side wireless communication that can use Formula includes the modes such as ZigBee, bluetooth, WiFi, 433MHz number biography, can include 485 buses, CAN in the wire communication mode used The modes such as bus, the Internet, power carrier.Communication module 103 can also receive the related work instruction of host computer, completes to specify Task.Host computer can be the server of administration section, it is also possible to be various work stations, or various mobile whole End.

Information acquisition module 101, message processing module 102, communication module 103 all or part of function can be integrated On a piece of SoC, reduce evaluator volume, convenient installation.

Different electric equipments has different starting current features.It is illustrated in figure 2 the start-up course of electric filament lamp desk lamp Current waveform.Electric filament lamp is by filament electrified regulation to incandescent state, utilizes heat radiation to send the electric light source of visible ray.Electric filament lamp Filament generally with resistant to elevated temperatures tungsten manufacture, but the resistance of tungsten varies with temperature greatly, with R_tRepresent that tungsten filament is when t DEG C Resistance, with R₀Represent the tungsten filament resistance when 0 DEG C, then both have following relation

R_t=R₀(1+0.0045t)

Such as, if the temperature that the filament of electric filament lamp (tungsten filament) is when normal work is 2000 DEG C, one " 220V 100W's " The filament of the electric filament lamp resistance when 2000 DEG C of normal work is

$R_t=\frac{U^2}{P}=\frac{220\×220}{100}=484\mathrm{\Ω}$

Its resistance of 0 DEG C when no power is

$R_0=\frac{R_t}{1+0.0045t}=\frac{484}{1+0.0045\×2000}=48.4\mathrm{\Ω}$

Its resistance of 20 DEG C when no power is

R₂₀=R₀(1+0.0045t)=52.8 Ω

I.e. electric filament lamp exceedes 9 times of its rated current at the immediate current starting energising, and maximum starting current occurs to exist Startup time.Along with the rising of electric filament lamp tungsten filament temperature, the load current of electric filament lamp exponentially reduces, subsequently into surely Determine state.

If electrical equipment steady-state current virtual value is I_W, and definition electric current virtual value entrance electrical equipment steady-state current virtual value Within one relative error range set and stably within this relative error range, then electrical equipment enters steady statue.Phase Range of error be can be set as 10%, it is also possible to be set as the value between the 2%-20% such as 2%, 5%, 15%, 20%.Fig. 2 In, the relative error range set is as 10%, when the load current of electric filament lamp is exponentially reduced to its I_W10% by mistake During difference scope, such as the moment T in Fig. 2_S, start-up course terminates.The start-up course time of electric filament lamp is T_S。I_WFor virtual value.

Select to start current rush I_G, start average current I_D, starting current momentum Q_IStarting current as electrical equipment is special Levy；Start current rush I_G, start average current I_DIt is per unit value.It is specifically defined and is: start current rush I_GFor appliance starting T after beginning₂Electric appliance load current average within time and electric appliance load steady-state current I_WRatio；Start average current I_D For appliance starting time T_SWithin electric appliance load current average and electric appliance load steady-state current I_WRatio；Starting current rushes Amount Q_IFor starting average current I_DWith start-up course time T_SProduct, dimension is ms.Electric appliance load electric current, electric appliance load stable state Electric current is virtual value.T₂Span be 20-100ms, or 1-5 power frequency period；Such as, T₂Value 40ms, i.e. 2 Individual power frequency period.Start current rush I_GReflection is the electric current impulse size in the short time after electric appliance load starts.In part In the start-up course of electrical equipment, as the actual start-up course time T having electrical equipment_SLess than the T set₂Time, when making the start-up course of electrical equipment Between T_SEqual to T₂.Start average current I_DReflection is the electric current entirety size in electric appliance load start-up course.Starting current momentum Q_IReflect is the bulk strength of electric appliance load startup.

In Fig. 2, the startup current rush I of electric filament lamp_GFor T₀(electric filament lamp Startup time, electric current is I₀) to T₂(setting time Carving, electric current is I₂The current average of electric filament lamp and the steady-state current I of electric filament lamp between)_WRatio.Start average current I_DFor T₀(electric filament lamp Startup time) is to T_SThe current average of electric filament lamp and electric filament lamp between (electric filament lamp start-up course end time) Steady-state current I_WRatio.Starting current momentum Q_IAverage current I is started for electric filament lamp_DWith start-up course time T_SProduct.

