CN104655928A - Method for detecting inter-harmonics of input voltage of electric automobile charger - Google Patents

Method for detecting inter-harmonics of input voltage of electric automobile charger Download PDF

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Publication number
CN104655928A
CN104655928A CN201310594914.0A CN201310594914A CN104655928A CN 104655928 A CN104655928 A CN 104655928A CN 201310594914 A CN201310594914 A CN 201310594914A CN 104655928 A CN104655928 A CN 104655928A
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acetyl chlorophosphonazo
frequency
amplitude
signal
detection method
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Inventor
刘理峰
徐国钧
刘永胜
杨朝阳
聂忠伟
杨侃
孙钢
胡晓琴
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State Grid Corp of China SGCC
State Grid Zhejiang Electric Power Co Ltd
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Hangzhou Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Publication of CN104655928A publication Critical patent/CN104655928A/en
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Abstract

The invention discloses a method for detecting inter-harmonics of input voltage of an electric automobile charger. The method comprises the following steps: firstly acquiring an inter-harmonics signal of the input voltage of the electric automobile charger, calculating a low-precision frequency, an amplitude value, a phase and the total number of times of the inter-harmonics of each inter-harmonic of the inter-harmonics signal by an FFT (Fast Fourier Transformation) interpolation algorithm with a Harming window, calculating the precise frequency, the amplitude value and the phase of each inter-harmonic by a linear neural network according to the low-precision frequency, the amplitude value, the phase and the total number of the inter-harmonics which are acquired, so that a good basis can be provided for inter-harmonic treatment.

Description

A kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger
Technical field
The application relates to technical field of electric power, more particularly, relates to a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger.
Background technology
The input voltage of electric automobile battery charger due to be subject to itself, the impact of electrical network or accumulator and there is m-Acetyl chlorophosphonazo, and m-Acetyl chlorophosphonazo can give the safety of electric automobile battery charger, economical operation brings great harm, therefore must administer m-Acetyl chlorophosphonazo, must be accurately detect the frequency of m-Acetyl chlorophosphonazo, amplitude and phase place for this reason, thus administer for m-Acetyl chlorophosphonazo good foundation is provided.
Summary of the invention
In view of this, the application provides the detection method of the input voltage m-Acetyl chlorophosphonazo of a kind of electric automobile battery charger, accurately to detect the frequency of the m-Acetyl chlorophosphonazo of the input voltage of electric automobile battery charger, amplitude and phase place.
To achieve these goals, the existing scheme proposed is as follows:
A detection method for the input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger, comprises the steps:
Obtain the m-Acetyl chlorophosphonazo signal of the input voltage of electric automobile battery charger;
Fast Fourier Transform (FFT) FFT(Fast Fourier Transformation with adding Hanning window) interpolation algorithm calculates the low precision frequency of each m-Acetyl chlorophosphonazo of described m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree;
According to the frequency of described low precision, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree, calculate the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place with linear neural network.
Preferably, the m-Acetyl chlorophosphonazo signal of the input voltage of described acquisition electric automobile battery charger, comprising:
The m-Acetyl chlorophosphonazo of the input end of described electric automobile battery charger is sampled;
The m-Acetyl chlorophosphonazo signal that record obtains.
Preferably, the sample frequency of sampling to described voltage signal is 5000 hertz.
Preferably, the writing time of described record sampled signal is 2 hours.
Preferably, the described FFT interpolation algorithm with adding Hanning window calculates the low precision frequency of each m-Acetyl chlorophosphonazo of described m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, comprising:
Hanning window process is added to described m-Acetyl chlorophosphonazo signal;
FFT conversion is carried out to the m-Acetyl chlorophosphonazo signal through Hanning window process, obtains spectrum sequence;
With interpolation algorithm, the low precision frequency of described each m-Acetyl chlorophosphonazo, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree are calculated to described spectrum sequence.
Preferably, the sample frequency of described Hanning window is 1600 hertz ~ 12800 hertz.
Preferably, the sampling time of described Hanning window is 0.2 second ~ 0.4 second.
Preferably, the described frequency according to described low precision, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree, calculate the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place with linear neural network, comprising:
Structure and initialization linear neural network;
Train described linear neural network;
Utilize described linear neural network to calculate described m-Acetyl chlorophosphonazo signal, obtain the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place.
Preferably, the neuron number of described linear neural network is identical with described harmonic wave total degree.
Preferably, the magnitude of voltage of described input voltage is 380 volts, fundamental frequency is 50.2 hertz.
