A meter remote reading carrier concentrator state detection method, medium and system

Abstract

The application provides a kind of electric meter remote meter reading carrier concentrator state detection method, medium and system, belong to the technical field of measurement electric variable, including: first, the load rate data and carrier signal data of the carrier concentrator to be measured are obtained, and power grid parameter data such as voltage, current, power factor, etc. are obtained. Next, the power grid parameter change curve is established, and the carrier signal data is decomposed to obtain stable components and variable components. Based on these data, a function relationship is established, a carrier signal variable coefficient matrix is constructed, and a variable comprehensive index is calculated. Finally, the stability state of the carrier concentrator to be measured is judged according to the load rate data and the variable comprehensive index. This process involves multiple steps such as data collection, signal analysis, function construction, etc., which can comprehensively evaluate the running state of the carrier concentrator and provide a basis for the stability of the power system. The application solves the technical problem that the existing technology cannot effectively monitor and diagnose the working state of the carrier concentrator.

Description

Technical Field

[0001] The present invention belongs to the technical field of measuring electric variables, and in particular relates to a method, medium and system for detecting the state of a carrier concentrator of a remote meter reading electric meter. Background Art

[0002] With the gradual construction and application of smart grids, power systems are rapidly developing towards automation and intelligence. Remote automatic meter reading, as a key component of smart grid construction, is playing an increasingly important role. A remote automatic meter reading system generally consists of three components: data collection terminals, a concentrator, and a master station system. The concentrator serves as a link between the upper and lower levels, collecting data from the data collection terminals and uploading it to the master station. The stable and reliable operation of the concentrator directly impacts the performance of the entire remote meter reading system.

[0003] Currently, remote meter reading systems widely utilize carrier communication technology to collect and transmit terminal data. Carrier communication, a communication technology that utilizes power lines as a communication channel, offers advantages such as wide coverage and low investment. It is widely used for data transmission between data collection terminals and concentrators in power systems. However, carrier signals are susceptible to interference from the power grid environment, and signal quality fluctuates significantly, placing a severe strain on the stability of the concentrator. A malfunction or malfunction in the concentrator severely impacts the normal operation of the entire remote meter reading system, significantly impacting the operational management of power companies.

[0004] Therefore, how to effectively monitor and diagnose the working status of carrier concentrators and promptly detect and address potential faults has become a technical challenge that needs to be solved. Existing methods, such as using the concentrator's own alarm information and manual inspections, have problems such as narrow detection range and crude detection methods, making it difficult to fully and accurately reflect the actual working status of the concentrator. Summary of the Invention

[0005] In view of this, the present invention provides a method, medium and system for detecting the status of a carrier concentrator for remote meter reading of an electric meter, which can solve the technical problem that it is difficult to effectively monitor and diagnose the working status of a carrier concentrator in the prior art.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides a method for detecting the state of a carrier concentrator for remote meter reading of an electric meter, comprising: acquiring in real time load rate data of the carrier concentrator to be tested and carrier data collected by a signal acquisition unit arranged at an uplink communication terminal and a downlink communication terminal of the carrier concentrator to be tested; acquiring power grid data collected by power grid parameter monitoring equipment installed in an area where the carrier concentrator to be tested is located; establishing a curve of power grid data changing over time, recorded as a power grid parameter changing curve; decomposing and calculating carrier signal data to obtain a stable component of the carrier signal and a variable component of the carrier signal; establishing a functional relationship between the stable component of the carrier signal, the power grid parameter changing curve, and the variable component of the carrier signal; constructing a carrier signal variation coefficient matrix based on the functional relationship; calculating a comprehensive variation index based on the carrier signal variation coefficient matrix; and judging the stability state of the carrier concentrator to be tested based on the load rate data and the comprehensive variation index.

[0008] The carrier data includes carrier signal data, carrier signal strength, carrier signal frequency, carrier signal phase, and carrier signal bit error rate.

[0009] Among them, the grid data includes grid voltage, grid current, power factor, and grid harmonics.

[0010] Among them, the collection period of the power grid parameter change curve is 24 hours.

[0011] The carrier signal data is decomposed by using harmonic decomposition and wavelet decomposition calculation.

[0012] Among them, the wavelet decomposition calculation adopts the Mallat algorithm.

[0013] The comprehensive variation index is obtained by weighted averaging the correlation coefficients in the carrier signal variation coefficient matrix.

[0014] Among them, the types of stability states include stable state, metastable state and unstable state.

