CN108596231A - A kind of students' dormitory electrical appliance sorting technique - Google Patents
A kind of students' dormitory electrical appliance sorting technique Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
- G06F18/24155—Bayesian classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/24765—Rule-based classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
- G06F18/254—Fusion techniques of classification results, e.g. of results related to same input data
Abstract
A kind of students' dormitory electrical appliance sorting technique, the characteristics of classification is identified, BP neural network grader and Bayes classifier are taken into account using the assembled classifier including BP neural network grader and Bayes classifier, carry out comprehensive identification, and recognition accuracy is high；Identification feature of the fundamental voltage current and phase difference and load current spectrum signature of the starting current feature, electric appliance that use electric appliance simultaneously as appliance type, characteristic information are abundant；Fundamental voltage current and phase difference and starting current feature, the load current spectrum signature acquisition methods of offer are simple, reliable.The method can be used in some collective public places for needing to carry out electrical appliance management such as collective of students dormitory, can be used for other occasions for needing to carry out electrical equipment management for needing to carry out appliance type identification with statistics.
Description
Present patent application is divisional application, and application No. is 201610213392.9, the applying date is in April, 2016 for original bill
8 days, entitled students' dormitory electrical appliance type detector.
Technical field
The present invention relates to a kind of identification of electrical equipment and sorting techniques, classify more particularly, to a kind of students' dormitory electrical appliance
Method.
Background technology
Currently, the electric appliance load property identification method of mainstream includes the electric appliance load based on bearing power coefficient of colligation algorithm
Recognition methods, the electric appliance load recognition methods based on electromagnetic induction, the electric appliance load recognition methods based on neural network algorithm, base
Become the electric appliance load recognition methods etc. of scaling method in cyclic dispersion.Various methods can be to realize that electric appliance is negative to a certain degree
The identification of property is carried, but since characteristic properties are single, means of identification is single, generally existing generalization ability is inadequate and cannot be completely accurate
Really the problem of identification.
Invention content
It is an object of the present invention to for the defect of present prior art, a kind of students' dormitory electrical appliance classification side is provided
Method.The method is using the assembled classifier progress appliance type knowledge for including BP neural network grader and Bayes classifier
Not, the input feature vector of the assembled classifier include the starting current feature of electric appliance, electric appliance load current spectrum signature and electricity
The fundamental voltage current and phase difference of device.
In the assembled classifier, BP neural network grader is main grader, and Bayes classifier is auxiliary grader.
The assembled classifier, which carries out appliance type, which knows method for distinguishing, is：When Main classification device successfully realizes appliance type identification, main point
The appliance type recognition result of class device is the recognition result of assembled classifier；It is identified when Main classification device fails realization appliance type,
And the recognition result of Main classification device is 2 kinds of 2 kinds or two or more electricity that either two or more appliance type exports Main classification device
In device type identification result, the highest appliance type of probability is known as the appliance type of assembled classifier in the output of subsidiary classification device
Other result；It is identified when Main classification device fails realization appliance type, and fails to provide the electricity of identification in the recognition result of Main classification device
When device type, the highest appliance type of probability identifies knot as the appliance type of assembled classifier during subsidiary classification device is exported
Fruit.
The load current spectrum signature is prepared by the following：
Step 1: obtaining the steady state current signals of electric appliance load, and it is converted into corresponding steady-state current digital signal；
Step 2: carrying out Fourier transform to steady-state current digital signal, load current spectral characteristic is obtained；
Step 3: using the nth harmonic signal relative magnitude in load current spectral characteristic as load current spectrum signature,
Wherein, n=1,2 ..., M；The M indicates harmonic wave highest number and M is more than or equal to 3.The harmonic signal relative magnitude is harmonic wave
The ratio of signal amplitude and electric appliance load steady-state current virtual value.
The fundamental voltage current and phase difference of the electric appliance is prepared by the following：
Step 1., after electric appliance load enters stable state, the synchronous steady state voltage signal for obtaining electric appliance load, stable state electricity
Signal is flowed, and is converted into corresponding steady state voltage digital signal, steady-state current digital signal；
2., to steady state voltage digital signal, steady-state current digital signal step carries out digital filtering respectively, extract fundamental wave
Voltage signal, fundamental current signal；
Step 3., analysis calculate the phase difference between fundamental voltage signal and fundamental current signal, by fundamental voltage signal
Fundamental voltage current and phase difference of the phase difference as electric appliance between fundamental current signal.
The judgement after electric appliance load enters stable state, according to each power frequency within nearest N number of power frequency period
The load current virtual value in period carries out, and specific method is：Continuous sampling is carried out to the load current of electric appliance, with power frequency period
For unit computational load current effective value and preserve；Calculate the average value of the load current virtual value of N number of power frequency period recently；When
The load current virtual value and the load current of N number of power frequency period of each power frequency period within nearest N number of power frequency period have
The average value of valid value compares, and when fluctuating range is respectively less than the relative error range E set, judgement electric appliance load enters stable shape
State.The value range of the N is 50-500, and the value range of E is 2%-20%.
