CN114201875A - Method for determining multi-sound-source noise equivalent model of transformer, terminal and storage medium - Google Patents
Method for determining multi-sound-source noise equivalent model of transformer, terminal and storage medium Download PDFInfo
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Abstract
The invention provides a method for determining a transformer multi-sound-source noise equivalent model, a terminal and a storage medium. The method comprises the following steps: acquiring space coordinates of a plurality of preset detection points around the transformer and actual sound pressure levels in a preset octave band; acquiring the number of equivalent sound sources of the transformer and the space coordinates of each equivalent sound source; according to the spatial coordinates of the preset detection points, the actual sound pressure level of the preset octave frequency band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, constructing a univariate linear regression model corresponding to the preset octave frequency band, and solving the univariate linear regression model corresponding to the preset octave frequency band to obtain the sound pressure levels of the equivalent sound sources in the preset octave frequency band; and obtaining a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source and the space coordinates of each equivalent sound source. The method has the advantages of convenient and simple equivalent process and small operand.
Description
Technical Field
The invention relates to the technical field of noise equivalent models, in particular to a method, a terminal and a storage medium for determining a transformer multi-sound-source noise equivalent model.
Background
As the site selection of the transformer substation is closer to the residential area, the problem of noise pollution caused by the transformer substation is more and more emphasized by people. The transformer is the largest single equipment in the transformer substation and is also the most main noise source in the transformer substation. An accurate transformer multi-sound-source noise equivalent model is constructed according to an acoustic equivalent source theory, and the method has very important significance for predicting the transformer substation noise model.
At present, a near-field acoustic holography technology is usually adopted to construct a transformer multi-sound-source noise equivalent model, however, the method is complex in calculation and large in calculation amount.
Disclosure of Invention
The embodiment of the invention provides a method for determining a multi-sound-source noise equivalent model of a transformer, a terminal and a storage medium, and aims to solve the problems of complex calculation and large calculation amount in the prior art.
In a first aspect, an embodiment of the present invention provides a method for determining a multi-sound-source noise equivalent model of a transformer, including:
acquiring space coordinates of a plurality of preset detection points around the transformer and actual sound pressure levels in a preset octave band;
acquiring the number of equivalent sound sources of the transformer and the space coordinates of each equivalent sound source;
according to the spatial coordinates of the preset detection points, the actual sound pressure level of the preset octave frequency band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, constructing a univariate linear regression model corresponding to the preset octave frequency band, and solving the univariate linear regression model corresponding to the preset octave frequency band to obtain the sound pressure levels of the equivalent sound sources in the preset octave frequency band;
and obtaining a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band and the space coordinates of each equivalent sound source.
In a possible implementation manner, constructing a univariate linear regression model corresponding to a preset octave band according to spatial coordinates of a plurality of preset detection points, actual sound pressure levels in the preset octave band, the number of equivalent sound sources, and spatial coordinates of each equivalent sound source, and solving the univariate linear regression model corresponding to the preset octave band to obtain the sound pressure levels of the plurality of equivalent sound sources in the preset octave band, the method includes:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of equivalent sound sources and the spatial coordinates of each equivalent sound source;
and solving the univariate linear regression model corresponding to the preset octave frequency band according to the predicted sound pressure level of each preset detection point in the preset octave frequency band and the actual sound pressure level of each preset detection point in the preset octave frequency band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave frequency band through double-layer optimization.
In a possible implementation manner, solving a univariate linear regression model corresponding to a preset octave band according to a predicted sound pressure level of each preset detection point in the preset octave band and an actual sound pressure level of each preset detection point in the preset octave band, and obtaining sound pressure levels of a plurality of equivalent sound sources in the preset octave band through double-layer optimization includes:
generating a first batch of random variables by adopting an average distribution method, wherein the first batch of random variables are used as input variables when a univariate linear regression model corresponding to a preset octave band is optimized for the first time;
performing first optimization according to the MSE loss function, and selecting error values obtained by calculating the MSE loss function from the first batch of random variables and arranging the error values in the random variables with the preset number in the order from small to large;
generating a second batch of random variables by adopting a normal distribution method according to the mathematical expected values and the variance values of the random variables in the preset number, wherein the second batch of random variables are used as input variables during second optimization of the univariate linear regression model corresponding to the preset octave frequency band;
and performing second optimization according to the cross entropy loss function, and finally solving to obtain the sound pressure level of each equivalent sound source in the preset octave frequency band from the second batch of random variables.
