CN114019236A - Power grid harmonic single-channel aliasing target signal detection method and device - Google Patents
Power grid harmonic single-channel aliasing target signal detection method and device Download PDFInfo
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Abstract
The application discloses a power grid harmonic single-channel aliasing target signal detection method, which comprises the following steps: oversampling is carried out on the received single-channel aliasing signals, the sampled signals are segmented, segmented Fourier transform is carried out on each segment of signals, and the obtained segmented frequency spectrums are arranged according to the time of each segment of signals, so that time-frequency domain data distribution is obtained; obtaining frequency values corresponding to the peak values of all the frequency ranges, forming time-varying data of the peak frequency, and calculating the frequency deviation degree of the time-varying data of the peak frequency; and judging whether the target signal exists or not according to the frequency offset degree. The application also includes a device implementing the method. The method and the device solve the problem of how to detect the target signal under the condition of power grid harmonic aliasing.
Description
Technical Field
The invention relates to the technical field of radio signal processing, in particular to a target detection method for aliasing target signals in power grid harmonic waves, which can be used for single-channel target signal detection.
Background
When a single channel receiver is used to receive a mixed signal, for example, in the following operating scenarios: shallow stratum profile data acquisition, biomedical signal processing, voice signal processing, image processing, state monitoring and fault diagnosis of a mechanical system and the like, target signals and noise signals are mixed together, sometimes, the noise signals also present obvious spectral domain characteristics,
in particular, in a power grid system, some background clutter originates from a power generation source, and due to the inherent nonlinearity of the power generation source in the power grid, various harmonics generated due to item shifting operation of a rectifying device in electric equipment and the like, the characteristic frequency of a target signal is just overlapped with the harmonics in the power grid, and the two harmonics cannot be distinguished, so that the existing target detection method such as constant false alarm probability detection is difficult to apply. In addition, since the multiple peak frequencies of the grid harmonics change slowly with time, and a long-time observation is required, the amount of data to be processed is also very large, and the efficiency of identifying the harmonics is also low.
Disclosure of Invention
The invention aims to provide a method and a device for detecting a harmonic single-channel aliasing target signal of a power grid, which solve the problem of how to detect the target signal under the condition of power grid harmonic aliasing.
The single-channel target detection method provided by the embodiment of the application judges whether a target signal appears by calculating the frequency deviation degree of an aliasing signal at a set frequency point.
Specifically, the received signals are subjected to segmented Fourier transform, the obtained results are spliced in a frequency domain, the rule that the peak value information changes along with time is extracted, and the frequency offset degree of the aliasing signals at a specific frequency point is obtained.
Because the frequency of the target signal has high stability, the embodiment of the application further utilizes the difference between the frequency offset characteristic of the power grid harmonic and the frequency offset characteristic of the target signal to distinguish the target signal.
The embodiment of the application also provides a device, a memory and a computer system for realizing the method.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
the single-channel mixed data is subjected to segmented fast Fourier transform processing, spliced and displayed in a frequency domain, and the condition that a specific frequency point of an aliasing signal changes along with time can be visually seen; in addition, because the power grid harmonic signal is a slowly-changing signal and needs to be observed for a long time, the method and the device use a segmented Fourier transform method to process each segment of data in parallel, save processing time and improve calculation efficiency.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a graph of background clutter signal phase over time;
FIG. 2 is a phase of a target signal over time;
FIG. 3 is a flow chart of an embodiment of the method of the present invention;
FIG. 4 is a schematic of a segmented Fourier transform;
FIG. 5 is a graph of the result of a segmented Fourier transform of a background clutter signal;
FIG. 6 is a graph of the results of a segmented Fourier transform of a target signal;
FIG. 7 is a graph of the degree of frequency offset versus frequency;
fig. 8 is an example of the structure of the device of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. 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 application.
In order to achieve the purpose, the invention provides a target signal detection method for a harmonic single-channel aliasing signal of a power grid.
The invention provides a method for detecting a harmonic single-channel aliasing target signal of a power grid, wherein the target signal refers to a signal with certain prior information, and a specific application scenario is that a certain device can generate a signal with stable frequency when being electrified to work.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
In order to detect a target signal in a single-channel aliasing signal, a long-time analysis needs to be performed on an acquired electromagnetic signal, so that prior cognition of a power grid harmonic signal and the target signal is obtained. Long-time analysis and experiments on single-channel electromagnetic signals show that the power grid harmonic wave has the characteristic of slow change, and the phase of a corresponding characteristic frequency signal shows nonlinear change along with slow time; and the stability of the characteristic frequency of the target signal is high, and the phase of the corresponding characteristic frequency signal is linearly changed or unchanged along with slow time. Fig. 1 and fig. 2 are the results of continuous observation of the background clutter signal and the target signal for 6 hours, respectively, and it can be seen from the graphs that the phase of the background clutter signal shows nonlinear change with time, which indicates that the frequency stability is poor, and the phase of the target signal shows approximately linear change with time, which indicates that the frequency stability is high.
