CN110672989B - Self-adaptive partial discharge spectrogram phase correction method and correction system - Google Patents
Self-adaptive partial discharge spectrogram phase correction method and correction system Download PDFInfo
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Abstract
The invention discloses a self-adaptive partial discharge spectrogram phase correction method and a correction system, wherein the correction method comprises the following steps of: firstly, acquiring pulse data by using a partial discharge pulse acquisition system, primarily processing the pulse data, and packaging the pulse data into an array form; step two, sequencing the phase information, wherein the numerical value of the phase information is between 0 and 360 degrees, and sequencing the phase information from small to large to form a sequenced phase array; step three, classifying the phase information, and dividing the sequenced phase arrays into two types; step four, dividing the amplitude array into two types; calculating the density of two kinds of amplitude value arrays of the standard spectrogram and the spectrogram to be corrected respectively, and searching for an amplitude value approximation class; and step six, carrying out normalization correction on the phase information corresponding to the amplitude approximation class. The invention can stabilize the pulse point on the spectrogram in a fixed range, so that the spectrogram can be correctly displayed, and correct spectrogram information is provided for the next partial discharge pattern recognition function.
Description
Technical Field
The present invention relates to a method and a system for correcting phase, and more particularly, to a method and a system for correcting phase of a local discharge spectrogram of a high voltage power device without changing hardware.
Background
In the use process of high-voltage power equipment, partial discharge problems can be caused due to the fact that the insulation performance is weakened, and therefore the partial discharge needs to be detected so as to evaluate the performance of the high-voltage power equipment. Currently, there are two methods for obtaining the phase of the partial discharge pulse in the partial discharge measurement device, i.e. inner trigger and outer trigger. The internal trigger is usually used for the handheld charged detection device, and generally, the external trigger cannot be adopted because the external power supply is not connected due to the power supply of the battery. The external trigger is used for portable measuring equipment and on-line monitoring equipment, and is generally connected with a low-voltage power frequency power supply, so that the sinusoidal power supply can be utilized. The internal triggering is a method which is not adopted for the needs of a phase statistical method, and is generally realized by software, and an obtained phase value is not a phase value corresponding to a real partial discharge pulse, but can help to realize statistical analysis from the aspect of statistical probability. The phase value obtained by the external trigger is much true and is generally realized by a hardware circuit, so the implementation is difficult and the cost is high.
Under the condition of internal triggering, the obtained spectrogram phase value is not the phase value corresponding to the real partial discharge pulse. For example, an external power supply is set to be 50Hz, fluctuation can occur in an actual situation, the fluctuation range is about 49.9-50.1 Hz, phase information is obtained by recording the time of pulse generation and corresponding to a 50Hz standard waveform, a certain error exists between a phase obtained by calculation due to frequency fluctuation and an actual phase, a small phase error may exist in one period, but within the total duration of collecting partial discharge pulses, the error accumulation can cause errors of tens of degrees or even hundreds of degrees. Fig. 1 shows a phase shift spectrum of the spike discharge, where (a) in fig. 1 shows a spectrum for generating a phase shift, and (b) in fig. 1 shows another spectrum for generating a phase shift. If the spectrogram has phase deviation, the recognition of a partial discharge mode can be misjudged, or the recognition type is inaccurate, if the measured data is inaccurate, the false alarm and the missing alarm rate are high (about 500 times of alarm is false alarm if the GIS ultrasonic partial discharge online monitoring is carried out in 2008), the overhaul frequency of workers is increased, and the operation stability of a power grid is seriously influenced.
Disclosure of Invention
In view of the above, the present invention provides an adaptive partial discharge spectrogram phase correction method and system for correcting a phase of a partial discharge spectrogram of a high-voltage power equipment.
