阅读说明：本技术 基于小波变换的变压器绕组ifra曲线去噪方法 (Transformer winding IFRA curve denoising method based on wavelet transformation ) 是由 赵仲勇 陈宇 谭珊 赵素涓 唐超 于 2019-11-19 设计创作，主要内容包括：本发明涉及一种基于小波变换的变压器绕组IFRA曲线去噪方法,属于电力设备检测技术领域。该方法包括以下步骤：步骤一,确定一个小波母函数；步骤二：确定小波母函数的层数并进行平移和伸缩得到一系列的小波序列,以此将信号分解为不同频率的分量；步骤三：进行对所要处理的原函数的小波变换,算出输入激励与输出响应的系数矩阵,即变换系数；步骤四：将尺度转换为伪频率；步骤五：将绕组的输入激励与输出响应的变换系数,根据定义,得到激励与响应信号幅度的频率域分布,再根据频率响应曲线的定义,获得绕组的脉冲频率响应曲线。本发明采用连续小波变换处理暂态信号,算法的运算速度较快。(The invention relates to a transformer winding IFRA curve denoising method based on wavelet transformation, and belongs to the technical field of power equipment detection. The method comprises the following steps: step one, determining a wavelet mother function; step two: determining the layer number of the wavelet mother function, translating and stretching to obtain a series of wavelet sequences, and decomposing signals into components with different frequencies; step three: performing wavelet transformation on the primitive function to be processed, and calculating a coefficient matrix of input excitation and output response, namely a transformation coefficient; step four: converting the scale to a pseudo frequency; step five: and obtaining the frequency domain distribution of the excitation and response signal amplitude according to the definition of the transformation coefficient of the input excitation and output response of the winding, and obtaining the pulse frequency response curve of the winding according to the definition of the frequency response curve. The invention adopts continuous wavelet transformation to process transient signals, and the arithmetic speed of the algorithm is higher.)

1. The transformer winding IFRA curve denoising method based on wavelet transformation is characterized by comprising the following steps: the method comprises the following steps:

step one, determining a wavelet mother function;

step two: determining the layer number of the wavelet mother function, translating and stretching to obtain a series of wavelet sequences, and decomposing signals into components with different frequencies;

step three: performing wavelet transformation on the primitive function to be processed, and calculating a coefficient matrix of input excitation and output response, namely a transformation coefficient;

step four: converting the scale to a pseudo frequency;

step five: and obtaining the frequency domain distribution of the excitation and response signal amplitude according to the definition of the transformation coefficient of the input excitation and output response of the winding, and obtaining the pulse frequency response curve of the winding according to the definition of the frequency response curve.

2. The transformer winding IFRA curve denoising method based on wavelet transformation as recited in claim 1, wherein: in said step one, the selection of the wavelet mother function ψ (t) must satisfy the descriptions of equations (1) to (3)

In the formula (I), the compound is shown in the specification,

3. The transformer winding IFRA curve denoising method based on wavelet transformation as claimed in claim 2, wherein: in the second step, the number of layers of the wavelet transform is determined, and then the wavelet transform obtains a series of wavelet sequences through translation and expansion of the mother wavelet function ψ (t), thereby decomposing the signal into different frequency components, which is described by equation (4)

Where b denotes a translation factor and a denotes a scale factor, and assuming that the scale a >0, b and a determine the position of the wavelet time-frequency window in the time domain and the frequency domain, respectively.

4. The transformer winding IFRA curve denoising method based on wavelet transformation as claimed in claim 3, wherein: in the third step, the definition formula (5) of the wavelet transformation is used,

the input excitation and output response time domain signals of the winding in the pulse frequency response method are respectively V_in(t) and R_out(t) performing a transform with successive wavelet transform coefficients WT_in(ω, b) and WT_out(ω,b)。

5. The transformer winding IFRA curve denoising method based on wavelet transformation as claimed in claim 4, wherein: in the fourth step, the scale is converted into pseudo frequency, and the conversion is performed according to the formula (6),

in the formula (f)_cFor the central frequency of the wavelet, one of the wavelets is describedGeneral characteristics; f. of_sIs the sampling rate of the signal; f. of_aThe actual frequency is often the pseudo frequency corresponding to the scale a.

6. The transformer winding IFRA curve denoising method based on wavelet transformation as recited in claim 5, wherein: in the fifth step, the transformation coefficient of the wavelet function in the third step is integrated in the time domain after the modulus of the wavelet coefficient is taken according to the definition of the marginal spectrum to obtain the frequency domain distribution of the excitation and response signal amplitude, and then the impulse frequency response curve H of the winding is obtained according to the definition of the frequency response curve_IFRAI.e. formula (7)

Judging whether the denoising effect of the pulse frequency response curve HIFRA is satisfactory or not, and if the denoising effect is satisfactory, ending the process; otherwise, turning to the step two, modifying the number of layers of the wavelet transformation, and repeating the steps until the final result is obtained.