In one possible implementation, the MSE loss function is:
wherein MSE is an error value obtained by calculating an MSE loss function; m is the number of the prediction detection points; lmjThe actual sound pressure level of the jth preset detection point in the preset octave band is determined; lwjFor the predicted sound pressure level of the jth preset detection point at the preset octave band,
Lwij＝Lpi-20lg(dij)-0.001*α*dij-11; n is the number of equivalent sound sources; lwijGenerating a sound pressure level in a preset octave frequency band for the ith equivalent sound source at the jth preset detection point; lpiThe sound pressure level to be solved for the ith equivalent sound source in the preset octave band is an unknown quantity; dijThe distance between the ith equivalent sound source and the jth preset detection point is calculated; alpha is the atmospheric absorption attenuation coefficient of noise in the propagation process.
In one possible implementation, the cross-entropy loss function is:
wherein, L is a value obtained by calculating a cross entropy loss function; p (Lp)i) Is LpiThe value probability in a normal distribution function.
In one possible implementation, the number of equivalent sound sources is 24;
the distribution mode of the equivalent sound source is as follows: the long box wall surfaces are arranged at equal intervals according to the specification of 4x2, and the short box wall surfaces are arranged at equal intervals according to the specification of 2x 2.
In a second aspect, an embodiment of the present invention provides an apparatus for determining a multiple sound source noise equivalent model of a transformer, including:
the first acquisition module is used for acquiring the space coordinates of a plurality of preset detection points around the transformer and the actual sound pressure level in a preset octave band;
the second acquisition module is used for acquiring the number of the equivalent sound sources of the transformer and the space coordinates of each equivalent sound source;
the solving module is used for constructing a univariate linear regression model corresponding to the preset octave frequency band according to the spatial coordinates of the preset detection points, the actual sound pressure level of the preset octave frequency band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, and solving the univariate linear regression model corresponding to the preset octave frequency band to obtain the sound pressure levels of the equivalent sound sources in the preset octave frequency band;
and the model determining module is used for obtaining a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band and the space coordinates of each equivalent sound source.
In a possible implementation manner, the solving module is specifically configured to:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of equivalent sound sources and the spatial coordinates of each equivalent sound source;
and solving the univariate linear regression model corresponding to the preset octave frequency band according to the predicted sound pressure level of each preset detection point in the preset octave frequency band and the actual sound pressure level of each preset detection point in the preset octave frequency band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave frequency band through double-layer optimization.
In a third aspect, an embodiment of the present invention provides a terminal, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor, when executing the computer program, implements the steps of the method for determining an equivalent model of multiple source noise of a transformer according to the first aspect or any possible implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the method for determining a multiple source noise equivalent model of a transformer according to the first aspect or any possible implementation manner of the first aspect.
The embodiment of the invention provides a method, a terminal and a storage medium for determining a multi-sound-source noise equivalent model of a transformer, which are characterized in that spatial coordinates of a plurality of preset detection points around the transformer and actual sound pressure levels in a preset octave band are obtained; acquiring the number of equivalent sound sources of the transformer and the space coordinates of each equivalent sound source; according to the spatial coordinates of the preset detection points, the actual sound pressure level of the preset octave frequency band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, constructing a univariate linear regression model corresponding to the preset octave frequency band, and solving the univariate linear regression model corresponding to the preset octave frequency band to obtain the sound pressure levels of the equivalent sound sources in the preset octave frequency band; the method comprises the steps of obtaining a transformer multi-sound-source noise equivalent model corresponding to a preset octave band according to the number of equivalent sound sources, the sound pressure level of each equivalent sound source and the space coordinates of each equivalent sound source, solving the problems of complex calculation and large calculation amount in a near-field sound holographic technology, measuring the sound pressure level of a small number of detection points near a transformer, and obtaining the transformer multi-sound-source noise equivalent model based on a univariate linear regression method, wherein the equivalent process is convenient and simple, and the calculation amount is small.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart of an implementation of a method for determining a multi-sound-source noise equivalent model of a transformer according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a multi-source noise equivalent model of a transformer according to an embodiment of the present invention;
FIG. 3 is a plan view of an equivalent source location distribution provided by an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a device for determining a multi-sound-source noise equivalent model of a transformer according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the following description is made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, it shows a flowchart of an implementation of the method for determining a noise equivalent model of multiple sound sources of a transformer according to an embodiment of the present invention. The execution subject of the determination method of the transformer multi-sound-source noise equivalent model can be a terminal.
