CN114374411A - Low-frequency power line carrier topology identification method - Google Patents
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Abstract
The invention discloses a low-frequency power line carrier topology identification method, which comprises the following steps: step 1: continuously sampling continuous voltage, and simultaneously processing the sampling data at the previous moment in parallel in a pipeline mode; step 2: carrying out high-density data analysis on the sampled data by adopting a method based on an FFT convolutional neural network; and step 3: averaging the analysis results of the plurality of sliding window data, and taking the averaged frequency spectrum module value as a judgment basis of the carrier signal; and 4, step 4: step classification is carried out according to the frequency spectrum module value amplitude of the signal, and 16-bit modulation signal codes are output; the signal detection mode based on the FFT convolutional neural network can utilize the effective duration of the modulation signal to the maximum extent, and improves the accuracy of topology identification monitoring while reducing the interference of power line noise on the modulation signal by using a mode of combining a digital filter and a multi-sliding-window FFT average.
Description
Technical Field
The invention belongs to the technical field of power line carrier communication, and particularly relates to a low-frequency power line carrier topology identification method.
Background
The power line carrier communication is a mode of utilizing a power line as a high-frequency signal to communicate in a transmission channel, and the high-frequency weak current signal and the low-frequency large current are transmitted together without laying a special power supply circuit, so that the power line carrier communication is widely applied to various fields such as intelligent homes, intelligent buildings, automatic meter reading and the like. In the field of power line carrier communication, the network topology analysis is to divide a bus and an electric island according to the on-off state of a power grid, and the technology is a basic module of power grid dispatching automation, an energy management system and a power distribution management system. The traditional topology identification technology generally carries out identification by matching a switch network with a specific voltage and current detection module, and the transmitted information is limited, so that the quick update of topology information is not facilitated. The carrier communication mode can realize richer information transmission, but the detection of the carrier signal is relatively complex, and the modulated carrier signal is strongly interfered due to strong noise on a power line.
At present, the carrier signal is mainly analyzed in an FFT mode, and the traditional FFT carrier parameter measurement method has the defect of sacrificing frequency resolution as a cost and has strict limits on the number of points and the number of cycles of data sampling.
Therefore, how to realize carrier detection with high frequency resolution and high detection accuracy is a problem to be solved in the prior art of the power line topology identification system.
Disclosure of Invention
The invention aims to provide a low-frequency power line carrier topology identification method to solve the problems in the background technology and realize high-speed and accurate topology identification of a power line network.
In order to achieve the purpose, the invention provides the following technical scheme: a low-frequency power line carrier topology identification method comprises the following steps:
step 1: continuously sampling continuous voltage, and simultaneously processing the sampling data at the previous moment in parallel in a pipeline mode;
step 2: carrying out high-density data analysis on the sampled data by adopting a method based on an FFT convolutional neural network;
and step 3: averaging the analysis results of the plurality of sliding window data, and taking the averaged frequency spectrum module value as a judgment basis of the carrier signal;
and 4, step 4: and carrying out step classification according to the magnitude of the frequency spectrum modulus of the signal and outputting a 16-bit modulation signal code.
Preferably, the analysis result measured at the start of the operation is a system noise level value.
Preferably, when the spectrum modulus is larger than the system noise amplitude, the carrier modulation signal is considered to arrive.
Preferably, the average value of the adjacent sliding window data is used as the decision basis.
Preferably, the data sliding window sequence number when the signal triggering determination is recorded, the windows corresponding to the 16-bit effective signals respectively are determined according to the duration of the signal and the time length of the window, and the frequency spectrum module values obtained by data processing are recorded in sequence.
Preferably, the high-low step classification is performed according to the amplitude of the signal spectrum modulus, a data bit with the amplitude higher than the system noise modulus is judged as 1, and a data bit with the amplitude lower than the system noise modulus is judged as 0.
Preferably, the convolution kernel in the convolution neural network based on FFT includes two digital signal processing means of digital filter and FFT.
Preferably, the sliding window processed data needs to be subjected to a digital filter to suppress out-of-band noise, and then subjected to FFT.