Technical Field

The invention belongs to the technical field of power equipment detection, and relates to a transformer winding IFRA curve denoising method based on wavelet transformation.

Background

The transformer is not directly replaced after the transformer is damaged and the transformer is inspected and repaired because of the complicated structural characteristics and the expensive material for manufacturing the transformer. While the winding deformation contributes about one third in the event of a failure of the transformer. The large electrodynamic force generated by the interaction of the short-circuit current outside the transformer and the internal magnetic field is the main cause of winding deformation. The deformation of the winding in different degrees is also caused by the inefficacy of transformer transportation, aging of winding insulation materials, explosion of gas dissolved in oil, earthquake and the like. It does not have a very serious effect on the operation of the transformer from the outset, but rather is cumulative in that if these effects are not dealt with in a timely manner, they can cumulatively induce more severe distortion until the transformer is destroyed. In conclusion, the detection of the deformation of the transformer winding is significant for the stable operation of the power grid.

At present, researchers propose that an Impulse Frequency Response (IFRA) method is used for detecting the winding deformation fault of the transformer on line, pulse signals are injected into the transformer on line, and response signals are measured on line, so that the winding fault is diagnosed according to a signal spectrogram. However, when the amplitude of the injected pulse signal is limited, the signal-to-noise ratio of the response signal measured in the field is not high, and in the present stage, Fast Fourier Transform (FFT) is mostly directly adopted to transform the time domain signal to the frequency domain to construct the IFRA curve. In fact, the FFT algorithm is based on a sinusoidal signal, and its property determines that it is suitable for processing a stationary signal, while the signal of the impulse frequency response method is a transient abrupt signal, and using FFT easily causes spectrum leakage and inter-spectrum interference, and easily causes a barrier phenomenon to a short-time sampling signal, resulting in a lack of frequency resolution. Therefore, the IFRA curve obtained by the FFT method is seriously affected by noise, and is liable to cause misjudgment and missed judgment of diagnosis.

Disclosure of Invention

In view of the above, the present invention provides a transformer winding IFRA curve denoising method based on wavelet transformation, which has the advantages of good time-frequency localization characteristic, good denoising effect, fast operation speed, and the like.

In order to achieve the purpose, the invention provides the following technical scheme:

a transformer winding IFRA curve denoising method based on wavelet transformation comprises the following steps:

step one, determining a wavelet mother function;

step two: determining the layer number of the wavelet mother function, translating and stretching to obtain a series of wavelet sequences, and decomposing signals into components with different frequencies;

step three: performing wavelet transformation on the primitive function to be processed, and calculating a coefficient matrix of input excitation and output response, namely a transformation coefficient;

step four: converting the scale to a pseudo frequency;

step five: and obtaining the frequency domain distribution of the excitation and response signal amplitude according to the definition of the transformation coefficient of the input excitation and output response of the winding, and obtaining the pulse frequency response curve of the winding according to the definition of the frequency response curve.

Optionally, in step one, the selection of the mother wavelet function ψ (t) must satisfy the descriptions of equations (1) to (3)

In the formula (I), the compound is shown in the specification,

is a Fourier transform of ψ (t).

Optionally, in the second step, the number of layers of the wavelet transform is determined, and then the wavelet transform obtains a series of wavelet sequences through translation and expansion of the mother wavelet function ψ (t), thereby decomposing the signal into different frequency components, which is described by equation (4)

Where b denotes a translation factor and a denotes a scale factor, and assuming that the scale a >0, b and a determine the position of the wavelet time-frequency window in the time domain and the frequency domain, respectively.

Optionally, in step three, using wavelet transform definition formula (5),

the input excitation and output response time domain signals of the winding in the pulse frequency response method are respectively V_in(t) and R_out(t) performing a transform with successive wavelet transform coefficients WT_in(ω, b) and WT_out(ω,b)。

Optionally, in the fourth step, the scale needs to be converted into a pseudo frequency, and the conversion is performed according to equation (6),

in the formula (f)_cThe general characteristics of the wavelet are described for the center frequency of the wavelet; f. of_sIs the sampling rate of the signal; f. of_aThe actual frequency is often the pseudo frequency corresponding to the scale a.