As can be seen from technique scheme, first the detection method of the input voltage m-Acetyl chlorophosphonazo of the electric automobile battery charger that the application provides obtains the m-Acetyl chlorophosphonazo signal of the input voltage of electric automobile battery charger, utilize the FFT(Fast Fourier Transformation adding Hanning window, fast Fourier transform) interpolation algorithm calculates the frequency of the low precision of each m-Acetyl chlorophosphonazo of m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, in the frequency according to the low precision obtained, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, the accurate frequency of each m-Acetyl chlorophosphonazo is calculated with linear neural network, amplitude and phase place, thus good foundation can be provided for m-Acetyl chlorophosphonazo improvement.
Accompanying drawing explanation
In order to be illustrated more clearly in the embodiment of the present application or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only some embodiments of the application, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to these accompanying drawings.
The process flow diagram of Fig. 1 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in the embodiment of the present application;
The process flow diagram of Fig. 2 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in another embodiment of the application;
The process flow diagram of Fig. 3 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in the another embodiment of the application.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present application, be clearly and completely described the technical scheme in the embodiment of the present application, obviously, described embodiment is only some embodiments of the present application, instead of whole embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making the every other embodiment obtained under creative work prerequisite, all belong to the scope of the application's protection.
Embodiment one
The process flow diagram of Fig. 1 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in the embodiment of the present application.
As shown in Figure 1, disclosed in the present embodiment, detection method comprises the steps:
S101: the m-Acetyl chlorophosphonazo signal obtaining the input voltage of electric automobile battery charger;
S102: with the Fast Fourier Transform (FFT) FFT(Fast Fourier Transformation adding Hanning window, fast Fourier transform) interpolation algorithm calculates the low precision frequency of each m-Acetyl chlorophosphonazo of m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree;
S103: according to the frequency of low precision, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, calculates the high-precision frequency of each m-Acetyl chlorophosphonazo, amplitude and phase place with linear neural network.
As can be seen from technique scheme, first the detection method of the input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in the present embodiment obtains the m-Acetyl chlorophosphonazo signal of the input voltage of electric automobile battery charger, utilize the FFT(Fast Fourier Transformation adding Hanning window, fast Fourier transform) interpolation algorithm calculates the frequency of the low precision of each m-Acetyl chlorophosphonazo of m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, in the frequency according to the low precision obtained, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, the accurate frequency of each m-Acetyl chlorophosphonazo is calculated with linear neural network, amplitude and phase place, thus good foundation can be provided for m-Acetyl chlorophosphonazo improvement.
Embodiment two
The process flow diagram of Fig. 2 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in another embodiment of the application.
As shown in Figure 2, disclosed in the present embodiment, detection method comprises the steps:
S201: the m-Acetyl chlorophosphonazo of the input voltage of electric automobile battery charger is sampled.
The lower end of the contactor at three-phase supply can be selected, sample frequency preferably 5000 hertz to the collection point of m-Acetyl chlorophosphonazo.
S202: the m-Acetyl chlorophosphonazo signal that record obtains.
To the m-Acetyl chlorophosphonazo signal that record while m-Acetyl chlorophosphonazo sampling obtains preferably 2 hours writing time.
S203: Hanning window process is added to m-Acetyl chlorophosphonazo signal.
Use F sthe sample frequency of=1600Hz ~ 12800Hz carries out the sampling of 0.2s ~ 0.4s to y (t), obtain N=(0.2 ~ 0.4) × F sindividual sample, sampling instant and the sampled value of the i-th+1 sample are respectively with i=0,1,2 ..., N-1, uses z ( i ) = 0.5 - 0.5 cos ( 2 π N i ) Right be weighted, obtain y z ( i ) = y ( i F s ) z ( i ) ;
S204: carry out FFT conversion to the m-Acetyl chlorophosphonazo signal through Hanning window process, obtains spectrum sequence.
By y zbe multiplied by i () sequence carries out FFT conversion after obtain frequency spectrum Y (n) sequence, wherein n=1,2 ... N
S205: with interpolation algorithm, spectrum sequence is calculated to the frequency of the low precision of each m-Acetyl chlorophosphonazo, amplitude, phase place and m-Acetyl chlorophosphonazo total degree.