The starting current feature includes start-up course time, starting current maximum value, starting current maximum value time, is led to
Cross following methods acquisition：
Before step 1, appliance starting, starts the load current continuous sampling to electric appliance and load current size is sentenced
It is disconnected；When load current virtual value is more than ε, judgement electric appliance starts to start and turns to step 2；The ε is the numerical value more than 0；
Step 2, after electric appliance load enters stable state, turn to step 3；
It is step 3, electric using the average value of the load current virtual value within nearest N number of power frequency period as electric appliance load stable state
Flow virtual value；Electric appliance is started into Startup time to the time between nearest N number of power frequency period initial time as when start-up course
Between；Electric appliance is started into the time within Startup time to start-up course time between the maximum power frequency period of load current virtual value
As the starting current maximum value time；By the load current virtual value and electric appliance of power frequency period where the starting current maximum value time
Ratio between load steady state current effective value is as starting current maximum value.
The input feature vector of the assembled classifier further includes electric appliance load steady-state current virtual value.
The students' dormitory electrical appliance sorting technique is by including information acquisition module, message processing module, communication module
Device is realized.Described information acquisition module is used to acquire the load current of electric appliance and load voltage information and send to information processing mould
Block；Described information processing module carries out appliance type identification according to the information of input；The communication module is for sending at information
The appliance type recognition result of module is managed to host computer.
Described information acquisition module includes current sensor, preamplifier, filter, A/D converter；At described information
The core for managing module is DSP, is either that ARM is either microcontroller or is FPGA.
The A/D converter that the core of message processing module includes may be used in the A/D converter.
Described information acquisition module, message processing module, communication module all or part of function be integrated in a piece of SoC
In (System on Chip, system on chip).
The communication module also receives the related work instruction of host computer；Communication between the communication module and host computer
Mode includes communication and wired communication mode；The communication include ZigBee, bluetooth, WiFi,
433MHz numbers pass mode；The wired communication mode includes 485 buses, CAN bus, internet, power carrier mode.
The beneficial effects of the invention are as follows：Using the assembled classifier for including BP neural network grader and Bayes classifier
The characteristics of classification is identified, takes into account BP neural network grader and Bayes classifier carries out comprehensive identification, generalization ability with
Recognition accuracy is high；Simultaneously using starting current feature, the load current spectrum signature of electric appliance and the fundamental wave of electric appliance of electric appliance
Identification feature of the voltage current phase difference as the electric appliance sorting technique, characteristic information are abundant；What is provided includes start-up course
Time, starting current maximum value, starting current characteristic-acquisition method and load current including the starting current maximum value time
Spectrum signature acquisition methods are simple, reliable.
Description of the drawings
Fig. 1 is the device embodiment structural schematic diagram for realizing students' dormitory electrical appliance sorting technique；
Fig. 2 is the start-up course current waveform of incandescent 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 that Switching Power Supply class loads；
Fig. 6 is students' dormitory electrical appliance sorting technique flow chart.
Specific implementation mode
Below in conjunction with attached drawing, the invention will be further described.
Fig. 1 is to realize the device embodiment structural schematic diagram of students' dormitory electrical appliance sorting technique, including information collection mould
Block 101, message processing module 102, communication module 103.
Information acquisition module 102 is used to acquire the load voltage of electric appliance, load current and turns load voltage, load current
Change voltage digital signal, current digital signal into, voltage digital signal, current digital signal are sent to message processing module 102.
Information acquisition module includes the compositions such as voltage sensor, current sensor, preamplifier, filter, A/D converter portion
Point, it is respectively completed load voltage, the sensing of load current signal, amplification, filtering and analog-digital conversion function.Work as load current range
When larger, the preamplifier with programmable function can be selected, or is further added by an independent journey before A/D converter
Amplifier is controlled, the load current larger to range carries out Discrete control amplification, make the voltage signal range for being input to A/D converter
It is maintained at rational section, ensures conversion accuracy.Filter avoids spectral aliasing for filtering out high fdrequency component.
Voltage digital signal, current digital signal of the message processing module 102 according to input, using including BP neural network
The assembled classifier of grader and Bayes classifier realizes appliance type identification.The input feature vector of assembled classifier includes electric appliance
Starting current feature, the load current spectrum signature of electric appliance and the fundamental voltage current and phase difference of electric appliance.Message processing module
102 core is DSP, ARM, microcontroller, or is FPGA.When include in the core of message processing module A/D converter and
When the A/D converter is met the requirements, the core of message processing module 102 may be used in the A/D converter in information acquisition module 101
The A/D converter for including in the heart.
Communication module 103 is sent to host computer for realizing the communication between host computer, by recognition result.Communication module
Communication mode between 102 and host computer includes communication and wired communication mode, the side wireless communication that may be used
Formula includes the modes such as ZigBee, bluetooth, WiFi, 433MHz number biography, and the wired communication mode that may be used includes 485 buses, CAN
The modes such as bus, internet, power carrier.Communication module 103 can also receive the related work instruction of host computer, complete specified
Task.Host computer can be the server of administrative department, can also be that various work stations or various movements are whole
End.
Information acquisition module 101, message processing module 102, communication module 103 all or part of function can integrate
On a piece of SoC, reduces device volume, facilitate installation.
