CN104935293B - High-power transformer adaptive active method for noise reduction control and control system - Google Patents
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Abstract
The invention discloses high-power transformer adaptive active method for noise reduction control, including, as being transferred to reference to signal in adaptive controller, adaptive controller exports a control signal according to this reference signal and sends secondary noise as secondary signal drive the speaker sensor acquisition primary noise；The primary sound field that the noise that power transformer is sent is established is superimposed with the secondary sound field generation that the secondary noise that loud speaker is sent is established, and is collected the acoustic pressure after superposition by error pick-up, and is formed error signal；After adaptive controller receives error signal, the phase and amplitude of secondary signal is adjusted using the LMS algorithm that convergence coefficient changes according to default object function, continues to meet object function to error signal, reaches stable state.It can accelerate convergence speed of the algorithm in the case where ensureing steady-state error performance, and improve noise reduction, and the selection of convergence coefficient initial value also becomes more freely.
Description
Technical field
The present invention relates to high-power transformer noise reduction technology field more particularly to a kind of high-power transformer adaptive actives
Method for noise reduction control and control system.
Background technology
Transformer noise be designed by body construction, type selecting layout, installation, during use, transformer body and cooling
Irregular, interval that system generates, it is continuous or random caused by mechanical noise and airborne noise summation.It makes an uproar caused by transformer
Sound influences the places such as residential quarters, commercial center, light station, airport, factories and miness, enterprise, hospital, school extensively.With people's environment
The raising and limitation of the environmental protection administration to each noise like of consciousness, especially because the continuous of city expands and city network transformation
Demand, some substations will be built in shopping centre and residential block sometimes, and then transformer noise problem just becomes very prominent
.
Adaptive active noise reduction system refers to generate secondary signal by adaptive controller computing to make loud speaker sounding
To offset a kind of method of noise, since primary sound source and the characteristic of ambient enviroment moment variation, adaptive controller are necessary
The amplitude, frequency and phase of secondary noise can be adjusted constantly, could obtain good noise reduction.Adaptive controller controls
Method quality decisive role is played to the quality of noise reduction, at present in traditional control algorithm each weight coefficient in iteration
In the process, it is all identical to restrain step-length, and which results in the contradictory problems between convergence rate and steady-state error performance.
The content of the invention
The purpose of the present invention is exactly to solve the above-mentioned problems, to provide a kind of high-power transformer adaptive active noise reduction control
Method and control system processed can voluntarily adjust the size of convergence coefficient, the direction of search and convergence so as to adjust control method
Speed, to obtain optimum weight coefficient and better adaptivity, noise reduction process effect is more stable, achieves good noise reduction effect
Fruit.
To achieve these goals, the present invention adopts the following technical scheme that：
High-power transformer adaptive active method for noise reduction control, adaptive controller median filter use FIR filter,
Comprise the following steps：
Step 1, sensor acquisition primary noise with reference to signal x (n) as being transferred in adaptive controller, adaptively
Controller exports a control signal according to this reference signal and sends secondary noise as secondary signal y (n) drive the speakers；
Step 2, the primary sound field that the noise that power transformer is sent is established are built with the secondary noise that loud speaker is sent
Vertical secondary sound field generates superposition, collects the acoustic pressure after superposition by error pick-up, and forms error signal e (n)；
After adaptive controller receives error signal e (n), receipts are utilized according to default object function J (n) for step 3
The LMS algorithm of index variation is held back to adjust the phase and amplitude of secondary signal, continues to meet object function J (n) to error signal,
Reach stable state.
Secondary signal y (n) outputs at the n-th moment are represented with vector form：
Y (n)=XT(n) W=WTX(n)
Wherein X (n)=[x (n), x (n-1) ..., x (n-L+1)]T, W=[w1,w2,...,wL]T, wl(n) it is weight coefficient,
L is the length of wave filter；The error signal e (n) at the n-th moment is：
E (n)=d (n)-y (n)=d (n)-WTX(n)
It can make the minimum target of the mean square error between desired signal d (n) and secondary signal y (n), by desired signal d
(n) and secondary signal y (n) obtains object function J (n).