[0015] The establishment of the grid parameter variation curve in S30 is specifically expressed as follows:

[0016]

[0017] Where, t is the time variable, ranging from [0, 24h]; V0, I0, PF0 are the reference values ​​of voltage, current, and power factor respectively; f k is the harmonic frequency;∈ v (t),∈ i (t),∈ pf (t) is the measurement error term; n is the harmonic number, generally 13; V(t) is the function of voltage changing with time; I(t) is the function of current changing with time; PF(t) is the function of power factor changing with time. k ,Bk ,C k is the amplitude of each harmonic component, φ k ,θ k ,ψ k is the phase angle, specifically, A k : amplitude of the kth harmonic component of voltage; B k : amplitude of the kth harmonic component of current; C k : amplitude of the kth harmonic component of the power factor; f k : kth harmonic frequency; φ k : voltage kth harmonic phase angle; θ k : phase angle of the kth harmonic of current; ψ k : The kth harmonic phase angle of the power factor; k is the summation variable.

[0018] The harmonic decomposition of the carrier signal in S40 is done by Fourier transform, which is specifically expressed as follows:

[0019]

[0020] Where s(t) is the time domain carrier signal; S(ω) is the frequency domain signal; ω is the angular frequency; and j is the imaginary unit.

[0021] The wavelet decomposition calculation adopts the Mallat algorithm, which is specifically expressed as follows:

[0022] c j+1,k =∑ n h n-2k c j,n ;

[0023] d j+1,k =∑ n g n-2k c j,n ;

[0024] Where c j,k is the low-frequency coefficient at the jth scale; d j,k is the high-frequency coefficient at the jth scale; k is the displacement index; h n ,g n is the orthogonal wavelet basis function coefficient, where h n is the low-pass filter coefficient, g n is the high-pass filter coefficient.

[0025] The functional relationship in S50 is established using multivariate nonlinear regression, which is specifically expressed in matrix form:

[0026]

[0027] Where Y t is the carrier signal variation component at time t; X irepresents the i-th characteristic variable, X ti is its specific value at time t, X ti is the i-th eigenvalue at time t; α i is the regression coefficient of the i-th feature; ∈ t is the regression error term at time t; T is the number of sampling points, which are carrier signal sampling points; and m is the number of features.

[0028] The variation coefficient matrix of the carrier signal in S60 is specifically expressed as follows:

[0029]

[0030] The coefficient of variation matrix elements are calculated as:

[0031]

[0032] Where λ is the regularization coefficient, which defaults to 0.01; Y at time t t The predicted value of .

[0033] The calculation of the comprehensive index of change in S70 is specifically expressed as follows:

[0034]

[0035] Where, I comp is the comprehensive index of change; w ij is the weight coefficient of the i-th factor to the j-th factor in the variation coefficient matrix; γ is the adjustment coefficient, which defaults to 0.5; eigenvalue(M) is the eigenvalue of the matrix M.

[0036] Weight coefficient w ij The analytic hierarchy process (AHP) is used to obtain the , and the specific steps are as follows:

[0037] 1) Construct judgment matrix A:

[0038]

[0039] where a ij It indicates the importance of the i-th factor relative to the j-th factor, using a 1-9 scale.

[0040] 2) Calculate the eigenvector:

[0041] AW=λ max W;

[0042] Where λ max is the maximum eigenvalue; W is the corresponding eigenvector.

[0043] 3) Consistency test:

[0044]

[0045] Where CI is the consistency index; RI is the random consistency index; when CR < 0.1, the judgment matrix has satisfactory consistency, and the standardized eigenvector is taken as the weight vector.

[0046] The stability status judgment in S80 is specifically expressed as follows:

[0047]

[0048] Where S state is the stability state index; L rate The load factor data.

[0049] The determination of the stable state judgment threshold adopts a combination of expert scoring method and statistical analysis:

[0050] 1) Collect stability scores under different operating conditions from 50 power system experts;

[0051] 2) Standardize the scoring data:

[0052]

[0053] 3) Calculate the probability distribution of each scoring interval using kernel density estimation:

[0054]

[0055] Where K(·) is the kernel function; h is the bandwidth parameter;

[0056] 4) Determine the threshold value based on the inflection point of the probability density curve: When S state <0.6 is considered stable. state <0.8 is considered metastable. state When ≥0.8, it is considered to be unstable.

[0057] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned method for detecting the state of a carrier concentrator for remote meter reading of an electricity meter.

[0058] A third aspect of the present invention provides a state detection system for a carrier concentrator of an electric meter remote meter reading, which includes the above-mentioned computer-readable storage medium.

[0059] Compared with existing technologies, the present invention provides a method, medium, and system for detecting the status of a carrier concentrator for remote meter reading. By conducting in-depth analysis of the operating data of the carrier concentrator and its power grid, a comprehensive evaluation index system for concentrator stability is established. This enables a more comprehensive and accurate diagnosis of the concentrator's operating status, offering the following significant technical advantages:

[0060] 1. Comprehensiveness: This method not only collects real-time operating parameters of the carrier concentrator under test, such as load factor, signal strength, and bit error rate, but also captures operational data from the power grid where the concentrator resides, such as voltage, current, and power factor. Through comprehensive analysis of this multi-source data, the concentrator's actual operating status can be more comprehensively reflected.

