WO2015169109A1 - 一种变压器噪声抑制方法 - Google Patents

一种变压器噪声抑制方法 Download PDF

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WO2015169109A1
WO2015169109A1 PCT/CN2015/070735 CN2015070735W WO2015169109A1 WO 2015169109 A1 WO2015169109 A1 WO 2015169109A1 CN 2015070735 W CN2015070735 W CN 2015070735W WO 2015169109 A1 WO2015169109 A1 WO 2015169109A1
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noise
particle
transformer
neural network
digital signal
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PCT/CN2015/070735
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English (en)
French (fr)
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姜宁
马宏忠
李凯
许洪华
王春宁
陈冰冰
姜鸿羽
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国家电网公司
江苏省电力公司
江苏省电力公司南京供电公司
河海大学
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Priority to US15/323,407 priority Critical patent/US10083709B2/en
Publication of WO2015169109A1 publication Critical patent/WO2015169109A1/zh

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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1781—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions
    • G10K11/17821—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase characterised by the analysis of input or output signals, e.g. frequency range, modes, transfer functions characterised by the analysis of the input signals only
    • G10K11/17825—Error signals
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    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17853—Methods, e.g. algorithms; Devices of the filter
    • G10K11/17854—Methods, e.g. algorithms; Devices of the filter the filter being an adaptive filter
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1785—Methods, e.g. algorithms; Devices
    • G10K11/17855—Methods, e.g. algorithms; Devices for improving speed or power requirements
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    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K11/00—Methods or devices for transmitting, conducting or directing sound in general; Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/16—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general
    • G10K11/175—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound
    • G10K11/178—Methods or devices for protecting against, or for damping, noise or other acoustic waves in general using interference effects; Masking sound by electro-acoustically regenerating the original acoustic waves in anti-phase
    • G10K11/1787—General system configurations
    • G10K11/17879—General system configurations using both a reference signal and an error signal
    • G10K11/17881—General system configurations using both a reference signal and an error signal the reference signal being an acoustic signal, e.g. recorded with a microphone
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0208—Noise filtering
    • G10L21/0216—Noise filtering characterised by the method used for estimating noise
    • G10L21/0232—Processing in the frequency domain
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01F—MAGNETS; INDUCTANCES; TRANSFORMERS; SELECTION OF MATERIALS FOR THEIR MAGNETIC PROPERTIES
    • H01F27/00—Details of transformers or inductances, in general
    • H01F27/33—Arrangements for noise damping
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/005—Circuits for transducers, loudspeakers or microphones for combining the signals of two or more microphones
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R3/00—Circuits for transducers, loudspeakers or microphones
    • H04R3/04—Circuits for transducers, loudspeakers or microphones for correcting frequency response
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/10—Applications
    • G10K2210/125—Transformers
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
    • G10K2210/3026—Feedback
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
    • G10K2210/301—Computational
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    • G—PHYSICS
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    • G10K—SOUND-PRODUCING DEVICES; METHODS OR DEVICES FOR PROTECTING AGAINST, OR FOR DAMPING, NOISE OR OTHER ACOUSTIC WAVES IN GENERAL; ACOUSTICS NOT OTHERWISE PROVIDED FOR
    • G10K2210/00—Details of active noise control [ANC] covered by G10K11/178 but not provided for in any of its subgroups
    • G10K2210/30—Means
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    • H04R—LOUDSPEAKERS, MICROPHONES, GRAMOPHONE PICK-UPS OR LIKE ACOUSTIC ELECTROMECHANICAL TRANSDUCERS; DEAF-AID SETS; PUBLIC ADDRESS SYSTEMS
    • H04R2410/00—Microphones
    • H04R2410/05—Noise reduction with a separate noise microphone

Definitions

  • the invention relates to a transformer noise suppression method, in particular to a transformer noise suppression method based on an improved particle BP neural network, belonging to the technical field of power supply.
  • the noise of the transformer body is mainly caused by the electromagnetic vibration generated by the magnetostriction caused by the magnetostriction of the silicon steel sheet and the electromagnetic force generated by the current passing through the winding.
  • the energy of the transformer is mainly concentrated at the fundamental frequency (100 Hz) integral frequency below 500 Hz, and has strong penetrating power.
  • the characteristics of slow decay, etc.; the noise of the transformer cooling system mainly comes from the vibration of the fan and the oil pump during operation.
  • the energy is mainly concentrated in the high frequency part above 500Hz, and the noise attenuation is faster.
  • the low frequency noise of the transformer is the main cause of environmental pollution, and it is necessary to control this part of the noise.
  • Chinese patent CN 102355233A uses a direct digital synthesis of a fixed frequency sine and cosine signal as a reference signal, the frequency value of which corresponds to the nominal frequency of harmonic noise.
  • CN 102176668A directly synthesizes a corresponding reference signal based on picking up the fundamental frequency components in the primary noise. All of the above patents use digital direct synthesis of reference signals, which only eliminates noise from certain frequency components.