The object function J (n) is the mean square error of desired signal d (n) and secondary signal y (n), i.e.,：
J (n)=E [e2(n)]=E [(d (n)-WTX(n))2]
=E [d2(n)]+WTE[X(n)XT(n)]W-2WTE[d(n)X(n)]
=E [d2(n)]+WTRW-2WTP
Wherein, R be input signal x (n) autocorrelation matrix, R=E [X (n) XT(n)]；P is cross-correlation vector, P=E [d
(n) X (n)], optimal weight vector W is obtained using iterative method during the LMS algorithm changed using convergence coefficient*, take optimal weight vector W*
When, object function is minimum.
Optimal weight vector W is obtained using iterative method during the LMS algorithm changed using convergence coefficient*It is used in the process to change
It is for function：
W (n+1)=+ 2 μ (n) X (n) e (n) of W (n)
Wherein, μ (n) is the diagonal matrix of L × L：
μl(n) (l=0,1 ..., L-1) is convergence coefficient, and specific obtaining value method is：
Wherein α is more than 1.
The stable state is the amplitude h of error signaltWith stable state moment amplitude h∞When within difference 5%.
High-power transformer adaptive active noise-reduction control system, including reference sensor, error pick-up and adaptive
Controller, the reference sensor connect the input terminal of adaptive controller, the error sensing by the first preamplifier
Device connects the input terminal of adaptive controller by the second preamplifier, and the output terminal of the adaptive controller passes through power
Amplifier connects loud speaker.
The reference sensor and error pick-up use condenser microphone.
Beneficial effects of the present invention：
This control method can accelerate convergence speed of the algorithm in the case where ensureing steady-state error performance, and improve noise reduction
Effect, the contradiction of convergence rate and steady-state error performance can not be taken into account simultaneously by alleviating traditional algorithm to a certain extent, and be received
Holding back the selection of coefficient initial value also becomes more freely, to be effectively reduced primary noise.
Description of the drawings
Fig. 1 is the system operating diagram of the present invention；
Fig. 2 is active noise reduction system mathematical model figure；
Fig. 3 (a) is active noise reduction system model continuous domain simplification figure；Fig. 3 (b) is the letter of active noise reduction system model discrete domain
Change figure；
Fig. 4 is Avaptive filtering system schematic diagram；
Fig. 5 is FIR filter schematic diagram；
Fig. 6 (a) is transformer raw noise time domain beamformer, and Fig. 6 (b) is transformer raw noise amplitude versus frequency characte figure；
Fig. 7 (a) is residual noise signal simulation figure after adaptive-filtering, and Fig. 7 (b) imitates for amplitude versus frequency characte after adaptive-filtering
True figure,
Fig. 7 (c) is secondary signal analogous diagram after adaptive-filtering；
Fig. 8 (a) is residual noise signal simulation figure of the present invention, and Fig. 8 (b) is amplitude versus frequency characte analogous diagram of the present invention,
Fig. 8 (c) is learning curve figure of the present invention, and Fig. 8 (d) is weight coefficient value.
Specific embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
As shown in Figure 1, high-power transformer adaptive active noise-reduction control system, including reference sensor, error sensing
Device and adaptive controller, the reference sensor connect the input terminal of adaptive controller, institute by the first preamplifier
State the input terminal that error pick-up connects adaptive controller by the second preamplifier, the output of the adaptive controller
End connects loud speaker by power amplifier.The reference sensor and error pick-up use condenser microphone.
The signal that reference sensor and error pick-up collect is generally weaker, therefore must pass through the first preamplifier
Amplification with the second preamplifier could be as the input of adaptive controller.Equally, the signal of adaptive controller output
It is not enough to drive the speaker sounding, it is therefore desirable to which power amplifier carries out ratio enlargement.
By Fig. 1 this it appears that entire adaptive noise reduction system mainly completes entire noise reduction process by three circuits,
The function of primary circuit is to pick up reference signal of the primary noise as system, the amplification of secondary loop completion secondary signal with it is defeated
Go out work, reference signal and error signal are then delivered to adaptive controller computing and generate secondary signal by backfeed loop.
