CN111614380A - PLC signal reconstruction method and system by using near-end gradient descent - Google Patents

PLC signal reconstruction method and system by using near-end gradient descent Download PDF

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CN111614380A
CN111614380A CN202010480085.3A CN202010480085A CN111614380A CN 111614380 A CN111614380 A CN 111614380A CN 202010480085 A CN202010480085 A CN 202010480085A CN 111614380 A CN111614380 A CN 111614380A
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approximation error
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翟明岳
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Guangdong University of Petrochemical Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H04B3/544—Setting up communications; Call and signalling arrangements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B3/00—Line transmission systems
    • H04B3/54—Systems for transmission via power distribution lines
    • H04B3/542—Systems for transmission via power distribution lines the information being in digital form

Abstract

The embodiment of the invention discloses a PLC signal reconstruction method and a system by using near-end gradient descent, wherein the method comprises the following steps: step 101, acquiring a signal sequence S acquired according to a time sequence; step 102 of obtaining an initial value x of a near-end sequence0Creating an iteration control parameter k and assigning a value of 1; step 103, obtaining the 1 st step value t of the iteration step1(ii) a Step 104, obtaining the k step value g of the gradient vector of descentk(ii) a Step 105 finds the kth step value t of the iteration stepk(ii) a Step 106 is to obtain the k step value d of the gradient decreasing function of the near endk(ii) a Step 107 is to obtain the k-th step x of the near-end sequencek(ii) a Step 108, solving an approximation error e; step 109 judges whether the approximation error e is greater than or equal to a preset threshold value0step; step 110 finds a noise-filtered signal sequence SNEW。