Referring to fig. 1, the method for determining the equivalent model of the multiple sound sources noise of the transformer includes:
in S101, spatial coordinates of a plurality of preset detection points around the transformer and an actual sound pressure level at a preset octave band are acquired.
In this embodiment, an actual typical substation may be selected, a plane distribution map and a three-dimensional space model of the substation are obtained, and a space rectangular coordinate system is established with a certain point on the ground of the substation as an origin of coordinates; sound pressure level measurement is carried out on a plurality of preset detection points (recorded as m) in free space around the transformer substation, and space coordinates (x) of the preset detection points are recordedRj，yRj，zRj) Actual sound pressure level Lm corresponding to the preset octave bandj. The detection point may also be referred to as a field point.
In one possible implementation, the origin of coordinates is used as a reference point, the east direction is used as the positive direction of the X-axis, the north direction is used as the positive direction of the Y-axis, and the up direction is the positive direction of the Z-axis. Establishing the space position coordinate of the detection point according to the space position relation between the detection point and the coordinate origin, and recording as (x)Rj，yRj，zRj) The unit is meter.
The present embodiment may use a noise real-time signal analyzer to measure the actual sound pressure level at each preset detection point. The noise real-time signal analyzer is a pocket real-time analyzer of digital signal processing technology, and can make frequency spectrum and amplitude analysis on noise, vibration or other electric signals.
When measuring the sound pressure level of a detection point, clear and windless weather is selected, and the temperature, the air humidity and the atmospheric pressure of the day are recorded. When the noise real-time signal analyzer is used for measuring the sound pressure level at the detection point, each sampling point needs to be continuously sampled for 30-60 seconds for 5 times, and the average value of the sampling points is obtained. And performing spectrum analysis on the sampling results of the sampling points, and extracting sound pressure levels of eight octave bands of 63HZ, 125HZ, 250HZ, 500HZ, 1000HZ, 2000HZ, 4000HZ and 8000HZ respectively.
The position of each preset detection point can be selected according to actual requirements, and the preset frequency doubling band can be any one of the eight frequency doubling bands.
In S102, the number of equivalent sound sources of the transformer and the spatial coordinates of each equivalent sound source are acquired.
Herein, the equivalent sound source may also be referred to as an equivalent point sound source.
In this embodiment, based on the multi-point equivalent source theory, according to the obtained spatial position parameters and geometric parameters of the transformer, geometric spatial information of the equivalent point sound source of the transformer, including the number of the equivalent point sound sources and the spatial distribution of the equivalent point sound sources, is obtained by using a correlation algorithm, so as to establish a noise radiation model of the transformer multi-point equivalent source (i.e., a noise equivalent model of the transformer multi-sound source), and the spatial position coordinates of n equivalent sources are recorded as (x) the spatial position coordinates of n equivalent sourcesNSi，yNSi，zNSi)。
The transformer can be a 500KV three-phase main transformer, and the geometric parameters of the transformer are 16.0m in length, 5.0m in width and 5.0m in height.
In some embodiments, the number of equivalent sound sources is 24;
the distribution mode of the equivalent sound source is as follows: the long box wall surfaces are arranged at equal intervals according to the specification of 4x2, and the short box wall surfaces are arranged at equal intervals according to the specification of 2x 2.