High-power transformer adaptive active method for noise reduction control, adaptive controller median filter use FIR filter,
Comprise the following steps：
High-power transformer adaptive active method for noise reduction control, adaptive controller median filter use FIR filter,
Comprise the following steps：
Step 1, sensor acquisition primary noise with reference to signal x (n) as being transferred in adaptive controller, adaptively
Controller exports a control signal according to this reference signal and sends secondary noise as secondary signal y (n) drive the speakers；
Step 2, the primary sound field that the noise that power transformer is sent is established are built with the secondary noise that loud speaker is sent
Vertical secondary sound field generates superposition, collects the acoustic pressure after superposition by error pick-up, and forms error signal e (n)；
After adaptive controller receives error signal e (n), receipts are utilized according to default object function J (n) for step 3
The LMS algorithm of index variation is held back to adjust the phase and amplitude of secondary signal, continues to meet object function J (n) to error signal,
Reach stable state.
Assuming that adaptive controller, reference signal sensor, the frequency response function of error pick-up are respectively W (ω), S1
(ω) and S2(ω), the frequency response of secondary sound source P is P (ω).Assuming that sound wave travels to reference signal sensor S from transformer1
Propagation path transmission function be HS1(ω) distinguishes from transformer and secondary sound source P to the transmission function error pick-up
For HS2(ω)、HPS(ω).Assuming that the response of the device frequencies such as preamplifier is N in primary circuit1(ω), power in secondary loop
The response of the device frequencies such as amplifier is N2The frequency response of the devices such as (ω), the preamplifier in backfeed loop is N3(ω).It is whole
The mathematical model of a intelligent transformer active noise reduction system is as shown in Figure 2.
In Fig. 2, p (t), x (t) and e (t) are respectively primary signal, reference signal and error signal.By the biography in each circuit
Delivery function makees following arrange：
Hr(ω)=S1(ω)HS1(ω)N1(ω) (1)
Hp(ω)=Hps(ω) (2)
HS(ω)=HS2(ω)N2(ω)P(ω) (3)
Hf(ω)=S2(ω)N3(ω) (4)
From the foregoing, it will be observed that Hr(ω)、Hp(ω)、Hs(ω) and Hf(ω) is respectively reference loop, primary circuit, secondary loop
With the transmission function of backfeed loop, therefore Fig. 2 can be reduced to Fig. 3 (a).It, can be by the simplification figure for the convenience subsequently calculated
Discrete domain is converted into, as shown in Fig. 3 (b), correspondingly the transmission function in controller and each circuit can be denoted as W (z), Hr(z)、Hp
(z)、Hs(z) and Hf(z)。
By Fig. 3, it is apparent that in the case of known each return transfer function, the core of the system is self-adaptive controlled
The control algolithm of device processed.
Sef-adapting filter is a kind of using least mean-square error or least square method as the optimum filter of criterion, can oneself
It is dynamic to adjust its unit pulse to reach optimum optimization effect.It can be divided into two parts, and a part is wave filter to complete to filter
Ripple task, another part are control algolithms to complete the adjustment task of coefficient.Its system principle is as shown in Figure 4.