Preferably, take
The modulus at the frequency is used as the basis for deciding the carrier signal, wherein (a)f c ) For a carrier modulated signal, (f 0 ) Is a power line.
Preferably, the data amount of each sliding window is:
whereinithe number of windows is indicated and is,
the data offset for each of the slips is represented,
representing the amount of data processed per sliding window.
The technical effects and advantages of the invention are that the low-frequency power line carrier topology identification method comprises the following steps:
1. the signal detection mode based on the FFT convolutional neural network can utilize the effective duration of the modulation signal to the maximum extent;
2. according to the invention, a mode of combining a digital filter and a multi-sliding window FFT average is adopted, so that the interference of power line noise on a modulation signal is reduced, and the accuracy of topology identification monitoring is improved;
3. the invention realizes the parallel operation of data sampling and data processing by using a pipeline mode, greatly reduces the requirement of a system on hardware resources, can realize topology identification by only using a single chip, and greatly controls the system cost.
Drawings
FIG. 1 is a flow chart of a topology identification method of the present invention;
fig. 2 illustrates a data sampling and processing method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to fig. 1-2 in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all 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 invention.
The invention provides a method for identifying a low-frequency power line carrier topology as shown in figures 1-2, which comprises the following steps:
step 1: in order to detect the carrier modulation signal which is possible to arrive at any time on the power line, continuous voltage sampling is continuously carried out when the system is started, and simultaneously, the sampling data at the previous moment are processed in parallel in a pipeline mode.
In practical cases, the system can perform continuous voltage acquisition through three channels of the ADC.
Step 2: because the sampling data is continuous in time, the sampling data is subjected to high-density data analysis by adopting a method based on an FFT convolutional neural network.
And step 3: the analysis results of a plurality of sliding window data are averaged, so that the interference of random noise on the power line can be reduced to a great extent, the technical defect caused by the fact that a single sliding window is adopted in convolution of a traditional convolution neural network is avoided, and then the averaged frequency spectrum modulus value is used as the judgment basis of the carrier signal.
In practical situations, by reading the spectrum modulus value of each channel at a specific frequency, the average value of the four adjacent sliding window data can be taken as a decision basis as required.
Specifically, the system is started, the analysis result measured when the system starts to work is the system noise level value, and when the frequency spectrum module value is larger than the system noise amplitude, the carrier modulation signal is considered to arrive.
At this time, the data sliding window sequence number when the signal triggering judgment is recorded and the signal coming edge are strictly aligned, windows respectively corresponding to the next 16-bit effective signals are determined according to the duration of each bit signal and the time length of the window, and each bit frequency spectrum module value obtained by data processing is recorded in sequence.
And 4, step 4: step classification is carried out according to the frequency spectrum module value amplitude of the signal, and 16-bit modulation signal codes are output;
specifically, high-low step classification is performed according to the amplitude of the signal spectrum modulus, a data bit with the amplitude higher than the system noise modulus is judged as 1, and a data bit with the amplitude lower than the system noise modulus is judged as 0.
In the invention, high-low step classification is carried out according to the magnitude of each signal spectrum module value, in the actual judgment, the data bit with the amplitude value obviously higher than the system noise module value is judged to be 1, otherwise, the data bit is judged to be 0, and finally, 16-bit modulation signal codes are output for one time.
Specifically, the average value of the adjacent sliding window data is used as a decision basis.
Specifically, the convolution kernel in the convolution neural network based on the FFT includes two digital signal processing means, namely a digital filter and the FFT.
In the invention, the data processing method is realized by carrying out high-density analysis and detection on signals by using a neural network method based on FFT convolution, wherein a convolution kernel mainly comprises two digital signal processing means of a digital filter and FFT, and the influence of random noise is reduced by averaging the data analysis results of a plurality of sliding windows, thereby ensuring the accurate analysis and judgment of carrier signals.