Based on the geometric parameter information of the transformer, when a multi-sound-source noise equivalent model of the transformer is constructed, the number of equivalent sound sources is set to be 24, the arrangement mode is equivalent to that of equivalent sound sources arranged on the wall surface of a long box at equal intervals of 4x2, equivalent sound sources arranged on the wall surface of a short box at equal intervals of 2x2 are equivalent, as shown in fig. 2, the cuboid in fig. 2 is a virtual model of the transformer, black points are equivalent sound sources, the equivalent sound sources are distributed on four side surfaces of the cuboid, and the upper surface and the lower surface are free of equivalent sound sources. It should be noted that, in order to make fig. 2 clearly show the equivalent sound source, fig. 2 only shows the equivalent sound source of one long box wall and one short box wall, and in the actual model, two opposite long box walls have the equivalent sound source and two opposite short box walls have the equivalent sound source.
The spatial position coordinate of the equivalent sound source is recorded as (x) according to the spatial position of the equivalent sound sourceNSi，yNSi，zNSi)。
In S103, a univariate linear regression model corresponding to the preset octave band is constructed according to the spatial coordinates of the plurality of preset detection points, the actual sound pressure level in the preset octave band, the number of the equivalent sound sources, and the spatial coordinates of each equivalent sound source, and the univariate linear regression model corresponding to the preset octave band is solved to obtain the sound pressure levels of the plurality of equivalent sound sources in the preset octave band.
In this embodiment, the sound pressure levels of a plurality of equivalent sound sources in the preset octave band can be obtained by constructing a univariate linear regression model corresponding to the preset octave band and solving the univariate linear regression model corresponding to the preset octave band.
In this embodiment, if it is desired to obtain the sound pressure levels of the eight octave bands of the equivalent sound source, the above-mentioned S101-S103 may be performed by using the eight octave bands as the preset octave bands, and finally, the sound pressure level of the equivalent sound source of the transformer multi-sound-source noise equivalent model obtained through S104 may include the sound pressure levels of the eight octave bands. That is to say, different univariate linear regression models need to be constructed for different octave bands and solved to obtain the sound pressure level of the equivalent sound source in the octave band.
In some embodiments, the S103 may include:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of equivalent sound sources and the spatial coordinates of each equivalent sound source;
and solving the univariate linear regression model corresponding to the preset octave frequency band according to the predicted sound pressure level of each preset detection point in the preset octave frequency band and the actual sound pressure level of each preset detection point in the preset octave frequency band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave frequency band through double-layer optimization.
According to the embodiment, the single-variable linear regression model corresponding to the preset octave frequency band is solved through double-layer optimization, and the accuracy can be improved.
In some embodiments, the solving the univariate linear regression model corresponding to the preset octave band according to the predicted sound pressure level of each preset detection point in the preset octave band and the actual sound pressure level of each preset detection point in the preset octave band, and obtaining the sound pressure levels of the plurality of equivalent sound sources in the preset octave band through double-layer optimization includes:
generating a first batch of random variables by adopting an average distribution method, wherein the first batch of random variables are used as input variables when a univariate linear regression model corresponding to a preset octave band is optimized for the first time;
performing first optimization according to the MSE loss function, and selecting error values obtained by calculating the MSE loss function from the first batch of random variables and arranging the error values in the random variables with the preset number in the order from small to large;
generating a second batch of random variables by adopting a normal distribution method according to the mathematical expected values and the variance values of the random variables in the preset number, wherein the second batch of random variables are used as input variables during second optimization of the univariate linear regression model corresponding to the preset octave frequency band;
the formula for normal distribution is as follows:
where μ is the mathematical expectation, σ, of a pre-set number of previously ranked random variables2Is the variance value of the previously preset number of random variables.
And performing second optimization according to the cross entropy loss function, and finally solving to obtain the sound pressure level of each equivalent sound source in the preset octave frequency band from the second batch of random variables.
Wherein the first random variables of the average distribution can be generated by using a Uniform function. Each random variable contains the sound pressure level of 24 equivalent sound sources in a preset octave band, and each random variable can be different.
In the embodiment, during the first optimization, a global error between a predicted value and an actual measurement value is calculated according to an MSE loss function, a preset number of random variables with smaller errors are selected, a second number of random variables are generated in normal distribution according to mathematical expected values and variance values of the preset number of random variables with smaller errors, and during the second optimization, a cross entropy loss function is adopted, and the sound pressure level of each equivalent sound source in a preset octave band is finally obtained through solving from the second number of random variables. And obtaining the sound pressure level of each equivalent sound source in the preset octave band through second suboptimal solution.