In order to achieve the above object, the present invention provides a phase correction method for a self-adaptive partial discharge spectrogram, comprising the following steps:
firstly, acquiring pulse data by using a partial discharge pulse acquisition system, primarily processing the pulse data, and packaging the pulse data into an array form;
step two, sequencing the phase information, wherein the numerical value of the phase information is between 0 and 360 degrees, and sequencing the phase information from small to large to form a sequenced phase array;
step three, classifying the phase information, and dividing the sequenced phase arrays into two types;
step four, carrying out same classification on the amplitude arrays, tracing back to the original two-dimensional array in the step one after the phase array is classified, finding amplitude information corresponding to indexes by searching phases, and equally classifying the amplitude arrays into two types;
calculating the density of two kinds of amplitude value arrays of the standard spectrogram and the spectrogram to be corrected respectively, and searching for an amplitude value approximation class;
and step six, carrying out normalization correction on the phase information corresponding to the amplitude approximation class.
In the first step, the array format is fixed, the first row is phase information, the second row is amplitude information, the third row is time for acquiring pulses, and the information of each column corresponds to one another.
The specific classification algorithm of the phase information in the third step is to make a difference on the sorted phase arrays, subtract the former value from the latter value to obtain a phase difference array, compare each value in the phase difference array with phi c, wherein phi c is a defined constant quantity, usually 10 degrees or 20 degrees is selected, and if the value is greater than phi c, the index at the moment is output to obtain an index array; inserting the 0 th bit of the index array into a value 0, inserting the last bit into the value with the size of the well-ordered phase array to form a new index array, and subtracting the nth bit from the n +1 th bit of the new index array to obtain an index difference array; and finding out the maximum value and the sub-maximum value of the index difference array, finding out two elements of the index array corresponding to the maximum value and the sub-maximum value, and dividing the sorted phase array into two types according to the two elements.
The two elements classify the ordered phase array according to the fact that the phase information indexed between the two elements is classified into a first type, the other phase information is classified into a second type, and the phase array is classified into two types.
The fifth step specifically includes, using a calculation formula:
where N is the number of points in one of the amplitude arrays, and N isGeneral assemblyRepresenting the total point number of the whole amplitude array, mu representing the central discrete degree of the amplitude array, the specific algorithm of mu is to take the standard deviation of the central position of the amplitude array, take the empirical constant 20, compare the array size of the two types of amplitude arrays with 20, take the minimum value x of the three numbers, respectively take the x values of the amplitude arrays from the central index position of each type of amplitude array, and solve the standard deviation of the x amplitudes, namely respectively obtain the central discrete degree mu of the two types of amplitude arrays1And mu2Obtaining the density sigma of two kinds of amplitude value arrays according to the formula1And σ2；
For both the standard spectrogram and the spectrogram to be corrected, the density σ of the two amplitude classes is calculated, and the larger one of the two amplitude classes is selected, so that the amplitude class corresponding to the relatively larger value is the approximate class.
The sixth step specifically includes calculating a difference between the phase center values of the amplitude approximation class
Wherein
Is the first phase value of the phase signal,
is the last phase value, and all phase values are reused
Plus with
Due to the fact that
Is in the range of 0-360 deg., the corrected phase value is in the range of-360 deg. -720 deg., and the range of the corrected phase value is limited to 0 deg. to 360 deg. by using the dispersion normalization method.
The dispersion normalization method utilizes a transfer function:
y＝(z-min)/(max-min)
wherein max is the maximum value of the sample data, and min is the minimum value of the sample data; the value of the phase value range between-360 and 720 degrees is z, the value of y is obtained through (z- (-360))/(720- (-360)) and is a value after standardization, the range is 0-1, then the value range of y is changed into 0-360 degrees through y x 360, and the phase value of the phase array is judged by the method and is in the range.