[0061] 2. Accuracy: This method utilizes advanced mathematical modeling and signal processing algorithms, including harmonic analysis, wavelet decomposition, and multivariate nonlinear regression, to deeply explore the inherent relationship between carrier signal characteristics and grid parameters, and construct a set of comprehensive evaluation indicators. These indicators can more accurately diagnose whether the concentrator is in a stable, metastable, or unstable state.

[0062] 3. Real-time: This method collects carrier and grid data within 24 hours, can track the changes in the working status of the concentrator in real time, promptly discover possible fault hazards, and provide timely and effective decision-making support for operation and maintenance personnel.

[0063] 4. Low resource consumption: This method uses matrix calculations to process data, significantly reducing the consumption of chip resources in the concentrator compared to traditional one-by-one calculations. Furthermore, by introducing regularization terms, it improves computational accuracy while reducing algorithm complexity, further alleviating the computational burden on the concentrator.

[0064] In general, the method for detecting the status of a carrier concentrator for remote meter reading of an electric meter proposed in the present invention can comprehensively, accurately, and in real time diagnose the operating status of the concentrator, effectively ensuring the reliability and stability of the remote meter reading system. It also greatly reduces the consumption of concentrator hardware resources during the implementation of the method, solving the technical problem that existing technologies have difficulty in effectively monitoring and diagnosing the operating status of the carrier concentrator. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A flow chart of the method provided by the present invention;

[0066] Figure 2 This is a 24-hour operating parameter change trend chart of carrier concentrator A;

[0067] Figure 3 Thermal maps for grid parameter correlation analysis;

[0068] Figure 4 This is the result diagram of harmonic analysis of carrier signal;

[0069] Figure 5 This is the distribution diagram of the comprehensive index of carrier concentrator changes. DETAILED DESCRIPTION

[0070] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions 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.

[0071] like Figure 1 FIG. 1 is a flow chart of a method for detecting the state of a carrier concentrator for remote meter reading of an electric meter provided by the first aspect of the present invention. The method comprises the following steps:

[0072] S10, real-time acquisition of load rate data of the carrier concentrator to be tested and real-time acquisition of carrier data collected by the signal acquisition unit of the uplink communication end and the downlink communication end of the carrier concentrator to be tested;

[0073] S20, obtaining grid data collected by grid parameter monitoring equipment installed in the area where the carrier concentrator to be tested is located, including grid voltage, grid current, power factor, and grid harmonics;

[0074] S30, establishing a curve of power grid data changing over time, recorded as a power grid parameter changing curve;

[0075] S40, decomposing and calculating the carrier signal data to obtain a carrier signal stable component and a carrier signal variable component;

[0076] S50, establishing a functional relationship between a stable component of a carrier signal, a grid parameter variation curve, and a variable component of a carrier signal;

[0077] S60, constructing a carrier signal variation coefficient matrix based on the functional relationship;

[0078] S70, calculating a comprehensive variation index based on a carrier signal variation coefficient matrix;

[0079] S80: Determine the stability status of the carrier concentrator to be tested according to the load rate data and the comprehensive change index.

[0080] The specific implementation of the above steps is described in detail below:

[0081] The specific implementation of step S10 is as follows: The main purpose of this step is to obtain various operating parameter data related to the carrier concentrator under test. First, a current sensor connected to the load terminal collects real-time load rate data of the carrier concentrator under test. Second, a dedicated signal acquisition unit is installed on the uplink and downlink communication interfaces of the carrier concentrator under test to collect real-time data characteristics of the carrier signal, including indicators such as amplitude, frequency, phase, and bit error rate. This data reflects the stability and reliability of the carrier communication signal. Analysis and modeling of this data facilitates further determination of the operating status of the carrier concentrator under test.

[0082] The specific implementation of step S20 is as follows: The primary purpose of this step is to obtain operational data related to the power grid system where the carrier concentrator under test resides. First, dedicated grid parameter monitoring equipment is installed on the power grid equipment in the area where the carrier concentrator under test resides to collect grid voltage, current, power factor, harmonics, and other indicators. These indicators reflect the operating status of the power grid and are correlated with the operating status of the carrier concentrator. Acquiring this grid parameter data provides an important reference for subsequent analysis of the carrier concentrator's operating status.

[0083] The specific implementation of step S30 is as follows: The main purpose of this step is to establish a mathematical model of the time-varying grid parameters based on the changing trends of the grid parameter data within 24 hours. First, the grid voltage V(t), current I(t), power factor PF(t) and other data collected in step S20 are modeled over time t. Among them, V(t) can be expressed as:

[0084]

[0085] Where V0 is the voltage reference value, A k is the amplitude of the kth harmonic component, f k is the harmonic frequency, φ k is the phase angle,∈ v (t) is the measurement error. Similarly, similar mathematical models can be established for I(t) and PF(t). By fitting grid parameter data over a 24-hour period, a curve reflecting the temporal changes in grid parameters can be obtained, providing an important reference for subsequent analysis.