It is illustrated in figure 3 the start-up course current waveform of the resistive loads such as resistance furnace.The resistive loads such as resistance furnace are led to Frequently with the lectrothermal alloy wire such as nickel chromium triangle, ferrum-chromium-aluminum, its common feature is that resistance temperature correction factor is little, and resistance value is stable.With board As a example by number being the nichrome wire of Cr20Ni80, its resistance correction factor when 1000 DEG C is 1.014, relative when i.e. 1000 DEG C In 20 DEG C time, the trade mark is that the nichrome wire resistance of Cr20Ni80 only increases by 1.4%.The resistive loads such as resistance furnace open in energising Steady statue is entered, the actual start-up course time T of the resistive load such as resistance furnace time dynamic_S=0, therefore, make resistance furnace etc. The actual start-up course time T of resistive load_S=T₂；Such as, T is worked as₂When being set as 40ms, the start-up course time the most now T_SAlso it is 40ms.Due to resistive load T₀Moment electric current I₀、T₂Moment electric current I₂Steady-state current I with resistive load_WIt is equal, Therefore, the startup current rush I of resistive load_G=1, start average current I_D=1.

It is illustrated in figure 4 the start-up course current waveform of monophase machine class load.The load of monophase machine class had both had inductance Property load characteristic, has again counter electromotive force load characteristic.Startup time, due to the effect of inductance, the starting current of Startup time I₀It is 0；Rise rapidly with after current, before counter electromotive force of motor does not sets up, reach current peak I_M；Hereafter, motor speed increases Adding, motor load electric current progressively reduces, until entering steady statue.In Fig. 4, the startup current rush I of monophase machine class load_G For T₀(monophase machine class load Startup time, electric current is I₀) to T₂(in the moment of setting, electric current is I₂Between), monophase machine class is born The current average carried and steady-state current I_WRatio.Start average current I_DFor T₀(monophase machine class load Startup time) extremely T_SThe current average of monophase machine class load and steady-state current I between (monophase machine class load start-up course end time)_W's Ratio.Starting current momentum Q_IAverage current I is started for the load of monophase machine class_DWith start-up course time T_SProduct.

It is illustrated in figure 5 computer and the start-up course current waveform of Switching Power Supply class load.Computer and Switching Power Supply Class load, because the impact on electric capacity charging, can produce a surge current the biggest in startup moment, and its peak value can reach surely State current effective value I_WSeveral times to tens times, the time is 1 to 2 power frequency period.Owing to computer and Switching Power Supply class load The startup time short, its start-up course time T_SLikely to be less than the T set₂；As its start-up course time T_SLess than the T set₂ Time, make T_SEqual to T₂.In Fig. 5, computer and the startup current rush I of Switching Power Supply class load_GFor T₀(computer and switch electricity Source class load Startup time, electric current is I₀) to T₂(in the moment of setting, electric current is I₂Computer and the load of Switching Power Supply class between) Current average and steady-state current I_WRatio.Start average current I_DFor T₀(when computer and the load of Switching Power Supply class start Carve) to T_SComputer and the electricity of Switching Power Supply class load between (computer and Switching Power Supply class load start-up course end time) Levelling average and steady-state current I_WRatio.Starting current momentum Q_ILoad for computer and Switching Power Supply class and start average current I_DWith start-up course time T_SProduct.

The method of the starting current feature obtaining electrical equipment is:

Before appliance starting, when load current value is 0 (being not keyed up) or the least (being in holding state), message processing module 102 i.e. start load current is carried out continuous sampling；The load current value virtual value obtained when sampling starts more than 0 or opens When beginning more than the standby current of electrical equipment, i.e. judging that electrical equipment has been started up, recording this moment is T₀.With a less non-negative threshold Value ε distinguishes the load current value before and after appliance starting, when special hour of ε value, such as, during ε value 1mA, described evaluator Not considering ideal case, i.e. thinking standby is also the starting state of electrical equipment；When ε value is less but it is more than the standby current of electrical equipment Time, such as, during ε value 20mA, the holding state of electrical equipment can be considered inactive state by described evaluator, but also can simultaneously The electrical equipment that Partial Power is the least causes and Lou identifies.

Message processing module 102 carries out continuous sampling to load current, and with power frequency period for unit computational load electric current Virtual value also preserves；When electrical equipment has been started up, and after continuous sampling reaches N number of power frequency period, while sampling, Continuous plus is Meansigma methods I of the load current virtual value of nearly N number of power frequency period_V；Within message processing module 102 is to nearest N number of power frequency period The load current virtual value of each power frequency period compares with the meansigma methods of the load current virtual value of this N number of power frequency period, When error (or fluctuation) amplitude is respectively less than the relative error range E set, it is determined that electrical equipment enters steady statue, this nearest N number of work Frequently the initial time in cycle is the finish time of start-up course, and recording this moment is T₁(as Figure 2-Figure 5).