Different electrical equipments has different starting current features.It is illustrated in figure 2 the start-up course of incandescent lamp desk lamp
Current waveform.Incandescent lamp is that filament electrified regulation to incandescent state is sent out the electric light source of visible light using heat radiation.Incandescent lamp
Filament usually manufactured with heat safe tungsten, but the resistance of tungsten varies with temperature greatly, with RtIndicate tungsten filament at t DEG C
Resistance, with R0Indicate resistance of the tungsten filament at 0 DEG C, then the two has following relationships
Rt=R0(1+0.0045t)
For example, set the temperature of the filament (tungsten filament) of incandescent lamp in normal work as 2000 DEG C, one " 220V 100W's "
The resistance when filament of incandescent lamp is worked normally at 2000 DEG C is
Its 0 DEG C resistance in no power is
Its 20 DEG C resistance in no power is
R20=R0(1+0.0045t)=52.8 Ω
I.e. incandescent lamp is in 9 times of the immediate current for starting energization more than its rated current, and maximum starting current is happened at
Startup time.With the raising of incandescent lamp tungsten filament temperature, the load current of incandescent lamp exponentially reduces, subsequently into steady
Determine state.
If electric appliance load steady-state current virtual value is IW, and define electric appliance load current effective value and enter electric appliance load stable state
Within the relative error range of one setting of current effective value and stablize within this relative error range, then electric appliance load
Into stable state.Relative error range can be set as 10%, can also be set as the 2%- such as 2%, 5%, 15%, 20%
Value between 20%.In Fig. 2, the relative error range that sets is 10%, when the load current of incandescent lamp exponentially subtracts
It is small to arrive its IW10% error range when, T at the time of as in Fig. 2S, start-up course terminates.The start-up course time of incandescent lamp is
TS。IWFor virtual value.
Select the startup electricity of start-up course time, starting current maximum value I*, starting current maximum value time as electric appliance
Flow feature；Starting current maximum value is per unit value, i.e. starting current maximum value I* is the maximum virtual value I of starting currentMWith electricity
Device load steady state current effective value IWRatio.
In Fig. 2, the start-up course time of incandescent lamp is TS；Starting current maximum value I* is IM/IW, value about 9-10 it
Between；The starting current maximum value time is TM, TM=0.
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 logical
Frequently with lectrothermal alloy wires such as nickel chromium triangle, ferrum-chromium-aluminums, common feature is that resistance temperature correction factor is small, resistance value stabilization.With board
For number for the nichrome wire of Cr20Ni80, resistance correction factor at 1000 DEG C is 1.014, i.e., 1000 DEG C whens are opposite
When 20 DEG C, the trade mark is that the nichrome wire resistance of Cr20Ni80 only increases by 1.4%.Therefore, the resistive loads such as resistance furnace exist
It is powered and enters stable state, the start-up course time T of the resistive loads such as resistance furnace when startingS=0；Starting current maximum value
I*=1；Starting current maximum value time TM=0.
It is illustrated in figure 4 the start-up course current waveform of monophase machine class load.The load of monophase machine class both has inductance
Property load characteristic, and have counter electromotive force load characteristic.Startup time, due to the effect of inductance, the starting current of Startup time
It is 0；Subsequent electric current rises rapidly, before counter electromotive force of motor is not set up, reaches current peak IM；Hereafter, motor speed increases
Add, motor load electric current gradually reduces, until entering stable state.In Fig. 4, the start-up course time of monophase machine class load is
TS；Starting current maximum value I* is IM/IW；The starting current maximum value time is TM。
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 will produce a prodigious surge current because of the influence to capacitor charging, in startup moment, and peak value can reach surely
State current effective value IWSeveral times to more than ten times, the time be 1 to 2 power frequency period.In Fig. 5, computer and Switching Power Supply class are negative
The start-up course time of load is TS, about 1 to 2 power frequency period；Starting current maximum value I* is IM/IW；When starting current maximum value
Between be TM=0.
Obtaining the method for starting current feature of electric appliance is：
Before appliance starting, when load current value is 0 (being not keyed up) or very little (being in standby), message processing module
102 start to carry out continuous sampling to load current；When the load current value virtual value that sampling obtains starts to be more than 0 or opens
When beginning to be more than the standby current of electric appliance, that is, judge that electric appliance has been started up, it is T to record the moment0.With a smaller non-negative threshold
Value ε distinguishes the load current value before and after appliance starting, and when ε values are especially small, for example, when ε value 1mA, described device is not
Consider ideal case, that is, it is also the starting state of electric appliance to think standby；When ε values are smaller but more than the standby current of electric appliance,
For example, when ε value 20mA, the standby mode of electric appliance can be considered inactive state by described device, but simultaneously also can part
The especially small electric appliance of power causes leakage to identify.
