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

Abstract

The invention provides an ammeter remote meter reading carrier concentrator state detection method, medium and system, and belongs to the technical field of electric variable measurement, and the method comprises the following steps: firstly, obtaining load rate data and carrier signal data of a carrier concentrator to be detected, and obtaining power grid parameter data, such as voltage, current, power factor and the like; establishing a power grid parameter change curve, and decomposing carrier signal data to obtain a stable component and a variable component; a function relation is established based on the data, a carrier signal variation coefficient matrix is constructed, and a variation comprehensive index is calculated. And finally, judging the stability state of the carrier concentrator to be tested according to the load rate data and the change comprehensive index. The process involves a plurality of steps of data acquisition, signal analysis, function construction and the like, the operation state of the carrier concentrator can be comprehensively evaluated, and a basis is provided for the stability of a power system. The technical problem that the working state of the carrier concentrator is difficult to effectively monitor and diagnose in the prior art is solved.

Description

Technical Field

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

[0002] Currently, with the gradual construction and application of smart grids, the power system is developing rapidly towards automation and intelligence. Among them, remote automatic meter reading, as one of the key links in the construction of smart grids, is playing an increasingly important role. A remote automatic meter reading system generally consists of three parts: a collection terminal, a concentrator, and a master station system. The concentrator plays a connecting role, realizing the collection of data from the collection terminal and the upload to the master station. The stable and reliable operation of the concentrator directly affects the working status of the entire remote meter reading system.

[0003] At present, carrier communication technology is commonly used in remote meter reading systems to achieve the collection and transmission of terminal data. Carrier communication is a communication technology that uses power lines as channels, with advantages such as wide coverage and low investment, and is widely used in data transmission between collection terminals and concentrators in the power system. However, carrier signals are easily interfered by the power grid environment, and the volatility of signal quality is relatively large, which poses a severe test to the stability of the concentrator. Once the concentrator fails or operates abnormally, it will seriously affect the normal operation of the entire remote meter reading system, bringing great inconvenience to the operation and management of power enterprises.

[0004] Therefore, how to effectively monitor and diagnose the working status of the carrier concentrator, and timely discover and handle potential faults has become a technical problem to be solved urgently. Some existing methods, such as through the alarm information of the concentrator itself, manual inspection, etc., all have problems such as narrow detection range and rough detection means, and it is difficult to comprehensively 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 electric meters, which can solve the technical problem that it is difficult to effectively monitor and diagnose the working status of the carrier concentrator in the prior art.

[0006] The present invention is implemented as follows:

[0007] The first aspect of the present invention provides a method for detecting the state of an electric meter remote reading carrier concentrator, including: obtaining in real time the load rate data of the carrier concentrator to be measured and the carrier data collected by signal acquisition units arranged at the upstream communication end and the downstream communication end of the carrier concentrator to be measured; obtaining the power grid data collected by power grid parameter monitoring equipment installed in the area where the carrier concentrator to be measured is located; establishing a change curve of the power grid data over time, denoted as the power grid parameter change curve; decomposing and calculating the carrier signal data to obtain a stable component and a variable component of the carrier signal; establishing a functional relationship among the stable component of the carrier signal, the power grid parameter change curve, and the variable component of the carrier signal; constructing a carrier signal variable coefficient matrix based on the functional relationship; calculating a change comprehensive index according to the carrier signal variable coefficient matrix; and judging the stability state of the carrier concentrator to be measured based on the load rate data and the change comprehensive index.

[0008] Among them, the carrier data includes carrier signal data, carrier signal strength, carrier signal frequency, carrier signal phase, and carrier signal error rate.

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

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

[0011] Among them, when decomposing the carrier signal data, harmonic decomposition and wavelet decomposition calculations are used.

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

[0013] Among them, the change comprehensive index is obtained by weighted averaging the relevant coefficients in the carrier signal variable coefficient matrix.

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

[0015] Among them, the establishment of the power grid parameter change curve in S30 is specifically represented as follows:

[0016]

[0017] In the formula, t is a time variable, and the range is [0, 24h]; V0, I0, and 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 a measurement error term; n is the harmonic order, generally taking 13; V(t) is the voltage change function over time; I(t) is the current change function over time; PF(t) is the power factor change function over time. A k , Bk , C k is the amplitude of each harmonic component, φ k , θ k , ψ k is the phase angle. Specifically, A k : the amplitude of the k-th harmonic component of the voltage; B k : the amplitude of the k-th harmonic component of the current; C k : the amplitude of the k-th harmonic component of the power factor; f k : the k-th harmonic frequency; φ k : the phase angle of the k-th harmonic component of the voltage; θ k : the phase angle of the k-th harmonic component of the current; ψ k : the phase angle of the k-th harmonic component of the power factor; k is the summation variable.