  • the traditional F-XLMS filtering algorithm can suppress all frequency component noise, but it can't deal with time-varying and nonlinear noise
  • scholars apply BP neural network algorithm with nonlinear processing ability, self-adaptive ability and robustness to the elimination.
  • the traditional BP neural network inherently has slow convergence speed and easy to fall into local minimum values, which makes the noise control effect not ideal. Therefore, it is necessary to find a filtering algorithm that can achieve better noise reduction effects.
  • the primary object of the present invention is to propose a transformer noise suppression method that can effectively eliminate noise.
  • a further object of the present invention is to provide a transformer noise suppression method based on an improved particle BP neural network that can overcome the above-mentioned prior art BP neural network with slow convergence speed and easy to fall into local minimum value defects.
  • the transformer single-channel noise suppression method of the present invention is mainly composed of a controller including a smart chip and an initial noise measuring microphone, a residual noise measuring microphone, and a speaker, wherein the controller implements noise according to the following steps. Suppression:
  • the first step is to receive an initial noise digital signal transmitted by the initial noise measurement microphone near the noise source and converted, as an input signal of a BP (Back Propagation) neural network;
  • BP Back Propagation
  • the secondary digital signal is converted into an analog signal, which is amplified and output to the speaker, and generates secondary noise that has an inhibitory effect on the initial noise;
  • the fourth step receiving the residual noise and the secondary noise superimposed by the residual noise measurement microphone and the converted residual noise digital signal, determining whether the amplitude of the residual noise digital signal has been continuously set the number of times, and if so, maintaining the secondary Digital signal output, if otherwise proceeding to the next step;
  • the BP neural network parameters are optimized and adjusted to minimize the amplitude of the residual noise digital signal, and the residual noise digital signal of the previous step is used as the input signal to repeat the second step.
  • the BP neural network parameters are optimized and adjusted by using an improved particle swarm algorithm, and the following process is performed:
  • Step 1 Determine the dimension of the particle according to the existing BP neural network structure, and randomly generate N initial particles;
  • Step 2 The weight coefficient ⁇ hi (n) between the input layer neuron i and the hidden layer neuron h in the neural network at the nth moment, and the weight coefficient W h between the hidden layer neuron h and the output layer ( n), the threshold GE h (n) of the hidden layer neuron h and the threshold ge(n) of the output layer neuron are real-coded in a predetermined order to form corresponding real numbers, each particle corresponding to a set of network parameters, Its coding form is;
  • Step 3 Select the residual noise signal e(n) of the system as the criterion for evaluating the network parameters, and use the following fitness function F(n) as the particle position coding and formula:
  • x(n) is the initial noise digital signal input at the time n;
  • H 1 (z) and H 2 (z) are the transfer functions of the primary channel and the secondary channel, respectively;
  • P g [p g1 ,p g2 ,...,p g(J(K+2)+1) ], otherwise, remain unchanged;
  • Step 5 If the number of iterations has reached the maximum number of iterations, the iteration is stopped, and the optimal position of the particle group is decoded to obtain the corresponding BP neural network parameters, otherwise the next step is performed;
  • Step 6 Define the evolution ⁇ of the particle swarm as:
  • f gbest (k) and f avg (k) are the global optimal fitness value and the average fitness value of the particle group at the kth iteration, respectively;
  • w 0 is the set initial value.
  • the formula for calculating the adaptive mutation probability is:
  • p m (k) is the mutation probability of the particle swarm at the kth iteration
  • p mmax and p mmin are the maximum and minimum of the mutation rate, respectively
  • f i (k) is the adaptation of the kth iteration of the particle i Degree value
  • ⁇ is the set constant
  • the new velocity and position calculation formula for defining particle variability is:
  • v id (k+1) w(k)v id (k)+c 1 r 1 (p id (k)-x id (k))+c 2 r 2 (p gd (k)-x id ( k)) (7)
  • v id (k) is the velocity of the d- th dimension of the particle i in the kth iteration
  • V i (k) is the velocity vector of the particle i in the kth iteration
  • p id (k) is the particle i k The best position of the dth dimension in the iteration
  • p gd (k) is the best position of the dth dimension in the kth iteration of the whole group
  • X i (k) is the position vector in the kth iteration of the particle i
  • c 1 And c 2 are non-negative acceleration constants
  • r 1 and r 2 are random numbers transformed in the range [0, 1]
  • R 1 and R 2 are unequal positive integers, in the range [1, N];
  • Step 7 First calculate the inertia factor and particle variability of this iteration from the above formulas (4) and (5), and then update the velocity and position of all particles by using equations (7) and (8) to generate a new generation of particles. Group, return to step four.
  • the digital signal x(n) at the nth time is input to the controller, it is first filtered by a BP neural network with random parameters to obtain an output value y(n), which is transmitted through the secondary acoustic channel. After the function is applied, the output value becomes s(n), that is, the secondary digital signal at the nth time, after superposition, the generated error digital signal is e(n).