Using the meansigma methods of the load current virtual value within nearest N number of power frequency period as electric appliance load steady-state current I_W； Electrical equipment is started Startup time T₀To nearest N number of power frequency period initial time T₁Between time as start-up course time T_S.Meter Calculate T₀To the T set₂Between the load current meansigma methods of (after i.e. electrical equipment starts to start within 1-5 power frequency period) electric with stable state Stream I_WRatio, using this ratio as the startup current rush I of electrical equipment_G.Calculate T₀To T_SBetween load current meansigma methods with steady State electric current I_WRatio, using this ratio as the startup average current I of electrical equipment_D.Calculate the startup average current I of electrical equipment_DWith startup Process time T_SProduct, using this product as the starting current momentum Q of electrical equipment_I。

Owing to not knowing electrical equipment steady-state current virtual value I in advance_W, therefore, by N number of power frequency period, i.e. one section duration T_P Within fluctuation range less than the meansigma methods of load current virtual value during the relative error range E set as electrical equipment steady-state current Virtual value I_W.Owing to the start-up course of ordinary appliances is very fast, so, T_PSpan be 1-10s, typical value is 2s, accordingly The typical value that span is 50-500, N of power frequency period quantity N be 100.The value model of described relative error range E Enclosing the typical value for 2%-20%, E is 10%.

The input feature vector of assembled classifier also includes the load current spectrum signature of electrical equipment.The load current frequency spectrum of electrical equipment is special Levy and controlled information acquisition module 101 by message processing module 102, obtained by following steps:

Step one, enter after steady statue until electrical equipment, obtain the steady state current signals of electrical equipment, and be converted into correspondence Steady-state current digital signal.

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic.For ensureing Fu Being smoothed out of vertical leaf transformation, at the steady state current signals of aforementioned acquisition electrical equipment, and is converted into the steady-state current number of correspondence During word signal, the accuracy and speed of A/D converter needs to meet the requirement of Fourier transform, and sample frequency can set For 10kHz, or other numerical value；Message processing module 102 carries out FFT computing to the steady-state current digital signal collected, Calculate its frequency spectrum.

Step 3, using the nth harmonic signal relative magnitude in load current spectral characteristic as load current spectrum signature, Wherein, n=1,2 ..., M；When forming the input feature value of assembled classifier, nth harmonic signal relative magnitude is input spy Levy according to 1 in vector, 2 ..., the order of M is arranged in order.Owing to load current spectral characteristic is mainly made up of odd harmonic, remove Outside minority electric equipment, even-order harmonic component is almost 0, accordingly it is also possible to be n by overtone order in load current spectral characteristic Secondary odd harmonic signal relative magnitude sequentially as load current spectrum signature, wherein, n=1,3 ..., M.During n=1 1 time Harmonic wave is fundamental frequency.Described harmonic signal relative magnitude is harmonic signal amplitude and electrical equipment steady-state current virtual value I_WRatio Value.Described M represents harmonic wave high reps, and generally, M is more than or equal to 3.

In assembled classifier, support vector machine classifier is Main classification device, and Bayes classifier is subsidiary classification device.Combination The input feature vector of grader includes aforesaid starting current feature and load current spectrum signature, the input feature vector of assembled classifier Input feature vector and the input feature vector of Bayes classifier simultaneously as support vector machine classifier.

Being illustrated in figure 6 evaluator identification appliance type method flow diagram, concrete steps include:

Step A, wait appliance starting；

Step B, collection appliance starting current data also preserve, until appliance starting process terminates；

The appliance starting current data that step C, analysis gather, obtains the starting current feature of electrical equipment；

Step D, gather electrical equipment steady operation time data and preserve；

Data during the electrical equipment steady operation that step E, analysis gather, obtain the load current spectrum signature of electrical equipment；

Step F, using starting current feature and load current spectrum signature as the input feature vector of assembled classifier；Combination point Class device carries out appliance type identification；

Step G, output appliance type recognition result.