The m-Acetyl chlorophosphonazo total degree H of y (t) and the frequency f of the h time low precision of m-Acetyl chlorophosphonazo is obtained with interpolation algorithm h(0), amplitude A h(0), phase place | Y (n) | be the amplitude of Y (n), m-Acetyl chlorophosphonazo total degree H is | Y (n) | in sequence maximum value number 1/2, h=1,2, ..., H, | Y (n) | in sequence, h maximum value U (h) is | Y (n) | l in sequence hindividual value, for | Y (n) | l in sequence h+ 1 value, f h ( 0 ) = ( l h + α h ) F s N , A h ( 0 ) = U ( h ) 2 π α h ( 1 - α h 2 ) sin ( π α h ) , wherein Y (l h) be l in Y (n) sequence hindividual value, angle (Y (l h)) represent Y (l h) phase place.
S206: structure and initialization linear neural network.
The number of hidden layer neuron is the total number H of m-Acetyl chlorophosphonazo, and hidden layer jth neuronic excitation function and the hidden layer jth weights between neuron and output neuron are respectively:
s j ( i ) = cos ( 2 π f h ( i ) i F s ) j = 2 h - 1 sin ( 2 π f h ( i ) i F s ) j = 2 h And w j(i), wherein, j=1,2 ..., 2H, h=1,2 ..., H; I=0,1,2 ..., N-1, the initial value α of setting learning rate α 0be the constant of 0.005 ~ 0.05, the initial value η of inertial coefficient η 0be the constant of 0.005 ~ 0.05, anticipation error ε is 10 -6~ 10 -4constant, frequency of training m=1, maximum frequency of training M is the integer of 100 ~ 300, and step-length regulation coefficient p is the constant of 0.3 ~ 0.7, and step-length maximum adjustment number of times K is the integer of 10 ~ 30, Δ w j(0)=0, Δ f h(0)=0, i=0.
S207: training linear neural network.
A. the input of i as linear neural network is got, corresponding sampled value for the desired output of linear neural network,
Calculate the output of linear neural network and output error output error index output error index V (i) and frequency f hthe local derviation of (i) output error index V (i) and frequency w jthe local derviation of (i) step-length adjustment number of times k=1, α=α 0, η=η 0, f ^ h = f h ( i ) , w ^ j = w j ( i ) ;
B. regularized learning algorithm rate α and inertial coefficient η: wherein, h=1,2 ..., H, j=1,2 ..., 2H, Δ f h(i)=-α g fh+ η Δ f h(i-1), Δ w j(i)=-α g wj+ η Δ w j(i-1), using i as the input of this linear neural network, corresponding sampled value for the desired output of this linear neural network, calculate the output of this linear neural network and the adjustment output error of this linear neural network when and during k<K, α=p α, η=p η, k=k+1, perform step b, until or during k>=K, f h(i+1)=f h(i), w j(i+1)=w j(i), i=i+1;
C. as i≤N-1, get next sample to train, perform step a to c, until i>N-1;
D. error of calculation root mean square as E (m) > ε and m<M time, m=m+1, i=0, perform step a to d, until during E (m)≤ε or m>=M, training end.
S208: utilize linear neural network to calculate m-Acetyl chlorophosphonazo signal, obtains the high-precision frequency of each m-Acetyl chlorophosphonazo, amplitude and phase place.
F is obtained after training terminates h, w j, wherein j=1,2 ..., 2H, h=1,2 ..., H, then the detected value of the frequency of y (t) the h time m-Acetyl chlorophosphonazo, amplitude, phase place is respectively: f h, A h, wherein,
Embodiment three
The process flow diagram of Fig. 3 a kind of detection method of input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger disclosed in the another embodiment of the application.
As shown in Figure 3, disclosed in the present embodiment, detection method comprises the steps:
S201: the m-Acetyl chlorophosphonazo of the input voltage of electric automobile battery charger is sampled.
The lower end of the contactor at three-phase supply can be selected, sample frequency preferably 5000 hertz to the collection point of m-Acetyl chlorophosphonazo.The voltage of the three-phase supply in the present embodiment is 380 volts.
S202: the m-Acetyl chlorophosphonazo signal that record obtains.
The fundamental frequency of m-Acetyl chlorophosphonazo is 50.2 hertz, preferably 2 hours writing time.
M-Acetyl chlorophosphonazo is the frequency of each the low precision of m-Acetyl chlorophosphonazo, amplitude, phase place and m-Acetyl chlorophosphonazo total degree H, wherein,
f ^ h = 75.30,170.68,356.42,512.04,682.72,788.14 ,
A ^ h = 22.58,35.07,60.34,53.93,96.94,100.26 ,
S203: Hanning window process is added to m-Acetyl chlorophosphonazo signal.