The invention provides a self-adaptive partial discharge spectrogram phase correction system, which comprises:
the partial discharge pulse acquisition unit is used for acquiring partial discharge pulse data, packaging the partial discharge pulse data into an array form after preliminary processing, wherein the array form is fixed, the first row is phase information, the second row is amplitude information, and the third row is the time for acquiring the pulse;
the phase information sequencing module is used for sequencing the phase information, the numerical value of the phase information is between 0 and 360 degrees, and a sequenced phase array is formed according to the sequence of the numerical values from small to large;
a phase information classification module for classifying the phase array into two categories;
the amplitude information classification module is used for classifying the amplitude arrays, tracing the phase arrays to the original two-dimensional arrays after the phase arrays are classified according to the classification mode of the phase arrays, and searching the phase to find the amplitude information corresponding to the index, so that the amplitude arrays are classified in the same way, and two types of phase arrays and two types of amplitude arrays are obtained;
the amplitude approximation module is used for searching an approximation class of the amplitude, calculating the densities of two amplitude classes for the standard spectrogram and the spectrogram to be corrected and selecting the larger one of the two amplitude classes, wherein the amplitude class corresponding to the relatively larger value is the approximation class; and
and the phase information normalization module is used for performing normalization correction on the phase information corresponding to the amplitude approximation class and performing range limitation on the corrected phase array by adopting a dispersion normalization method.
The phase information classification module comprises:
the phase difference array module is used for carrying out difference on the sequenced phase arrays, namely subtracting the former value from the latter value to obtain a phase difference array;
the index array module is used for comparing each value in the phase difference array with phi c, wherein phi c is a defined constant quantity, usually 10 degrees or 20 degrees is selected, and if the value is greater than phi c, the index at the moment is output to obtain an index array;
the index difference array module inserts the 0 th bit of the index array into a value 0, inserts the last bit into the value with the size of the well-ordered phase array to form a new index array, and subtracts the nth bit from the n +1 th bit of the new index array to obtain an index difference array; finding out the maximum value and the sub-maximum value of the index difference array, and finding out two elements of the index array which are worth corresponding to the two values; and
and the phase array splitting module splits the phase array according to the two elements, the phase information indexed between the two elements is classified into a first type, and other phase information is classified into a second type, so that the two types of classification processing are carried out on the phase array.
Calculating the difference value of the phase center values of the amplitude approximation class by using the phase information normalization module
Wherein
Is the first phase value of the phase signal,
is the last phase value, and all phase values are reused
Plus with
Due to the fact that
Is in the range of 0-360 deg., the corrected phase value is in the range of-360 deg. -720 deg., and the corrected phase value array is limited to the range of 0 deg. to 360 deg. by the dispersion normalization method.
The phase correction method and the correction system can correct the phase deviation on the spectrogram due to frequency fluctuation through software, stabilize the pulse point on the spectrogram in a fixed range, enable the spectrogram to be correctly displayed, provide correct spectrogram information for the next partial discharge mode identification function and improve the accuracy of electric leakage alarm.
Drawings
FIG. 1 is a graph of a prior art phase shift;
FIG. 2 is a flowchart illustrating a phase correction method according to the present invention;
FIG. 3 illustrates an exemplary embodiment of the present invention in which partial discharge pulse data is packed into an array;
FIG. 4 is a standard partial discharge spectrum of the present invention;
FIG. 5 is a flowchart of a process for classifying the phase array according to the present invention;
FIG. 6 is a flowchart of the procedure for finding the magnitude approximation in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 2, in the method for correcting the phase of the partial discharge spectrogram according to the present invention, step one s1, pulse data is acquired by using a partial discharge pulse acquisition system, and after being subjected to preliminary processing, the pulse data is packaged into an array format, as shown in fig. 3, which is an example of an array of partial discharge pulse data, the array format is fixed, the first row is phase information, the second row is amplitude information, the third row is time for acquiring pulses, and information in each row corresponds to each other one by one. And step two s2, sequencing the phase information, wherein the numerical value of the phase information is between 0 and 360 degrees, sequencing according to the sequence from small to large of the numerical value to form a sequenced phase array, and classifying the phases. According to the characteristics of the partial discharge spectrogram, except for the discharge types of particle discharge, two types of point sets, namely a dense point set and a dispersed point set, are presented on the spectrogram after interference is removed, as shown in fig. 4, the phase distribution of the standard partial discharge spectrogram is fixed, and the phase characteristics are obvious, so that the phase information is initially divided into two types. The dispersion of the original two kinds of point sets into three kinds of point sets around 0 °, the middle part, and around 360 ° (as shown in fig. 1 (a)) may be caused by the spectral phase shift, and a plurality of point sets may occur due to the dispersion of the point sets at the time of air gap discharge, and a dispersion of all phases may occur in the case of particle discharge.