Described assembled classifier carries out appliance type knowledge method for distinguishing: know when Main classification device successfully realizes appliance type Not, when i.e. the recognition result of Main classification device output is that in unique appliance type, i.e. recognition result, unique appliance type is for being, Using the appliance type of Main classification device identification as the appliance type recognition result of assembled classifier；When Main classification device fails to realize electricity Device type identification, and the recognition result of Main classification device be 2 kinds or two or more appliance type, i.e. recognition result have 2 kinds or When two or more appliance type is for being, by Main classification device export 2 kinds or two or more appliance type recognition result in, auxiliary point The appliance type that in the output of class device, probability is the highest is as the appliance type recognition result of assembled classifier；When Main classification device fails reality Failing to be given in the appliance type of identification, i.e. recognition result in existing appliance type identification, and the recognition result of Main classification device does not has When appliance type is for being, the appliance type that in being exported by subsidiary classification device, probability is the highest is known as the appliance type of assembled classifier Other result.

As a example by a simple embodiment 1, illustrate that assembled classifier carries out appliance type and knows method for distinguishing.It is provided with one Individual assembled classifier, its input feature vector is x={I_G, I_D, Q_I, A₁, A₂, A₃, A₄, A₅, wherein, I_GIt is to start current rush；I_DIt is Start average current；Q_IIt it is starting current momentum；A₁、A₂、A₃、A₄、A₅For the 1-5 rd harmonic signal in load current spectral characteristic Relative magnitude.The output of assembled classifier is { B₁, B₂, B₃, B₄, B₁、B₂、B₃、B₄Represent respectively assembled classifier to electric filament lamp, Resistance furnace, hair-dryer, the recognition result output of computer, recognition result B₁、B₂、B₃、B₄Value be two-value key words sorting. The input feature vector of Main classification device is also x={I_G, I_D, Q_I, A₁, A₂, A₃, A₄, A₅, its output is { F₁, F₂, F₃, F₄, F₁、F₂、F₃、 F₄Represent Main classification device respectively the recognition result of electric filament lamp, resistance furnace, hair-dryer, computer is exported, recognition result F₁、F₂、 F₃、F₄Value be also two-value key words sorting.The input feature vector of subsidiary classification device is similarly x={I_G, I_D, Q_I, A₁, A₂, A₃, A₄, A₅, its output is { P (y₁| x), P (y₂| x), P (y₃| x), P (y₄| x) }, P (y₁|x)、P(y₂|x)、P(y₃|x)、P(y₄|x) For the posterior probability of subsidiary classification device output, P (y₁|x)、P(y₂|x)、P(y₃|x)、P(y₄| the mutual size between x) shows auxiliary The current input feature helping grader represents that identified electrical equipment belongs to the probability of electric filament lamp, resistance furnace, hair-dryer, computer Size.

In embodiment 1, B₁、B₂、B₃、B₄Key words sorting and F₁、F₂、F₃、F₄Key words sorting all take 1,0.Key words sorting When being 1, corresponding appliance type mates with current input feature, for the recognition result confirmed, in other words corresponding appliance type Recognition result is yes；When key words sorting is 0, corresponding appliance type does not mates with input feature vector, fails to become the identification of confirmation As a result, corresponding appliance type recognition result is no in other words.

In embodiment 1, if the identification key words sorting of certain Main classification device is F₁F₂F₃F₄=0100, then it is assumed that main point Class device successfully realizes appliance type identification, therefore, does not consider the recognition result of subsidiary classification device, directly makes B₁B₂B₃B₄=0100, The i.e. recognition result of assembled classifier is: identified electrical equipment is resistance furnace.

In embodiment 1, if the identification key words sorting of certain Main classification device is F₁F₂F₃F₄=1010, then it is assumed that main point Class device fails to realize appliance type identification, and the recognition result of Main classification device is 2 kinds or two or more appliance type；Set this again Time subsidiary classification device recognition result meet P (y₁|x)<P(y₃| x), then make B₁B₂B₃B₄=0010, i.e. the identification of assembled classifier Result is: identified electrical equipment is hair-dryer.

In embodiment 1, if the identification key words sorting of certain Main classification device is F₁F₂F₃F₄=0000, then it is assumed that main point Class device fails to realize failing to provide in appliance type identification, and the recognition result of Main classification device the appliance type of identification；Set this again Time subsidiary classification device recognition result meet P (y₁|x)>P(y₂| x) and P (y₁|x)>P(y₃| x) and P (y₁|x)>P(y₄| x), then Make B₁B₂B₃B₄=1000, i.e. the recognition result of assembled classifier is: identified electrical equipment is electric filament lamp.

