CN113932912A - Transformer substation noise anti-interference estimation method, system and medium - Google Patents
Transformer substation noise anti-interference estimation method, system and medium Download PDFInfo
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Abstract
The invention discloses a transformer substation noise anti-interference estimation method, a system and a medium, and the method comprises the steps of estimating the transformer substation noise existence probability of a current time frequency point for a noise signal y (n), and integrating the transformer substation noise existence probability into 1/3 octave interference signal existence probability; and comprehensively outputting 1/3 octave sound pressure level of the transformer substation noise according to the noise signals corresponding to 1/3 octaves and the existence probability of 1/3 octave interference signals of the noise signals y (n) and adding energy to obtain the total sound pressure level of the transformer substation. The invention achieves the purpose of measuring the 1/3 octave sound pressure level with anti-interference by utilizing the statistical stability of the interference noise signals of the transformer substation noise, the bird singing and the like, can overcome the problem of transient noise interference in the current transformer substation noise measurement, can more accurately obtain 1/3 octave sound pressure level, and can further obtain the total sound pressure level according to the requirement.
Description
Technical Field
The invention relates to an acoustic measurement technology, in particular to a transformer substation noise anti-interference estimation method, a transformer substation noise anti-interference estimation system and a transformer substation noise anti-interference estimation medium.
Background
With the development of economic society, land resources are becoming increasingly scarce, and the power load is continuously increased. The transformer substation is used as a hub of power transmission, and plays an important role in the aspect of guaranteeing the power consumption of users. On the other hand, however, the substation noise problem is also receiving more and more attention. Power companies may periodically conduct substation boundary and equipment noise measurements. In actual measurement, transient interference problems such as bug singing and bird calling are often encountered, which have adverse effects on the test, and workers usually wait for the bug singing and bird calling to finish and restart the measurement through manual judgment so as to ensure the accuracy of the measurement and greatly influence the test efficiency. At present, no better method for reducing the noise interference of the burst environment exists.
Disclosure of Invention
The technical problems to be solved by the invention are as follows: aiming at the problems in the prior art, the transformer substation noise anti-interference estimation method, the transformer substation noise anti-interference estimation system and the transformer substation noise anti-interference estimation medium are provided, the problem of transient noise interference in current transformer substation noise measurement can be solved, 1/3 octave sound pressure levels can be obtained more accurately, and the total sound pressure level can be further obtained according to needs.
In order to solve the technical problems, the invention adopts the technical scheme that:
a transformer substation noise anti-interference estimation method comprises the following steps:
1) collecting a noise signal y (n) of the current time domain of the transformer substation;
2) estimating the existence probability of the transformer substation noise at the current time frequency point for the noise signal y (n), and integrating the existence probability of the transformer substation noise into the existence probability of 1/3 octave interference signals; adopting an 1/3 frequency multiplication layer filter to obtain noise signals y (omega, i) corresponding to each 1/3 octave for the noise signals y (n);
3) and (3) comprehensively outputting the 1/3 octave sound pressure level of the substation noise according to the noise signal y (omega, i) corresponding to each 1/3 octave and the existence probability of the 1/3 octave interference signal.
Optionally, energy addition is further performed on the 1/3 octave sound pressure level after the step 3) to obtain the total sound pressure level of the substation.
Optionally, when the noise signal y (n) of the current time domain of the substation is acquired in step 1), the sampling rate is not less than 40 k.
Optionally, the step 2) of estimating the substation noise existence probability of the current time frequency point for the noise signal y (n), and integrating the substation noise existence probability into 1/3 octave interference signal existence probability includes:
2.1) framing, windowing and Fourier transforming the noise signal y (n);
2.2) smoothing frequency points and time frames of input signal frequency spectrums obtained by Fourier transform in a frequency domain;
2.3) obtaining the minimum value of the smooth power spectrum through comparison and tracking;
2.4) defining the existence probability of the noise of the transformer substation at the current time frequency point based on the minimum value of the smooth power spectrum;
2.5) integrating the existence probability of the noise of the transformer substation into the existence probability of 1/3 octave interference signals.