As shown in the sliding window data processing of fig. 2:
the data of the windows is system background noise, and a lower background noise amplitude value can be obtained by averaging the frequency spectrum analysis results of the window signals;
⑬ - ⑯ windows of data are effective carrier signals, and a more stable carrier signal amplitude can be obtained by averaging the results of the spectral analysis of these windows of signals.
The stable carrier signal amplitude and the stable background noise amplitude both increase the accuracy of system discrimination.
On the other hand, the data of the value of sixty- ⑫ is a transition value and should be avoided as much as possible for discrimination.
In addition, in the invention, when data analysis is carried out on each window signal, digital filtering is carried out on the sampling signal, and then FFT conversion is carried out on the data.
As shown in figure 2 of the drawings, in which,
is to be treatedThe detection of the signal is carried out by detecting the signal,
wherein
is shown inm i The coded 16-bit frequency is (f c ) Is injected into a power line containing noise: (f 0 ) The above.
Specifically, the data amount of each sliding window is as follows:
whereinithe number of windows is indicated and is,
the data offset for each of the slips is represented,
representing the amount of data processed per sliding window.
Specifically, in the present invention, the data subjected to the sliding window processing each time needs to be subjected to a digital filter to suppress out-of-band noise, and then subjected to FFT.
Specifically, in the present invention, take
The modulus at the frequency is used as the basis for deciding the carrier signal, wherein (a)f c ) For a carrier modulated signal, (f 0 ) Is a power line.
In the present invention, the digital filter is
Here, the
Wherein
For a second order chebyshev low-pass prototype function,
in order to normalize the center frequency of the signal,
in order to normalize the frequency of the upper sideband,
to normalize the lower sideband frequency.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (10)
1. A low-frequency power line carrier topology identification method is characterized by comprising the following steps:
step 1: continuously sampling continuous voltage, and simultaneously processing the sampling data at the previous moment in parallel in a pipeline mode;
step 2: carrying out high-density data analysis on the sampled data by adopting a method based on an FFT convolutional neural network;
and step 3: averaging the analysis results of the plurality of sliding window data, and taking the averaged frequency spectrum module value as a judgment basis of the carrier signal;
and 4, step 4: and carrying out step classification according to the magnitude of the frequency spectrum modulus of the signal and outputting a 16-bit modulation signal code.
2. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: the analysis result measured at the start of the operation is a system noise level value.
3. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: and when the frequency spectrum module value is larger than the system noise amplitude, the carrier modulation signal is considered to arrive.
4. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: and taking the average value of the adjacent sliding window data as a judgment basis.
5. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: and recording the data sliding window serial number when the signal is triggered and judged, determining windows corresponding to the 16-bit effective signals respectively according to the duration time of the signal and the time length of the window, and sequentially recording the frequency spectrum module value obtained by data processing.
6. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: and carrying out high-low step classification according to the amplitude of the signal frequency spectrum module value, judging that the data bit with the amplitude value higher than the system noise module value is 1, and judging that the data bit with the amplitude value lower than the system noise module value is 0.
7. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: a convolution kernel in the convolution neural network based on FFT comprises two digital signal processing means of a digital filter and FFT.
8. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: the data processed by the sliding window is firstly subjected to the suppression of out-of-band noise by a digital filter and then subjected to FFT (fast Fourier transform).
9. The method for identifying the topology of the low-frequency power line carrier according to claim 1, wherein: get
The modulus at the frequency is used as the basis for deciding the carrier signal, wherein (a)f c ) For a carrier modulated signal, (f 0 ) Is a power line.
10. The method for identifying the carrier topology of the low-frequency power line according to claim 1, wherein the data amount of the sliding window at each time is as follows:
，
wherein,ithe number of windows is indicated and is,
the data offset for each of the slips is represented,
representing the amount of data processed per sliding window.
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Cited By (2)
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| CN114553263A (en) * | 2022-04-27 | 2022-05-27 | 杭州禾迈电力电子股份有限公司 | Power line carrier communication device and method |
| CN116029533A (en) * | 2023-03-23 | 2023-04-28 | 江苏米特物联网科技有限公司 | Management method and device for ordered charging system of electric automobile |
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