  • the improved particle swarm optimization algorithm is used to correct the relevant parameters in the BP neural network in combination with the reference digital signal x(n) at the nth time.
  • the new weight coefficient and the threshold coefficient corresponding to the BP neural network at the nth time can be obtained, and then the new parameters are replaced with the original parameters.
  • the reference digital signal x(n+1) at the n+1th time is input to the controller, it is filtered by a BP neural network with updated parameters to obtain a new output value, which is subjected to a secondary acoustic channel transfer function. The output value then becomes s(n+1), which is the secondary digital signal at the n+1th time.
  • the invention not only establishes a complete single-channel transformer noise reduction system, but also can effectively realize noise reduction of noise sources such as transformers in the substation, and through scientific operation processing and multiple sets of single channel drops.
  • the coordination of the noise system can quickly suppress noise within a certain range of noise sources such as transformers, achieve an ideal noise reduction effect, and solve the problems of high cost, poor performance, and poor portability of the conventional noise reduction method.
  • FIG. 1 is a schematic diagram of a single channel noise reduction structure according to an embodiment of the present invention.
  • FIG. 2 is a flow chart showing the basic processing of the digital signal of the embodiment of FIG. 1.
  • FIG. 3 is a block diagram of an improved particle BP neural network system of the embodiment of FIG. 1.
  • FIG. 4 is a comparison diagram of the time domain effect of the residual noise measurement microphone pickup signal before and after noise reduction in the embodiment of FIG. 1.
  • FIG. 5 is a frequency domain effect diagram of the residual noise measurement microphone pickup signal before noise reduction in the embodiment of FIG. 1.
  • FIG. 6 is a frequency domain effect diagram of a residual noise measurement microphone pickup signal after noise reduction in the embodiment of FIG. 1.
  • Figure 7 is a multi-channel noise reduction system constructed in the embodiment of Figure 1.
  • This embodiment is a transformer noise suppression method based on the improved particle BP neural network.
  • the main components of the single channel noise reduction system include: a controller including a smart chip type TMS320VC5509 DSP chip, respectively as an initial noise measurement microphone And 2 PCM6110 microphones for residual noise measurement microphones, Swans S6.5 woofer.
  • the initial noise measuring microphone and the residual noise measuring microphone are respectively connected to the input port corresponding to the DSP chip of the controller through the sound processing chip (TLV320AIC23 audio chip) and the A/D conversion chip of the automatic gain adjuster, and the output end of the DSP chip passes through The D/A converter chip and power amplifier are connected to the speaker.
  • the signal acquisition frequency is set to 5000Hz
  • the BP neural network structure is set to 2-10-1 filtering algorithm.
  • the excitation functions of the hidden layer and the output layer are respectively Sigmoid function and Purelin function.
  • the primary noise measurement microphone is 10 cm from the transformer, the speaker is 30 cm from the transformer, and the residual noise measurement microphone is 20 cm from the speaker.
  • the controller When working, the controller implements noise suppression as follows (see Figure 2):
  • the first step is to receive an initial noise digital signal transmitted by the initial noise measurement microphone near the noise source and converted, as an input signal of a BP (Back Propagation) neural network;
  • BP Back Propagation
  • the secondary digital signal is converted into an analog signal, which is amplified and output to the speaker, and generates secondary noise that has an inhibitory effect on the initial noise;
  • the fourth step receiving the residual noise and the secondary noise superimposed by the residual noise measurement microphone and the converted residual noise digital signal, determining whether the amplitude of the residual noise digital signal has been continuously set the number of times, and if so, maintaining the secondary Digital signal output, if otherwise proceeding to the next step;
  • the BP neural network parameters are optimized and adjusted to minimize the amplitude of the residual noise digital signal, and the residual noise digital signal of the previous step is used as the input signal to repeat the second step; wherein the BP neural network parameters are optimized and adjusted.
  • Step 1 Determine the dimension of the particle according to the existing BP neural network structure, and randomly generate N initial particles;
  • Step 2 The weight coefficient ⁇ hi (n) between the input layer neuron i and the hidden layer neuron h in the neural network at the nth moment, and the weight coefficient W h between the hidden layer neuron h and the output layer ( n), the threshold GE h (n) of the hidden layer neuron h and the threshold ge(n) of the output layer neuron are real-coded in a predetermined order to form corresponding real numbers, each particle corresponding to a set of network parameters, Its coding form is;
  • Step 3 Select the residual noise signal e(n) of the system as the criterion for evaluating the network parameters, and use the following fitness function F(n) as the particle position coding and formula:
  • x(n) is the initial noise digital signal input at the time n;
  • H 1 (z) and H 2 (z) are the transfer functions of the primary channel and the secondary channel, respectively;
  • P g [p g1 ,p g2 ,...,p g(J(K+2)+1) ], otherwise, remain unchanged;
  • Step 5 If the number of iterations has reached the maximum number of iterations, the iteration is stopped, and the optimal position of the particle group is decoded to obtain the corresponding BP neural network parameters, otherwise the next step is performed;
  • Step 6 Define the evolution ⁇ of the particle swarm as:
  • f gbest (k) and f avg (k) are the global optimal fitness value and the average fitness value of the particle group at the kth iteration, respectively;
  • w 0 is the set initial value.