Optionally, the functional expression for performing overlapping windowing on the noise signal y (n) and performing fourier transform in step 2.1) is as follows:
in the above formula, Y (N + lM) is an input signal of the (N + lM) th time frequency point, k and l are respectively a frequency band index and a time frame index, N is a serial number of a current sampling point, w (N) is a window function, N is a length of the window function, M is a frame step length, and Y (k, l) is a power spectrum of the kth frequency point corresponding to the l frame input signal Y (N + lM);
the function expression for frequency point smoothing in step 2.2) is:
in the above formula, Sf(k, l-1) the power spectrum of the smoothed input signal at the frequency point of the (l-1) time frame, b (i) a normalized window function, the length of the normalized window function is 2 ω +1, ω is a positive integer used for determining the number of smoothed frames, and Y (k-i, l-1) is the power spectrum of the input signal at the k-i frequency point corresponding to the l-1 frame;
the function expression for smoothing between time frames in step 2.2) is:
S(k,l-1)＝αsS(k,l-2)+(1-αs)Sf(k,l-1)
in the above formula, S (k, l-1) is the power spectrum of the interframe smooth input signal of (l-1) time frame, alphasFor a smoothing parameter size in the (0,1) interval, S (k, l-2) represents the power spectrum of the smoothed input signal between frames of the (l-2) time frame.
Optionally, the function expression of the minimum value of the smoothed power spectrum obtained by the comparison tracking in step 2.3) is:
Smin(k,l-1)＝min{Smin(k,l-2),S(k,l-1)}
in the above formula, Smin(k, l-1) is the minimum value of the power spectrum of the input signal in the (l-1) time frame, S (k, l-1) is the power spectrum of the inter-frame smooth input signal in the (l-1) time frame, SminAnd (k, l-2) is the minimum value of the power spectrum of the input signal of the (l-2) time frame, and min is a function of the minimum value.
Optionally, the function expression of the substation noise existence probability of the current time frequency point defined based on the minimum value of the smoothed power spectrum in step 2.4) is as follows:
in the above formula, γmin(k, l-1) is the existence probability of posterior signal of (l-1) th time frame, Y (k, l-1) is the power spectrum of input signal of (l-1) th time frame, BminEstimating a bias compensation factor for the noise spectrum minimum, wherein zeta (k, l-1) is the existence probability of the signal in the (l-1) time frame prior; step 2.5), the function expression of the existence probability of the interference signals of the substation comprehensively presenting 1/3 octaves is as follows:
in the above formula, I (k, l-1) is the final determination result of the presence or absence of the interference signal, a value of 1 represents the absence of the interference signal, a value of 0 represents the absence of the interference signal, and γ is0For a posterior signal having a probability threshold, ζ0A probability threshold exists for the prior signal.
Optionally, when the 1/3 frequency-doubling layer filter is used for the noise signal y (n) in step 2) to obtain the noise signal y (Ω, i) corresponding to each 1/3 octave, the method includes, for 1/3 octaves with a center frequency greater than or equal to 50Hz, filtering with a 48kHz sampling frequency, then obtaining the noise signal corresponding to the 1/3 octave with a band-pass filter, for 1/3 octaves with a center frequency less than 50Hz, first performing low-pass filtering with 4kHz, then sampling the low-pass filtered noise signal from the 48kHz sampling frequency to a 8kHz sampling frequency, and finally obtaining the noise signal corresponding to the 1/3 octave with the band-pass filter, thereby obtaining the final noise signal y (Ω, i) corresponding to each 1/3 octave.
Optionally, the functional expression of 1/3 octave sound pressure level of the substation noise comprehensively output according to both the noise signal y (Ω, i) corresponding to each 1/3 octave and the existence probability of 1/3 octave interference signal in step 3) is as follows:
in the above formula, P (Ω, l) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, Ω, l are frequency-doubled layer and time frame, respectively, P (Ω, l-1) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, α is a smoothing factor, M is a frame step, y (Ω, I) is each frequency-doubled layer signal, I' (Ω, l) is 1/3 octave interference signal existence probability, I (Ω, l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signal, I (Ω, l) is a total of three times of the frequency-doubled layer signal, I (l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signaltAnd (omega) is a threshold value preset for 1/3 octaves.