[0086] The specific implementation of step S40 is as follows: The main purpose of this step is to conduct an in-depth analysis of the collected carrier signal and decompose it into a stable component and a variable component. First, the carrier signal s(t) is harmonically decomposed using Fourier transform to obtain the frequency domain signal S(ω):

[0087]

[0088] Where ω is the angular frequency and j is the imaginary unit. Harmonic decomposition can reflect the spectral characteristics of the carrier signal, including the stable fundamental component and the fluctuating harmonic components.

[0089] Secondly, the Mallat algorithm is used to perform wavelet decomposition on the carrier signal, which can further obtain the low-frequency stable component c j,k and high frequency variation component d j,k :

[0090] c j+1,k =∑ n h n-2k c j,n ;

[0091] d j+1,k =∑ n g n-2k c j,n ;

[0092] Where, h n and g n is the orthogonal wavelet basis function coefficient. Through harmonic decomposition and wavelet decomposition, the stable basic components and the changing interference components in the carrier signal can be effectively distinguished, providing basic data for subsequent model establishment and state judgment.

[0093] The specific implementation of step S50 is: The main purpose of this step is to explore the intrinsic relationship between the carrier signal characteristics and the grid parameters. The multivariate nonlinear regression method is used to establish the carrier signal variation component Y t and the grid parameter characteristic value X ti The functional relationship between:

[0094]

[0095] Among them, α i is the regression coefficient,∈ t is the residual term, T is the number of sampling points, and m is the number of features. Through this regression model, the intrinsic relationship between the variability of the carrier signal and the power grid parameters can be reflected, providing a basis for subsequent state assessment.

[0096] The specific implementation of step S60 is: the main purpose of this step is to further construct a coefficient matrix reflecting the variation characteristics of the carrier signal based on the model established in step S50. Specifically, the variation coefficient matrix M of the following form is constructed:

[0097]

[0098] Among them, the matrix element m ij The calculation formula is:

[0099]

[0100] Where λ is the regularization coefficient, which defaults to 0.01. is the predicted value at time t. The variation coefficient matrix M reflects the influence of each eigenvalue on the carrier signal variation, providing an important basis for subsequent comprehensive evaluation.

[0101] The specific implementation of step S70 is as follows: The main purpose of this step is to calculate a comprehensive index reflecting the stability of the carrier concentrator based on the variation coefficient matrix M constructed in step S60. The specific calculation formula is as follows:

[0102]

[0103] Among them, I comp is the comprehensive index of change, w ij is the weight coefficient, γ is the adjustment coefficient, and the default value is 0.5. ij The analytic hierarchy process (AHP) is used to obtain the specific steps including: 1) constructing the judgment matrix A, element a ij Indicates the importance of the i-th factor relative to the j-th factor; 2) Calculate the eigenvector W and the maximum eigenvalue λ max ; 3) Perform consistency test. When the consistency ratio CR<0.1, the standardized feature vector W is used as the weight vector w ij The comprehensive index of this change is I comp It comprehensively reflects the variation characteristics of the carrier signal and provides a basis for subsequent stability judgment.

[0104] The specific implementation of step S80 is as follows: The main purpose of this step is to comprehensively consider the load rate and the comprehensive index of change to determine the stability state of the carrier concentrator to be tested. The specific calculation formula is as follows:

[0105]

[0106] Where, S state is the stability state index, L rate The stability threshold is determined by combining expert scoring and statistical analysis: 1) collecting stability scores from 50 power system experts under different working conditions; 2) standardizing the scoring data; 3) calculating the probability density distribution of each scoring interval; 4) determining the threshold based on the inflection point of the probability density curve. state <0.6 is considered stable, and when 0.6≤S state <0.8 is considered metastable. state When the value is ≥0.8, it is considered to be unstable. By comprehensively considering the load conditions and the variation characteristics, the actual working state of the carrier concentrator under test can be more accurately determined.

[0107] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned method for detecting the state of a carrier concentrator for remote meter reading of an electricity meter.

[0108] A third aspect of the present invention provides a state detection system for a carrier concentrator of an electric meter remote meter reading, which includes the above-mentioned computer-readable storage medium.

[0109] Specifically, the principle of the present invention is:

[0110] First, the method collects real-time load data from the carrier concentrator under test, as well as carrier signal characteristic data collected by signal acquisition units installed on the concentrator's uplink and downlink communication interfaces. This includes carrier signal waveform, strength, frequency, phase, and bit error rate. Simultaneously, it also acquires operating parameter data from the power grid where the concentrator resides, such as voltage, current, and power factor. This data provides a foundation for subsequent analysis and modeling.

[0111] Secondly, by performing harmonic decomposition and wavelet decomposition on the collected carrier signal data, it can be decomposed into stable basic components and variable interference components. Harmonic decomposition can reflect the spectral characteristics of the carrier signal, and wavelet decomposition can further distinguish low-frequency stable components and high-frequency variable components.