  • the formula for calculating the adaptive mutation probability is:
  • p m (k) is the mutation probability of the particle swarm at the kth iteration
  • p mmax and p mmin are the maximum and minimum of the mutation rate, respectively
  • f i (k) is the adaptation of the kth iteration of particle i Degree value
  • ⁇ is the set constant
  • the new velocity and position calculation formula for defining particle variability is:
  • v id (k+1) w(k)v id (k)+c 1 r 1 (p id (k)-x id (k))+c 2 r 2 (p gd (k)-x id ( k)) (7)
  • v id (k) is the velocity of the d- th dimension of the particle i in the kth iteration
  • V i (k) is the velocity vector of the particle i in the kth iteration
  • p id (k) is the particle i k The best position of the dth dimension in the iteration
  • p gd (k) is the best position of the dth dimension in the kth iteration of the whole group
  • X i (k) is the position vector in the kth iteration of the particle i
  • c 1 And c 2 are non-negative acceleration constants
  • r 1 and r 2 are random numbers transformed in the range [0, 1]
  • R 1 and R 2 are unequal positive integers, in the range [1, N];
  • Step 7 First calculate the inertia factor and particle variability of this iteration from the above formulas (4) and (5), and then update the velocity and position of all particles by using equations (7) and (8) to generate a new generation of particles. Group, return to step four.
  • this embodiment not only constructs a reasonable single-channel transformer noise suppression system, but also combines the particle swarm optimization algorithm based on BP neural network filtering, so as to improve the weight and threshold of BP neural network by using improved particle swarm optimization algorithm.
  • the correction method overcomes the inherent defects of the algorithm such as gradient descent optimization, and can effectively suppress the noise of all frequency components of the transformer.
  • a multi-channel noise reduction system consisting of multiple sets of single-channel noise reduction systems can effectively control the transformer noise within the allowable range.

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Abstract