Furthermore, the present invention also provides a substation noise immunity estimation system comprising a microprocessor and a memory connected to each other, the microprocessor being programmed or configured to perform the steps of the substation noise immunity estimation method, or the memory having stored therein a computer program of the substation noise immunity estimation method.
In addition, the invention also provides a computer readable storage medium, and the computer readable storage medium stores the computer program of the transformer substation noise anti-interference estimation method.
Compared with the prior art, the invention has the following advantages: the method comprises the steps of collecting a noise signal y (n) of the current time domain of the transformer substation; estimating the existence probability of the transformer substation noise at the current time frequency point for the noise signal y (n), and integrating the existence probability of the transformer substation noise into the existence probability of 1/3 octave interference signals; adopting an 1/3 frequency multiplication layer filter to obtain noise signals corresponding to each 1/3 octave for the noise signals y (n); comprehensively outputting 1/3 octave sound pressure level of transformer substation noise according to noise signals corresponding to 1/3 octaves and 1/3 octave interference signal existence probabilities; and energy addition is carried out on the 1/3 octave sound pressure level to obtain the total sound pressure level of the transformer substation. According to the invention, the purpose of measuring the anti-interference 1/3 octave sound pressure level is achieved by utilizing the statistical stability characteristics of interference noise signals such as substation noise, bird singing and the like, the probability of existence of the substation noise at each time frequency point is estimated in real time, then the probability of existence of each 1/3 octave interference signal is synthesized, and 1/3 octave noise updating is not carried out on the 1/3 octave interference signal, so that the influence of the interference signal on noise measurement is avoided, the problem of transient noise interference in the current substation noise measurement can be overcome, the 1/3 octave sound pressure level can be more accurately obtained, and the total sound pressure level can be further obtained according to the requirement.
Drawings
FIG. 1 is a schematic diagram of a basic flow of a method according to an embodiment of the present invention.
Fig. 2 is a diagram of pure time-domain waveform measurement of substation noise in the embodiment of the present invention.
Fig. 3 is a time-domain waveform measurement diagram of bird cry in the embodiment of the present invention.
FIG. 4 is a time-domain waveform measurement of a chirp signal in an embodiment of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings.
As shown in fig. 1, the method for estimating noise immunity of a substation in this embodiment includes:
1) collecting a noise signal y (n) of the current time domain of the transformer substation;
2) estimating the existence probability of the transformer substation noise at the current time frequency point for the noise signal y (n), and integrating the existence probability of the transformer substation noise into the existence probability of 1/3 octave interference signals; adopting an 1/3 frequency multiplication layer filter to obtain noise signals y (omega, i) corresponding to each 1/3 octave for the noise signals y (n);
3) and (3) comprehensively outputting the 1/3 octave sound pressure level of the substation noise according to the noise signal y (omega, i) corresponding to each 1/3 octave and the existence probability of the 1/3 octave interference signal.
In this embodiment, when the noise signal y (n) of the current time domain of the substation is acquired in step 1), the sampling rate is not less than 40 k. The equipment adopted when acquiring the noise signal y (n) of the current time domain of the transformer substation is a microphone, specifically, the noise of the transformer substation is picked up at a sampling rate of 48kHz, and a signal model is as follows:
y(n)＝s(n)+v(n)
in the above formula, s (n) is a substation noise signal to be measured, and v (n) is a noise interference signal during measurement, including a bird call, a bug singing and the like. Fig. 2 is a pure transformer substation noise time domain waveform measurement diagram, fig. 3 is a bird cry time domain waveform measurement diagram, and fig. 4 is a bug singing time domain waveform measurement diagram. In fig. 2 to 4, the abscissa axes are time axes, and the ordinate axes are sound pressure amplitude axes, and it can be seen from the graphs that pure substation noise is steady-state noise, and the interference signal is unsteady-state noise. Thus, the noise signal received at the microphone is the substation noise combined with the interference signal to obtain the interference signal containing the substation noise.