In some embodiments, the MSE loss function is:
wherein MSE is an error value obtained by calculating an MSE loss function; m is the number of the prediction detection points; lmjThe actual sound pressure level of the jth preset detection point in the preset octave band is determined; lwjFor the predicted sound pressure level of the jth preset detection point at the preset octave band,
Lwij＝Lpi-20lg(dij)-0.001*α*dij-11; n is the number of equivalent sound sources; lwijGenerating a sound pressure level in a preset octave frequency band for the ith equivalent sound source at the jth preset detection point; lpiThe sound pressure level to be solved for the ith equivalent sound source in the preset octave band is an unknown quantity; dijThe distance between the ith equivalent sound source and the jth preset detection point is calculated; alpha is the atmospheric absorption attenuation coefficient of noise in the propagation process.
Wherein the content of the first and second substances,
as shown in fig. 3, fig. 3 shows 4 field points (field point 1, field point 2, field point 3, and field point 4) and an equivalent source plane having 4 equivalent sound sources in the equivalent source plane, and d in fig. 3 represents a distance between one of the field points (detection points) and one of the equivalent sound sources.
The MSE penalty function is a globally sensitive penalty function used to locate Lp for the second optimizationiThe candidate section of (2).
In this embodiment, the sound pressure level of the equivalent sound source in space is a candidate, and is first set to Lpi. According to the attenuation formula of noise in free field propagation, in the propagation process of each equivalent sound source from a transformer to a detection point, the noise propagation only undergoes geometric divergence attenuation and atmospheric absorption attenuation, and the sound pressure level generated at the detection point is as follows: lwij＝Lpi-20lg(dij)-0.001*α*dij11, where α can be found by looking up table 1, temperature and relative humidity are recorded when measuring the sound pressure level at a predetermined detection point. Propagation distance dijDetermines the geometrical divergence attenuation and the atmospheric absorption attenuation in the propagation process.
Superposing the sound pressure level generated by all equivalent sound sources at the detection point, namely predicting the sound pressure level at the detection point, wherein the superposition formula is as follows:
TABLE 1 atmospheric absorption attenuation coefficient table
In some embodiments, the cross-entropy loss function is:
wherein L isCalculating a value obtained by a cross entropy loss function; p (Lp)i) Is LpiThe value probability in a normal distribution function.
Cross entropy is used as a loss function, data is provided for gradient descent in an iterative convergence process, Lp is converged through small-batch random gradient descentiTo obtain LpiThe value of (1) is the sound pressure level of the equivalent sound source of the transformer multi-sound-source noise equivalent model in the preset octave band.
The above calculation is only the sound pressure level of the single frequency band of the equivalent sound source, if the sound pressure levels of eight frequency bands of 63HZ, 125HZ, 250HZ, 500HZ, 1000HZ, 2000HZ, 4000HZ and 8000HZ are to be obtained, 8 models need to be constructed, and a double-layer optimization method is used for carrying out 8 optimal solutions, so that the sound pressure level of 8 frequency bands of the equivalent sound source can be obtained.
In S104, a transformer multi-sound-source noise equivalent model corresponding to the preset octave band is obtained according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band, and the spatial coordinates of each equivalent sound source.
After the sound pressure level of each equivalent sound source in the preset octave band is obtained through solving, according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band and the space coordinates of each equivalent sound source, a transformer multi-sound-source noise equivalent model corresponding to the preset octave band can be obtained.
If the sound pressure levels of the equivalent sound sources in the eight octave frequency bands are obtained through solving, a final transformer multi-sound-source noise equivalent model can be obtained.
The method comprises the steps of obtaining space coordinates of a plurality of preset detection points around a transformer and actual sound pressure levels in a preset octave band; acquiring the number of equivalent sound sources of the transformer and the space coordinates of each equivalent sound source; according to the spatial coordinates of the preset detection points, the actual sound pressure level of the preset octave frequency band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, constructing a univariate linear regression model corresponding to the preset octave frequency band, and solving the univariate linear regression model corresponding to the preset octave frequency band to obtain the sound pressure levels of the equivalent sound sources in the preset octave frequency band; the method comprises the steps of obtaining a transformer multi-sound-source noise equivalent model corresponding to a preset octave band according to the number of equivalent sound sources, the sound pressure level of each equivalent sound source and the space coordinates of each equivalent sound source, solving the problems of complex calculation and large calculation amount in a near-field sound holographic technology, measuring the sound pressure level of a small number of detection points near a transformer, and obtaining the transformer multi-sound-source noise equivalent model based on a univariate linear regression method, wherein the equivalent process is convenient and simple, and the calculation amount is small.