Referring to fig. 2 and 5, step three s3 of the phase correction method of the present invention classifies the phase information, and the phase position component is divided into two types, the specific classification algorithm of the present invention for the phase information is to subtract the sorted phase position component, that is, subtract the former value from the latter value to obtain a phase difference component, compare each value in the phase difference component with Φ c, where Φ c is a defined constant quantity, usually 10 degrees or 20 degrees is selected, and if it is greater than Φ c, output the index at this time to obtain an index component. And inserting the 0 th bit of the index array into a value 0, inserting the last bit into the value (the array size is the number of elements in the array) of the sorted phase array size to form a new index array, and subtracting the nth bit from the n +1 th bit of the new index array to obtain an index difference array. And finding out the maximum value and the sub-maximum value of the index difference array, finding out two elements of the index array corresponding to the two values at the moment, and classifying the sorted phase array according to the two elements. The specific classification step is to split the phase array according to the two elements, the phase information indexed between the two elements is classified into a first class, and the other phase information is classified into a second class, so that the two classes of classification processing are performed on the phase array.
Step four s4, the amplitude arrays are classified into two types, the phase arrays are classified and then traced back to the original two-dimensional arrays, amplitude information corresponding to the indexes is found by searching the phases, and therefore the amplitude arrays are classified and processed similarly, and two types of phase arrays and two types of amplitude arrays are obtained. And step five s5, calculating the density of two kinds of amplitude value arrays of the standard spectrogram and the spectrogram to be corrected respectively, and searching for amplitude value approximation. The metric for the selected approximation class is a custom eigenvalue defined as the density of the class. The magnitude of the eigenvalue is related to the degree of center dispersion and phase density of the sorted amplitude array. The calculation formula is as follows:
where N is the number of points in one of the amplitude arrays, and N isGeneral assemblyRepresenting the total number of points of the entire amplitude array. These two comparisons yield a density normalized value for the class of amplitude values. Mu represents the central discrete degree of the amplitude array, the specific algorithm of mu is to take the standard deviation at the center of the amplitude array, because the array sizes of the two types of amplitude arrays are not necessarily equal, the empirical constant 20 is taken, the array sizes of the two types of amplitude arrays are compared with 20, the minimum value x of the three numbers is taken, the x values of the amplitude array are respectively taken from the central index of each type of amplitude array, and the standard deviation of the x amplitudes is solved, namely the central discrete degree mu of the two types of amplitude arrays is respectively obtained1And mu2Obtaining the density sigma of two kinds of amplitude value arrays according to the formula1And σ2。
For both the standard spectrogram and the spectrogram to be corrected, the density σ of the two amplitude classes is calculated, and the larger one of the two amplitude classes is selected, so that the amplitude class corresponding to the relatively larger value is the approximate class. FIG. 6 is a flowchart of the procedure for finding the magnitude approximation in the present invention.
Step six s6, carrying out normalization correction on the phase information corresponding to the amplitude approximation class, specifically including calculating the difference of the phase center values of the amplitude approximation class
Wherein
Is the first phase value of the phase signal,
is the last phase value, and all phase values are reused
Plus with
Due to the possibility of exceeding after correctionThe 0 deg. to 360 deg. range, so its value is limited to between 0-360 deg.. Due to the fact that
Is in the range of 0-360 deg., the corrected phase value is in the range of-360 deg. -720 deg., and since the range of the spectrogram is limited to 0 deg. to 360 deg., the range of the corrected phase array needs to be limited. In the invention, a dispersion standardization method is adopted for limiting the phase range, and the linear transformation of the original data is adopted to map the result value to [ 0-1%]In the meantime. The transfer function is as follows:
y＝(z-min)/(max-min)
where max is the maximum value of the sample data and min is the minimum value of the sample data. The value z of the phase value range between-360 and 720 degrees is obtained through (x- (-360))/(720- (-360)) to obtain the value y which is a normalized value and ranges from 0 to 1, then the value range of the phase value can be changed into 0 to 360 degrees by y x 360, and the phase value of the phase array can be judged and is in the range by the method.