In this embodiment, estimating the substation noise existence probability of the current time frequency point for the noise signal y (n) in step 2), and integrating the substation noise existence probability into 1/3 octave interference signal existence probability includes:
2.1) framing, windowing and Fourier transforming the noise signal y (n);
2.2) smoothing frequency points and time frames of input signal frequency spectrums obtained by Fourier transform in a frequency domain;
2.3) obtaining the minimum value of the smooth power spectrum through comparison and tracking;
2.4) defining the existence probability of the noise of the transformer substation at the current time frequency point based on the minimum value of the smooth power spectrum;
2.5) integrating the existence probability of the noise of the transformer substation into the existence probability of 1/3 octave interference signals.
In this embodiment, the function expression for performing the overlapping windowing on the noise signal y (n) and performing the fourier transform in step 2.1) is as follows:
in the above formula, Y (N + lM) is an input signal of the nth + lM th time frequency point, k and l are respectively a frequency band index and a time frame index, N is a sequence number of a current sampling point, w (N) is a window function, N is a length of the window function, M is a frame step length, and Y (k, l) is a power spectrum of the kth frequency point corresponding to the ith frame input signal Y (N + lM).
In this embodiment, the function expression for frequency point smoothing in step 2.2) is:
in the above formula, Sf(k, l-1) is the power spectrum of the smoothed input signal at the frequency point of the (l-1) time frame, b (i) is a normalized window function, the length of the normalized window function is 2 ω +1, ω is a positive integer used for determining the number of smoothed frames, and Y (k-i, l-1) is the power spectrum of the input signal at the k-i frequency point corresponding to the l-1 th frame.
In this embodiment, the function expression for performing inter-time-frame smoothing in step 2.2) is as follows:
S(k,l-1)＝αsS(k,l-2)+(1-αs)Sf(k,l-1)
in the above formula, S (k, l-1) is the power spectrum of the interframe smooth input signal of (l-1) time frame, alphasFor a smoothing parameter size in the (0,1) interval, S (k, l-2) represents the power spectrum of the smoothed input signal between frames of the (l-2) time frame.
In this embodiment, the function expression of the minimum value of the smoothed power spectrum obtained by comparing and tracking in step 2.3) is:
Smin(k,l-1)＝min{Smin(k,l-2),S(k,l-1)}
in the above formula, Smin(k, l-1) is the minimum value of the power spectrum of the input signal in the (l-1) time frame, S (k, l-1) is the power spectrum of the inter-frame smooth input signal in the (l-1) time frame, SminAnd (k, l-2) is the minimum value of the power spectrum of the input signal of the (l-2) time frame, and min is a function of the minimum value.
In this embodiment, the function expression defining the existence probability of the noise of the substation at the current time frequency point based on the minimum value of the smoothed power spectrum in step 2.4) is as follows:
in the above formula, γmin(k, l-1) is the existence probability of posterior signal of (l-1) th time frame, Y (k, l-1) is the power spectrum of input signal of (l-1) th time frame, BminThe bias compensation factor is estimated for the noise spectrum minimum, and ζ (k, l-1) is the (l-1) time frame prior signal existence probability.
In this embodiment, the functional expression of the existence probability of the substation noise, which is obtained by integrating the existence probability of the substation noise in 1/3 octaves in step 2.5), is as follows:
in the above formula, I (k, l-1) is the final determination result of the presence or absence of the interference signal, a value of 1 represents the absence of the interference signal, a value of 0 represents the absence of the interference signal, and γ is0For a posterior signal having a probability threshold, ζ0A probability threshold exists for the prior signal.
For substation noise, the highest test frequency is 20kHz, and in this embodiment, when a 1/3 frequency-doubling layer filter is used for a noise signal y (n) in step 2) to obtain a noise signal y (Ω, i) corresponding to each 1/3 octave, a multi-rate processing technique is used, which includes:
aiming at 1/3 octaves with the center frequency of more than or equal to 50Hz, filtering is carried out by adopting a 48kHz sampling frequency, and then a noise signal corresponding to 1/3 octaves is obtained by adopting a band-pass filter;
aiming at 1/3 octaves with the center frequency less than 50Hz, firstly, low-pass filtering of 4kHz is adopted, then the noise signals after low-pass filtering are extracted from the sampling frequency of 48kHz to be the sampling frequency of 8kHz, and finally, a band-pass filter is adopted to obtain the noise signals corresponding to 1/3 octaves, so that the final noise signals y (omega, i) corresponding to 1/3 octaves are obtained. In this embodiment, the low-pass filtering is 5-order IIR filtering. The band-pass filtering corresponding to each 1/3 octave is realized by adopting 3 second-order IIR cascade filters, and the IIR filter coefficient corresponding to each 1/3 octave is shown in Table 1.