Message processing module 102 carries out continuous sampling to load current, and using power frequency period as unit computational load electric current
Virtual value simultaneously preserves；Continuous plus is most after electric appliance has been started up, and continuous sampling reaches N number of power frequency period, while sampling
The average value I of the load current virtual value of nearly N number of power frequency periodV；Message processing module 102 to nearest N number of power frequency period within
The load current virtual value of each power frequency period is compared with the average value of the load current virtual value of N number of power frequency period,
When error (or fluctuation) amplitude is respectively less than the relative error range E set, judgement electric appliance load enters stable state, the nearest N
The initial time of a power frequency period is the finish time of start-up course, and it is T to record the moment1。
The average value of load current virtual value within nearest N number of power frequency period is effective as electric appliance load steady-state current
Value IW；Electric appliance is started into Startup time T0To nearest N number of power frequency period initial time T1Between time as the start-up course time
TS；By T0To T1Within the moment where the maximum power frequency period of load current virtual value be recorded as T2, by T0To T2Between time
As starting current maximum value time TM；By T2The load current virtual value of place power frequency period has with electric appliance load steady-state current
Valid value IWBetween ratio as starting current maximum value I*.
Due to not knowing electric appliance load steady-state current virtual value I in advanceW, therefore, by N number of power frequency period, i.e., one section continues
Time TPWithin load current virtual value of fluctuation range when being less than the relative error range E of setting average value it is negative as electric appliance
Carry steady-state current virtual value IW.Since the start-up course of ordinary appliances load is very fast, so, TPValue range be 1-10s, allusion quotation
Type value is 2s, and the value range of corresponding power frequency period quantity N is 50-500, and the typical value of N is 100.It is described to miss relatively
The value range of poor range E is 2%-20%, and the typical value of E is 10%.
The input feature vector of assembled classifier further includes the load current spectrum signature of electric appliance.The load current frequency spectrum of electric appliance is special
Sign controls information acquisition module 101 by message processing module 102, is obtained by following steps：
Step 1: after electric appliance load enters stable state, the steady state current signals of electric appliance load are obtained, and are converted
For corresponding steady-state current digital signal.
Step 2: carrying out Fourier transform to steady-state current digital signal, load current spectral characteristic is obtained.To ensure Fu
Vertical leaf transformation is smoothed out, and the steady state current signals of electric appliance load are obtained aforementioned, and is converted into corresponding stable state electricity
During streaming digital signal, the accuracy and speed of A/D converter needs the requirement for meeting Fourier transform, and sample frequency can be with
It is set as 10kHz or other numerical value；Message processing module 102 carries out FFT fortune to collected steady-state current digital signal
It calculates, calculates 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 special in input
According to 1,2 in sign vector ..., the sequence of M is arranged in order.Since load current spectral characteristic is mainly made of odd harmonic, remove
Outside a small number of electrical equipments, 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 as load current spectrum signature, wherein n=1,3 ..., M.The harmonic signal phase
It is harmonic signal amplitude and electric appliance load steady-state current virtual value I to amplitudeWRatio.1 subharmonic when n=1 is power frequency base
Wave.The M indicates harmonic wave highest number, and under normal circumstances, M is more than or equal to 3.
The input feature vector of assembled classifier can also include the fundamental voltage current and phase difference of electric appliance.Fundamental voltage electric current phase
Potential difference can distinguish resistive, capacitive character, inductive load, can also be to general inductive load and big inductive
Load distinguishes.The fundamental voltage current and phase difference of electric appliance controls information acquisition module 101 by message processing module 102, leads to
Cross following steps acquisition：
Step 1., after electric appliance load enters stable state, obtain electric appliance load steady state voltage signal, steady-state current letter
Number, and it is converted into corresponding steady state voltage digital signal, steady-state current digital signal.
2., to steady state voltage digital signal, steady-state current digital signal step carries out digital filtering respectively, extract fundamental wave
Voltage signal, fundamental current signal.
Step 3., analysis calculate the phase difference between fundamental voltage signal and fundamental current signal, by fundamental voltage signal
Fundamental voltage current and phase difference of the phase difference as electric appliance between fundamental current signal.
Step 2. in digital filtering carried out respectively to steady state voltage digital signal, steady-state current digital signal, number filter
Wave algorithm can select the digital filter algorithms such as Kalman filtering method, Wavelet Transform, Wiener Filter Method, adaptive-filtering.
In assembled classifier, BP neural network grader is main grader, and Bayes classifier is auxiliary grader.Combination
The input feature vector of grader includes starting current feature above-mentioned and load current spectrum signature, the input feature vector of assembled classifier
Simultaneously as the input feature vector of BP neural network grader and the input feature vector of Bayes classifier.
It is illustrated in figure 6 students' dormitory electrical appliance sorting technique flow chart, including：
Step A, appliance starting is waited for；
Step B, it acquires appliance starting current data and preserves, until appliance starting process terminates；
Step C, the appliance starting current data of analysis acquisition, obtains the starting current feature of electric appliance；
Voltage when step D, acquiring electric appliance steady operation and preserves current data；
Step E, the voltage when electric appliance steady operation of analysis acquisition, current data, obtain the load current frequency spectrum of electric appliance
Feature, fundamental voltage current and phase difference；
Step F, using starting current feature, load current spectrum signature, fundamental voltage current and phase difference as assembled classification
The input feature vector of device；Assembled classifier carries out appliance type identification；
Step G, appliance type recognition result is exported.