In Fig. 4, x (n) and y (n) are respectively reference signal and secondary signal, and d (n) is desired signal, and e (n) is then error
Signal.Sef-adapting filter can make the mean square error between d (n) and y (n) minimum, therefore the mean square error is system
Object function is represented, i.e., with J (n)
J (n)=E [e2(n)]=E [(d (n)-y (n))2] (5)
Sef-adapting filter structure is that the transversary made using tap delay has limit for length's shock response (Finite-
Duration Impulse Response, FIR) wave filter, its tap weighting coefficients collection is exactly equal to its shock response.FIR
Wave filter is a kind of wave filter of onrecurrent structure, and there are two features for tool：It has linear phase-frequency characteristic first, can
Ensure that distortion will not occur in passband for signal；Secondly it is the causal system of a unconditional stability, without backfeed loop, only
Include zero point.Its structure is as shown in figure 5, wl(n) it is weight coefficient, it is assumed that the length of wave filter is L, then the output at the n-th moment is：
From formula (6) as can be seen that output signal y (n) is the sum of linear weighted function of preceding L input signal x (n), then use
Vector form represents the input signal and weight coefficient of wave filter respectively：
X (n)=[x (n), x (n-1) ..., x (n-L+1)]T (7)
W=[w1,w2,...,wL]T (8)
So, formula (6) can be write as：
Y (n)=XT(n) W=WTX(n) (9)
Thus the error signal at the n-th moment can be written：
E (n)=d (n)-y (n)=d (n)-WTX(n) (10)
Thus it is possible to object function is done such as down conversion：
J (n)=E [e2(n)]=E [(d (n)-WTX(n))2]
=E [d2(n)]+WTE[X(n)XT(n)]W-2WTE[d(n)X(n)]
=E [d2(n)]+WTRW-2WTP (11)
In formula, the autocorrelation matrix of R-input signal x (n), R=E [X (n) XT(n)]；
P-cross-correlation vector, P=E [d (n) X (n)]
From formula (11) as can be seen that object function J (n) is the Quadratic Function Optimization of weight coefficient vector W, then its shape is
Concave Iy curved, therefore with unique minimum.So when object function J (n) gradients are equal to zero, it is possible to obtain optimal
Weight vector W*.So object function J (n) is made to weight vector W*Gradient equations be zero：
If matrix R is full rank battle array, and its inverse matrix R-1In the presence of can then acquire optimal weight vector is：
W*=R-1P (13)
Although formula (13) gives the optimal weight vector for obtaining minimum target function, input letter must be precomputed
Number autocorrelation matrix R of x (n) and cross-correlation vector P, and invert to autocorrelation matrix.However in Practical Project, it is difficult to directly
It obtains derived from correlation matrix R and cross-correlation matrix P, it is necessary to be obtained by calculating.But when filter length L is bigger,
Autocorrelation matrix R and cross-correlation matrix P computation complexities will certainly be seriously affected.Therefore, estimated most using the method for iteration
Excellent weight vector becomes a feasible program.At present, adaptive algorithm there are many kinds of, most of is by LMSE method
(LMS) it is derived with least square method (LS),
Adaptive process is exactly gradually to approach the process of object function minimum value by successively adjusting weight vector,
Final purpose is to seek optimal weight vector.And least mean square algorithm can realize this function, it is a kind of simple and effective
Iterative algorithm, also need not be to matrix inversion without autocorrelation matrix is known in advance, it is only necessary to be weighed using steepest descent method
The recurrence formula of vector.
The process following article of existing LMS algorithm.
According to steepest descent method principle, the weight vector W (n+1) at the (n+1)th moment is equal to the weight vector W (n) at the n-th moment and subtracts
A variable quantity of gradient ▽ (n) is proportional to, as shown in formula (14)：
W (n+1)=W (n)-μ ▽ (n) (14)
In formula, μ-convergence coefficient influences convergence speed of the algorithm；
▽ (n)-object function J (n) is to weight vector W*Gradient, i.e.,
In order to improve the real-time of system, estimate of the instantaneous value as formula (15) can be used, that is, is had：
It can be proved that estimation gradient vector value is the unbiased esti-mator of true gradient vector, then have：
But the two still has certain difference：
In formula, N (n)-gradient noise.
The appearance of gradient noise is because in each iterative process, using only limited input quantity, therefore causes ladder
Spending estimate, there are certain deviations.
So, useTo replace the ▽ (n) in formula (14) that can obtain：
Above with iterative algorithm come to derive the method for weight vector be exactly existing least-mean-square error algorithm.
In existing LMS algorithm each weight coefficient in an iterative process, convergence step-length be all it is identical, which results in
Contradictory problems between convergence rate and steady-state error performance.For this problem, this patent, which makes traditional LMS algorithm, to be changed
Into algorithm being enable voluntarily to adjust the size of convergence coefficient, so as to adjust the direction of search and convergence rate of algorithm, to obtain most
Excellent weight coefficient and better adaptivity.