Table 1: and an IIR filter coefficient table corresponding to each 1/3 octave.
In this embodiment, the functional expression of 1/3 octave sound pressure level of the substation noise comprehensively output according to both the existence probabilities of the noise signal y (Ω, i) and the 1/3 octave interference signal corresponding to each 1/3 octave in step 3) is as follows:
in the above formula, P (Ω, l) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, Ω, l are frequency-doubled layer and time frame, respectively, P (Ω, l-1) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, α is a smoothing factor, M is a frame step, y (Ω, I) is each frequency-doubled layer signal, I' (Ω, l) is 1/3 octave interference signal existence probability, I (Ω, l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signal, I (Ω, l) is a total of three times of the frequency-doubled layer signal, I (l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signaltAnd (omega) is a threshold value preset for 1/3 octaves. As can be seen from the above formula, in the present embodiment, when the existence probability of the interference signal is high, the 1/3 octave sound pressure level is not updated; when the existence probability of the interference signal is small, 1/3 octave sound pressure level updating output is carried out.
As shown in fig. 1, in this embodiment, in order to further achieve obtaining the total sound pressure level of the substation, energy addition is further performed on the 1/3 octave sound pressure level after step 3) to obtain the total sound pressure level of the substation.
Table 2 shows the result of measuring 1/3 octave sound pressure level of substation noise containing a bug-ringing interference signal by using the method of the present embodiment.
Table 2: and the transformer substation contains a bug buzz interference noise processing result.
Referring to table 2, it can be seen that in the frequency bands of 50 to 300Hz and 2000 to 10000Hz, the 1/3 octave measurement results are significantly affected by the interference. By adopting the method of the embodiment, the measurement result can be closer to the true value.
Table 3 shows the results of measuring 1/3 octave sound pressure level of the substation noise containing bird call interference signals by using the method of the present embodiment.
Table 3: the transformer substation contains a bird call interference noise processing result.
| Centre frequency Hz | True value | Conventional methods | Method of the present embodiment |
| 25.12 | 25.07 | 23.53 | 23.56 |
| 31.62 | 25.26 | 24.88 | 24.58 |
| 39.81 | 26.98 | 27.03 | 27.00 |
| 50.12 | 30.29 | 30.48 | 30.36 |
| 63.10 | 37.75 | 37.78 | 36.44 |
| 79.43 | 54.98 | 54.91 | 54.84 |
| 100.00 | 73.11 | 73.05 | 73.01 |
| 125.89 | 54.97 | 54.91 | 54.84 |
| 158.49 | 38.09 | 38.00 | 36.79 |
| 199.53 | 40.08 | 38.43 | 37.40 |
| 251.19 | 31.96 | 32.17 | 30.66 |
| 316.23 | 42.45 | 43.17 | 42.89 |
| 398.11 | 56.80 | 58.06 | 57.97 |
| 501.19 | 54.14 | 53.95 | 53.59 |
| 630.96 | 60.70 | 62.41 | 60.75 |
| 794.33 | 45.12 | 56.57 | 45.74 |
| 1000.00 | 43.58 | 50.34 | 42.64 |
| 1258.93 | 53.59 | 61.29 | 55.22 |
| 1584.89 | 35.70 | 58.58 | 43.00 |
| 1995.26 | 25.98 | 44.13 | 25.29 |
| 2511.89 | 23.33 | 41.43 | 20.35 |
| 3162.28 | 21.41 | 38.18 | 18.46 |
| 3981.07 | 21.97 | 33.85 | 19.45 |
| 5011.87 | 23.02 | 45.93 | 20.52 |
| 6309.57 | 26.85 | 40.42 | 28.39 |
| 7943.28 | 23.20 | 30.33 | 22.19 |
| 10000.00 | 23.86 | 27.62 | 22.86 |
| 12589.25 | 23.98 | 24.56 | 23.48 |
| 15848.93 | 24.97 | 25.13 | 24.98 |
| 19952.62 | 25.88 | 25.89 | 25.81 |
Referring to table 3, it can be seen that the interference frequency is mainly concentrated in the middle-high frequency band, and the 1/3 octave sound pressure level and the total sound pressure level can be more accurately obtained by using the method of this embodiment.