[0018] In S40, the harmonic decomposition of the carrier signal adopts Fourier transform, which is specifically expressed as follows:

[0019]

[0020] In the formula, s(t) is the carrier signal in the time domain; S(ω) is the signal in the frequency domain; ω is the angular frequency; 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] In the formula, c j,k is the low-frequency coefficient at the j-th scale; d j,k is the high-frequency coefficient at the j-th scale; k is the displacement subscript; h n , g n are the coefficients of the orthogonal wavelet basis function, where h n is the low-pass filter coefficient, and g n is the high-pass filter coefficient.

[0025] In S50, the establishment of the functional relationship adopts multiple nonlinear regression, which is specifically expressed in matrix form:

[0026]

[0027] In the formula, Y t is the variable component of the carrier signal at time t; X iDenote the \(i\)-th feature variable as \(X\). ti Its specific value at time \(t\) is \(X_{t}\). ti The \(i\)-th eigenvalue at time \(t\) is \(\alpha_{t}\). The regression coefficient of the \(i\)-th feature is \(\alpha\). i \(\epsilon_{t}\) is the regression error term at time \(t\). \(T\) is the number of sampling points, and the sampling points are the sampling points of the carrier signal. \(m\) is the number of features. t

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

[0029]

[0030] Among them, the calculation of the coefficient of variation matrix elements is:

[0031]

[0032] In the formula, \(\lambda\) is the regularization coefficient, and the default value is 0.01. \(\hat{Y}_{t}\) is the predicted value of \(Y\) at time \(t\). t

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

[0034]

[0035] In the formula, \(I\) is the comprehensive variation index; \(w_{ij}\) is the weight coefficient of the \(i\)-th factor on the \(j\)-th factor in the coefficient of variation matrix; \(\gamma\) is the adjustment coefficient, and the default value is 0.5; eigenvalue(\(M\)) is the eigenvalue of matrix \(M\). comp ij

[0036] The weight coefficient \(w_{ij}\) is obtained by the Analytic Hierarchy Process (AHP). The specific steps are as follows: ij

[0037] 1) Construct the judgment matrix \(A\):

[0038]

[0039] where \(a_{ij}\) represents the importance degree of the \(i\)-th factor relative to the \(j\)-th factor, and the 1-9 scale method is adopted. ij

[0040] 2) Calculate the eigenvector:

[0041] \(AW = \lambda_{max}W\). max

[0042] In the formula, \(\lambda_{max}\) is the maximum eigenvalue; \(W\) is the corresponding eigenvector. max

[0043] 3) Consistency test: ​​​​​​​​​

[0044]

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

[0046] The specific representation of the stability state judgment in S80 is as follows:

[0047]

[0048] Wherein, S state is the stability state index; L rate is the load rate data.

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

[0050] 1) Collect the stability scores of 50 power system experts under different working conditions;

[0051] 2) Standardize the scoring data:

[0052]

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

[0054]

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

[0056] 4) Determine the threshold according to the inflection point of the probability density curve: when S state < 0.6, it is determined as the stable state; when 0.6 ≤ S state < 0.8, it is determined as the metastable state; when S state ≥ 0.8, it is determined as the unstable state.

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

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

[0059] Compared with the prior art, a method, medium and system for detecting the status of a carrier concentrator for remote meter reading of electric meters provided by the present invention can establish a comprehensive evaluation index system for the stability of the concentrator by deeply analyzing the operation data of the carrier concentrator and the power grid where it is located, so as to more comprehensively and accurately diagnose the working status of the concentrator, and has the following remarkable technical advantages:

[0060] 1. Comprehensiveness: This method not only collects the operation parameters of the carrier concentrator to be measured in real time, such as load rate, signal strength, error rate, etc., but also obtains the operation data of the power grid where the concentrator is located, such as voltage, current, power factor, etc. Through the comprehensive analysis of these multi-source data, the actual working status of the concentrator can be more comprehensively reflected.

[0061] 2. Accuracy: This method uses advanced mathematical modeling and signal processing algorithms such as harmonic analysis, wavelet decomposition, and multivariate nonlinear regression to deeply explore the internal relationship between the characteristics of carrier signals and power grid parameters, and constructs a set of comprehensive evaluation indexes. This index can more accurately diagnose whether the concentrator is in a stable state, a metastable state or an unstable state.

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

[0063] 4. Low resource consumption: This method uses matrix calculation to process data. Compared with the traditional one-by-one calculation method, it greatly reduces the consumption of chip resources in the concentrator. At the same time, by introducing a regularization term, while improving the calculation accuracy, it also reduces the complexity of the algorithm, further reducing the calculation burden of the concentrator.