Assembled classifier, the recognition result key words sorting of Main classification device can also use other scheme, such as, use respectively Key words sorting 1 ,-1, or 0,1, or-1,1, and other schemes represent corresponding electric appliance recognition result be yes, no. The key words sorting scheme of assembled classifier and Main classification device can be identical, it is also possible to differs.

In the input feature vector of described assembled classifier, it is also possible to include electrical equipment steady-state current virtual value I_W.Such as, there are 2 kinds Different electrical equipment, electric cautery and resistance furnace need to identify, electric cautery, resistance furnace are all pure resistor loads, and all have resistance temperature Degree correction factor is little, the common feature that resistance value is stable.Therefore, the aforesaid starting current feature of simple dependence and load current frequency They cannot be made a distinction by spectrum signature.Input feature vector increases electrical equipment steady-state current virtual value I_WAfter, electric cautery power is little, electricity Device steady-state current virtual value I_WLittle；Resistance furnace power is big, electrical equipment steady-state current virtual value I_WGreatly, feature is different, and assembled classifier can To carry out and to complete identifying.

Subsidiary classification device is Bayes classifier.NBC grader (Naive Bayes Classifier), TAN can be selected to classify Three kinds of Bayes classifiers such as device (crown pruning), BAN grader (Bayes classifier of enhancing) it In one as subsidiary classification device.

Embodiment 2 selects NBC grader as subsidiary classification device.Naive Bayes Classification is defined as follows:

(1) set x={a₁,a₂,…,a_mIt is an item to be sorted, and each a is x characteristic attribute；

(2) there is category set C={y₁,y₂,…,y_n}；

(3) calculate P (y₁|x),P(y₂|x),…,P(y_n|x)；

If (4) P (y_k| x)=max{P (y₁|x),P(y₂|x),…,P(y_n| x) }, then x ∈ y_k。

The concrete grammar calculating the (3) each conditional probability in step is:

1. find the item set to be sorted of a known classification as training sample set；

2. statistics obtains the conditional probability estimation of each characteristic attribute lower of all categories；

P(a₁|y₁),P(a₂|y₁),…,P(a_m|y₁)；

P(a₁|y₂),P(a₂|y₂),…,P(a_m|y₂)；

…；

P(a₁|y_n),P(a₂|y_n),…,P(a_m|y_n)。

3. according to Bayes theorem, have:

$P\left(y_i\right|x)=\frac{P\left(x\right|y_i)P\left(y_i\right)}{P\left(x\right)}---\left(1\right)$

Because denominator is constant for all categories, as long as therefore molecule is maximized by we；Again because in simplicity In Bayes, each characteristic attribute is conditional sampling, so having:

$P\left(x\right|y_i)P\left(y_i\right)=P\left(a_1\right|y_i)P\left(a_2\right|y_i)...P\left(a_m\right|y_i)P\left(y_i\right)=P\left(y_i\right)\underset{j=1}{\overset{m}{\&Sigma;}}P\left(a_j\right|y_i)$

In embodiment 2, the input feature vector of assembled classifier is { I_G, I_D, Q_I, A₁, A₃, I_W, wherein, I_GIt is to start impulse electricity Stream；I_DIt is to start average current；Q_IIt it is starting current momentum；A₁、A₃For 1 in load current spectral characteristic, 3 odd harmonics Signal relative magnitude；I_WFor electrical equipment steady-state current virtual value, unit is ampere.Require that the electrical equipment classification identified is electric filament lamp, electricity Resistance stove, electric fan, computer, electric cautery.Make the characteristic attribute combination x={a of Naive Bayes Classifier₁,a₂,a₃,a₄,a₅, a₆Element in } and the element sequentially { I in the input feature vector set of assembled classifier_G, I_D, Q_I, A₁, A₃, I_WOne_to_one corresponding；Piao The output category set C={y of element Bayes classifier₁,y₂,y₃,y₄,y₅The most respectively with electrical equipment classification electric filament lamp, resistance furnace, electricity Fan, computer, electric cautery one_to_one corresponding.

