Fault indicator waveform compression method

Abstract

The invention provides a fault indicator waveform compression method, which comprises the following steps of: 1, regularly sampling data according to a set time interval, and carrying out noise reduction processing on each sampled value to obtain noise reduction data; 2, filtering the noise reduction data obtained in the step 1 to obtain filtered data; 3, performing incremental calculation on the filtering data obtained in the step 2, and comparing the filtering data with an old sampling value of a previous sampling period until a complete minimum-digit data sequence is obtained; and step 4, compressing the obtained fine noise reduction data by using a compression algorithm. The recording file transmission efficiency is improved through the waveform compression algorithm, and the problems that the recording file of the high-precision fault indicator is large and the transmission efficiency is low are solved.

Description

Technical Field

[0001] The present invention mainly relates to the technical field of fault indicators, and particularly relates to a waveform compression method for fault indicators. Background Art

[0002] Fault diagnosis, power quality analysis, and research on the operating state of the power grid during the operation of the power system all rely on power waveform recording data, and the acquisition of power waveforms is usually completed by waveform recording devices. The recording files of high-precision fault indicators are relatively large and the transmission efficiency is relatively low. It is necessary to study a waveform compression algorithm to improve the transmission efficiency of the recording files. Summary of the Invention

[0003] The present invention mainly provides a waveform compression method for fault indicators to solve the technical problems raised in the above background art.

[0004] The technical solution adopted by the present invention to solve the above technical problems is as follows:

[0005] A waveform compression method for fault indicators, comprising the following steps:

[0006] Step 1, regularly sample the data at a set time interval, and perform noise reduction processing on each sampled value to obtain noise-reduced data;

[0007] Step 2, perform filtering processing on the noise-reduced data obtained in Step 1 to obtain filtered data;

[0008] Step 3, perform incremental calculation on the filtered data obtained in Step 2, and compare it with the old sampled value of the previous sampling period until a complete minimum-bit data sequence is obtained;

[0009] Step 4, use a compression algorithm to compress the obtained highly noise-reduced data.

[0010] Further, in Step 1, the Kalman filtering method is used to perform noise reduction processing on each sampled value to obtain pre-noise-reduced data, and the pre-noise-reduced data is further processed by the wavelet transform method to obtain noise-reduced data, and an ideal waveform is further generated, so as to effectively separate the useful signal from the noise and obtain a clearer waveform image.

[0011] Further, in Step 1, wavelet decomposition is performed on the pre-noise-reduced data, appropriate wavelet basis functions and decomposition levels are selected, wavelet coefficients are calculated and normalized, and the pre-noise-reduced data is synthesized using the wavelet coefficients to update the noise-reduced data.

[0012] Further, in Step 2, the noise-reduced data obtained in Step 1 is filtered by a low-pass filter, a high-pass filter, a band-stop filter, an all-pass filter, and an adaptive filter.

[0013] Further, in step three, the new sampling value is compared with the old sampling value of the previous sampling period, and the minimum bit data sequence is updated.

[0014] Further, in step three, for each new sampling value, it is compared with the value sampled in the previous period to find the minimum of the two values, and it is stored in a local minimum value array. The current new sampling value is replaced with the first value of the local minimum value array, this value is deleted from the minimum value array, and the next element of the local minimum value array is added to the minimum value array. Repeat the above until all sampling values have been processed. Before generating the ideal waveform, the minimum value array is used as the input of the new sampling value. The new sampling value is compared with the old sampling value of the previous sampling period, and the minimum bit data sequence is updated.

[0015] Further, in step four, the compression methods include Huffman coding, run-length coding or DCT coding. The main purpose of these compression algorithms is to reduce the size of the data to save space during storage and transmission.

[0016] Further, in step four, the data is divided into multiple subtasks and assigned to multiple independent computing nodes for processing. In this way, each node can execute multiple subtasks simultaneously, thereby improving the overall parallelism.

[0017] Further, in step four, the subtasks are simultaneously executed on multiple parallel processing units to achieve data parallel compression processing. This can significantly improve the processing speed.