The assembled classifier, which carries out appliance type, which knows method for distinguishing, is：When Main classification device successfully realizes that appliance type is known
Not, i.e., Main classification device output recognition result be unique appliance type, i.e., in recognition result unique appliance type for be when,
The appliance type that Main classification device is identified is 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 either have in two or more appliance type, that is, recognition result 2 kinds or
Two or more appliance type is when being, in 2 kinds that Main classification device is exported or two or more appliance type recognition result, auxiliary point
Appliance type recognition result of the highest appliance type of probability as assembled classifier in the output of class device；When Main classification device fails reality
Existing appliance type identification, and fail to provide the appliance type of identification in the recognition result of Main classification device, i.e., do not have in recognition result
Appliance type is when being, the highest appliance type of probability is known as the appliance type of assembled classifier during subsidiary classification device is exported
Other result.
By taking a simple embodiment 1 as an example, method for distinguishing is known to illustrate that assembled classifier carries out appliance type.Equipped with one
A assembled classifier, input feature vector areWherein, TSIt is to open
Dynamic process time, unit is ms；I* is starting current maximum value；TMIt it is the starting current maximum value time, unit is ms；A1、A2、
A3、A4、A5For the 1-5 rd harmonic signal relative magnitudes in load current spectral characteristic,For the fundamental voltage electric current of power load
Phase difference.The output of assembled classifier is { B1, B2, B3, B4, B1、B2、B3、B4Assembled classifier is respectively represented to incandescent lamp, electricity
Hinder the recognition result output of stove, hair-dryer, computer, recognition result B1、B2、B3、B4Value be two-value classification marker.It is main
The input feature vector of grader is also Its output is { F1, F2, F3,
F4, F1、F2、F3、F4It respectively represents Main classification device to export the recognition result of incandescent lamp, resistance furnace, hair-dryer, computer, identification
As a result F1、F2、F3、F4Value be also two-value classification marker.The input feature vector of subsidiary classification device is similarlyIts 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) it is the posterior probability for assisting grader output, P (y1|x)、P(y2|x)、P
(y3|x)、P(y4| the mutual size between x) shows that the current input feature of subsidiary classification device indicates that identified electric appliance belongs to white
The possibility size of vehement lamp, resistance furnace, hair-dryer, computer.
In embodiment 1, B1、B2、B3、B4Classification marker and F1、F2、F3、F4Classification marker take 1,0.Classification marker
When being 1, corresponding appliance type is matched with current input feature, for the recognition result of confirmation, corresponding appliance type in other words
Recognition result is yes；When classification marker is 0, corresponding appliance type is mismatched with input feature vector, is failed as the identification confirmed
As a result, corresponding appliance type recognition result is no in other words.
In embodiment 1, if the recognition result classification marker of certain Main classification device is F1F2F3F4=0100, then it is assumed that
Main classification device successfully realizes that appliance type identifies, therefore, does not consider the recognition result of subsidiary classification device, directly enables B1B2B3B4=
0100, i.e. the recognition result of assembled classifier is：Identified electric appliance is resistance furnace.
In embodiment 1, if the recognition result classification marker of certain Main classification device is F1F2F3F4=1010, then it is assumed that
Main classification device fails to realize appliance type identification, and the recognition result of Main classification device is 2 kinds or two or more appliance type；Again
If the recognition result of subsidiary classification device meets P (y at this time1|x)<P(y3| x), then enable B1B2B3B4=0010, i.e. assembled classifier
Recognition result is：Identified electric appliance is hair-dryer.
In embodiment 1, if the recognition result classification marker of certain Main classification device is F1F2F3F4=0000, then it is assumed that
Main classification device fails to realize appliance type identification, and fails to provide the appliance type of identification in the recognition result of Main classification device；Again
If the recognition result of subsidiary classification device meets P (y at this time1|x)>P(y2| x) and P (y1|x)>P(y3| x) and P (y1|x)>P(y4|
X), then B is enabled1B2B3B4=1000, i.e. the recognition result of assembled classifier is：Identified electric appliance is incandescent lamp.
The recognition result classification marker of assembled classifier, Main classification device can also use other schemes, for example, using respectively
Classification marker 1, -1 or 0,1, either -1,1 and other schemes come indicate corresponding electric appliance recognition result be yes, it is no.
Assembled classifier can be identical with the classification marker scheme of Main classification device, can not also be identical.
Can also include electric appliance load steady-state current virtual value I in the input feature vector of the assembled classifierW.For example, having
2 kinds of different electric appliances, electric iron and resistance furnace need to identify, electric iron, resistance furnace are all pure resistor loads, and all has resistance
Temperature correction coefficient is small, the common feature of resistance value stabilization.Therefore, starting current feature above-mentioned and load current are relied on merely
Spectrum signature, electric appliance fundamental voltage current and phase difference feature they can not be distinguished.It is negative to increase electric appliance in input feature vector
Carry steady-state current virtual value IWAfterwards, electric iron power is small, electric appliance load steady-state current virtual value IWIt is small；Resistance furnace power is big, electric appliance
Load steady state current effective value IWGreatly, feature is different, and assembled classifier can carry out and complete to identify.