The process of training NBC grader includes:

1, characteristic attribute is carried out segmentation division, carry out sliding-model control.In embodiment 2, the characteristic attribute taked is discrete Change method is:

a₁: { a₁<3,3≤a₁≤6,a₁>6}；

a₂: { a₂<1.2,1.2≤a₂≤2.4,a₂>2.4}；

a₃: { a₃＜ 120,120≤a₃≤450,a₃>450}；

a₄: { a₄＜ 0.7,0.7≤a₄≤0.9,a₄>0.9}；

a₅: { a₅＜ 0.02,0.02≤a₅≤0.05,a₅>0.05}；

a₆: { a₆＜ 0.45, a₆≥0.45}。

2, every electric appliances type is all gathered, and how group sample, as training sample, calculates every electric appliances type sample simultaneously and exists The ratio occupied in all appliance type samples, calculates P (y the most respectively₁)、P(y₂)、P(y₃)、P(y₄)、P(y₅).When every class electricity When device all gathers identical sample size, such as, every electric appliances all gathers the sample more than 100 groups, and wherein every electric appliances is random Selecting 100 groups of samples as training sample, other are then as test sample, and total training sample is 500 groups, and has

P(y₁)=P (y₂)=P (y₃)=P (y₄)=P (y₅)=0.2.

3, calculating the frequency (ratio) of each characteristic attribute segmentation under each class condition of training sample, statistics obtains all kinds of Not Xia each characteristic attribute conditional probability estimate, statistical computation the most respectively

P(a₁＜ 3 | y₁)、P(3≤a₁≤6|y₁)、P(a₁>6|y₁)；

P(a₁＜ 3 | y₂)、P(3≤a₁≤6|y₂)、P(a₁>6|y₂)；

…；

P(a₁＜ 3 | y₅)、P(3≤a₁≤6|y₅)、P(a₁>6|y₅)；

P(a₂＜ 1.2 | y₁)、P(1.2≤a₂≤2.4|y₁)、P(a₂>2.4|y₁)；

P(a₂<1.2|y₂)、P(1.2≤a₂≤2.4|y₂)、P(a₂>2.4|y₂)；

…；

P(a₂＜ 1.2 | y₅)、P(1.2≤a₂≤2.4|y₅)、P(a₂>2.4|y₅)；

P(a₃<120|y₁)、P(120≤a₃≤450|y₁)、P(a₃>450|y₁)；

P(a₃＜ 120 | y₂)、P(120≤a₃≤450|y₂)、P(a₃>450|y₂)；

…；

P(a₃＜ 120 | y₅)、P(120≤a₃≤450|y₅)、P(a₃>450|y₅)；

P(a₄＜ 0.7 | y₁)、P(0.7≤a₄≤0.9|y₁)、P(a₄>0.9|y₁)；

P(a₄＜ 0.7 | y₂)、P(0.7≤a₄≤0.9|y₂)、P(a₄>0.9|y₂)；

…；

P(a₄<0.7|y₅)、P(0.7≤a₄≤0.9|y₅)、P(a₄>0.9|y₅)；

P(a₅＜ 0.02 | y₁)、P(0.02≤a₅≤0.05|y₁)、P(a₅>0.05|y₁)；

P(a₅＜ 0.02 | y₂)、P(0.02≤a₅≤0.05|y₂)、P(a₅>0.05|y₂)；

P(a₅＜ 0.02 | y₅)、P(0.02≤a₅≤0.05|y₅)、P(a₅>0.05|y₅)；

P(a₆＜ 0.45 | y₁)、P(a₆≥0.45|y₁)；

P(a₆＜ 0.45 | y₂)、P(a₆≥0.45|y₂)；

…；

P(a₆＜ 0.45 | y₅)、P(a₆≥0.45|y₅)。

Through above-mentioned step 1, step 2, step 3, NBC classifier training completes.Wherein, characteristic attribute is entered by step 1 Row segmentation divides by manually determining, when each input feature vector is carried out disperse segmentaly, the quantity of segmentation is 2 sections or 2 sections Above, such as, in embodiment 2, feature a₁-a₅All it is divided into 3 sections, feature a₆It is divided into 2 sections.Each feature is specifically divided into how many sections, Result after test sample can be tested by the selection of fragmentation threshold according to the Bayes classifier after training is adjusted.Step 2, step 3 has been calculated by message processing module 102 or computer.

The method using Bayes classifier to carry out classifying in the present invention is:

1, using the input feature vector of assembled classifier as the input feature vector of Bayes classifier.In example 2, will combination Input feature vector set { the I of grader_G, I_D, Q_I, A₁, A₃, I_WAs the input feature vector x of Bayes classifier, and have x={a₁, a₂,a₃,a₄,a₅,a₆}。

2, the conditional probability of each characteristic attribute of all categories lower obtained according to training is estimated, determines each input feature vector respectively The segmentation place of attribute also determines its probability P (a to every electric appliances classification₁|y₁)～P (a_m|y_n), wherein, electrical equipment category set For C={y₁,y₂,…,y_n}.In embodiment 2, electrical equipment category set C={y₁,y₂,y₃,y₄,y₅The corresponding electrical equipment classification that represents is Electric filament lamp, resistance furnace, electric fan, computer, electric cautery, determine P (a₁|y₁)～P (a₆|y₅) method be use training NBC divide The conditional probability of each characteristic attribute obtained during class device is estimated.