The self-adaptive partial discharge spectrogram phase correction system comprises a partial discharge pulse acquisition unit, a phase information sorting module, a phase information classification module, an amplitude approximation module and a phase information normalization module. The partial discharge pulse acquisition system is used for acquiring partial discharge pulse data, packaging the partial discharge pulse data into an array form after preliminary processing, wherein the array form is fixed, the first row is phase information, the second row is amplitude information, and the third row is time for acquiring pulses.
The phase information sequencing module is used for sequencing phase information, the numerical value of the phase information is between 0 and 360 degrees, and a sequenced phase array is formed according to the sequence of the numerical values from small to large. The phase information classification module is used for classifying the phase arrays into two types, and specifically comprises the steps of utilizing the phase difference array module to make a difference on the sequenced phase arrays, namely subtracting the former value from the latter value to obtain the phase difference array, utilizing the index array module to compare each value in the phase difference array with phi c, wherein the phi c is a defined constant quantity, usually 10 degrees or 20 degrees is selected, and if the value is larger than the phi c, the index at the moment is output to obtain an index array. And inserting the 0 th bit of the index array into a value 0 by using an index difference array module, inserting the last bit into the value with the size of the well-ordered phase array to form a new index array, and subtracting the nth bit from the n +1 th bit of the new index array to obtain the index difference array. And finding out the maximum value and the sub-maximum value of the index difference array, finding out two elements of the index array which are worth corresponding at the moment, and classifying the sorted phase array according to the two elements. And splitting the phase array according to the two elements by using a phase array splitting module, wherein the phase information indexed between the two elements is classified into a first type, and other phase information is classified into a second type, so that the two types of classification processing are carried out on the phase array.
The amplitude information classification module is used for classifying the amplitude arrays, referring to the phase array classification mode, tracing back the original two-dimensional arrays after the phase arrays are classified, and searching the phase to find the amplitude information corresponding to the index, so that the amplitude arrays are classified, and two types of phase arrays and two types of amplitude arrays are obtained. The amplitude approximation class module is used for finding an approximation class of the amplitude by using the following calculation formula:
where N is the number of points in one of the amplitude arrays, and N isGeneral assemblyRepresenting the total number of points of the entire amplitude array. These two comparisons yield a density normalized value for the class of amplitude values. Mu represents the central discrete degree of the amplitude array, the specific algorithm of mu is to take the standard deviation at the center of the amplitude array, because the array sizes of the two types of amplitude arrays are not necessarily equal, the empirical constant 20 is taken, the array sizes of the two types of amplitude arrays are compared with 20, the minimum value x of the three numbers is taken, the x values of the amplitude array are respectively taken from the central index of each type of amplitude array, and the standard deviation of the x amplitudes is solved, namely the central discrete degree mu of the two types of amplitude arrays is respectively obtained1And mu2Obtaining the density sigma of two kinds of amplitude value arrays according to the formula1And σ2。
The phase information normalization module is used for performing normalization correction on the phase information corresponding to the amplitude approximation class, and specifically comprises the step of calculating the difference value of the phase center values of the amplitude approximation class
Reuse all phase values
Plus with
Since the correction may exceed the range of 0 ° to 360 °, the value is limited to 0 to 360 °. Due to the fact that
The range of the phase value after correction is between-360 and 720 degrees, and the range of the phase value after correction is limited to be between 0 and 360 degrees, and the range of the phase value after correction is limited by adopting a dispersion standardization method. Thus, the phase correction is completed.