[0112] Next, this method established a multivariate nonlinear regression model between the carrier signal's varying components and grid parameters, exploring the inherent relationship between the two. Compared to the traditional, one-by-one calculation approach, this modeling approach significantly improves computational efficiency and reduces concentrator chip resource consumption. Furthermore, a regularization term is introduced to enhance the model's stability and generalization performance.

[0113] Based on the above regression model, this method constructs an evaluation index that comprehensively reflects the stability of the carrier concentrator. This index not only considers the influence of each eigenvalue on the carrier signal fluctuation, but also introduces the largest eigenvalue of the matrix, which can more comprehensively characterize the working status of the concentrator.

[0114] Finally, the method uses load rate data and comprehensive indicators as the basis for judging the stability of the concentrator. By combining expert scoring method and statistical analysis, the thresholds of three stable states are determined, which can accurately determine whether the concentrator under test is in a stable state, a metastable state, or an unstable state.

[0115] A specific embodiment 1 of the method of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows: The specific implementation of step S10 is as follows:

[0116] First, the load rate data L of the carrier concentrator to be tested is collected in real time by connecting the current sensor at the load end. rate The load rate is an important indicator reflecting the system load status and is of great significance for evaluating the working performance of the carrier concentrator.

[0117] Secondly, a dedicated signal acquisition unit is set up at the uplink communication end and the downlink communication end of the carrier concentrator to be tested to collect the following carrier signal characteristic parameters in real time:

[0118] Carrier signal data s(t): represents the carrier signal waveform in the time domain;

[0119] Carrier signal strength S amp : Indicates the amplitude of the carrier signal;

[0120] Carrier signal frequency S freq : Indicates the frequency of the carrier signal;

[0121] Carrier signal phase S phase : Indicates the phase of the carrier signal;

[0122] Carrier signal bit error rate S ber : Indicates the proportion of bit errors detected by the receiving end in digital communication.

[0123] These parameters can reflect the stability and reliability of the carrier signal and provide basic data for subsequent status analysis and evaluation.

[0124] The specific implementation of step S20 is as follows:

[0125] Install dedicated grid parameter monitoring equipment on the grid equipment in the area where the carrier concentrator to be tested is located to collect the following grid operation data in real time:

[0126] Grid voltage V(t): represents the change of grid voltage over time;

[0127] Grid current I(t): represents the change of grid current over time;

[0128] Grid power factor PF(t): represents the change of grid power factor over time.

[0129] These grid parameter data can reflect the working status of the grid and have a certain correlation with the operating status of the carrier concentrator, providing a basis for analyzing the intrinsic connection between the two.

[0130] The specific implementation of step S30 is as follows:

[0131] By modeling the grid parameter data collected in step S20, a mathematical model reflecting the trend of grid parameter changes over time can be obtained.

[0132] First, the grid voltage V(t) can be expressed as:

[0133]

[0134] Among them, V0 is the reference value of voltage, A k is the amplitude of the kth harmonic component, f k is the harmonic frequency, φ k is the phase angle,∈ v (t) is the measurement error.

[0135] Similarly, a similar mathematical model can be established for the grid current I(t):

[0136]

[0137] Among them, I0 is the reference value of current, B k is the amplitude of the kth harmonic component, θ k is the phase angle,∈ i (t) is the measurement error.

[0138] The power factor PF(t) of the power grid can be modeled as follows:

[0139]

[0140] Among them, PF0 is the reference value of power factor, C k is the amplitude of the kth harmonic component, ψ k is the phase angle,∈ pf (t) is the measurement error.

[0141] By fitting the grid parameter data within 24 hours, a mathematical model reflecting the changes of grid parameters over time can be obtained, providing an important basis for subsequent analysis.

[0142] The specific implementation of step S40 is as follows:

[0143] First, the Fourier transform is used to perform harmonic decomposition on the collected carrier signal s(t) to obtain the frequency domain signal S(ω):

[0144]

[0145] Where ω is the angular frequency and j is the imaginary unit. Harmonic decomposition can reflect the spectral characteristics of the carrier signal, including the stable fundamental component and the fluctuating harmonic components.

[0146] Secondly, the Mallat algorithm is used to perform wavelet decomposition on the carrier signal, which can further obtain the low-frequency stable component c j,j and high frequency variation component d j,k :

[0147] c j+1,k =∑ n h n-2k c j,n ;

[0148] d j+1,k =∑ n g n-2k c j,n ;

[0149] Where, h n and g n are the orthogonal wavelet basis function coefficients.

[0150] Through harmonic decomposition and wavelet decomposition, the stable basic components and the changing interference components in the carrier signal can be effectively distinguished, providing basic data for subsequent model establishment and state judgment.