一种变压器噪声抑制方法，属于电力供应技术领域。单个有源降噪系统抑制噪声方法步骤为：（1）接收并转换初始噪声数字信号作为BP神经网络的输入信号；（2）对输入信号进行处理，生成次级数字信号；（3）将次级数字信号输出到扬声器，产生次级噪声；（4）接收初始噪声和次级噪声叠加的残余噪声数字信号，判断是否已连续设定次数不变，如是则保持次级数字信号输出；（5）如否则以使残余噪声数字信号幅值最小为优化原则，优化、调整BP神经网络参数，将上一步的残余噪声数字信号作为输入信号重复第（2）步。在变压器油箱附近适当位置上布置多个单通道降噪系统，这样便构成变压器噪声的多通道抑制系统。该变压器噪声抑制方法能够迅速抑制变压器等噪声源一定范围内的噪声，取得理想的降噪效果，解决了传统降噪方法存在的费用高、效果差及可移植性差等问题。

Description

一种变压器噪声抑制方法 技术领域
本发明涉及一种变压器噪声抑制方法，尤其是一种基于改进粒子BP神经网络的变压器噪声抑制方法，属于电力供应技术领域。
背景技术
研究表明，变电站噪声主要为大型电力变压器噪声，而电力变压器噪声来源于变压器本体和冷却系统两个方面。
变压器本体噪声主要源于硅钢片磁致伸缩所引起的铁心振动和电流通过绕组所产生的电磁力，其能量主要集中在500Hz以下的基频(100Hz)整数倍频率处，具有穿透能力强、衰减慢等特点；变压器冷却系统噪声主要源于风扇和油泵运行时的振动，其能量主要集中在500Hz以上的高频部分，该部分噪声衰减较快。变压器低频噪声是造成环境污染的主要因素，控制该部分噪声很有必要。
中国专利CN 102355233A采用直接数字合成固定频率的正余弦信号作为参考信号，其频率值对应谐波噪声的额定频率。CN 102176668A根据拾取初级噪声中的基波频率成分，直接数字合成相应的参考信号。以上专利均利用数字直接合成参考信号，只能消除某些频率成分的噪声。虽然传统的F-XLMS滤波算法可以抑制所有频率成分噪声，但是其无法处理时变、非线性噪声，故学者将具有非线性处理能力、自适应能力及鲁棒性的BP神经网络算法应用到消噪领域中，而传统BP神经网络固有的收敛速度慢和容易陷入局部极小值等缺陷，使其噪声控制的效果并不理想。因此，需寻求一种能够取得更好降噪效果的滤波算法。
发明内容
本发明的首要目的在于：提出一种可以有效消除噪声的变压器噪声抑制方法。
本发明进一步的目的：提出一种可以克服上述现有技术BP神经网络收敛速度慢和容易陷入局部极小值缺陷的基于改进粒子BP神经网络的变压器噪声抑制方法。
为了达到以上首要目的，本发明的变压器单通道噪声抑制方法在主要由含智能芯片的控制器以及初始噪声测量传声器、残余噪声测量传声器、扬声器组成的系统中，所述控制器按以下步骤实现噪声抑制：
第一步、接收位于噪声源附近初始噪声测量传声器传输并经转换的初始噪声数字信号，作为BP(Back Propagation)神经网络的输入信号；
第二步、借助BP神经网络对输入信号进行处理后，生成相位与输入信号偏离的次级数字信号；
第三步、将次级数字信号转换成模拟信号经放大后输出到扬声器，产生对初始噪声具有抑制作用的次级噪声；
第四步、接收残余噪声测量传声器拾取的初始噪声和次级噪声叠加并经转换后的残余噪声数字信号，判断残余噪声数字信号的幅值是否已连续设定次数不变，如是则保持次级数字信号输出，如否则进行下步；
第五步、以使残余噪声数字信号幅值最小为优化原则，优化、调整BP神经网络参数，将上一步的残余噪声数字信号作为输入信号重复第二步。
为了达到进一步的目的，本发明变压器单通道噪声抑制方法中，所述第五步中优化、调整BP神经网络参数采用改进粒子群算法，按以下过程进行：
步骤一、根据已有的BP神经网络结构确定粒子的维数，并随机产生N个初始粒子；
步骤二、将第n时刻神经网络中输入层神经元i与隐含层神经元h之间的权系数ωhi(n)、隐含层神经元h与输出层之间的权系数Wh(n)、隐含层神经元h的阈值GEh(n)及输出层神经元的阈值ge(n)按预定的次序进行实数编码，形成相应的实数粒子，每个粒子对应一组网络参数，其编码形式为；
ω11 ω12 … ω1K W1 GE1 … ωJ1 ωJ2 … ωJK WJ GEJ ge
步骤三、选择系统的残余噪声信号e(n)作为网络参数的评判标准，以如下适应度函数F(n)作为粒子位置编码和公式：
Figure PCTCN2015070735-appb-000001
上式中，x(n)为第n时刻输入的初始噪声数字信号；H1(z)和H2(z)分别为初级通道和次级通道的传递函数；
Figure PCTCN2015070735-appb-000002
上式中，xi(n)＝x(n-i+1)表示输入层神经元i的输入；以f(x)＝2/(1+exp(-2x))-1表示网络隐含层的激励函数；
步骤四、根据粒子位置编码和公式(1)，计算出各粒子的适应度值；将粒子当前位置适应度值与之前迭代时该粒子的位置适应度值作比较，若前者比后者小，则更新该粒子最优位置Pi＝[pi1,pi2,…,pi(J(K+2)+1)]，否则，保持不变；同时，将该粒子对应的适应度值与之前迭代时的适应度值作比较，若前者比后者小，则更新整个粒子群的最优位置Pg＝[pg1,pg2,…,pg(J(K+2)+1)]，否则，保持不变；
步骤五、若迭代次数已达到最大迭代次数，则停止迭代，对粒子群最优位置进行解码得到相应BP神经网络参数，否则进行下一步；
步骤六、定义粒子群的进化度σ为：