Description

PLC signal reconstruction method and system by using near-end gradient descent
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 3k 500 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 utilizing near-end gradient descent. 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 near-end gradient descent, comprising:
step 101 acquires a time-sequentially acquired signal sequence S
Step 102 of obtaining an initial value x of a near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
step 103, obtaining the 1 st step value t of the iteration step1Is concretely provided with
Figure BDA0002517008590000021
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 104, obtaining the k step value g of the gradient vector of descentkThe method specifically comprises the following steps:
Figure BDA0002517008590000022
wherein the gradient vector initial value g01 st element of (1)
Figure BDA0002517008590000023
Is 0; the gradient vector initial value g0The ith element of
Figure BDA0002517008590000024
Is composed of
Figure BDA0002517008590000025
Wherein Δ T is a sampling interval of the signal vector S; i is the element serial number, and the value range is i ═ 2,3, ·, N;
step 105 of obtainingTaking the kth step value t of the iteration stepkThe method specifically comprises the following steps:
Figure BDA0002517008590000026
wherein λ isminIs the minimum eigenvalue of the average matrix B;
step 106 is to obtain the k step value d of the gradient decreasing function of the near endkIs concretely provided with
Figure BDA0002517008590000027
Figure BDA0002517008590000028
Wherein z is an intermediate parameter vector;
step 107 is to obtain the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
Step 108, solving an approximation error e; in particular, e ═ xk-xk-1|；
Step 109 judges whether the approximation error e is greater than or equal to a preset threshold value0And obtaining a first judgment result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0Adding 1 to the value of the iterative control parameter k and returning to the step 104, the step 105, the step 106, the step 107, the step 108 and the step 109 until the first judgment result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
Step 110 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
A PLC signal reconstruction system utilizing near-end gradient descent, comprising:
the module 201 acquires a time-sequentially acquired signal sequence S
Module 202 finds the initial value x of the near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. It is composed ofM in0Represents the mean value of the signal sequence S;
module 203 determines the value t of step 1 of the iteration step1Is concretely provided with
Figure BDA0002517008590000029
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
the module 204 finds the k-th step g of the gradient descent vectorkThe method specifically comprises the following steps:
Figure BDA0002517008590000031
wherein the gradient vector initial value g01 st element of (1)
Figure BDA0002517008590000032
Is 0; the gradient vector initial value g0The ith element of
Figure BDA0002517008590000033
Is composed of
Figure BDA0002517008590000034
Wherein Δ T is a sampling interval of the signal vector S; i is the element serial number, and the value range is i ═ 2,3, ·, N;
the module 205 finds the value t of the kth step of the iteration stepkThe method specifically comprises the following steps:
Figure BDA0002517008590000035
wherein λ isminIs the minimum eigenvalue of the average matrix B;
module 206 finds the k-th step d of the gradient descent function of the near endkIs concretely provided with
Figure BDA0002517008590000036
Figure BDA0002517008590000037
Wherein z is an intermediate parameter vector;
module 207 determines the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
The module 208 finds the approximation error e; in particular, e ═ xk-xk-1|；
The module 209 determines whether the approximation error e is greater than or equal to a preset threshold0And obtaining a first judgment result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0If the value of the iterative control parameter k is increased by 1 and returned to the module 204, the module 205, the module 206, the module 207, the module 208, and the module 209 until the first determination result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
The module 210 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
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 utilizing near-end gradient descent. 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 schematic flow chart of a PLC signal reconstruction method using near-end gradient descent
Fig. 1 is a schematic flow chart of a PLC signal reconstruction method using a near-end gradient descent according to the present invention. As shown in fig. 1, the method for reconstructing a PLC signal using a near-end gradient descent specifically includes the following steps:
step 101 acquires a time-sequentially acquired signal sequence S
Step 102 of obtaining an initial value x of a near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
step 103, obtaining the 1 st step value t of the iteration step1Is concretely provided with
Figure 1
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 104, obtaining the k step value g of the gradient vector of descentkThe method specifically comprises the following steps:
Figure BDA0002517008590000042
wherein the gradient vector initial value g01 st element of (1)
Figure BDA0002517008590000043
Is 0; the gradient vector initial value g0The ith element of
Figure BDA0002517008590000044
Is composed of
Figure BDA0002517008590000045
Wherein Δ T is a sampling interval of the signal vector S; i is the element serial number, and the value range is i ═ 2,3, ·, N;
step 105 finds the kth step value t of the iteration stepkThe method specifically comprises the following steps:
Figure BDA0002517008590000046
wherein λ isminIs the minimum eigenvalue of the average matrix B;
step 106 is to obtain the k step value d of the gradient decreasing function of the near endkIs concretely provided with
Figure BDA0002517008590000047
Figure BDA0002517008590000048
Wherein z is an intermediate parameter vector;
step 107 is to obtain the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
Step 108, solving an approximation error e; in particular, e ═ xk-xk-1|；
Step 109 judges whether the approximation error e is greater than or equal to a preset threshold value0And obtaining a first judgment result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0Adding 1 to the value of the iterative control parameter k and returning to the step 104, the step 105, the step 106, the step 107, the step 108 and the step 109 until the first judgment result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
Step 110 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
FIG. 2 is a schematic diagram of a PLC signal reconstruction system using near-end gradient descent
Fig. 2 is a schematic structural diagram of a PLC signal reconstruction system using near-end gradient descent according to the present invention. As shown in fig. 2, the PLC signal reconstruction system using the near-end gradient descent includes the following structures:
the module 201 acquires a time-sequentially acquired signal sequence S
Module 202 finds the initial value x of the near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
module 203 determines the value t of step 1 of the iteration step1Is concretely provided with
Figure BDA0002517008590000051
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
the module 204 finds the k-th step g of the gradient descent vectorkThe method specifically comprises the following steps:
Figure BDA0002517008590000052
wherein the gradient vector initial value g01 st element of (1)
Figure BDA0002517008590000053
Is 0; the gradient vector initial value g0The ith element of
Figure BDA0002517008590000054
Is composed of
Figure BDA0002517008590000055
Wherein Δ T is a sampling interval of the signal vector S; i is the element serial number, and the value range is i ═ 2,3, ·, N;
the module 205 finds the value t of the kth step of the iteration stepkThe method specifically comprises the following steps:
Figure BDA0002517008590000056
wherein λ isminIs the minimum eigenvalue of the average matrix B;
module 206 finds the k-th step d of the gradient descent function of the near endkIs concretely provided with