Use F sthe sample frequency of=3200Hz carries out the sampling of 0.2s to y (t), obtain N=640 sample, sampling instant and the sampled value of the i-th+1 sample are respectively with i=0,1,2 ..., 639, use z ( i ) = 0.5 - 0.5 cos ( 2 &pi; 640 i ) Right be weighted, obtain:
y z ( i ) = y ( i 3200 ) z ( i ) = 0,0.004954 , - 0.027349 , - 0.075363 , . . . , - 0.002181 .
S204: carry out FFT conversion to the m-Acetyl chlorophosphonazo signal through Hanning window process, obtains spectrum sequence.
By y zbe multiplied by i () sequence carries out FFT conversion after obtain Y (n)=-0.00088071 ,-0.00089882-8.95*10 -5i ,-0.00095509-0.00018471i ,-0.00105566-0.00029212i ... ,-0.00089882+8.95*10 -5i,
Wherein n=1,2 ..., 640.
S205: with interpolation algorithm, spectrum sequence is calculated to the frequency of the low precision of each m-Acetyl chlorophosphonazo, amplitude, phase place and m-Acetyl chlorophosphonazo total degree.
Phase place | Y (n) |=0.000454,0.000466,0.000502,0.000568 ..., 0.000466, H=6, h=1,2 ..., 6, l h=16,35,72,103,138,159,
Y(l h)=10.83803+3.066618i,8.799310+14.92612i,-3.05286+28.46976i,23.52900+5.622922i,-37.382706-19.81066i,45.74991+2.430355i，
U(h)=11.26,17.33,28.63,24.19,42.31,45.81，
U ^ ( h ) = 6.15,10.56,21.42,21.40,9.37,12.13 ,
By &beta; h = U ( h ) U ^ ( h ) , &alpha; h = 2 - &beta; h 1 + &beta; h , f h ( 0 ) = ( l h + &alpha; h ) F s N , A h ( 0 ) = U ( h ) 2 &pi; &alpha; h ( 1 - &alpha; h 2 ) sin ( &pi; &alpha; h ) , obtain:
β h=1.83,1.64,1.35,1.13,4.51,3.78，
α h=0.06,0.14,0.28,0.41,-0.46,-0.37，
f h(0)=75.30027,170.6800,356.4200,512.0401,682.7206,788.1397，
A h(0)=22.57952,35.06992,60.34000,53.92996,96.92954,100.2665，
Wherein h=1,2 ..., 6.
S206: structure and initialization linear neural network.
The number of hidden layer neuron is the total number H=6 of m-Acetyl chlorophosphonazo, and hidden layer jth neuronic excitation function and the hidden layer jth weights between neuron and output neuron are respectively:
s j ( i ) = cos ( 2 &pi; f h ( i ) i 3200 ) j = 2 h - 1 sin ( 2 &pi; f h ( i ) i 3200 ) j = 2 h And w j(i), wherein, j=1,2 ..., 12, h=1,2 ..., 6, i=0,1,2 ..., 639, w j(0)=22.49397 ,-1.96365,28.72825 ,-20.1144,42.66656 ,-42.6671,26.96283,46.70595,33.12165,91.09496,34.27171 ,-94.2275, the initial value α of setting learning rate α 0the initial value η of=0.02, inertial coefficient η 0=0.02, anticipation error ε=10 -6, frequency of training m=1, maximum frequency of training M=300, step-length regulation coefficient p=0.5, step-length maximum adjustment number of times K=20, Δ w j(0)=0, Δ f h(0)=0, i=0.
S207: training linear neural network.
A. the input of i as linear neural network is got, corresponding sampled value for the desired output of linear neural network, calculate the output of linear neural network and output error output error index output error index V (i) and frequency f hthe local derviation of (i) output error index V (i) and frequency w jthe local derviation of (i) step-length adjustment number of times k=1, α=0.02, η=0.02;
B. regularized learning algorithm rate α and inertial coefficient η: wherein, h=1,2 ..., 6, j=1,2 ..., 12, Δ f h(i)=-α g fh+ η Δ f h(i-1), Δ w j(i)=-α g wj+ η Δ w j(i-1), using i as the input of this linear neural network, corresponding sampled value for the desired output of this linear neural network, calculate the output of this linear neural network and the adjustment output error of this linear neural network when and during k<20, α=0.5 α, η=0.5 η, k=k+1, perform step b, until or during k>=20, f h(i+1)=f h(i), w j(i+1)=w j(i), i=i+1;
C. when i≤639, get next sample to train, perform step a to c, until i>639;
D. error of calculation root mean square as E (m) >10 -6and during m<300, m=m+1, i=0, perform step a to d, until E (m)≤10 -6or during m>=30, training terminates;
Linear neural network is after m=102 training, and training terminates, and error mean square root E (102)=9.642522e-07 is less than anticipation error ε=10 of setting -6.