Figure PCTCN2015070735-appb-000003
上式中，fgbest(k)和favg(k)分别为第k次迭代时粒子群的全局最优适应度值和平均适应度值；
定义动态变化的惯性因子计算公式为：
Figure PCTCN2015070735-appb-000004
上式中，w0为设定的初始值。
定义自适应变异概率计算公式为：
Figure PCTCN2015070735-appb-000005
Figure PCTCN2015070735-appb-000006
其中，pm(k)为粒子群在第k次迭代的变异概率；pmmax和pmmin分别为变异率的 最大值和最小值，fi(k)为粒子i第k次迭代时的适应度值，ε为设定的常数；
定义粒子变异后新的速度和位置计算公式为：
vid(k+1)＝w(k)vid(k)+c1r1(pid(k)-xid(k))+c2r2(pgd(k)-xid(k))   (7)
Figure PCTCN2015070735-appb-000007
其中，vid(k)为粒子i在第k次迭代中第d维的速度；Vi(k)为粒子i在第k次迭代中的速度向量；pid(k)为粒子i第k次迭代中第d维的最好位置；pgd(k)为整个群体第k次迭代中第d维的最好位置；Xi(k)为粒子i第k次迭代中位置向量；c1和c2为非负加速常数；r1和r2为[0,1]范围内变换的随机数；R1和R2为不相等正整数，在[1,N]范围内；
步骤七、先由以上公式(4)和(5)计算出本次迭代时的惯性因子和粒子变异率，再利用公式(7)和(8)更新所有粒子的速度和位置，生成新一代粒子群，返回步骤四。
这样即可实现，当第n时刻的数字信号x(n)输入控制器时，先将其通过一个具有随机参数的BP神经网络滤波，得到一个输出值y(n)，经过次级声通道传递函数作用后该输出值变为s(n)，即第n时刻的次级数字信号，经过叠加后，产生的误差数字信号为e(n)。当第n时刻的误差数字信号e(n)反馈输入控制器时，结合第n时刻的参考数字信号x(n)，利用改进粒子群优化算法修正BP神经网络中相关参数。经过上述运算处理后，便可以得到第n时刻BP神经网络对应的新的权系数和阈系数，然后将这些新的参数替换原来的参数。当第n+1时刻的参考数字信号x(n+1)输入控制器时，便将其通过一个更新过参数的BP神经网络滤波，得到一个新的输出值，经过次级声通道传递函数作用后该输出值变为s(n+1)，即第n+1时刻的次级数字信号。
本发明与现有技术相比，不仅由于建立了一套完整的单通道变压器降噪系统，可以切实实现对变电站内变压器等噪声源的降噪，而且通过科学的运算处理和多组单通道降噪系统的配合，能够迅速抑制变压器等噪声源一定范围内的噪声，取得理想的降噪效果，解决了传统降噪方法存在的费用高、效果差及可移植性差等问题。
附图说明
图1是本发明一个实施例的单通道降噪结构示意图。
图2是图1实施例的数字信号基本处理过程流程图。
图3是图1实施例的改进粒子BP神经网络系统框图。
图4是图1实施例降噪前后残余噪声测量传声器拾取信号的时域效果对照图。
图5是图1实施例降噪前残余噪声测量传声器拾取信号的频域效果图。
图6是图1实施例降噪后残余噪声测量传声器拾取信号的频域效果图。
图7是图1实施例组成的多通道降噪系统。
具体实施方式
实施例一
下面结合附图对本发明的技术方案做进一步的详细说明。
本实施例为基于改进粒子BP神经网络的变压器噪声抑制方法，如图1所示，单通道降噪系统主要构成包括：含智能芯片——型号为TMS320VC5509DSP芯片的控制器，分别作为初始噪声测量传声器和残余噪声测量传声器的2只PCM6110麦克风，惠威S6.5低频扬声器。其中初始噪声测量传声器和残余噪声测量传声器分别经过自动增益调节器的声音处理芯片(TLV320AIC23音频芯片)和A/D转换芯片后与控制器的DSP芯片对应的输入端口连接，DSP芯片的输出端通过D/A转换芯片和功率放大器后接扬声器。具体针对某变压器现场，信号采集频率设定为5000Hz，BP神经网络结构设为2-10-1滤波算法，其中隐含层和输出层的激励函数分别为Sigmoid函数和Purelin函数。P粒子群的规模为30，最大遗传代数为10。根据粒子的编码方式，粒子的维数为78，c1＝c2＝1.3，ω0＝0.9，pmmax＝0.1，pmmin＝0.05。初级噪声测量传声器距离变压器10cm,扬声器距离变压器30cm，残余噪声测量传声器距离扬声器20cm。
工作时，控制器按以下步骤实现噪声抑制(参见图2)：
第一步、接收位于噪声源附近初始噪声测量传声器传输并经转换的初始噪声数字信号，作为BP(Back Propagation)神经网络的输入信号；
第二步、借助BP神经网络对输入信号进行滤波处理后，生成相位与输入信号偏离的次级数字信号；
第三步、将次级数字信号转换成模拟信号经放大后输出到扬声器，产生对初始噪声具有抑制作用的次级噪声；
第四步、接收残余噪声测量传声器拾取的初始噪声和次级噪声叠加并经转换后的残余噪声数字信号，判断残余噪声数字信号的幅值是否已连续设定次数不变，如是则保持次级数字信号输出，如否则进行下步；
第五步、以使残余噪声数字信号幅值最小为优化原则，优化、调整BP神经网络参数，将上一步的残余噪声数字信号作为输入信号重复第二步；其中优化、调整BP神经网络参数采用改进粒子群算法，按以下基本过程进行(参见图3)：