Subsidiary classification device is Bayes classifier.It can select NBC graders (Naive Bayes Classifier), TAN classification
Three kinds of Bayes classifiers such as device (crown pruning), BAN graders (Bayes classifier of enhancing) it
In it is a kind of be used as subsidiary classification device.
Embodiment 2 selects NBC graders as subsidiary classification device.Naive Bayes Classification is defined as follows：
(1) x={ a are set1, a2..., amIt is an item to be sorted, and the characteristic attribute that each a is x；
(2) category set C={ y are had1, y2..., yn}；
(3) P (y are calculated1| x), P (y2| x) ..., P (yn|x)；
(4) if P (yk| x)=max { P (y1| x), P (y2| x) ..., P (yn| x) }, then x ∈ yk。
Calculate the (3) walk in the specific method of each conditional probability be：
1. it is training sample set to find the item collection cooperation to be sorted classified known to one；
2. statistics obtain it is of all categories under each characteristic attribute conditional probability estimation；
P(a1|y1), P (a2|y1) ..., P (am|y1)；
P(a1|y2), P (a2|y2) ..., P (am|y2)；
…；
P(a1|yn), P (a2|yn) ..., P (am|yn)。
3. according to Bayes' theorem, have：
Because denominator for all categories be constant, as long as therefore we molecule is maximized；Again because in simplicity
Each characteristic attribute is conditional sampling in Bayes, so having：
In embodiment 2, the input feature vector of assembled classifier is { TS, I*, TM, A1, A3,IW, wherein TSIt is start-up course
Time, unit are ms；I* is starting current maximum value；TMIt it is the starting current maximum value time, unit is ms；A1、A3For load electricity
Flow 1,3 odd harmonic signal relative magnitude in spectral characteristic；For the fundamental voltage current and phase difference of electric appliance, unit is degree,
And fundamental voltage is when being ahead of fundamental current,IWFor electric appliance load steady-state current virtual value, unit is ampere.It is required that knowing
Other electric appliance classification is incandescent lamp, resistance furnace, electric fan, computer, electric iron.Enable the characteristic attribute of Naive Bayes Classifier
Combine x={ a1, a2, a3, a4, a5, a6, a7In element and assembled classifier input feature vector set in element sequentially { TS,
I*, TM, A1, A3,IWCorrespond；The output category set C={ y of Naive Bayes Classifier1, y2, y3, y4, y5Then divide
It is not corresponded with electric appliance classification incandescent lamp, resistance furnace, electric fan, computer, electric iron.
The process of training NBC graders includes：
1, segmentation division is carried out to characteristic attribute, carries out sliding-model control.In embodiment 2, the characteristic attribute taken is discrete
Change method is：
a1：{a1＜ 50,50≤a1≤ 1000, a1＞ 1000 }；
a2：{a2＜ 7,7≤a2≤ 11, a2＞ 11 }；
a3：{a3＜ 20,20≤a3≤ 300, a3＞ 300 }；
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＜ -6, -6≤a6≤ 18, a6＞ 18 }；
a7：{a7＜ 0.45, a7≥0.45}。
2, multigroup sample is acquired as training sample to every electric appliances type, while calculates and exists per electric appliances type sample
The ratio occupied in all appliance type samples, that is, calculate separately P (y1)、P(y2)、P(y3)、P(y4)、P(y5).When every class electricity
When device acquires identical sample size, for example, the sample more than 100 groups is acquired per electric appliances, wherein random per electric appliances
Select 100 groups of samples as training sample, other are then used as test sample, and total training sample is 500 groups, and is had
P(y1)=P (y2)=P (y3)=P (y4)=P (y5)=0.2.
3, the frequency (ratio) of each characteristic attribute segmentation under each class condition of training sample is calculated, statistics obtains all kinds of
The conditional probability estimation of each characteristic attribute under other, i.e., statistics calculates respectively
P(a1＜ 50 | y1)、P(50≤a1≤1000|y1)、P(a1＞ 1000 | y1)；
P(a1＜ 50 | y2)、P(50≤a1≤1000|y2)、P(a1＞ 1000 | y2)；
…；
P(a1＜ 50 | y5)、P(50≤a1≤1000|y5)、P(a1＞ 1000 | y5)；
P(a2＜ 7 | y1)、P(7≤a2≤11|y1)、P(a2＞ 11 | y1)；
P(a2＜ 7 | y2)、P(7≤a2≤11|y2)、P(a2＞ 11 | y2)；
…；
P(a2＜ 7 | y5)、P(7≤a2≤11|y5)、P(a2＞ 11 | y5)；
P(a3＜ 20 | y1)、P(20≤a3≤300|y1)、P(a3＞ 300 | y1)；
P(a3＜ 20 | y2)、P(20≤a3≤300|y2)、P(a3＞ 300 | y2)；
…；
P(a3＜ 20 | y5)、P(20≤a3≤300|y5)、P(a3＞ 300 | y5)；
P(a4＜ 0.7 | y1)、P(0.7≤a4≤0.9|y1)、P(a4＞ 0.9 | y1)；
((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)、((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＜ -6 | y1)、P(-6≤a6≤18|y1)、P(a6＞ 18 | y1)；
P(a6＜ -6 | y2)、P(-6≤a6≤18|y2)、P(a6＞ 18 | y2)；
…；
P(a6＜ -6 | y5)、P(-6≤a6≤18|y5)、P(a6＞ 18 | y5)；
P(a7＜ 0.45 | y1)、P(a7≥0.45|y1)；
P(a7＜ 0.45 | y2)、P(a7≥0.45|y2)；
…；
P(a7＜ 0.45 | y5)、P(a7≥0.45|y5)。
By above-mentioned step 1, step 2, step 3, NBC classifier trainings are completed.Wherein, step 1 to characteristic attribute into
Row segmentation is divided by manually determining, when carrying out disperse segmentaly to each input feature vector, the quantity of segmentation is 2 sections or 2 sections
More than, for example, in embodiment 2, feature a1-a6All it is divided into 3 sections, feature a7It is divided into 2 sections.Each feature is specifically divided into how many sections,
Result after the selection of fragmentation threshold can test test sample according to the Bayes classifier after training is adjusted.Step
2, step 3 is calculated by the either computer of message processing module 102 and is completed.