Formula (19) is changed as shown below：
W (n+1)=+ 2 μ (n) X (n) e (n) of W (n) (21)
In formula, the diagonal matrix of μ (n)-L × L：
In order to ensure the convergence of adaptive algorithm, it is necessary to by μl(n) value range of (l=0,1 ..., L-1) limits
Between a minimum value and a maximum value, i.e., formula (9) must be met.Under this condition, value can be divided into three kinds of situations.1、
If x (n-1) e (n) is after continuous multiple moment, symbol does not change, then by μl(n) α times is increased；If the 2nd, x
(n-1) e (n) is after continuous multiple moment, and symbol changes, then by μl(n) α times is reduced；Other situations are then constant.
It can obtain equation below：
α is more than 1.
In order to verify effect of the adaptive filter algorithm during active noise reduction, the present invention also carries out existing algorithm
Experiment.Using the original noise of transformer as the input signal of sef-adapting filter, as shown in Fig. 6 (a)-Fig. 6 (b),
Then by the output signal of sef-adapting filter and the superimposed analog error signal of noise signal of transformer.The signal is to drop
Signal after making an uproar, and sef-adapting filter is introduced as object function.Sef-adapting filter length is set to 256 ranks, convergence coefficient
For 0.05.The output signal of error signal and its amplitude versus frequency characte and sef-adapting filter is set forth in Fig. 7 (a)-Fig. 7 (c).
To understand the convergence rate of adaptive algorithm, present invention definition is as the amplitude h of certain moment error signaltDuring with stable state
Carve amplitude h∞When within difference 5%, then it is assumed that reach stable state in the moment algorithm, i.e.,：
As can be seen that error signal reaches stable state after 295ms, amplitude significantly decreases comparison diagram 6.Control
The amplitude that amplitude versus frequency characte can be seen that at 100Hz and its frequency multiplication drops to less than 0.006 by more than 0.02, there is apparent noise reduction
Decline at effect, especially 300Hz the most apparent.Secondary signal is compared with primary signal it can be found that its phase almost differs
180 °, therefore the amplitude of error signal can be obviously reduced after the two superposition.
Simulating, verifying is also carried out to the control method of the present invention, it is assumed that convergence coefficient μl(l=0,1 ..., L-1) initial value
0.05 is taken, the length of wave filter is set to 256 ranks, and α values are 2, can be obtained such as Fig. 8 (a)-Fig. 8 (d) results.
From Fig. 8 (a)-Fig. 8 (d) as can be seen that residual noise has reached stable state in 300ms or so, amplitude is finally received
It holds back 0.015 or so, achieves good noise reduction；Learning curve shows that the amplitude of residual noise has dropped 39dB；From width
It is seen in frequency characteristic, primary noise main frequency composition amplitude drops to less than 0.0016,300Hz groups in especially former primary noise
Divide decline the most apparent；The final value of weight coefficient is fluctuated in zero crossings.
Improved adaptive algorithm can add algorithm to a certain extent in the case where ensureing steady-state error performance
Speed convergence, and improve noise reduction.In conclusion improved adaptive algorithm solve to a certain extent traditional algorithm without
Method takes into account the problem of convergence rate and steady-state error performance, and the selection of convergence coefficient initial value is also more convenient.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (4)
1. high-power transformer adaptive active method for noise reduction control, adaptive controller median filter uses FIR filter,
It is characterized in, comprises the following steps：
Step 1, sensor acquisition primary noise, which is used as, to be transferred to reference to signal x (n) in adaptive controller, self adaptive control
Device exports a control signal according to this reference signal and sends secondary noise as secondary signal y (n) drive the speakers；
Step 2, the primary sound field that the noise that power transformer is sent is established are established with the secondary noise that loud speaker is sent
Secondary sound field generates superposition, collects the acoustic pressure after superposition by error pick-up, and forms error signal e (n)；
After adaptive controller receives error signal e (n), convergence system is utilized according to default object function J (n) for step 3
The LMS algorithm of variation is counted to adjust the phase and amplitude of secondary signal, continues to meet object function J (n) to error signal, reach