步骤一、根据已有的BP神经网络结构确定粒子的维数，并随机产生N个初始粒子；
步骤二、将第n时刻神经网络中输入层神经元i与隐含层神经元h之间的权系数ωhi(n)、隐含层神经元h与输出层之间的权系数Wh(n)、隐含层神经元h的阈值GEh(n)及输出层神经元的阈值ge(n)按预定的次序进行实数编码，形成相应的实数粒子，每个粒子对应一组网络参数，其编码形式为；
ω11 ω12 … ω1K W1 GE1 … ωJ1 ωJ2 … ωJK WJ GEJ ge
步骤三、选择系统的残余噪声信号e(n)作为网络参数的评判标准，以如下适应度函数F(n)作为粒子位置编码和公式：
Figure PCTCN2015070735-appb-000008
上式中，x(n)为第n时刻输入的初始噪声数字信号；H1(z)和H2(z)分别为初级通道和次级通道的传递函数；
Figure PCTCN2015070735-appb-000009
上式中，xi(n)＝x(n-i+1)表示输入层神经元i的输入；以f(x)＝2/(1+exp(-2x))-1表示网络隐含层的激励函数；
步骤四、根据粒子位置编码和公式(1)，计算出各粒子的适应度值；将粒子当前位置适应度值与之前迭代时该粒子的位置适应度值作比较，若前者比后者小，则更新该粒子最优位置Pi＝[pi1,pi2,…,pi(J(K+2)+1)]，否则，保持不变；同时，将该粒子对应的适应度值与之前迭代时的适应度值作比较，若前者比后者小，则更新整个粒子群的 最优位置Pg＝[pg1,pg2,…,pg(J(K+2)+1)]，否则，保持不变；
步骤五、若迭代次数已达到最大迭代次数，则停止迭代，对粒子群最优位置进行解码得到相应BP神经网络参数，否则进行下一步；
步骤六、定义粒子群的进化度σ为：
Figure PCTCN2015070735-appb-000010
上式中，fgbest(k)和favg(k)分别为第k次迭代时粒子群的全局最优适应度值和平均适应度值；
定义动态变化的惯性因子计算公式为：
Figure PCTCN2015070735-appb-000011
上式中，w0为设定的初始值。
定义自适应变异概率计算公式为：
Figure PCTCN2015070735-appb-000012
Figure PCTCN2015070735-appb-000013
其中，pm(k)为粒子群在第k次迭代的变异概率；pmmax和pmmin分别为变异率的最大值和最小值，fi(k)为粒子i第k次迭代时的适应度值，ε为设定的常数；
定义粒子变异后新的速度和位置计算公式为：
vid(k+1)＝w(k)vid(k)+c1r1(pid(k)-xid(k))+c2r2(pgd(k)-xid(k))   (7)
Figure PCTCN2015070735-appb-000014
其中，vid(k)为粒子i在第k次迭代中第d维的速度；Vi(k)为粒子i在第k次迭代中的速度向量；pid(k)为粒子i第k次迭代中第d维的最好位置；pgd(k)为整个群体第 k次迭代中第d维的最好位置；Xi(k)为粒子i第k次迭代中位置向量；c1和c2为非负加速常数；r1和r2为[0,1]范围内变换的随机数；R1和R2为不相等正整数，在[1,N]范围内；
步骤七、先由以上公式(4)和(5)计算出本次迭代时的惯性因子和粒子变异率，再利用公式(7)和(8)更新所有粒子的速度和位置，生成新一代粒子群，返回步骤四。
结果，降噪前、后残余噪声测量传声器所拾取信号对比情况如图4、图5和图6所示，效果十分显著。由此可见，本实施例不仅构建了合理的单通道变压器噪声抑制系统，而且在BP神经网络滤波基础上融合了粒子群优化算法，从而利用改进粒子群优化算法改善BP神经网络中权、阈值的修正方式，克服了梯度下降优化等算法所具有的固有缺陷，能够有效地抑制变压器所有频率成分的噪声。此外，由多组单通道降噪系统组成的变压器多通道降噪系统能够有效地将变压器噪声控制在允许的范围内。
以上所述，仅为本发明中的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉该技术的人在本发明所揭露的技术范围内，可理解想到的变换或替换，都应涵盖在本发明的包含范围之内，因此，本发明的保护范围应该以权利要求书的保护范围为准。

Claims (7)

  1. 一种变压器噪声抑制方法，其特征在于：在主要由含智能芯片的控制器以及初始噪声测量传声器、残余噪声测量传声器、扬声器组成的系统中，所述控制器按以下步骤实现噪声抑制：
    第一步、接收位于噪声源附近初始噪声测量传声器传输并经转换的初始噪声数字信号，作为BP(BackPropagation)神经网络的输入信号；
    第二步、借助BP神经网络对输入信号进行处理后，生成相位与输入信号偏离的次级数字信号；
    第三步、将次级数字信号转换成模拟信号经放大后输出到扬声器，产生对初始噪声具有抑制作用的次级噪声；
    第四步、接收残余噪声测量传声器拾取的初始噪声和次级噪声叠加并经转换后的残余噪声数字信号，判断残余噪声数字信号的幅值是否已连续设定次数不变，如是则保持次级数字信号输出，如否则进行下步；
    第五步、以使残余噪声数字信号幅值最小为优化原则，优化、调整BP神经网络参数，将上一步的残余噪声数字信号作为输入信号重复第二步。