Optionally, in the fifth step, the transform coefficient of the wavelet function in the third step is defined according to the marginal spectrum, the wavelet coefficient is subjected to modulo and then integrated in the time domain to obtain the frequency domain distribution of the excitation and response signal amplitudes, and then the impulse frequency response curve H of the winding is obtained according to the definition of the frequency response curve_IFRAI.e. formula (7)

Judging whether the denoising effect of the pulse frequency response curve HIFRA is satisfactory or not, and if the denoising effect is satisfactory, ending the process; otherwise, turning to the step two, modifying the number of layers of the wavelet transformation, and repeating the steps until the final result is obtained.

The invention has the beneficial effects that:

1. the wavelet transform is a multi-resolution analysis, has good time-frequency localization characteristics, is naturally converged compared with the FFT algorithm with the basis of a sine function, and is more suitable for processing excitation and response transient signals of the transformer.

2. The wavelet transformation is adopted to prevent the inter-spectrum interference and the barrier phenomenon caused by transient sudden change signal processing, reduce the deficiency of frequency resolution, enable the pulse frequency response curve to be smoother and clearer, and be less influenced by noise, and the denoising effect of the method is better.

3. The transient signal is processed by adopting continuous wavelet transformation, and the arithmetic speed of the algorithm is higher.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the means of the instrumentalities and combinations particularly pointed out hereinafter.

Drawings

For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:

FIG. 1 is a flow chart of the method of the present invention;

FIG. 2 shows the de-noising effect of the IFRA curve of the transformer winding according to the invention.

Detailed Description

The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.

Wherein the showings are for the purpose of illustrating the invention only and not for the purpose of limiting the same, and in which there is shown by way of illustration only and not in the drawings in which there is no intention to limit the invention thereto; to better illustrate the embodiments of the present invention, some parts of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product; it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.

The same or similar reference numerals in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there is an orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "front", "rear", etc., based on the orientation or positional relationship shown in the drawings, it is only for convenience of description and simplification of description, but it is not an indication or suggestion that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and therefore, the terms describing the positional relationship in the drawings are only used for illustrative purposes, and are not to be construed as limiting the present invention, and the specific meaning of the terms may be understood by those skilled in the art according to specific situations.

FIG. 1 is a flow chart of the process of the present invention comprising the steps of: step one, determining a wavelet mother function. Step two: and determining the layer number of the wavelet mother function, translating and stretching to obtain a series of wavelet sequences, and decomposing the signal into components with different frequencies. Step three: wavelet transform is performed on the primitive function to be processed, and a coefficient matrix (transform coefficient) of the input excitation and output response is calculated. Step four: converting said scale to a pseudo frequency. Step five: and obtaining the frequency domain distribution of the excitation and response signal amplitude according to the definition of the transformation coefficient of the input excitation and output response of the winding, and obtaining the pulse frequency response curve of the winding according to the definition of the frequency response curve.

Wherein, in step one, the wavelet mother function ψ (t) is selected to satisfy three expressions described by the following expressions (1) to (3),

in the formula (I), the compound is shown in the specification,

a Fourier transform of ψ (t);

in step two, the number of layers of the wavelet transform is determined, and then the wavelet transform obtains a series of wavelet sequences through the translation and expansion of the mother wavelet functions ψ (t), thereby decomposing the signal into different frequency components, which can be described by the following formula (4),

in the formula, b represents a translation factor, a represents a scale factor, and assuming that the scale a is greater than 0, b and a respectively determine the positions of a time-frequency window of the wavelet in a time domain and a frequency domain;

further, the definition of the wavelet transform in step three is shown in formula (5),

the input excitation and output response time domain signals of the winding in the pulse frequency response method are respectively V_in(t) and R_out(t) performing a transform with successive wavelet transform coefficients WT_in(ω, b) and WT_out(ω,b)。

In step four, since the wavelet transform is performed in the scale domain, it is necessary to convert the scale into pseudo frequencies, which can be converted according to the following equation (6),

in the formula (f)_cThe general characteristics of the wavelet are described for the center frequency of the wavelet; f. of_sIs the sampling rate of the signal; f. of_aA pseudo frequency corresponding to the scale a, often referred to as the actual frequency;

in the fifth step, the transform coefficient of the wavelet function in the third step is integrated in the time domain after the modulus of the wavelet coefficient is taken according to the definition of the marginal spectrum to obtain the frequency domain distribution of the excitation and response signal amplitude, and then the impulse frequency response curve H of the winding is obtained according to the definition of the frequency response curve_IFRAAs shown in the following formula (7),

judging whether the denoising effect of the pulse frequency response curve HIFRA is satisfactory or not, and if the denoising effect is satisfactory, ending the process; otherwise, turning to the step two, modifying the number of layers of the wavelet transformation, and repeating the steps until the final result is obtained.

FIG. 2 shows the denoising effect of the IFRA curve of the phase A winding on the high-voltage side of a 10kV three-phase transformer, and the results are compared by using the FFT method.

Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.