In the embodiment, a single-variable linear regression model is constructed through noise radiation attenuation characteristics, MSE and cross entropy are used as loss functions, and the sound pressure level of the transformer multi-sound-source equivalent model is obtained through double-layer optimization. The equivalent process is convenient and simple, the operand is small, and the equivalent result is more accurate through double-layer optimization.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The following are embodiments of the apparatus of the invention, reference being made to the corresponding method embodiments described above for details which are not described in detail therein.
Fig. 4 is a schematic structural diagram of a device for determining a multi-sound-source noise equivalent model of a transformer according to an embodiment of the present invention, and for convenience of description, only the parts related to the embodiment of the present invention are shown, which are detailed as follows:
as shown in fig. 4, the apparatus 30 for determining the equivalent noise model of the transformer multi-sound source includes: a first acquisition module 31, a second acquisition module 32, a solving module 33 and a model determination module 34.
The first obtaining module 31 is configured to obtain spatial coordinates of a plurality of preset detection points around the transformer and an actual sound pressure level at a preset octave band;
a second obtaining module 32, configured to obtain the number of equivalent sound sources of the transformer and spatial coordinates of each equivalent sound source;
the solving module 33 is configured to construct a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of the multiple preset detection points, the actual sound pressure levels in the preset octave band, the number of the equivalent sound sources, and the spatial coordinates of each equivalent sound source, and solve the univariate linear regression model corresponding to the preset octave band to obtain the sound pressure levels of the multiple equivalent sound sources in the preset octave band;
and the model determining module 34 is configured to obtain a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band, and the spatial coordinates of each equivalent sound source.
In a possible implementation, the solving module 33 is specifically configured to:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of equivalent sound sources and the spatial coordinates of each equivalent sound source;
and solving the univariate linear regression model corresponding to the preset octave frequency band according to the predicted sound pressure level of each preset detection point in the preset octave frequency band and the actual sound pressure level of each preset detection point in the preset octave frequency band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave frequency band through double-layer optimization.
In a possible implementation, the solving module 33 is specifically configured to:
generating a first batch of random variables by adopting an average distribution method, wherein the first batch of random variables are used as input variables when a univariate linear regression model corresponding to a preset octave band is optimized for the first time;
performing first optimization according to the MSE loss function, and selecting error values obtained by calculating the MSE loss function from the first batch of random variables and arranging the error values in the random variables with the preset number in the order from small to large;
generating a second batch of random variables by adopting a normal distribution method according to the mathematical expected values and the variance values of the random variables in the preset number, wherein the second batch of random variables are used as input variables during second optimization of the univariate linear regression model corresponding to the preset octave frequency band;
and performing second optimization according to the cross entropy loss function, and finally solving to obtain the sound pressure level of each equivalent sound source in the preset octave frequency band from the second batch of random variables.
In one possible implementation, the MSE loss function is:
wherein MSE is an error value obtained by calculating an MSE loss function; m is the number of the prediction detection points; lmjThe actual sound pressure level of the jth preset detection point in the preset octave band is determined; lwjFor the predicted sound pressure level of the jth preset detection point at the preset octave band,
Lwij＝Lpi-20lg(dij)-0.001*α*dij-11; n is the number of equivalent sound sources; lwijGenerating a sound pressure level in a preset octave frequency band for the ith equivalent sound source at the jth preset detection point; lpiThe sound pressure level to be solved for the ith equivalent sound source in the preset octave band is an unknown quantity; dijThe distance between the ith equivalent sound source and the jth preset detection point is calculated; alpha is the atmospheric absorption attenuation coefficient of noise in the propagation process.
In one possible implementation, the cross-entropy loss function is:
wherein, L is a value obtained by calculating a cross entropy loss function; p (Lp)i) Is LpiProbability of values in the positive-too distribution function.