S208: utilize linear neural network to calculate m-Acetyl chlorophosphonazo signal, obtains the high-precision frequency of each m-Acetyl chlorophosphonazo, amplitude and phase place.
Vector is obtained after training terminates:
w j=22.4940762,-1.9679767,28.7276621,-20.1153257,142.6668228,-42.6668235,26.9649994,46.7047503,33.1554293,91.0938040,34.2909431,-94.2135803
f h=75.30000,170.6800,356.4200,512.0400,682.7200,788.1400，
Wherein, j=1,2 ..., 12, h=1,2 ..., 6, by obtain the frequency f of y (t) the h time m-Acetyl chlorophosphonazo h, amplitude A h, phase place detected value:
f h=75.30000,170.6800,356.4200,512.0400,682.7200,788.1400,
A h=22.58000，35.07000，60.34000，53.93000，96.94000，100.2600,
Finally, also it should be noted that, in this article, the such as relational terms of first and second grades and so on is only used for an entity or operation to separate with another entity or operational zone, and not necessarily requires or imply the relation that there is any this reality between these entities or operation or sequentially.And, term " comprises ", " comprising " or its any other variant are intended to contain comprising of nonexcludability, thus make to comprise the process of a series of key element, method, article or equipment and not only comprise those key elements, but also comprise other key elements clearly do not listed, or also comprise by the intrinsic key element of this process, method, article or equipment.When not more restrictions, the key element limited by statement " comprising ... ", and be not precluded within process, method, article or the equipment comprising described key element and also there is other identical element.
In this instructions, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.
To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the application.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein when not departing from the spirit or scope of the application, can realize in other embodiments.Therefore, the application can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (10)

1. a detection method for the input voltage m-Acetyl chlorophosphonazo of electric automobile battery charger, is characterized in that, comprise the steps:
Obtain the m-Acetyl chlorophosphonazo signal of the input voltage of electric automobile battery charger;
Fast Fourier Transform (FFT) FFT(Fast Fourier Transformation with adding Hanning window) interpolation algorithm calculates the low precision frequency of each m-Acetyl chlorophosphonazo of described m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree;
According to the frequency of described low precision, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree, calculate the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place with linear neural network.
2. detection method as claimed in claim 1, it is characterized in that, the m-Acetyl chlorophosphonazo signal of the input voltage of described acquisition electric automobile battery charger, comprising:
The m-Acetyl chlorophosphonazo of the input end of described electric automobile battery charger is sampled;
The m-Acetyl chlorophosphonazo signal that record obtains.
3. detection method as claimed in claim 2, it is characterized in that, the sample frequency of sampling to described voltage signal is 5000 hertz.
4. detection method as claimed in claim 2, it is characterized in that, the writing time of described record sampled signal is 2 hours.
5. detection method as claimed in claim 1, is characterized in that, the described FFT interpolation algorithm with adding Hanning window calculates the low precision frequency of each m-Acetyl chlorophosphonazo of described m-Acetyl chlorophosphonazo signal, amplitude, phase place and m-Acetyl chlorophosphonazo total degree, comprising:
Hanning window process is added to described m-Acetyl chlorophosphonazo signal;
FFT conversion is carried out to the m-Acetyl chlorophosphonazo signal through Hanning window process, obtains spectrum sequence;
With interpolation algorithm, the low precision frequency of described each m-Acetyl chlorophosphonazo, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree are calculated to described spectrum sequence.
6. detection method as claimed in claim 5, it is characterized in that, the sample frequency of described Hanning window is 1600 hertz ~ 12800 hertz.
7. detection method as claimed in claim 5, it is characterized in that, the sampling time of described Hanning window is 0.2 second ~ 0.4 second.
8. detection method as claimed in claim 1, it is characterized in that, the described frequency according to described low precision, amplitude, phase place and described m-Acetyl chlorophosphonazo total degree, calculate the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place with linear neural network, comprising:
Structure and initialization linear neural network;
Train described linear neural network;
Utilize described linear neural network to calculate described m-Acetyl chlorophosphonazo signal, obtain the high-precision frequency of described each m-Acetyl chlorophosphonazo, amplitude and phase place.
9. detection method as claimed in claim 8, it is characterized in that, the neuron number of described linear neural network is identical with described harmonic wave total degree.
10. the detection method as described in claim 1 ~ 9, is characterized in that, the magnitude of voltage of described input voltage is 380 volts, fundamental frequency is 50.2 hertz.
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