CN111900993A - PLC signal reconstruction method and system by using self-adaptive generalization transformation - Google Patents

PLC signal reconstruction method and system by using self-adaptive generalization transformation Download PDF

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CN111900993A
CN111900993A CN202010805167.0A CN202010805167A CN111900993A CN 111900993 A CN111900993 A CN 111900993A CN 202010805167 A CN202010805167 A CN 202010805167A CN 111900993 A CN111900993 A CN 111900993A
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翟明岳
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Guangdong University of Petrochemical Technology
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Abstract

The embodiment of the invention discloses a PLC signal reconstruction method and a system by utilizing self-adaptive generalization transformation, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102, solving a signal second-order difference sequence; step 103, obtaining an adaptive generalized transformation adjustment factor; step 104, calculating a space factor; step 105 finds the reconstructed signal sequence.

Description

PLC signal reconstruction method and system by using self-adaptive generalization transformation
Technical Field
The invention relates to the field of electric power, in particular to a PLC signal reconstruction method and a system.
Background
Compared with various wired communication technologies, the power line communication has the advantages of no need of rewiring, easiness in networking and the like, and has wide application prospect. The power line communication technology is divided into Narrowband over power line (NPL) and Broadband over power line (BPL); the narrow-band power line communication refers to a power line carrier communication technology with the bandwidth limited to 3k500 kHz; the power line communication technology includes a prescribed bandwidth (3148.5kHz) of european CENELEC, a prescribed bandwidth (9490kHz) of the Federal Communications Commission (FCC) in the united states, a prescribed bandwidth (9450kHz) of the Association of Radio Industries and Businesses (ARIB) in japan, and a prescribed bandwidth (3500kHz) in china. The narrow-band power line communication technology mainly adopts a single carrier modulation technology, such as a PSK technology, a DSSS technology, a Chirp technology and the like, and the communication speed is less than 1 Mbits/s; the broadband power line communication technology refers to a power line carrier communication technology with a bandwidth limited between 1.630MHz and a communication rate generally above 1Mbps, and adopts various spread spectrum communication technologies with OFDM as a core.
Although power line communication systems are widely used and the technology is relatively mature, a large number of branches and electrical devices in the power line communication system generate a large amount of noise in the power line channel; random impulse noise has high randomness and high noise intensity, and seriously damages a power line communication system, so that the technology for inhibiting the random impulse noise is always the key point for the research of scholars at home and abroad; and the noise model does not fit into a gaussian distribution. Therefore, the traditional communication system designed aiming at the gaussian noise is not suitable for a power line carrier communication system any more, and a corresponding noise suppression technology must be researched to improve the signal-to-noise ratio of the power line communication system, reduce the bit error rate and ensure the quality of the power line communication system.
In practical applications, some simple non-linear techniques are often applied to eliminate power line channel noise, such as Clip-ping, Blanking and Clipping/Blanking techniques, but these research methods all have to work well under a certain signal-to-noise ratio condition, and only consider the elimination of impulse noise, in a power line communication system, some commercial power line transmitters are characterized by low transmission power, and in some special cases, the transmission power may be even lower than 18w, so that in some special cases, signals are submerged in a large amount of noise, resulting in a low signal-to-noise ratio condition of the power line communication system.
Disclosure of Invention
As described above, with the application and popularization of nonlinear electrical appliances, background noise in a medium and low voltage power transmission and distribution network presents obvious non-stationarity and non-gaussian characteristics, so that the phenomenon of data loss in a power line communication system is more serious, the communication quality is obviously reduced, and the performance of a PLC communication system is seriously affected.
The invention aims to provide a PLC signal reconstruction method and a system by using self-adaptive generalization transformation. The method has better signal reconstruction performance and simpler calculation.
In order to achieve the purpose, the invention provides the following scheme:
a PLC signal reconstruction method using adaptive generalization transformation comprises the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102 of obtainingThe signal second-order difference sequence specifically comprises: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
step 103, obtaining an adaptive generalized transformation adjustment factor, specifically: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure BDA0002628860320000021
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
step 104, obtaining a space factor, specifically: the space factor is k, and the calculation formula is
Figure BDA0002628860320000022
Figure BDA0002628860320000023
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
step 105, obtaining a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure BDA0002628860320000024
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
A PLC signal reconstruction system using an adaptive generalization transform, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a second order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
the module 203 calculates an adaptive generalized transformation adjustment factor, which specifically includes: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure BDA0002628860320000025
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
the module 204 calculates a space factor, specifically: the space factor is k, and the calculation formula is
Figure BDA0002628860320000026
Figure BDA0002628860320000027
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
the module 205 obtains a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure BDA0002628860320000028
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
as described above, with the application and popularization of nonlinear electrical appliances, background noise in a medium and low voltage power transmission and distribution network presents obvious non-stationarity and non-gaussian characteristics, so that the phenomenon of data loss in a power line communication system is more serious, the communication quality is obviously reduced, and the performance of a PLC communication system is seriously affected.
The invention aims to provide a PLC signal reconstruction method and a system by using self-adaptive generalization transformation. The method has better signal reconstruction performance and simpler calculation.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art, other drawings can be derived from them without inventive effort.
FIG. 1 is a schematic flow diagram of the process of the present invention;
FIG. 2 is a schematic flow chart of the system of the present invention;
FIG. 3 is a flow chart illustrating an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope 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.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a flow chart illustrating a PLC signal reconstruction method using adaptive generalization transform
Fig. 1 is a flow chart illustrating a PLC signal reconstruction method using adaptive generalization transformation according to the present invention. As shown in fig. 1, the PLC signal reconstruction method using adaptive generalization conversion specifically includes the following steps:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, calculating a second-order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
step 103, obtaining an adaptive generalized transformation adjustment factor, specifically: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure BDA0002628860320000031
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
step 104, obtaining a space factor, specifically: the space factor is k, and the calculation formula is
Figure BDA0002628860320000041
Figure BDA0002628860320000042
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
step 105, obtaining a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure BDA0002628860320000043
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
FIG. 2 is a schematic diagram of a PLC signal reconstruction system using adaptive generalization transform
Fig. 2 is a schematic structural diagram of a PLC signal reconstruction system using adaptive generalization transformation according to the present invention. As shown in fig. 2, the PLC signal reconstruction system using the adaptive generalization transform includes the following structures:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a second order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
the module 203 calculates an adaptive generalized transformation adjustment factor, which specifically includes: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure BDA0002628860320000044
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
the module 204 calculates a space factor, specifically: the space factor is k, and the calculation formula is
Figure BDA0002628860320000045
Figure BDA0002628860320000046
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
the module 205 obtains a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure BDA0002628860320000047
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
The following provides an embodiment for further illustrating the invention
FIG. 3 is a flow chart illustrating an embodiment of the present invention. As shown in fig. 3, the method specifically includes the following steps:
step 301, acquiring a signal sequence S acquired according to a time sequence;
step 302, calculating a second order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the letterThe nth element of the number sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
step 303, obtaining an adaptive generalized transformation adjustment factor, specifically: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure BDA0002628860320000051
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
step 304, obtaining a space factor, specifically: the space factor is k, and the calculation formula is
Figure BDA0002628860320000052
Figure BDA0002628860320000053
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
step 305 obtains a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure BDA0002628860320000054
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is simple because the system corresponds to the method disclosed by the embodiment, and the relevant part can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (2)