The phase correction method and the correction system can correct the phase deviation on the spectrogram due to frequency fluctuation through software, stabilize the pulse point on the spectrogram in a fixed range, enable the spectrogram to be correctly displayed, provide correct spectrogram information for the next partial discharge mode identification function and improve the accuracy of electric leakage alarm.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (5)
1. A phase correction method for a self-adaptive partial discharge spectrogram is characterized by comprising the following steps:
firstly, acquiring pulse data by using a partial discharge pulse acquisition system, primarily processing the pulse data, and packaging the pulse data into an array form;
step two, sequencing the phase information, wherein the numerical value of the phase information is between 0 and 360 degrees, and sequencing the phase information from small to large to form a sequenced phase array;
step three, classifying the phase information, and dividing the sequenced phase arrays into two types;
the specific classification algorithm of the phase information is that the sorted phase arrays are subjected to subtraction, the former value is subtracted from the latter value to obtain a phase difference array, each value in the phase difference array is compared with phi c, wherein phi c is a defined constant number, 10 degrees or 20 degrees is selected, and if phi c is larger than phi c, the index at the moment is output to obtain an index array; inserting the 0 th bit of the index array into a value 0, inserting the last bit into the value with the size of the well-ordered phase array to form a new index array, and subtracting the nth bit from the n +1 th bit of the new index array to obtain an index difference array; finding out the maximum value and the sub-maximum value of the index difference array, finding out two elements of the index array corresponding to the maximum value and the sub-maximum value, and dividing the sorted phase array into two types according to the two elements;
step four, carrying out same classification on the amplitude arrays, tracing back to the original two-dimensional array in the step one after the phase array is classified, finding amplitude information corresponding to indexes by searching phases, and equally classifying the amplitude arrays into two types;
calculating the density of two kinds of amplitude value arrays of the standard spectrogram and the spectrogram to be corrected respectively, and searching for an amplitude value approximation class;
specifically, the method comprises the following steps of:
where N is the number of points in one of the amplitude arrays, and N isGeneral assemblyRepresenting the total number of points of the whole amplitude array, mu representing the central discrete degree of the amplitude array, the specific algorithm of mu is to take the standard deviation of the center of the amplitude class, take the empirical constant 20, compare the array size of the two classes of amplitude classes with 20, take the minimum value x of the three numbers, respectively take the x values of the amplitude classes from the center index of each class of amplitude array, and solve the x valuesStandard deviation, i.e. obtaining the central dispersion degree mu of two kinds of amplitude arrays1And mu2Obtaining the density sigma of two kinds of amplitude value arrays according to the formula1And σ2；
For both the standard spectrogram and the spectrogram to be corrected, calculating the densities sigma of the two amplitude classes and selecting the larger one of the two amplitude classes, wherein the amplitude class corresponding to the relatively larger value is the approximate class;
step six, carrying out normalization correction on the phase information corresponding to the amplitude approximation class;
specifically, the method comprises calculating the difference of the phase center values of the amplitude approximation class
Wherein
Is the first phase value of the phase signal,
is the last phase value, and then the sum of all phase values
Plus with
Due to the fact that
Is in the range of 0 deg. -360 deg., the corrected phase value is in the range of-360 deg. -720 deg., and the range of the corrected phase value is limited to 0 deg. to 360 deg. by means of dispersion normalization.
2. The adaptive partial discharge spectrogram phase correction method according to claim 1, wherein in step one, the array format is fixed, the first row is phase information, the second row is amplitude information, the third row is time for acquiring the pulse, and information in each column corresponds to one another.
3. The adaptive partial discharge spectrogram phase correction method of claim 1, wherein said two elements classify the ordered phase array according to the phase information indexed between said two elements being classified into a first class, the other phase information being classified into a second class, and the phase array being classified into two classes.