  2. 根据权利要求1所述的变压器噪声抑制方法，其特征在于：所述第五步中优化、调整BP神经网络参数采用改进粒子群算法，按以下过程进行：
    步骤一、根据已有的BP神经网络结构确定粒子的维数，并随机产生N个初始粒子；步骤二、将第n时刻神经网络中输入层神经元i与隐含层神经元h之间的权系数ωhi(n)、隐含层神经元h与输出层之间的权系数Wh(n)、隐含层神经元h的阈值GEh(n)及输出层神经元的阈值ge(n)按预定的次序进行实数编码，形成相应的实数粒子，每个粒子对应一组网络参数，其编码形式为；
    ω11 ω12 … ω1K W1 GE1 … ωJ1 ωJ2 … ωJK WJ GEJ ge
    步骤三、选择系统的残余噪声信号e(n)作为网络参数的评判标准，以如下适应度函数F(n)作为粒子位置编码和公式：
    Figure PCTCN2015070735-appb-100001
    上式中，x(n)为第n时刻输入的初始噪声数字信号；H1(z)和H2(z)分别为初级通道和次级通道的传递函数；
    Figure PCTCN2015070735-appb-100002
    上式中，xi(n)＝x(n-i+1)表示输入层神经元i的输入；以f(x)＝2/(1+exp(-2x))-1表示网络隐含层的激励函数；
    步骤四、根据粒子位置编码和公式(1)，计算出各粒子的适应度值；将粒子当前位置适应度值与之前迭代时该粒子的位置适应度值作比较，若前者比后者小，则更新该粒子最优位置Pi＝[pi1,pi2,…,pi(J(K+2)+1)]，否则，保持不变；同时，将该粒子对应的适应度值与之前迭代时的适应度值作比较，若前者比后者小，则更新整个粒子群的最优位置Pg＝[pg1,pg2,…,pg(J(K+2)+1)]，否则，保持不变；
    步骤五、若迭代次数已达到最大迭代次数，则停止迭代，对粒子群最优位置进行解码得到相应BP神经网络参数，否则进行下一步；
    步骤六、定义粒子群的进化度σ为：
    Figure PCTCN2015070735-appb-100003
    上式中，fgbest(k)和favg(k)分别为第k次迭代时粒子群的全局最优适应度值和平均适应度值；
    定义动态变化的惯性因子计算公式为：
    Figure PCTCN2015070735-appb-100004
    上式中，w0为设定的初始值。
    定义自适应变异概率计算公式为：
    Figure PCTCN2015070735-appb-100005
    Figure PCTCN2015070735-appb-100006
    其中，pm(k)为粒子群在第k次迭代的变异概率；pmmax和pmmin分别为变异率的最大值和最小值，fi(k)为粒子i第k次迭代时的适应度值，ε为设定的常数；
    定义粒子变异后新的速度和位置计算公式为：
    vid(k+1)＝w(k)vid(k)+c1r1(pid(k)-xid(k))+c2r2(pgd(k)-xid(k))       (7)
    Figure PCTCN2015070735-appb-100007
    其中，vid(k)为粒子i在第k次迭代中第d维的速度；Vi(k)为粒子i在第k次迭代中的速度向量；pid(k)为粒子i第k次迭代中第d维的最好位置；pgd(k)为整个群体第k次迭代中第d维的最好位置；Xi(k)为粒子i第k次迭代中位置向量；c1和c2为非负加速常数；r1和r2为[0,1]范围内变换的随机数；R1和R2为不相等正整数，在[1,N]范围内；
    步骤七、先由以上公式(4)和(5)计算出本次迭代时的惯性因子和粒子变异率，再利用公式(7)和(8)更新所有粒子的速度和位置，生成新一代粒子群，返回步骤四。
  3. 根据权利要求2所述的变压器噪声抑制方法，其特征在于：所述智能芯片为DSP芯片，所述初始噪声测量传声器和残余噪声测量传声器分别经过自动增益调节器的声音处理芯片和A/D转换芯片后与DSP芯片对应的输入端口连接，所述DSP芯片的输出端通过D/A转换芯片和功率放大器后接扬声器。
  4. 根据权利要求3所述的变压器噪声抑制方法，其特征在于：所述BP神经网络结构设为2-10-1滤波算法，其中隐含层和输出层的激励函数分别为Sigmoid函数和Purelin函数。
  5. 根据权利要求4所述的变压器噪声抑制方法，其特征在于：所述粒子群的规模为30，最大遗传代数为10，所述粒子的维数为78。
  6. 根据权利要求5所述的变压器噪声抑制方法，其特征在于：
    所述初始噪声测量传声器距变压器10cm，其高度位于油箱中部；扬声器距初始噪声测量传声器20cm，其高度位于油箱中部；残余噪声测量传声器距扬声器20cm，其高度位于油箱中部。上述三种器件按顺序排成一列，构成单通道降噪系统。
  7. 根据权利要求5所述的变压器噪声抑制方法，其特征在于：
    所述变压器多通道降噪系统由8个单通道降噪系统组成。在变压器正和反两面均放置3组单通道降噪系统，其位置分别与变压器的A、B、C三相对应；在变压器测面的中间位置放置1组单通道降噪系统。
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