1. A PLC signal reconstruction method using an adaptive generalization transform, comprising:
step 101, acquiring a signal sequence S acquired according to a time sequence;
step 102, calculating a second-order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
step 103, obtaining an adaptive generalized transformation adjustment factor, specifically: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure FDA0002628860310000011
Wherein σSIs the mean square error of the signal sequence S; sigmaΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
step 104, obtaining a space factor, specifically: the space factor is k, and the calculation formula is
Figure FDA0002628860310000012
Wherein m isΔSIs that it isThe mean value of the second order difference sequence Delta S of the signal; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
step 105, obtaining a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure FDA0002628860310000013
Figure FDA0002628860310000014
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
2. A PLC signal reconstruction system using an adaptive generalization transform, comprising:
the module 201 acquires a signal sequence S acquired in time sequence;
the module 202 calculates a second order difference sequence of the signal, specifically: the second order differential sequence of the signal is Delta S, and the 1 st element of the second order differential sequence is Delta S1A value of 0; the 2 nd element being Δ s2A value of 0; the nth element being Δ snThe calculation formula is Deltasn＝sn-sn-2(ii) a Wherein s isnIs the nth element of the signal sequence S; sn-2Is the n-2 th element of the signal sequence S; n is the element serial number, and the value range is N-3, 4, ·, N; n is the length of the signal sequence S;
the module 203 calculates an adaptive generalized transformation adjustment factor, which specifically includes: the self-adaptive generalized Jordan transformation adjustment factor is lambda, and the calculation formula is
Figure FDA0002628860310000015
Wherein σSIs the mean square error of the signal sequence S；σΔSThe mean square error of the second order difference sequence Delta S of the signal is obtained;
the module 204 calculates a space factor, specifically: the space factor is k, and the calculation formula is
Figure FDA0002628860310000016
Wherein m isΔSThe mean value of the second order difference sequence delta S of the signal is obtained; m isSIs the mean of the signal sequence S; snr is the signal-to-noise ratio of the signal sequence S; Δ f is the sampling frequency of the signal sequence S;
the module 205 obtains a reconstructed signal sequence, specifically: the reconstructed signal sequence is SnewThe first element is
Figure FDA0002628860310000021
Figure FDA0002628860310000022
Wherein, l is a reconstruction element serial number, and the value range thereof is l ═ 1,2, ·, N; slIs the l-th element of the signal sequence S; Δ sm-qIs the m-q element of the second order difference sequence delta S of the signal, if m-q is less than or equal to 0, the corresponding element delta Sm-qThe value of (d) is 0.
CN202010805167.0A 2020-08-12 2020-08-12 PLC signal reconstruction method and system by using self-adaptive generalization transformation Withdrawn CN111900993A (en)

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