4. The adaptive partial discharge spectrogram phase correction method of claim 1, wherein said dispersion normalization method utilizes a transfer function:
y＝(z-min)/(max-min)
wherein max is the maximum value of the sample data, and min is the minimum value of the sample data; the value of the phase value range between-360 degrees and 720 degrees is z, the value of y is obtained through (z- (-360))/(720- (-360)) and is a value after standardization, the range is 0-1, then the value range of y is changed into 0-360 degrees through y x 360, and the phase value of the phase array is judged by the method and is in the measuring range.
5. An adaptive partial discharge spectrogram phase correction system, comprising:
the partial discharge pulse acquisition unit is used for acquiring partial discharge pulse data, packaging the partial discharge pulse data into an array form after preliminary processing, wherein the array form is fixed, the first row is phase information, the second row is amplitude information, and the third row is the time for acquiring the pulse;
the phase information sequencing module is used for sequencing the phase information, the numerical value of the phase information is between 0 and 360 degrees, and a sequenced phase array is formed according to the sequence of the numerical values from small to large;
a phase information classification module for classifying the phase array into two categories;
the phase information classification module comprises:
the phase difference array module is used for carrying out difference on the sequenced phase arrays, namely subtracting the former value from the latter value to obtain a phase difference array;
the index array module compares each value in the phase difference array with phi c, wherein phi c is a defined constant number, 10 degrees or 20 degrees is selected, and if the value is greater than phi c, the index at the moment is output to obtain an index array;
the index difference array module inserts the 0 th bit of the index array into a value 0, inserts the last bit into the value with the size of the well-ordered phase array to form a new index array, and subtracts the nth bit from the n +1 th bit of the new index array to obtain an index difference array; finding out the maximum value and the sub-maximum value of the index difference array, and finding out two elements of the index array corresponding to the maximum value and the sub-maximum value at the moment; and
the phase array splitting module splits the phase array according to the two elements, the phase information indexed between the two elements is classified into a first type, and other phase information is classified into a second type, so that the two types of classification processing are carried out on the phase array;
the amplitude information classification module is used for classifying the amplitude arrays, tracing the phase arrays to the original two-dimensional arrays after the phase arrays are classified according to the classification mode of the phase arrays, and searching the phase to find the amplitude information corresponding to the index, so that the amplitude arrays are classified in the same way, and two types of phase arrays and two types of amplitude arrays are obtained;
the amplitude approximation module is used for searching an approximation class of the amplitude, calculating the densities of two amplitude classes for the standard spectrogram and the spectrogram to be corrected and selecting the larger one of the two amplitude classes, wherein the amplitude class corresponding to the relatively larger value is the approximation class;
the following calculation formula is used:
where N is the number of points in one of the amplitude arrays, and N isGeneral assemblyRepresenting the total number of points of the whole amplitude array, obtaining the density normalization value of the amplitude by comparing the two values, wherein mu represents the central discrete degree of the amplitude array, and the specific algorithm of mu isTaking the standard deviation of the center of the amplitude class, because the array sizes of the two amplitude classes are not necessarily equal, taking the empirical constant 20, comparing the array sizes of the two amplitude classes with 20, taking the minimum value x of the three numbers, respectively taking the x values of the amplitude classes from the center index of each amplitude array, and calculating the standard deviation of the x amplitudes, namely respectively obtaining the center dispersion degree mu of the two amplitude arrays1And mu2Obtaining the density sigma of two kinds of amplitude value arrays according to the formula1And σ2；
The phase information normalization module is used for performing normalization correction on the phase information corresponding to the amplitude approximation class and performing range limitation on the corrected phase array by adopting a dispersion standardization method;
calculating the difference value of the phase center values of the amplitude approximation class by using the phase information normalization module
Wherein
Is the first phase value of the phase signal,
is the last phase value, and then the sum of all phase values
Plus with
Due to the fact that
Is in the range of 0 to 360, the corrected phase value is in the range of-360 to 720, and the range of the corrected phase value array is limited to 0 to 360 by the dispersion normalization method.
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