In one possible implementation, the number of equivalent sound sources is 24;
the distribution mode of the equivalent sound source is as follows: the long box wall surfaces are arranged at equal intervals according to the specification of 4x2, and the short box wall surfaces are arranged at equal intervals according to the specification of 2x 2.
Fig. 5 is a schematic diagram of a terminal according to an embodiment of the present invention. As shown in fig. 5, the terminal 4 of this embodiment includes: a processor 40, a memory 41 and a computer program 42 stored in said memory 41 and executable on said processor 40. The processor 40, when executing the computer program 42, implements the steps in the above-described method embodiment for determining a noise equivalent model of multiple sound sources of each transformer, for example, S101 to S104 shown in fig. 1. Alternatively, the processor 40, when executing the computer program 42, implements the functions of the modules/units in the above-mentioned device embodiments, such as the modules/units 31 to 34 shown in fig. 4.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 42 in the terminal 4. For example, the computer program 42 may be divided into the modules/units 31 to 34 shown in fig. 4.
The terminal 4 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal 4 may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 5 is only an example of a terminal 4 and is not intended to be limiting of terminal 4, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the terminal may also include input-output devices, network access devices, buses, etc.
The Processor 40 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used for storing the computer program and other programs and data required by the terminal. The memory 41 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal and method may be implemented in other ways. For example, the above-described apparatus/terminal embodiments are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the method according to the above embodiments may be implemented by a computer program, which may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the method for determining the multiple-sound-source noise equivalent model of each transformer may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for determining a transformer multi-sound-source noise equivalent model is characterized by comprising the following steps:
acquiring space coordinates of a plurality of preset detection points around the transformer and actual sound pressure levels in a preset octave band;
acquiring the number of equivalent sound sources of the transformer and the space coordinates of each equivalent sound source;
according to the space coordinates of a plurality of preset detection points, the actual sound pressure level of a preset octave band, the number of the equivalent sound sources and the space coordinates of each equivalent sound source, constructing a univariate linear regression model corresponding to the preset octave band, and solving the univariate linear regression model corresponding to the preset octave band to obtain the sound pressure levels of the equivalent sound sources in the preset octave band;
and obtaining a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band and the space coordinates of each equivalent sound source.
2. The method for determining the transformer multi-sound-source noise equivalent model according to claim 1, wherein the step of constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level in the preset octave band, the number of the equivalent sound sources, and the spatial coordinates of each equivalent sound source, and solving the univariate linear regression model corresponding to the preset octave band to obtain the sound pressure levels of the plurality of equivalent sound sources in the preset octave band comprises:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source;
according to the predicted sound pressure level of each preset detection point in the preset octave band and the actual sound pressure level of each preset detection point in the preset octave band, solving a univariate linear regression model corresponding to the preset octave band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave band through double-layer optimization.
3. The method for determining the transformer multi-sound-source noise equivalent model according to claim 2, wherein the step of solving the univariate linear regression model corresponding to the preset octave band according to the predicted sound pressure level of each preset detection point in the preset octave band and the actual sound pressure level of each preset detection point in the preset octave band to obtain the sound pressure levels of a plurality of equivalent sound sources in the preset octave band through double-layer optimization comprises:
generating a first batch of random variables by adopting an average distribution method, wherein the first batch of random variables are used as input variables when a univariate linear regression model corresponding to the preset octave band is optimized for the first time;
performing first optimization according to the MSE loss function, and selecting error values obtained by calculating the MSE loss function from the first batch of random variables and arranging the error values in a preset number of random variables in the order from small to large;
generating a second batch of random variables by adopting a normal distribution method according to the mathematical expected values and the variance values of the random variables in the preset number, wherein the second batch of random variables are used as input variables during second optimization of the univariate linear regression model corresponding to the preset octave frequency band;
and performing second optimization according to the cross entropy loss function, and finally solving to obtain the sound pressure level of each equivalent sound source in the preset octave frequency band from the second batch of random variables.