[0018] Further, in step four, the characteristics of each type of data are collected and analyzed. After compression, the data is decompressed and tested to ensure that the compression and decompression processes are correct. If it is found that the compression rate of some data does not increase significantly, or the compression and decompression time is too long, other compression techniques are tried. We can iterate this process repeatedly until the best data body format is found. This can be carried out by generating the original data set (compressed) and the compressed data set. The characteristics include the data volume, dynamics and complexity. This information will help us determine the most suitable compression technique for each type of data.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0020] First, aiming at the problems of large recording files, low transmission efficiency, low transmission success rate and large traffic consumption of high-precision transient recording fault indicators, the present invention conducts research on waveform compression methods, selects an appropriate compression rate according to the data characteristics of each cycle, and designs the compressed data body format for each cycle.

[0021] Second, the present invention improves the transmission efficiency of the recording wave file through a waveform compression algorithm to address the problem that the recording wave file of the high-precision fault indicator is relatively large and the transmission efficiency is relatively low.

[0022] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. Description of the Drawings

[0023] Figure 1 It is a flowchart of the present invention. Specific Embodiments

[0024] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant accompanying drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in different forms and is not limited to the embodiments described in the text. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0025] It should be noted that when an element is referred to as "fixedly provided on" another element, it can be directly on the other element or there can be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0026] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0027] Embodiment, please refer to the attached Figure 1 , a waveform compression method for a fault indicator, comprising the following steps:

[0028] Step 1: Regularly sample the data at a set time interval. For each sampled value, perform noise reduction processing to obtain noise-reduced data;

[0029] Step 2: Perform filtering processing on the noise-reduced data obtained in Step 1 to obtain filtered data;

[0030] Step 3: Perform incremental calculation on the filtered data obtained in Step 2 and compare it with the old sampled value of the previous sampling period until a complete minimum-bit data sequence is obtained;

[0031] Step 4: Use a compression algorithm to compress the obtained highly noise-reduced data.

[0032] Specifically, please refer to the appendix Figure 1 In the first step, for each sampled value, the Kalman filtering method is used for noise reduction processing to obtain pre-noise-reduced data. The pre-noise-reduced data is further processed by the wavelet transform method to obtain noise-reduced data, and an ideal waveform is further generated, so as to effectively separate the useful signal from the noise and obtain a clearer waveform image.

[0033] Furthermore, in the first step, the pre-noise-reduced data is wavelet decomposed, a suitable wavelet basis function and decomposition level are selected, the wavelet coefficients are calculated and normalized, and the pre-noise-reduced data is synthesized using the wavelet coefficients to update the noise-reduced data.

[0034] Specifically, please refer to the appendix Figure 1 In the second step, the noise-reduced data obtained in the first step is filtered by a low-pass filter, a high-pass filter, a band-stop filter, a full-response filter, and an adaptive filter.

[0035] Furthermore, the low-pass filter is mainly used to remove high-frequency noise. It allows only the lower-frequency sine wave components of the signal to pass through, while blocking or attenuating the higher-frequency noise components. A simple low-pass filter can be implemented by a first-order high-pass filter and a one-dimensional convolution kernel;

[0036] The high-pass filter is mainly used to eliminate low-frequency noise. It allows the lower-frequency sine wave components of the signal to pass through, while blocking or attenuating the higher-frequency noise components. A simple high-pass filter can be implemented by a first-order low-pass filter and a one-dimensional convolution kernel;

[0037] The band-stop filter can limit a specific frequency range. For example, a band-stop filter can retain low-frequency signals while eliminating high-frequency signals. A band-stop filter can be implemented by a one-dimensional convolution kernel;

[0038] The full-response filter is a relatively complex filter that can provide a higher cut-off frequency. It can eliminate all noise components above the cut-off frequency, but may cause data loss;

[0039] The adaptive filter can automatically adjust parameters according to the characteristics of the input signal and the noise characteristics to achieve the best filtering effect. The adaptive filter is usually designed based on objective functions such as the minimum mean square error (MMSE) or the multi-criterion maximization (MCME).

[0040] Specifically, please refer to the appendix Figure 1 In the third step, the new sampled value is compared with the old sampled value in the previous sampling period, and the minimum-bit data sequence is updated.

[0041] Further, in Step 3, for each new sampling value, compare it with the value sampled in the previous cycle, find the smaller of the two values, store it in a local minimum value array, replace the current new sampling value with the first value of the local minimum value array, delete this value from the minimum value array, add the next element of the local minimum value array to the minimum value array, and repeat the above until all sampling values have been processed. Before generating the ideal waveform, use the minimum value array as the input of the new sampling value, compare the new sampling value with the old sampling value of the previous sampling cycle, and update the minimum bit data sequence.