Stable state；
Secondary signal y (n) outputs at the n-th moment are represented with vector form：
Y (n)=XT(n) W=WTX(n)
Wherein X (n)=[x (n), x (n-1) ..., x (n-L+1)]T, W=[w1,w2,...,wL]T, wl(n) it is weight coefficient, L is
The length of wave filter；The error signal e (n) at the n-th moment is：
E (n)=d (n)-y (n)=d (n)-WTX(n)
Can make the minimum target of the mean square error between desired signal d (n) and secondary signal y (n), by desired signal d (n) and
Secondary signal y (n) obtains object function J (n)；
The object function J (n) is the mean square error of desired signal d (n) and secondary signal y (n), i.e.,：
J (n)=E [e2(n)]=E [(d (n)-WTX(n))2]
=E [d2(n)]+WTE[X(n)XT(n)]W-2WTE[d(n)X(n)]
=E [d2(n)]+WTRW-2WTP
Wherein, R be input signal x (n) autocorrelation matrix, R=E [X (n) XT(n)]；P is cross-correlation vector, P=E [d (n) X
(n)] optimal weight vector W is obtained using iterative method during the LMS algorithm, changed using convergence coefficient*, take optimal weight vector W*When, mesh
Scalar functions are minimum；
Optimal weight vector W is obtained using iterative method during the LMS algorithm changed using convergence coefficient*Used iteration letter in the process
Number is：
W (n+1)=+ 2 μ (n) X (n) e (n) of W (n)
Wherein, μ (n) is the diagonal matrix of L × L：
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μl(n) it is convergence coefficient, l=0,1 ..., L-1；Specifically obtaining value method is：
Wherein α is more than 1.
2. high-power transformer adaptive active method for noise reduction control as described in claim 1, it is characterized in that, the stable state
For the amplitude h of error signaltWith stable state moment amplitude h∞When within difference 5%.
3. high-power transformer adaptive active method for noise reduction control as described in claim 1, it is characterized in that, using high-power change
The control system of depressor adaptive active method for noise reduction control includes reference sensor, error pick-up and adaptive controller,
The reference sensor connects the input terminal of adaptive controller by the first preamplifier, and the error pick-up passes through the
Two preamplifiers connect the input terminal of adaptive controller, and the output terminal of the adaptive controller is connected by power amplifier
Connect loud speaker.
4. high-power transformer adaptive active method for noise reduction control as claimed in claim 3, it is characterized in that, it is described with reference to sensing
Device and error pick-up use condenser microphone.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510358670.5A CN104935293B (en) | 2015-06-25 | 2015-06-25 | High-power transformer adaptive active method for noise reduction control and control system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510358670.5A CN104935293B (en) | 2015-06-25 | 2015-06-25 | High-power transformer adaptive active method for noise reduction control and control system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104935293A CN104935293A (en) | 2015-09-23 |
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| CN105575381A (en) * | 2015-10-28 | 2016-05-11 | 长沙学院 | Train air-cooled current transformer noise active control method |
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| CN106094654B (en) * | 2016-08-16 | 2018-10-26 | 武汉大学 | A kind of power transformer active noise control system based on disturbance observation method |
| CN106303885B (en) * | 2016-09-29 | 2020-02-28 | 歌尔股份有限公司 | Noise control method and system and sound insulation box |
| CN106409281A (en) * | 2016-10-11 | 2017-02-15 | 哈尔滨理工大学 | Power transformer noise active control system |
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| CN107351741A (en) * | 2017-06-28 | 2017-11-17 | 邢优胜 | A kind of active noise reduction headrest of automotive seat |
| CN107393545B (en) * | 2017-07-17 | 2020-12-08 | 会听声学科技（北京）有限公司 | Feedback type active noise reduction system and method with flexible gain |
| CN109714023B (en) * | 2018-12-28 | 2023-07-11 | 歌尔股份有限公司 | Adaptive filtering method, adaptive filter and noise control system |
| CN110459197B (en) * | 2019-07-10 | 2021-09-24 | 哈尔滨工业大学（深圳） | Signal enhancer and method for denoising and extracting weak blind signals |
| CN112037752A (en) * | 2020-09-08 | 2020-12-04 | 珠海格力电器股份有限公司 | Household appliance noise reduction method and device, computer equipment and storage medium |
| CN113223491B (en) * | 2021-04-15 | 2022-10-21 | 天津工业大学 | Active noise reduction method for electrical equipment |
| CN115068824B (en) * | 2022-08-23 | 2022-11-18 | 深圳市健怡康医疗器械科技有限公司 | Electric shock massager |
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