4. The method for determining the transformer multi-sound-source noise equivalent model according to claim 3, wherein the MSE loss function is:
wherein MSE is an error value obtained by calculating an MSE loss function; m is the number of the prediction detection points; lmjThe actual sound pressure level of the jth preset detection point in the preset octave band is determined; lwjFor the predicted sound pressure level of the jth preset detection point at the preset octave band,
Lwij＝Lpi-20lg(dij)-0.001*α*dij-11; n is the number of equivalent sound sources; lwijFor the ith equivalent sound sourceSound pressure levels in preset frequency doubling bands generated by the j preset detection points; lpiThe sound pressure level to be solved for the ith equivalent sound source in the preset octave band is an unknown quantity; dijThe distance between the ith equivalent sound source and the jth preset detection point is calculated; alpha is the atmospheric absorption attenuation coefficient of noise in the propagation process.
5. The method for determining the transformer multi-sound-source noise equivalent model according to claim 4, wherein the cross-entropy loss function is:
wherein, L is a value obtained by calculating a cross entropy loss function; p (Lp)i) Is LpiProbability of values in the positive-too distribution function.
6. The method for determining the equivalent noise model of the transformer multi-sound source according to any one of claims 1 to 5, wherein the number of the equivalent sound sources is 24;
the distribution mode of the equivalent sound source is as follows: the long box wall surfaces are arranged at equal intervals according to the specification of 4x2, and the short box wall surfaces are arranged at equal intervals according to the specification of 2x 2.
7. An apparatus for determining a multi-source noise equivalent model of a transformer, comprising:
the first acquisition module is used for acquiring the space coordinates of a plurality of preset detection points around the transformer and the actual sound pressure level in a preset octave band;
the second acquisition module is used for acquiring the number of the equivalent sound sources of the transformer and the space coordinates of each equivalent sound source;
the solving module is used for constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source, and solving the univariate linear regression model corresponding to the preset octave band to obtain the sound pressure levels of the equivalent sound sources in the preset octave band;
and the model determining module is used for obtaining a transformer multi-sound-source noise equivalent model corresponding to the preset octave band according to the number of the equivalent sound sources, the sound pressure level of each equivalent sound source in the preset octave band and the space coordinates of each equivalent sound source.
8. The apparatus for determining a transformer multi-source noise equivalent model according to claim 7, wherein the solving module is specifically configured to:
constructing a univariate linear regression model corresponding to a preset octave band according to the spatial coordinates of a plurality of preset detection points, the actual sound pressure level of the preset octave band, the number of the equivalent sound sources and the spatial coordinates of each equivalent sound source;
according to the predicted sound pressure level of each preset detection point in the preset octave band and the actual sound pressure level of each preset detection point in the preset octave band, solving a univariate linear regression model corresponding to the preset octave band, and obtaining the sound pressure levels of a plurality of equivalent sound sources in the preset octave band through double-layer optimization.
9. A terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method for determining the equivalent model of multi-source noise of a transformer as claimed in any one of claims 1 to 6 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, implements the steps of the method for determining a multiple source noise equivalent model of a transformer according to any one of claims 1 to 6.
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| PCT/CN2022/115386 WO2023103468A1 (en) | 2021-12-10 | 2022-08-29 | Method for determining transformer multi-sound-source noise equivalent model, terminal, and storage medium |
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| WO2023103468A1 (en) * | 2021-12-10 | 2023-06-15 | 国网河北省电力有限公司经济技术研究院 | Method for determining transformer multi-sound-source noise equivalent model, terminal, and storage medium |
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| JP2001159559A (en) * | 1999-12-03 | 2001-06-12 | Chubu Electric Power Co Inc | Noise measuring method |
| CN111159928A (en) * | 2019-11-26 | 2020-05-15 | 中国电力科学研究院有限公司 | Transformer noise calculation method and system based on multi-line sound source model |
| CN111261188B (en) * | 2020-01-20 | 2022-10-18 | 中国电力科学研究院有限公司 | Method and device for determining noise frequency spectrum of high-voltage transformer |
| CN114201875A (en) * | 2021-12-10 | 2022-03-18 | 国网河北省电力有限公司经济技术研究院 | Method for determining multi-sound-source noise equivalent model of transformer, terminal and storage medium |
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| WO2023103468A1 (en) * | 2021-12-10 | 2023-06-15 | 国网河北省电力有限公司经济技术研究院 | Method for determining transformer multi-sound-source noise equivalent model, terminal, and storage medium |
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