[0151] The specific implementation of step S50 is as follows:

[0152] The carrier signal variation component Y is established by using the multivariate nonlinear regression method. t and the grid parameter characteristic value X ti The functional relationship between:

[0153]

[0154] Among them, α i is the regression coefficient,∈ t is the residual term, T is the number of sampling points, and m is the number of features.

[0155] Through this regression model, the intrinsic relationship between the variability of the carrier signal and the power grid parameters can be reflected, providing a basis for subsequent status assessment.

[0156] The specific implementation of step S60 is as follows:

[0157] According to the regression model established in step S50, a coefficient matrix M reflecting the variation characteristics of the carrier signal can be constructed:

[0158]

[0159] Matrix element m ij The calculation formula is:

[0160]

[0161] Among them, λ is the regularization coefficient, which defaults to 0.01. is the predicted value at time t.

[0162] The variation coefficient matrix M reflects the degree of influence of each eigenvalue on the carrier signal variation, providing an important basis for subsequent comprehensive evaluation.

[0163] The specific implementation of step S70 is as follows:

[0164] According to the variation coefficient matrix M constructed in step S60, the evaluation index I that comprehensively reflects the stability of the carrier concentrator can be calculated. comp :

[0165]

[0166] Among them, w ij is the weight coefficient, γ is the adjustment coefficient, and the default value is 0.5.

[0167] Weight coefficient w ij The analytic hierarchy process (AHP) is used to obtain the specific steps including:

[0168] 1) Construct judgment matrix A, element a ij Indicates the importance of the i-th factor relative to the j-th factor;

[0169] 2) Calculate the eigenvector W and the maximum eigenvalue λ max ;

[0170] 3) Perform consistency test. When the consistency ratio CR<0.1, the normalized feature vector W is used as the weight vector w ij .

[0171] The comprehensive index of change I comp It comprehensively reflects the variation characteristics of the carrier signal and provides a basis for subsequent stability judgment.

[0172] The specific implementation of step S80 is as follows:

[0173] Comprehensive consideration of load rate L rate and comprehensive index of change I comp , the index S reflecting the stability state of the carrier concentrator under test can be calculated state :

[0174]

[0175] The determination of the stable state judgment threshold adopts a combination of expert scoring method and statistical analysis:

[0176] 1) Collect stability scores under different operating conditions from 50 power system experts;

[0177] 2) Standardize the scoring data:

[0178] 3) Calculate the probability density distribution of each scoring interval Where K(·) is the kernel function and h is the bandwidth parameter;

[0179] 4) Determine the threshold value based on the inflection point of the probability density curve: When S state <0.6 is considered stable, and when 0.6≤S state <0.8 is considered metastable. state When ≥0.8, it is considered to be unstable.

[0180] By comprehensively considering the load conditions and variation characteristics, the actual working status of the carrier concentrator under test can be judged more accurately.

[0181] To better understand and implement the present invention, Example 2 of a specific application scenario is provided below: A power company is responsible for remote automatic meter reading within a certain area. Within this area, there are 10 carrier concentrators, responsible for collecting meter data from over 100 data collection terminals and transmitting this data to a master station system via carrier communication. To comprehensively assess the operating status of these carrier concentrators, the power company decided to employ the detection method proposed in this invention for diagnostic purposes.

[0182] First, the power company installed dedicated signal acquisition units on each of the ten carrier concentrators to collect real-time data on each concentrator's load factor and carrier signal characteristics. Grid parameter monitoring equipment was also installed on the main distribution lines within the area to collect real-time data on grid voltage, current, power factor, and other indicators. All of this data was transmitted to the power company's monitoring center via fiber optic communications, providing a foundation for subsequent analysis and evaluation.

[0183] At the monitoring center, the staff first pre-processes and organizes the collected data. Taking the carrier concentrator numbered Concentrator A as an example, some of the data collected within 24 hours is shown in Table 1.

[0184] Table 1 - Operational data of carrier concentrator A

[0185]

[0186] like Figure 2 The figure below shows a 24-hour operating parameter trend chart for carrier concentrator A. This chart uses hyperbolic curves to illustrate the load factor and signal strength trends for carrier concentrator A over the course of 24 hours. The red curve represents load factor changes, and the blue curve represents signal strength changes. The horizontal axis represents time (hours), and the vertical axis represents parameter values. As can be seen from the figure, the load factor fluctuates around 65%, with a fluctuation range of approximately ±5%. The signal strength fluctuates around 36dB, with a relatively small fluctuation range of approximately ±1dB. Both parameters exhibit a certain periodic variation.

[0187] At the same time, the power grid parameter monitoring equipment in the area also collected corresponding power grid operation data, some of which are shown in Table 2.