Figure BDA0002517008590000057
Figure BDA0002517008590000058
Wherein z is an intermediate parameter vector;
module 207 determines the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
The module 208 finds the approximation error e; in particular, e ═ xk-xk-1|；
The module 209 determines whether the approximation error e is greater than or equal to a preset threshold0And obtaining a first judgment result. If it is saidThe first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0If the value of the iterative control parameter k is increased by 1 and returned to the module 204, the module 205, the module 206, the module 207, the module 208, and the module 209 until the first determination result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
The module 210 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
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 acquires a time-sequentially acquired signal sequence S
Step 302 finds the initial value x of the near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
step 303 obtains the 1 st step value t of the iteration step1Is concretely provided with
Figure BDA0002517008590000059
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, ·, N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 304, calculating the k step value g of the gradient vector of descentkThe method specifically comprises the following steps:
Figure BDA0002517008590000061
wherein the gradient vector initial value g01 st element of (1)
Figure BDA0002517008590000062
Is 0; the gradient vector initial value g0The ith element of
Figure BDA0002517008590000063
Is composed of
Figure BDA0002517008590000064
Wherein Δ T is a sampling interval of the signal vector S; i is the element serial number, and the value range is i ═ 2,3, ·, N;
step 305 finds the kth step value t of the iteration stepkThe method specifically comprises the following steps:
Figure BDA0002517008590000065
wherein λ isminIs the minimum eigenvalue of the average matrix B;
step 306 obtains the k-th step d of the gradient descent function of the near endkIs concretely provided with
Figure BDA0002517008590000066
Figure BDA0002517008590000067
Wherein z is an intermediate parameter vector;
step 307 is to obtain the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
Step 308, solving an approximation error e; in particular, e ═ xk-xk-1|；
Step 309, determining whether the approximation error e is greater than or equal to a preset threshold0And obtaining a first judgment result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0Adding 1 to the value of the iterative control parameter k and returning to the step 304, the step 305, the step 306, the step 307, the step 308 and the step 309 until the first judgment result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
Step 310 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
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. The PLC signal reconstruction method utilizing the near-end gradient descent is characterized by comprising the following steps of:
step 101 acquires a time-sequentially acquired signal sequence S
Step 102 of obtaining an initial value x of a near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
step 103, obtaining the 1 st step value t of the iteration step1Is concretely provided with
Figure FDA0002517008580000011
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, … and N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
step 104, obtaining the k step value g of the gradient vector of descentkThe method specifically comprises the following steps:
Figure FDA0002517008580000012
wherein the gradient vector initial value g01 st element of (1)
Figure FDA0002517008580000013
Is 0; the gradient vector initial value g0The ith element of
Figure FDA0002517008580000014
Is composed of
Figure FDA0002517008580000015
Wherein Δ T is a sampling interval of the signal vector S; i is an element serial number, and the value range of i is 2,3, … and N;
step 105 finds the kth step value t of the iteration stepkThe method specifically comprises the following steps:
Figure FDA0002517008580000016
wherein λ isminIs the minimum eigenvalue of the average matrix B;
step 106 is to obtain the k step value d of the gradient decreasing function of the near endkIs concretely provided with
Figure FDA0002517008580000017
Figure FDA0002517008580000018
Wherein z is an intermediate parameter vector;
step 107 is to obtain the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
Step 108, solving an approximation error e; in particular, e ═ xk-xk-1|；
Step 109 judges whether the approximation error e is greater than or equal to a preset threshold value0To obtain the first judgmentAnd (5) cutting to obtain a result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0Adding 1 to the value of the iterative control parameter k and returning to the step 104, the step 105, the step 106, the step 107, the step 108 and the step 109 until the first judgment result shows that the approximation error e is smaller than the preset threshold value0. Wherein the preset threshold is0＝0.001；
Step 110 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
2. The PLC signal reconstruction system utilizing the near-end gradient descent comprises:
the module 201 acquires a time-sequentially acquired signal sequence S
Module 202 finds the initial value x of the near-end sequence0An iteration control parameter k is created and assigned a value of 1. The near-end sequence initial value x0Is given by the formula x0＝S-m0. Wherein m is0Represents the mean value of the signal sequence S;
module 203 determines the value t of step 1 of the iteration step1Is concretely provided with
Figure FDA0002517008580000019
Wherein λ isjIs the jth eigenvalue of the average matrix B; the solving formula of the average matrix B is B ═ S-m0]T[S-m0](ii) a j is a characteristic value serial number, and the value range of j is 1,2, … and N; n is the length of the signal sequence S; lambda [ alpha ]maxIs the maximum eigenvalue of the average matrix B;
the module 204 finds the k-th step g of the gradient descent vectorkThe method specifically comprises the following steps:
Figure FDA0002517008580000021
wherein the gradient vector initial value g01 st element of (1)
Figure FDA0002517008580000022
Is 0; the gradient vector initial value g0The ith element of
Figure FDA0002517008580000023
Is composed of
Figure FDA0002517008580000024
Wherein Δ T is a sampling interval of the signal vector S; i is an element serial number, and the value range of i is 2,3, … and N;
the module 205 finds the value t of the kth step of the iteration stepkThe method specifically comprises the following steps:
Figure FDA0002517008580000025
wherein λ isminIs the minimum eigenvalue of the average matrix B;
module 206 finds the k-th step d of the gradient descent function of the near endkIs concretely provided with
Figure FDA0002517008580000026
Figure FDA0002517008580000027
Wherein z is an intermediate parameter vector;
module 207 determines the k-th step x of the near-end sequencekAnd is specifically xk＝xk-1+ΔTdk-BS；
The module 208 finds the approximation error e; in particular, e ═ xk-xk-1|；
The module 209 determines whether the approximation error e is greater than or equal to a preset threshold0And obtaining a first judgment result. If the first judgment result shows that the approximation error e is greater than or equal to the preset threshold value0If the value of the iterative control parameter k is increased by 1 and returned to the module 204, the module 205, the module 206, the module 207, the module 208, and the module 209 until the first determination result shows that the approximation error e is smaller than the preset threshold value0. Wherein the presettingThe threshold value is0＝0.001；
The module 210 finds a noise-filtered signal sequence SNEWAnd is specifically SNEW＝xk+B[xk-S]。
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070276610A1 (en) * 2000-11-20 2007-11-29 Michael Korenberg Method for classifying genetic data
CN102111360A (en) * 2011-03-14 2011-06-29 中国人民解放军海军航空工程学院 Algorithm for dynamically switching channel equalization based on real-time signal-to-noise ratio estimation
CN110611522A (en) * 2019-09-20 2019-12-24 广东石油化工学院 PLC signal reconstruction method and system using multiple regular optimization theory
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