[0042] Specifically, please refer to the attached Figure 1 In Step 4, the compression methods include Huffman coding, run-length coding or DCT coding. The main purpose of these compression algorithms is to reduce the size of the data in order to save space during storage and transmission.

[0043] Further, in Step 4, divide the data into multiple subtasks and assign them to multiple independent computing nodes for processing. In this way, each node can execute multiple subtasks simultaneously, thereby improving the overall parallelism.

[0044] Further, in Step 4, execute the subtasks simultaneously on multiple parallel processing units to achieve data parallel compression processing. This can significantly improve the processing speed.

[0045] Further, in Step 4, collect and analyze the characteristics of each type of data. After compression is completed, perform a decompression test on the data to ensure that the compression and decompression processes are correct. If it is found that the compression rate of some data does not increase significantly, or the compression and decompression time is too long, then try using other compression techniques. We can iterate this process repeatedly until the best data body format is found. This can be done by generating the original data set (compressed) and the compressed data set. The characteristics include the data volume, dynamicity and complexity, and this information will help us determine the most suitable compression technique for each type of data.

[0046] The above has made an exemplary description of the present invention in conjunction with the accompanying drawings. Obviously, the specific implementation of the present invention is not limited by the above methods. As long as this non-substantial improvement is made using the method concept and technical solution of the present invention, or the concept and technical solution of the present invention are directly applied to other occasions without improvement, they are all within the protection scope of the present invention.

Claims

1. A fault indicator waveform compression method, characterized in that: The following steps are involved: Step 1: regularly sample the data at a set time interval, and perform noise reduction processing on each sampled value to obtain noise-reduced data; Step 2, filtering the noise reduction data obtained in step 1 to obtain filtered data; Step 3, performing incremental calculation on the filtered data obtained in step 2, and comparing it with the old sampling value of the previous sampling period, until a complete minimum bit data sequence is obtained; Step 4: Use a compression algorithm to compress the obtained refined noise reduction data.

2. A fault indicator waveform compression method according to claim 1, characterized in that: In the step 1, each sampled value is subjected to denoising processing by a Kalman filter method to obtain pre-denoising data, and the pre-denoising data is further processed by a wavelet transform method to obtain denoised data, thereby further generating an ideal waveform.

3. A fault indicator waveform compression method according to claim 1, characterized in that: In the step 1, the pre-denoised data is subjected to wavelet decomposition, a suitable wavelet basis function and decomposition layer number are selected, wavelet coefficients are calculated and normalized, and the pre-denoised data is synthesized using the wavelet coefficients to update the denoised data.

4. A method for compressing a fault indicator waveform according to claim 1, characterized in that: In the step 2, the noise reduction data obtained in the step 1 is filtered by a low-pass filter, a high-pass filter, a band-stop filter, a full response filter and an adaptive filter.

5. A fault indicator waveform compression method according to claim 1, characterized in that: In the step three, the new sampling value is compared with the old sampling value of the previous sampling cycle, and the minimum bit data sequence is updated.

6. A method for compressing a fault indicator waveform according to claim 1, characterized in that: In step three, for each new sample value, compare it with the value sampled in the previous cycle, find the smallest of the two values, and store it in a local minimum array, replace the current new sample value with the first value of the local minimum array, delete the value from the minimum array, add the next element of the local minimum array to the minimum array, repeat the above until all sample values ​​have been processed, and before generating the ideal waveform, use the minimum array as the input of the new sample value, compare the new sample value with the old sample value of the previous sampling cycle, and update the minimum bit data sequence.

7. A fault indicator waveform compression method according to claim 1, characterized in that: In the step 4, the compression method includes Huffman coding, run-length coding or DCT coding.

8. A fault indicator waveform compression method according to claim 1, characterized in that: In step 4, the data is divided into multiple subtasks and assigned to multiple independent computing nodes for processing. In this way, each node can execute multiple subtasks at the same time.

9. A method for compressing a fault indicator waveform according to claim 1, characterized in that: In the step 4, subtasks are executed simultaneously on multiple parallel processing units to achieve data parallel compression processing.

10. A fault indicator waveform compression method according to claim 1, characterized in that: In step 4, the characteristics of each type of data are collected and analyzed. After compression, the data is decompressed and tested. If it is found that the compression rate of some data is not significantly improved, or the compression and decompression time is too long, other compression technologies are tried. The characteristics include data volume, dynamics and complexity.

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