According to the signal characteristics, the method for detecting the harmonic single-channel aliasing target signal of the power grid achieves the purpose of target detection by analyzing the amplitude difference of the frequency change of the harmonic single-channel aliasing target signal and the frequency change along with time. In order to achieve the purpose, the technical scheme adopted by the invention is shown in fig. 3 and comprises the following steps 11-15:
step 11: oversampling a received single-channel aliasing signal at a sampling frequency fsGenerating a sampling signal;
step 12: dividing a sampling signal with the length of N into L sections, wherein the length of each section of signal is m, and the value of m is sampling frequency fsIs an integral multiple of (1), wherein the number of segments L, the length of the segments m, and the sampling frequency are fsAre the operating parameters of the method or apparatus of the present application.
Step 13: and (3) performing fast Fourier transform on each section of signals in the step (12), and arranging the obtained segmented frequency spectrums in a time domain according to the time of each section of signals to obtain time-frequency domain data distribution, as shown in FIG. 4. That is, the segmented fourier transform of the present application is to perform fast fourier transform on each segmented signal.
Note that the difference between the segmented fourier transform and the short-time fourier transform (STFT) is described. For the calculation of STFT, a time window t is first selected1To t2The FFT of this segment is performed, the result is denoted as s1, then the time window is slid and t is selected1To t2' (Window duration t2-t1＝t2’-t1') and the result is recorded as s2, … … window is continuously slid and calculated in sequence, the time length of one time of window sliding is time resolution, and the time length and the time resolution of the window are respectively and independently configured. The segmented Fourier transform method provided by the patent firstly segments data according to a set segment length, then simultaneously carries out FFT on each segment of data, and finally splices the obtained segmented FFT result in a time-frequency domain to obtain corresponding time-frequency distribution. In conclusion, the parallel computing mode provided by the patent saves time for the situation of large data volume, and greatly improves the computing efficiency.
Fig. 5 is a result of a segmented fast fourier transform of the background clutter signal, fig. 6 is a result of a segmented fast fourier transform of the target signal, and a comparison between fig. 5 and fig. 6 shows that there is a significant difference in frequency stability.
The main frequency components of the grid harmonic signals are 50Hz and odd multiples of 50 Hz. In both FIG. 5 and FIG. 6, m is 2fsThe results obtained.
It should be noted that, the main focus of the patent is on the relationship of the frequency variation with time at the frequency point overlapping with the target signal, as an example, the target signal is located in the frequency range of 1340 to 1360Hz, so fig. 5 to 6 only show the partial time-frequency domain data distribution in the frequency range of 1340 to 1360Hz, and not the whole time-frequency domain data of the received signal.
And 14, after performing segmented fast Fourier transform on the aliasing signal, obtaining frequency values corresponding to the peak values of each segment of the frequency spectrum to form time-varying data of peak frequency, and calculating the frequency offset degree of the time-varying data of the peak frequency.
The degree of frequency shift may be a statistical parameter such as mean or standard deviation.
For example, the calculation formula of the degree of frequency shift is as follows:
where a is a random variable consisting of N scalar observations, μ is the mean of a, i.e.:
and step 15, judging whether the target signal exists according to the frequency offset degree. For example, a frequency offset threshold is set, and when the degree of frequency offset is smaller than the frequency offset threshold, the target signal is determined to be present. The center frequency of the target signal is μ.
And detecting whether the target signal exists at the specific frequency point according to the characteristic that the target signal is relatively stable, for example, setting a threshold value for judging whether the target signal exists to be 0.1, and if the calculated deviation degree is less than 0.1, indicating that the target signal is detected, otherwise, not detecting the target signal.
Fig. 7 is a deviation degree of each odd harmonic frequency in the power grid with time, wherein the higher the deviation degree of the frequency is, the more unstable the frequency component corresponding to the signal is, and it can be seen from the graph that as the number of the odd harmonics increases, the frequency deviation amplitude of the odd harmonics almost linearly increases, and the frequency deviation amplitude of the odd harmonics suddenly decreases between the frequencies 1200 to 1400Hz, which is related to the high stability of the frequency of the target signal.
To further improve processing efficiency, the segmented Fourier computation routine may be trained using known clutter data and target signal data to achieve optimal computational efficiency before performing the above example steps 11-15 on the real-time signal. Specifically, the sampling frequency, the segment length, and the number of segments are changed, clutter data and target signal data are processed, and the minimum calculation configuration is obtained when the frequency offset degree is not changed. The term "unchanged" in this application means less than the stated range.