It is using the method that Bayes classifier is classified in the present invention：
1, using the input feature vector of assembled classifier as the input feature vector of Bayes classifier.In example 2, it will combine
Input feature vector set { the T of graderS, I*, TM, A1, A3,IWInput feature vector x as Bayes classifier, and have x=
{a1, a2, a3, a4, a5, a6, a7}。
2, according to training obtain it is of all categories under each characteristic attribute conditional probability estimate, respectively determine each input feature vector
Where the segmentation of attribute and determine its probability P (a to every electric appliances classification1|y1)~P (am|yn), wherein electric appliance category set
For C={ y1, y2..., yn}.In embodiment 2, electric appliance category set C={ y1, y2, y3, y4, y5The corresponding electric appliance classification represented is
Incandescent lamp, resistance furnace, electric fan, computer, electric iron determine P (a1|y1)~P (a7|y5) method be using training NBC point
The conditional probability estimation of each characteristic attribute obtained during class device.
3, according to formula
Calculate the other posterior probability of each electric type.Because denominator P (x) is constant for all electric appliance classifications, P (x) is enabled
=1 substitutes actual P (x) value, and the mutual size not influenced between each electric appliance classification posterior probability compares, has at this time
In embodiment 2, have
Trained Bayes classifier is tested using test sample, adjustment pair is decided whether according to test result
The discretization method (adjusting number of fragments and threshold value) of input feature vector, re -training Bayes classifier.
Main classification device is BP neural network grader, selects 3 layers of BP neural network grader as Main classification device.By BP god
Quantity through element in network classifier input feature value, i.e. number of nodes of the quantity of input feature vector as input layer, for example,
9 in embodiment 1 or 7 in embodiment 2.The quantity of the appliance type identified will be needed as output node layer
Number, for example, in embodiment 1, output node layer is 4, respectively the knot of output identification incandescent lamp, resistance furnace, hair-dryer, computer
Fruit；In embodiment 2, output node layer is 5, respectively output identification incandescent lamp, resistance furnace, electric fan, computer, electric iron
As a result.The number of nodes of intermediate hidden layer rule of thumb takes, for example, the number of nodes of hidden layer can be in embodiment 1, embodiment 2
It is chosen in the range of 6-18.Multigroup sample is acquired to every electric appliances type, for example, acquiring 200 groups of samples；It randomly selects
Several groups therein, such as 150 groups of samples are as training sample, it is remaining to be used as test sample, to BP neural network grader
It is trained and tests.Multi input, multi output 3 layers of BP neural network grader due to the coupling between multi output, have
Sample may completely cannot be identified in training or test；Even in training or test can to sample into
Row identification completely, is restricted, when a certain characteristic attribute newly inputted is identified, Main classification device is possible to by generalization ability
The recognition result of output is that either recognition result is 2 kinds and either two or more appliance type or fails unique appliance type
Provide the appliance type of identification.
Main classification device is also an option that 3 layers of BP neural network grader of multiple single node outputs collectively constitute, Mei Gedan
3 layers of BP neural network grader of node output, which correspond to, identifies a kind of appliance type, for example, 4 lists may be used in embodiment 1
3 layers of BP neural network grader of node output identify incandescent lamp, resistance furnace, hair-dryer, computer respectively；It can in embodiment 2
With using 5 single nodes output 3 layers of BP neural network grader identify respectively incandescent lamp, resistance furnace, electric fan, computer,
Electric iron.When Main classification device selects 3 layers of BP neural network grader of multiple single node outputs to collectively constitute, all single nodes are defeated
The input layer number of the 3 layers of BP neural network grader gone out is the quantity of element in main grader input feature value；It is intermediate
The number of nodes of hidden layer rule of thumb takes, the number of nodes of hidden layer among 3 layers of BP neural network grader of each single node output
Amount may be the same or different, and need to select according to respective.As the neural network of non-single node output, need pair
Multigroup sample is acquired per electric appliances type, for example, acquiring 200 groups of samples；Randomly select several groups therein, such as 150
Group sample is as training sample, remaining to be used as test sample, the BP neural network grader progress to the output of each single node
Training and test.When Main classification device selects 3 layers of BP neural network grader of multiple single node outputs to collectively constitute, each single-unit
3 layers of BP neural network grader of point output only need to be performed a kind of identification of appliance type, and the training of each network is relatively simple
It is single.Since Main classification device is made of 3 layers of BP neural network grader that multiple single nodes export at this time, the 3 of each single node output
Between layer BP neural network grader independently of each other, therefore, when a certain characteristic attribute is identified, Main classification device is possible to defeated
The recognition result gone out be unique appliance type either recognition result be 2 kinds either two or more appliance type or fail to
Go out the appliance type of identification.