[0188] Table 2 - Grid operation data

[0189] time Voltage (kV) Current (A) Power Factor 00:00 10.5 285.6 0.92 01:00 10.4 281.2 0.91 02:00 10.3 276.5 0.90 …… …… …… …… 23:00 10.7 291.3 0.93

[0190] With these basic data, the detection method proposed by the present invention is specifically implemented as follows:

[0191] Step S10: Obtain real-time load rate data and collected carrier signal characteristic data for the carrier concentrator under test. Taking concentrator A as an example, its load rate data and carrier signal characteristic data over a 24-hour period are shown in Table 1. This data provides a foundation for subsequent analysis and evaluation.

[0192] Step S20: Obtain grid operating data for the area where the carrier concentrator under test is located. For example, the grid voltage, current, and power factor data for the area are shown in Table 2. These grid parameters correlate with the operating status of the carrier concentrator, making them useful for analyzing the inherent connection between the two.

[0193] like Figure 3 The figure below shows a heat map of grid parameter correlation analysis. This figure uses a heat map format to illustrate the correlations between three grid parameters: voltage, current, and power factor. Redder colors indicate stronger positive correlations, and values ​​closer to 1 indicate stronger correlations. As can be seen from the figure, the correlation coefficient between voltage and current is 0.85, indicating a strong positive correlation. The correlation coefficients between voltage and power factor are 0.72, and the correlation coefficients between current and power factor are 0.68, both showing moderate positive correlations.

[0194] Step S30: Establish a mathematical model of how grid parameters change over time. Taking grid voltage V(t) as an example, the following model can be established:

[0195]

[0196] Among them, V0 is the reference value of voltage, which is 10.5kV; A k is the amplitude of the kth harmonic component, f k is the harmonic frequency, which is [100, 200, 300, ..., 1300] Hz; φ k is the phase angle; v (t) is the measurement error. Similarly, similar mathematical models can be established for the grid current I(t) and power factor PF(t). By fitting data over a 24-hour period, a mathematical curve can be obtained that reflects the temporal trends of grid parameters.

[0197] like Figure 4 The figure below shows the results of harmonic analysis of the carrier signal. This figure uses a bar graph to display the amplitude distribution of the carrier signal at different frequencies. The horizontal axis represents frequency (Hz), ranging from 0 to 1300 Hz; the vertical axis represents amplitude, in V·s⁻¹. As can be seen from the figure, the amplitude of the harmonic components decreases exponentially with increasing frequency, indicating that the carrier signal's primary energy is concentrated in the low-frequency range.

[0198] Step S40: Perform harmonic decomposition and wavelet decomposition on the collected carrier signal data. Taking the carrier signal s(t) of concentrator A as an example, first perform harmonic decomposition using Fourier transform to obtain the frequency domain signal S(ω):

[0199]

[0200] This harmonic decomposition can reflect the spectral characteristics of the carrier signal, including the stable fundamental component and the fluctuating harmonic components.

[0201] Secondly, the Mallat algorithm is used to perform wavelet decomposition on the carrier signal, which can further obtain the low-frequency stable component c j,k and high frequency variation component d j,k :

[0202] c j+1,k =∑ n h n-2k c j,n ;

[0203] d j+1,k =∑ n g n-2k c j,n ;

[0204] Where, h n and g n is the orthogonal wavelet basis function coefficient. Through harmonic decomposition and wavelet decomposition, the stable basic components and the changing interference components in the carrier signal can be effectively distinguished.

[0205] Step S50: Establish a functional relationship between the carrier signal variation component and the grid parameters. Take concentrator A as an example, its carrier signal variation component Y t As the dependent variable, the grid voltage X 1t , grid current X 2t and grid power factor X 3t As the independent variable, the following multiple nonlinear regression model is established:

[0206]

[0207] Through fitting calculation, the regression coefficient α can be obtained i and the residual term ∈ t This modeling approach can reflect the inherent relationship between the variability of the carrier signal and the grid parameters.

[0208] Step S60: Construct a coefficient matrix reflecting the carrier signal variation characteristics. Again, taking concentrator A as an example, based on the regression model established in step S50, construct the following variation coefficient matrix M:

[0209]

[0210] Among them, the matrix element m ij The calculation formula is:

[0211]

[0212] The variation coefficient matrix M reflects the influence of each grid parameter on the carrier signal variation.

[0213] Step S70: Calculate the evaluation index that comprehensively reflects the stability of the carrier concentrator. Based on the variation coefficient matrix M constructed in step S60, the variation comprehensive index I can be calculated. comp :

[0214]

[0215] Among them, w ij is the weight coefficient, which is determined to be [0.45, 0.35, 0.20] by the analytic hierarchy process (AHP). comp The index comprehensively reflects the variation characteristics of the carrier signal and provides a basis for subsequent stability judgment.