3, according to formula

$P\left(y_i\right|x)=\frac{P\left(x\right|y_i)P\left(y_i\right)}{P\left(x\right)}$

Calculate every kind of other posterior probability of electric type.Because denominator P (x) is constant for all electrical equipment classifications, make P (x) =1 substitutes actual P (x) value, and the mutual size not affected between every kind of electrical equipment classification posterior probability compares, and now has

$P\left(y_i\right|x)=P\left(x\right|y_i)P\left(y_i\right)=P\left(y_i\right)\underset{j=1}{\overset{m}{\&Pi;}}P\left(a_j\right|y_i)$

In embodiment 2, have

$P\left(y_1\right|x)=P\left(x\right|y_1)P\left(y_1\right)=P\left(y_1\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_1);$

$P\left(y_2\right|x)=P\left(x\right|y_2)P\left(y_2\right)=P\left(y_2\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_2);$

$P\left(y_3\right|x)=P\left(x\right|y_3)P\left(y_3\right)=P\left(y_3\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_3);$

$P\left(y_4\right|x)=P\left(x\right|y_4)P\left(y_4\right)=P\left(y_4\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_4);$

$P\left(y_5\right|x)=P\left(x\right|y_5)P\left(y_5\right)=P\left(y_5\right)\underset{j=1}{\overset{6}{\&Pi;}}P\left(a_j\right|y_5).$

Using test sample to test the Bayes classifier trained, it is right to decide whether to adjust according to test result The discretization method (i.e. adjusting number of fragments and threshold value) of input feature vector, re-training Bayes classifier.

Main classification device is support vector machine classifier, or referred to as SVM classifier.SVM classifier is particularly suitable for solving two-value Classification situation, therefore, Main classification device uses multiple two class output SVM classifier compositions, and each two class output SVM classifier are corresponding Identify a kind of appliance type, such as, embodiment 1 can use 4 two class output SVM classifier identify electric filament lamp, electricity respectively Resistance stove, hair-dryer, computer, can use 5 two class output SVM classifier to identify electric filament lamp, resistance respectively in embodiment 2 Stove, electric fan, computer, electric cautery.When Main classification device selects multiple two class output SVM classifier to collectively constitute, all two classes The input feature vector of output SVM classifier is the input feature vector of Main classification device.

When training each two class output SVM classifier, every electric appliances type is all gathered and organizes sample more, randomly draw part and make For training sample, remaining is as test sample.Sample collection uses the method for the starting current feature of aforesaid acquisition electrical equipment Method with the fundamental voltage current and phase difference feature of the load current spectrum signature and acquisition electrical equipment obtaining electrical equipment.All of Training sample is all as the training sample of each two class output SVM classifier.Such as, in example 2, can be respectively to white heat Lamp, resistance furnace, electric fan, computer, electric cautery even load all gather more than 100 groups of samples, randomly draw wherein every kind 100 Group, totally 500 groups of sample composition training samples, remaining sample composition test sample；Certainly, certain load or all loads are adopted The sample size of collection does not reaches 100 groups of samples, and SVM classifier also is able to obtain preferable classifying quality.

Two class output SVM classifier selected by Main classification device select radially base RBF kernel function, and use particle cluster algorithm Or each two classes output punishment parameters C of SVM classifier and nuclear parameter Y are in optimized selection by genetic algorithm (PSO).

Each two class output SVM classifier only need to be performed the identification of a kind of appliance type, and the training of SVM classifier is relative Simply.Main classification device is made up of multiple two class output SVM classifier, separate between each two class output SVM classifier, because of This, when being identified a certain characteristic attribute, the recognition result that Main classification device likely exports is unique appliance type, or Recognition result is 2 kinds or two or more appliance type, or fails to provide the appliance type of identification.

Claims (10)

1. an appliance type evaluator, it is characterised in that include information acquisition module, message processing module, communication module；

Described information acquisition module is for gathering the load current of electrical equipment and being converted into current digital signal；Described current digital is believed Number it is sent to message processing module；

Described message processing module, according to the current digital signal of input, uses assembled classifier to carry out appliance type identification；

Described communication module is for sending the appliance type recognition result of message processing module to host computer；

The input feature vector of described assembled classifier includes the starting current feature of electrical equipment and the load current spectrum signature of electrical equipment；

Described assembled classifier includes support vector machine classifier and Bayes classifier；

Described starting current feature includes starting current rush, starting average current, starting current momentum.