To sum up, the present embodiment utilizes the statistical stability of the interference noise signals such as the substation noise and the bird singing, to achieve the purpose of measuring the anti-interference 1/3 octave sound pressure level. The embodiment adopts the multi-rate sampling technology to design an 1/3 octave filter, and reduces the required filter order at the lower central frequency. The probability of the input signal having an interfering signal at each 1/3 octave is then estimated. The embodiment is suitable for realizing more accurate 1/3 octave sound pressure level measurement and total sound pressure level measurement under the condition that transient interference signals exist.
In addition, the present embodiment also provides a substation noise immunity estimation system, which includes a microprocessor and a memory, which are connected to each other, where the microprocessor is programmed or configured to execute the steps of the foregoing substation noise immunity estimation method, or the memory stores a computer program of the foregoing substation noise immunity estimation method.
In addition, the present embodiment also provides a computer-readable storage medium, where a computer program of the foregoing substation noise anti-interference estimation method is stored in the computer-readable storage medium.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments, and all technical solutions belonging to the idea of the present invention belong to the protection scope of the present invention. It should be noted that modifications and embellishments within the scope of the invention may occur to those skilled in the art without departing from the principle of the invention, and are considered to be within the scope of the invention.
Claims (10)
1. A transformer substation noise anti-interference estimation method is characterized by comprising the following steps:
1) collecting a noise signal y (n) of the current time domain of the transformer substation;
2) estimating the existence probability of the transformer substation noise at the current time frequency point for the noise signal y (n), and integrating the existence probability of the transformer substation noise into the existence probability of 1/3 octave interference signals; adopting an 1/3 frequency multiplication layer filter to obtain noise signals y (omega, i) corresponding to each 1/3 octave for the noise signals y (n);
3) and (3) comprehensively outputting the 1/3 octave sound pressure level of the substation noise according to the noise signal y (omega, i) corresponding to each 1/3 octave and the existence probability of the 1/3 octave interference signal.
2. The substation noise anti-interference estimation method according to claim 1, further comprising energy addition of 1/3 octaves sound pressure level to obtain the total sound pressure level of the substation after step 3).
3. The substation noise anti-interference estimation method according to claim 2, wherein the step 2) of estimating the substation noise existence probability of the current time frequency point for the noise signal y (n), and the step of integrating the substation noise existence probability into 1/3 octave interference signal existence probability comprises:
2.1) framing, windowing and Fourier transforming the noise signal y (n);
2.2) smoothing frequency points and time frames of input signal frequency spectrums obtained by Fourier transform in a frequency domain;
2.3) obtaining the minimum value of the smooth power spectrum through comparison and tracking;
2.4) defining the existence probability of the noise of the transformer substation at the current time frequency point based on the minimum value of the smooth power spectrum;
2.5) integrating the existence probability of the noise of the transformer substation into the existence probability of 1/3 octave interference signals.
4. The substation noise anti-interference estimation method according to claim 3, wherein in step 2.1), the noise signal y (n) is subjected to overlap windowing and fourier transform, and a function expression of the overlap windowing and the fourier transform is as follows:
in the above formula, Y (N + lM) is an input signal of the (N + lM) th time frequency point, k and l are respectively a frequency band index and a time frame index, N is a serial number of a current sampling point, w (N) is a window function, N is a length of the window function, M is a frame step length, and Y (k, l) is a power spectrum of the kth frequency point corresponding to the l frame input signal Y (N + lM);
the function expression for frequency point smoothing in step 2.2) is:
in the above formula, Sf(k, l-1) the power spectrum of the smoothed input signal at the frequency point of the (l-1) time frame, b (i) a normalized window function, the length of the normalized window function is 2 ω +1, ω is a positive integer used for determining the number of smoothed frames, and Y (k-i, l-1) is the power spectrum of the input signal at the k-i frequency point corresponding to the l-1 frame;
the function expression for smoothing between time frames in step 2.2) is:
S(k,l-1)＝αsS(k,l-2)+(1-αs)Sf(k,l-1)
in the above formula, S (k, l-1) is the power spectrum of the interframe smooth input signal of (l-1) time frame, alphasFor a smoothing parameter size in the (0,1) interval, S (k, l-2) represents the power spectrum of the smoothed input signal between frames of the (l-2) time frame.