[0216] Step S80: Determine the stability of the carrier concentrator based on the load rate data and the comprehensive index of change. Take concentrator A as an example, its load rate data L within 24 hours is rate and comprehensive index of change I comp as follows:

[0217] L rate ={65.2,68.1,72.4,...,63.8};

[0218] I com p = 6.72;

[0219] Substitute these two indicators into the stability state evaluation formula:

[0220]

[0221] According to the pre-determined threshold, when S state <0.6 is considered stable, and when 0.6≤Sstate <0.8 is considered metastable. state When the value is ≥0.8, it is considered unstable. Therefore, the stability evaluation result of "Concentrator A" is unstable.

[0222] Through the above analysis process, the power company conducted a comprehensive diagnosis of the working status of the 10 carrier concentrators. The results are shown in Table 3.

[0223] Table 3-10 Stability Status Evaluation Results of Carrier Concentrators

[0224]

[0225]

[0226] As can be seen from Table 3, among the 10 carrier concentrators, 3 are in an unstable state, 4 are in a metastable state, and 3 are in a stable state.

[0227] like Figure 5 The figure shows the distribution of carrier concentrator change comprehensive index, which uses a bar chart to show the distribution of change comprehensive index of 10 carrier concentrators (AJ). The horizontal axis is the concentrator number, and the vertical axis is the change comprehensive index I comp The color of the bar graph also reflects the stability status: green means stable (I comp <5.5, yellow indicates metastable state (5.5≤I comp ≤6.5), red indicates unstable (I comp >6.5). As can be seen from the figure, concentrator F has the highest comprehensive index of change, reaching 7.08, while concentrator G has the lowest comprehensive index of change, at 4.56.

[0228] Power companies can take corresponding maintenance measures for concentrators in different states: for unstable concentrators, emergency repairs are needed as soon as possible to eliminate possible hardware failures; for concentrators in metastable states, monitoring and maintenance need to be strengthened to promptly discover and eliminate potential hidden dangers; for stable concentrators, inspection efforts can be appropriately relaxed to save maintenance costs while ensuring normal operation.

[0229] The above description is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered by the scope of protection of the present invention.

Claims

1. A method for detecting the state of a carrier concentrator for remote meter reading of an electric meter, characterized in that: include: Acquire in real time the load rate data of the carrier concentrator to be tested and the carrier data collected by the signal collection units arranged at the uplink communication end and the downlink communication end of the carrier concentrator to be tested; Obtain grid data collected by grid parameter monitoring equipment installed in the area where the carrier concentrator to be tested is located; establish a curve of grid data change over time, recorded as a grid parameter change curve; decompose and calculate the carrier signal data using harmonic decomposition and wavelet decomposition to obtain a stable component of the carrier signal and a variable component of the carrier signal; establish a functional relationship between the stable component of the carrier signal, the grid parameter change curve, and the variable component of the carrier signal; construct a variation coefficient matrix of the carrier signal based on the functional relationship; calculate a comprehensive variation index based on the variation coefficient matrix of the carrier signal; and determine the stability state of the carrier concentrator to be tested based on the load rate data and the comprehensive variation index; The carrier data includes carrier signal data, carrier signal strength, carrier signal frequency, carrier signal phase, and carrier signal bit error rate; Among them, the grid data includes grid voltage, grid current, power factor, and grid harmonics; Among them, the variation coefficient matrix of the carrier signal is specifically expressed as follows: The coefficient of variation matrix elements are calculated as: Where λ is the regularization coefficient, which defaults to 0.01; Y at time t t The predicted value of Where Y t is the carrier signal variation component at time t; X i represents the i-th characteristic variable, X ti is its specific value at time t, X ti is the i-th eigenvalue at time t; α i is the regression coefficient of the i-th feature; ∈ t is the regression error term at time t; T is the number of sampling points, which are carrier signal sampling points; m is the number of features; The calculation of the comprehensive index of changes is as follows: Where, I comp is the comprehensive index of change; w ij is the weight coefficient of the i-th factor to the j-th factor in the variation coefficient matrix; γ is the adjustment coefficient; eigenvalue(M) is the eigenvalue of the matrix M.

2. The method for detecting the state of a carrier concentrator for remote meter reading of an electric meter according to claim 1, characterized in that: The collection period of the grid parameter change curve is 24 hours.

3. The method for detecting the state of a carrier concentrator for remote meter reading of an electric meter according to claim 2, characterized in that: The Mallat algorithm is used for wavelet decomposition calculation.

4. The method for detecting the state of a carrier concentrator for remote meter reading of an electric meter according to claim 3, characterized in that: The comprehensive variation index is obtained by weighted averaging the correlation coefficients in the carrier signal variation coefficient matrix.

5. The method for detecting the state of a carrier concentrator for remote meter reading of an electric meter according to claim 4, characterized in that: Types of stability states include stable, metastable, and unstable states.

6. A computer-readable storage medium, characterized in that The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the method for detecting the state of a carrier concentrator for remote meter reading of an electric meter according to any one of claims 1 to 5.

7. A meter remote meter reading carrier concentrator status detection system, characterized in that: Contains the computer-readable storage medium of claim 6.

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