In order to further improve the processing efficiency, in the step 13 of the segmented fourier transform, when at least a part of the segmented data is subjected to fourier transform, a peak frequency of the part of the segmented data is obtained, the steps 14 to 15 are executed to calculate the frequency offset degree, and whether the aliasing signal contains the target signal is determined according to the frequency offset threshold. The selection of the part of the segments may be k segments randomly selected from the L segments, or k segments selected according to a set order. Preferably, when the number k of segments participating in the calculation increases, the steps 14-15 are repeated to calculate the frequency offset degree, and the calculation is stopped when the single-channel aliasing signal is determined to contain the target signal.
In order to further improve the processing efficiency, in the step 13 of the segmented fourier transform, because the power grid harmonic signal is a signal slowly changing along with time, the signal is firstly acquired for a long time, the long time is realized by using a set time length, and then the received long-time observation data is segmented and processed in parallel by using the segmented fast fourier transform technical scheme.
Fig. 8 is an example of the structure of the device of the present application. Further, the application also comprises a power grid harmonic single-channel aliasing target signal detection device which comprises an acquisition module 81, a transformation module 82 and a comparison module 83 which are connected in sequence. The data acquisition device is used for sampling the received single-channel aliasing signal; the transformation module is used for carrying out segmented Fourier transformation processing on the sampling signal to obtain time-frequency domain distribution data so as to obtain the peak frequency of each section of frequency spectrum; and the comparison module is used for calculating the frequency deviation degree according to the peak frequency time-varying data and judging a target signal.
Further preferably, the detection apparatus further comprises a training module 84 for changing the sampling frequency, the segment length, and the number of segments using the known clutter data and target signal data, and processing the clutter data and the target signal data to obtain a minimum calculation configuration with a constant frequency offset. The minimum calculation configuration comprises at least one parameter of minimum sampling frequency, minimum segment length and minimum segment number, and is used for setting working parameters of the transformation module. The training module, when operating, obtains a degree of frequency offset from the comparison module.
According to the embodiment, the target signal detection is realized by utilizing the prior information of the background clutter and the target signal, and the time-frequency analysis is carried out on the received single-channel aliasing signal through experimental verification, so that the calculation efficiency is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application therefore also proposes a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of the embodiments of the present application.
Further, the present application also proposes an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any of the embodiments of the present application.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The computer program instruction firstly carries out segmentation processing on a single-channel aliasing signal received by a receiver, then simultaneously carries out fast Fourier transform on each segmented sub-signal, and splices the obtained results in a frequency domain to obtain a time-frequency analysis curve of the single-channel aliasing signal, and then judges whether a target signal appears or not by calculating the frequency offset degree of the time-frequency analysis curve at a specific frequency point.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A power grid harmonic single-channel aliasing target signal detection method is characterized by comprising the following steps:
oversampling a received single-channel aliasing signal, and dividing the sampling signal into L sections;
performing segmented Fourier transform on each segment of signal, and arranging the obtained segmented frequency spectrum according to the time of each segment of signal to obtain time-frequency domain data distribution;
obtaining frequency values corresponding to the peak values of all the frequency ranges, forming time-varying data of the peak frequency, and calculating the frequency deviation degree of the time-varying data of the peak frequency;
and judging whether the target signal exists or not according to the frequency offset degree.
2. The power grid harmonic single-channel aliasing target signal detection method of claim 1, wherein a frequency offset threshold is set, and when the frequency offset degree is less than the frequency offset threshold, the target signal is determined to exist.
3. The power grid harmonic single channel aliasing target signal detection method of claim 1, further comprising the steps of:
the known clutter data and target signal data are used for training a segmented Fourier calculation program, the sampling frequency, the segment length and the segment number are changed, the clutter data and the target signal data are processed, and the minimum calculation configuration when the frequency offset degree is unchanged is obtained.
4. The power grid harmonic single-channel aliasing target signal detection method of claim 1, wherein when at least a part of segmented data is subjected to Fourier transform, a peak frequency of a part of segmented data is obtained, a frequency offset degree is calculated, and whether an aliasing signal contains a target signal is determined according to a frequency offset threshold.
5. The power grid harmonic single-channel aliasing target signal detection method of claim 4, wherein the selection of the part of the segments is k segments selected randomly from L segments or k segments selected according to a set order; when the number k of segments participating in the calculation increases, the frequency offset degree is repeatedly calculated.
6. The power grid harmonic single-channel aliasing target signal detection method according to claim 1, characterized in that a signal is acquired for a long time, the long time is realized by a set time length, and then the received long-time observation data is processed in a segmented parallel mode through a segmented fast Fourier transform technical scheme.