5. The substation noise anti-interference estimation method according to claim 4, wherein the function expression of the minimum value of the smoothed power spectrum obtained by the comparison tracking in step 2.3) is as follows:
Smin(k,l-1)＝min{Smin(k,l-2),S(k,l-1)}
in the above formula, Smin(k, l-1) is the minimum value of the power spectrum of the input signal in the (l-1) time frame, S (k, l-1) is the power spectrum of the inter-frame smooth input signal in the (l-1) time frame, SminAnd (k, l-2) is the minimum value of the power spectrum of the input signal of the (l-2) time frame, and min is a function of the minimum value.
6. The substation noise anti-interference estimation method according to claim 5, wherein in step 2.4), the functional expression for defining the substation noise existence probability of the current time frequency point based on the minimum value of the smoothed power spectrum is as follows:
in the above formula, γmin(k, l-1) is the existence probability of posterior signal of (l-1) th time frame, Y (k, l-1) is the power spectrum of input signal of (l-1) th time frame, BminEstimating a bias compensation factor for the noise spectrum minimum, wherein zeta (k, l-1) is the existence probability of the signal in the (l-1) time frame prior; step 2.5), the function expression of the existence probability of the interference signals of the substation comprehensively presenting 1/3 octaves is as follows:
in the above formula, I (k, l-1) is the final determination result of the presence or absence of the interference signal, a value of 1 represents the absence of the interference signal, a value of 0 represents the absence of the interference signal, and γ is0For a posterior signal having a probability threshold, ζ0A probability threshold exists for the prior signal.
7. The substation noise anti-interference estimation method according to claim 1, wherein in step 2), when the 1/3 frequency multiplication layer filter is used for obtaining the noise signal y (Ω, i) corresponding to each 1/3 octave in the noise signal y (n), the method includes, for 1/3 octave with a center frequency greater than or equal to 50Hz, firstly filtering with a 48kHz sampling frequency, then obtaining the noise signal corresponding to 1/3 octave with a band-pass filter, and for 1/3 octave with a center frequency less than 50Hz, firstly filtering with a 4kHz low-pass filter, then sampling the low-pass filtered noise signal from the 48kHz sampling frequency to a 8kHz sampling frequency, and finally obtaining the noise signal corresponding to 1/3 octave with a band-pass filter, thereby obtaining the final noise signal y (Ω, i) in that respect
8. The substation noise anti-interference estimation method according to claim 1, wherein in step 3), the 1/3 octave sound pressure level function expression of the substation noise is comprehensively output according to both the existence probabilities of the noise signals y (Ω, i) and 1/3 octave interference signals corresponding to 1/3 octaves:
in the above formula, P (Ω, l) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, Ω, l are frequency-doubled layer and time frame, respectively, P (Ω, l-1) is 1/3 frequency-doubled layer sound pressure level estimated at the ith time frame, α is a smoothing factor, M is a frame step, y (Ω, I) is each frequency-doubled layer signal, I' (Ω, l) is 1/3 octave interference signal existence probability, I (Ω, l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signal, I (Ω, l) is a total of three times of the frequency-doubled layer signal, I (l) is a total of three times of the frequency-doubled layer signal, and I (l) is a total of three times of the frequency-doubled layer signaltAnd (omega) is a threshold value preset for 1/3 octaves.
9. A substation noise immunity estimation system comprising a microprocessor and a memory connected to each other, characterized in that the microprocessor is programmed or configured to perform the steps of the substation noise immunity estimation method according to any one of claims 1 to 8, or that the memory has stored therein a computer program of the substation noise immunity estimation method according to any one of claims 1 to 8.