2. appliance type evaluator as claimed in claim 1, it is characterised in that described information acquisition module includes current sense Device, preamplifier, wave filter, A/D converter；The core of described message processing module is DSP, or is ARM, or is single Sheet machine, or be FPGA.

3. appliance type evaluator as claimed in claim 2, it is characterised in that described A/D converter uses information processing mould The A/D converter that the core of block includes.

4. appliance type evaluator as claimed in claim 1, it is characterised in that described information acquisition module, information processing mould Block, all or part of function of communication module are integrated on a piece of SoC.

5. appliance type evaluator as claimed in claim 1, it is characterised in that described communication module also receives the phase of host computer Close work order；Communication mode between described communication module and host computer includes communication and wire communication mode； Described communication includes that ZigBee, bluetooth, WiFi, 433MHz number pass mode；Described wire communication mode includes that 485 is total Line, CAN, the Internet, power carrier mode.

6. the appliance type evaluator as according to any one of claim 1-5, it is characterised in that in described assembled classifier, Support vector machine classifier is Main classification device, and Bayes classifier is subsidiary classification device.

7. appliance type evaluator as claimed in claim 6, it is characterised in that described assembled classifier carries out appliance type knowledge Method for distinguishing is: when Main classification device successfully realizes appliance type identification, and the appliance type recognition result of Main classification device is combination The recognition result of grader；When Main classification device fails to realize appliance type identification, and the recognition result of Main classification device be 2 kinds or Two or more appliance type, in 2 kinds exported by Main classification device or two or more appliance type recognition result, subsidiary classification device is defeated Go out the middle probability the highest appliance type appliance type recognition result as assembled classifier；When Main classification device fails to realize electrical equipment Type identification, and the recognition result of Main classification device fail the appliance type providing identification time, general during subsidiary classification device is exported The highest appliance type of rate is as the appliance type recognition result of assembled classifier.

8. appliance type evaluator as claimed in claim 6, it is characterised in that described load current spectrum signature is by following Method obtains:

Step one, the steady state current signals of acquisition electrical equipment, and it is converted into the steady-state current digital signal of correspondence；

Step 2, steady-state current digital signal is carried out Fourier transform, obtain load current spectral characteristic；

Step 3, using odd harmonic signal relative magnitude that overtone order in load current spectral characteristic is n time as load electricity Stream spectrum signature, wherein, n=1,3 ..., M；Described M represents that harmonic wave high reps and M are more than or equal to 3.

9. appliance type evaluator as claimed in claim 6, it is characterised in that described starting current feature is by information processing mould Block is prepared by the following:

Before step 1, appliance starting, start the load current continuous sampling to electrical equipment and load current size is judged；When When load current virtual value is more than ε, it is determined that electrical equipment starts start and turn to step 2；Described ε is the numerical value more than 0；

Step 2, load current to electrical equipment carry out continuous sampling, and protect with power frequency period for unit computational load current effective value Deposit；Calculate the meansigma methods of the load current virtual value of nearest N number of power frequency period；Each work within nearest N number of power frequency period Frequently the load current virtual value in cycle is compared with the meansigma methods of the load current virtual value of this N number of power frequency period, fluctuating margin When being respectively less than the relative error range E set, it is determined that electrical equipment enters steady statue, turns to step 3；The span of described N is 50-500；The span of described E is 2%-20%；

Step 3, using the meansigma methods of the load current virtual value within nearest N number of power frequency period as electric appliance load steady-state current； Electrical equipment is started Startup time to the time between nearest N number of power frequency period initial time as the start-up course time；Calculate electricity Device start to start after electric appliance load current effective value within L power frequency period meansigma methods and electric appliance load steady-state current between Ratio, using this ratio as the startup current rush of electrical equipment；Calculate the electric appliance load electricity within the start-up course time of electrical equipment Ratio between meansigma methods and the electric appliance load steady-state current of stream virtual value, using this ratio as the startup average current of electrical equipment； Calculate electrical equipment starts average current and the product between the start-up course time, is rushed as the starting current of electrical equipment by this product Amount；The span of described L is 1-5.

10. appliance type evaluator as claimed in claim 9, it is characterised in that the input feature vector of described assembled classifier is also Including electrical equipment steady-state current.