7. A power grid harmonic single-channel aliasing target signal detection device is used for realizing the method of any one of claims 1-6, and is characterized by comprising an acquisition module, a transformation module and a comparison module which are sequentially connected;
the data acquisition device is used for sampling the received single-channel aliasing signal; the transformation module is used for carrying out segmented Fourier transformation processing on the sampling signals to obtain time-frequency domain distribution data; and the comparison module is used for calculating the frequency deviation degree according to the peak frequency time-varying data and judging a target signal.
8. The grid harmonic single channel aliased target signal detection device of claim 7 further comprising a training module for using known clutter data and target signal data to change sampling frequency, segment length, and number of segments, and processing the clutter data and the target signal data to obtain a minimum calculation configuration with a constant degree of frequency offset; the minimum calculation configuration comprises at least one parameter of minimum sampling frequency, minimum segment length and minimum segment number, and is used for setting working parameters of the transformation module.
9. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method of any one of claims 1 to 6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method according to any of claims 1 to 6 when executing the computer program.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115542101A (en) * | 2022-11-30 | 2022-12-30 | 杭州兆华电子股份有限公司 | Voiceprint preprocessing method of transformer voiceprint detection system |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1245897A (en) * | 1998-08-26 | 2000-03-01 | 贺守正 | Harmonic quantitative measurement method of power system and its measurement instrument |
| US20150219756A1 (en) * | 2014-01-31 | 2015-08-06 | Fujitsu Ten Limited | Radar apparatus |
| US20160103216A1 (en) * | 2013-04-03 | 2016-04-14 | The Boeing Company | Using frequency diversity to detect objects |
| CN105717359A (en) * | 2016-02-19 | 2016-06-29 | 云南电网有限责任公司电力科学研究院 | Harmonic analysis algorithm |
| CN107222226A (en) * | 2016-03-21 | 2017-09-29 | 华为技术有限公司 | The method and apparatus of harmonic carcellation interference |
| CN109581045A (en) * | 2018-12-24 | 2019-04-05 | 中国船舶重工集团公司第七〇九研究所 | A kind of m-Acetyl chlorophosphonazo power measurement method meeting IEC standard frame |
| CN111404853A (en) * | 2020-03-13 | 2020-07-10 | 西安电子科技大学 | Carrier frequency offset estimation method, device and computer storage medium |
| CN111708006A (en) * | 2020-05-28 | 2020-09-25 | 哈尔滨工程大学 | Target line spectrum detection method suitable for unmanned platform detection sonar |
| CN111782861A (en) * | 2020-06-12 | 2020-10-16 | Oppo广东移动通信有限公司 | Noise detection method and device and storage medium |
| CN112462355A (en) * | 2020-11-11 | 2021-03-09 | 西北工业大学 | Sea target intelligent detection method based on time-frequency three-feature extraction |
-
2021
- 2021-10-12 CN CN202111189155.0A patent/CN114019236A/en active Pending
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1245897A (en) * | 1998-08-26 | 2000-03-01 | 贺守正 | Harmonic quantitative measurement method of power system and its measurement instrument |
| US20160103216A1 (en) * | 2013-04-03 | 2016-04-14 | The Boeing Company | Using frequency diversity to detect objects |
| US20150219756A1 (en) * | 2014-01-31 | 2015-08-06 | Fujitsu Ten Limited | Radar apparatus |
| CN105717359A (en) * | 2016-02-19 | 2016-06-29 | 云南电网有限责任公司电力科学研究院 | Harmonic analysis algorithm |
| CN107222226A (en) * | 2016-03-21 | 2017-09-29 | 华为技术有限公司 | The method and apparatus of harmonic carcellation interference |
| CN109581045A (en) * | 2018-12-24 | 2019-04-05 | 中国船舶重工集团公司第七〇九研究所 | A kind of m-Acetyl chlorophosphonazo power measurement method meeting IEC standard frame |
| CN111404853A (en) * | 2020-03-13 | 2020-07-10 | 西安电子科技大学 | Carrier frequency offset estimation method, device and computer storage medium |
| CN111708006A (en) * | 2020-05-28 | 2020-09-25 | 哈尔滨工程大学 | Target line spectrum detection method suitable for unmanned platform detection sonar |
| CN111782861A (en) * | 2020-06-12 | 2020-10-16 | Oppo广东移动通信有限公司 | Noise detection method and device and storage medium |
| CN112462355A (en) * | 2020-11-11 | 2021-03-09 | 西北工业大学 | Sea target intelligent detection method based on time-frequency three-feature extraction |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115542101A (en) * | 2022-11-30 | 2022-12-30 | 杭州兆华电子股份有限公司 | Voiceprint preprocessing method of transformer voiceprint detection system |
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