10. A computer-readable storage medium, wherein a computer program of the substation noise immunity estimation method according to any one of claims 1 to 8 is stored in the computer-readable storage medium.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354569A (en) * | 2007-07-25 | 2009-01-28 | 索尼株式会社 | Information processing apparatus, information processing method, and computer program |
| JP2013097176A (en) * | 2011-11-01 | 2013-05-20 | Nippon Telegr & Teleph Corp <Ntt> | Sound source separation device, sound source separation method, and program |
| US20160331237A1 (en) * | 2014-05-13 | 2016-11-17 | The Seaberg Company, Inc. | Acoustic Detection of Bone Fracture |
| JP2017161635A (en) * | 2016-03-08 | 2017-09-14 | 沖電気工業株式会社 | Gain processing device and program, and acoustic signal processing device and program |
| CN108226854A (en) * | 2016-12-09 | 2018-06-29 | 现代自动车株式会社 | The device and method that the visual information of rear car is provided |
| CN108922554A (en) * | 2018-06-04 | 2018-11-30 | 南京信息工程大学 | The constant Wave beam forming voice enhancement algorithm of LCMV frequency based on logarithm Power estimation |
| CN110600051A (en) * | 2019-11-12 | 2019-12-20 | 乐鑫信息科技（上海）股份有限公司 | Method for selecting output beams of a microphone array |
| US20200213794A1 (en) * | 2018-12-31 | 2020-07-02 | AAC Technologies Pte. Ltd. | Method and system for measuring total sound pressure level of noise, and computer readable storage medium |
| CN111899752A (en) * | 2020-07-13 | 2020-11-06 | 紫光展锐(重庆)科技有限公司 | Noise suppression method and device for rapidly calculating voice existence probability, storage medium and terminal |
| CN112700787A (en) * | 2021-03-24 | 2021-04-23 | 深圳市中科蓝讯科技股份有限公司 | Noise reduction method, nonvolatile readable storage medium and electronic device |
| CN113008361A (en) * | 2021-02-04 | 2021-06-22 | 国网湖南省电力有限公司 | Substation boundary noise anti-environmental interference detection method and device |
-
2021
- 2021-10-13 CN CN202111192339.2A patent/CN113932912B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101354569A (en) * | 2007-07-25 | 2009-01-28 | 索尼株式会社 | Information processing apparatus, information processing method, and computer program |
| JP2013097176A (en) * | 2011-11-01 | 2013-05-20 | Nippon Telegr & Teleph Corp <Ntt> | Sound source separation device, sound source separation method, and program |
| US20160331237A1 (en) * | 2014-05-13 | 2016-11-17 | The Seaberg Company, Inc. | Acoustic Detection of Bone Fracture |
| JP2017161635A (en) * | 2016-03-08 | 2017-09-14 | 沖電気工業株式会社 | Gain processing device and program, and acoustic signal processing device and program |
| CN108226854A (en) * | 2016-12-09 | 2018-06-29 | 现代自动车株式会社 | The device and method that the visual information of rear car is provided |
| CN108922554A (en) * | 2018-06-04 | 2018-11-30 | 南京信息工程大学 | The constant Wave beam forming voice enhancement algorithm of LCMV frequency based on logarithm Power estimation |
| US20200213794A1 (en) * | 2018-12-31 | 2020-07-02 | AAC Technologies Pte. Ltd. | Method and system for measuring total sound pressure level of noise, and computer readable storage medium |
| CN110600051A (en) * | 2019-11-12 | 2019-12-20 | 乐鑫信息科技（上海）股份有限公司 | Method for selecting output beams of a microphone array |
| CN111899752A (en) * | 2020-07-13 | 2020-11-06 | 紫光展锐(重庆)科技有限公司 | Noise suppression method and device for rapidly calculating voice existence probability, storage medium and terminal |
| CN113008361A (en) * | 2021-02-04 | 2021-06-22 | 国网湖南省电力有限公司 | Substation boundary noise anti-environmental interference detection method and device |
| CN112700787A (en) * | 2021-03-24 | 2021-04-23 | 深圳市中科蓝讯科技股份有限公司 | Noise reduction method, nonvolatile readable storage medium and electronic device |
Non-Patent Citations (1)
| Title |
|---|
| 满娟 等: "骨导气导结合的语音增强算法", 《电声技术》, vol. 35, no. 06, pages 42 - 46 * |
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