Gradient descent method may be used in the training method of BP neural network grader, can also use particle group optimizing, lose
The optimization methods such as propagation algorithm.Sample collection is using the method for the starting current feature above-mentioned for obtaining electric appliance and bearing for acquisition electric appliance
The method for carrying current spectrum feature and obtaining the fundamental voltage current and phase difference feature of electric appliance.
Claims (9)
1. a kind of students' dormitory electrical appliance sorting technique, which is characterized in that it includes BP neural network grader and Bayes to use
The assembled classifier of grader carries out appliance type identification, and the input feature vector of the assembled classifier includes the starting current of electric appliance
The fundamental voltage current and phase difference of feature, the load current spectrum signature of electric appliance and electric appliance.
2. electrical appliance sorting technique in students' dormitory as described in claim 1, which is characterized in that in the assembled classifier, BP
Neural network classifier is main grader, and Bayes classifier is auxiliary grader.
3. electrical appliance sorting technique in students' dormitory as claimed in claim 2, which is characterized in that the assembled classifier carries out electricity
The method of device type identification is：When Main classification device successfully realizes appliance type identification, the appliance type identification knot of Main classification device
Fruit is the recognition result of assembled classifier；When Main classification device fails to realize appliance type identification, and the recognition result of Main classification device
It is auxiliary in 2 kinds either two or more appliance type export Main classification device 2 kinds or two or more appliance type recognition result
Appliance type recognition result of the highest appliance type of probability as assembled classifier in helping grader to export；When Main classification device not
When can realize that appliance type identifies, and fail to provide the appliance type of identification in the recognition result of Main classification device, by subsidiary classification
Appliance type recognition result of the highest appliance type of probability as assembled classifier in device output.
4. electrical appliance sorting technique in students' dormitory as claimed in any one of claims 1-3, which is characterized in that the load electricity
Stream spectrum signature is prepared by the following：
Step 1: obtaining the steady state current signals of electric appliance load, and it is converted into corresponding steady-state current digital signal；
Step 2: carrying out Fourier transform to steady-state current digital signal, load current spectral characteristic is obtained；
Step 3: using the nth harmonic signal relative magnitude in load current spectral characteristic as load current spectrum signature,
In, n=1,2 ..., M；The M indicates harmonic wave highest number and M is more than or equal to 3.
5. electrical appliance sorting technique in students' dormitory as claimed in claim 4, which is characterized in that the harmonic signal relative magnitude
For the ratio of harmonic signal amplitude and electric appliance load steady-state current virtual value.
6. electrical appliance sorting technique in students' dormitory as claimed in any one of claims 1-3, which is characterized in that the startup electricity
It includes start-up course time, starting current maximum value, starting current maximum value time to flow feature.
7. electrical appliance sorting technique in students' dormitory as claimed in any one of claims 1-3, which is characterized in that the electric appliance
Fundamental voltage current and phase difference is prepared by the following：
Step 1., after electric appliance load enters stable state, the synchronous steady state voltage signal for obtaining electric appliance load, steady-state current letter
Number, and it is converted into corresponding steady state voltage digital signal, steady-state current digital signal；
2., to steady state voltage digital signal, steady-state current digital signal step carries out digital filtering respectively, extract fundamental voltage
Signal, fundamental current signal；
Step 3., analysis calculate the phase difference between fundamental voltage signal and fundamental current signal, by fundamental voltage signal and base
Fundamental voltage current and phase difference of the phase difference as electric appliance between signal wave current.
8. electrical appliance sorting technique in students' dormitory as claimed in claim 7, which is characterized in that described to wait for that electric appliance load enters surely
Determine the judgement after state, is carried out according to the load current virtual value of each power frequency period within nearest N number of power frequency period；Institute
The value range for stating N is 50-500.
9. electrical appliance sorting technique in students' dormitory as claimed in claim 8, which is characterized in that carried out to the load current of electric appliance
Continuous sampling as unit computational load current effective value and is preserved using power frequency period；Calculate the load electricity of N number of power frequency period recently
Flow the average value of virtual value；The load current virtual value and N number of work of each power frequency period within nearest N number of power frequency period
The average value of the load current virtual value in frequency period compares, when fluctuating range is respectively less than the relative error range E set, judgement
Electric appliance load enters stable state；The value range of the E is 2%-20%.
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