[0064] Generally speaking, the method for detecting the status of a carrier concentrator for remote meter reading of electric meters proposed by the present invention can comprehensively, accurately and real-time diagnose the working status of the concentrator, provide effective guarantee for the reliability and stability of the remote meter reading system, and at the same time greatly reduce the consumption of hardware resources of the concentrator during the implementation of the method, solving the technical problem that it is difficult to effectively monitor and diagnose the working status of the carrier concentrator in the prior art. Brief Description of the Drawings

[0065] Figure 1 is the flowchart of the method provided by the present invention;

[0066] Figure 2 is the 24-hour operation parameter change trend chart of carrier concentrator A;

[0067] Figure 3 is the heat map of power grid parameter correlation analysis;

[0068] Figure 4 It is a graph of the harmonic analysis result of the carrier signal;

[0069] Figure 5 It is a distribution graph of the comprehensive index of changes in the carrier concentrator. Specific implementation manners

[0070] To make the objectives, 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] As Figure 1 shown, it is a flowchart of a method for detecting the status of an electric meter remote reading carrier concentrator provided in the first aspect of the present invention. This method includes the following steps:

[0072] S10. Real-time obtain the load rate data of the carrier concentrator to be measured and real-time obtain the carrier data collected by the signal acquisition units arranged at the upstream communication end and the downstream communication end of the carrier concentrator to be measured;

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

[0074] S30. Establish a curve of the change of the power grid data over time, denoted as the power grid parameter change curve;

[0075] S40. Decompose and calculate the carrier signal data to obtain the stable component and the variable component of the carrier signal;

[0076] S50. Establish a functional relationship among the stable component of the carrier signal, the power grid parameter change curve, and the variable component of the carrier signal;

[0077] S60. Construct a carrier signal variable coefficient matrix based on the functional relationship;

[0078] S70. Calculate the comprehensive index of changes according to the carrier signal variable coefficient matrix;

[0079] S80. Judge the stability status of the carrier concentrator to be measured according to the load rate data and the comprehensive index of changes.

[0080] The specific implementation manners of the above steps will be 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 measured carrier concentrator. First, the load rate data of the carrier concentrator to be measured is collected in real time through a current sensor connected to the load end. Secondly, a dedicated signal acquisition unit is set at the upstream and downstream communication interfaces of the carrier concentrator to be measured to collect the data characteristics of the carrier signal in real time, including indicators such as amplitude, frequency, phase, and bit error rate. These data reflect the stability and reliability of the carrier communication signal. Through the analysis and modeling of these data, it helps to further judge the working state of the carrier concentrator to be measured.

[0082] The specific implementation of step S20 is as follows: The main purpose of this step is to obtain the operating data related to the power grid system where the measured carrier concentrator is located. First, dedicated power grid parameter monitoring equipment is installed on the power grid equipment in the area where the carrier concentrator to be measured is located to collect indicators such as power grid voltage, current, power factor, and harmonics. These indicators reflect the working state of the power grid and have a certain correlation with the operating state of the carrier concentrator. By obtaining these power grid parameter data, it can provide an important reference basis for subsequent analysis of the working state of the carrier concentrator.

[0083] The specific implementation of step S30 is as follows: The main purpose of this step is to establish a mathematical model of the power grid parameters changing with time through the change trend of the power grid parameter data within 24 hours. First, the data such as the power grid voltage V(t), current I(t), power factor PF(t), etc. collected in step S20 are modeled with respect to time t. Among them, V(t) can be expressed as:

[0084]

[0085] In the formula, V0 is the voltage reference value, A k is the amplitude of the k-th 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 the power grid parameter data within 24 hours, a curve reflecting the change of the power grid parameters with time can be obtained, providing an important reference basis for subsequent analysis.

[0086] The specific implementation of step S40 is as follows: The main purpose of this step is to deeply analyze the collected carrier signal and decompose it into a stable component and a variable component. First, the Fourier transform is used to perform harmonic decomposition on the carrier signal s(t) to obtain the frequency-domain signal S(ω):

[0087]

[0088] Among them, 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 varying harmonic components.

[0089] Secondly, the Mallat algorithm is used to perform wavelet decomposition on the carrier signal, and the low-frequency stable component c j,k and the high-frequency varying 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] In the formula, h n and g n are the coefficients of the orthogonal wavelet basis function. Through harmonic decomposition and wavelet decomposition, the stable basic components and the varying 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 as follows: The main purpose of this step is to explore the internal relationship between the characteristics of the carrier signal and the power grid parameters. Using the method of multiple nonlinear regression, a functional relationship between the varying component Y t of the carrier signal and the eigenvalue X ti of the power grid parameters is established:

[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 characteristics. Through this regression model, the internal 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 as follows: The main purpose of this step is to further construct a coefficient matrix reflecting the varying characteristics of the carrier signal according to the model established in step S50. Specifically, a varying coefficient matrix M in the following form is constructed:

[0097]

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

[0099]

[0100] In the formula, λ is the regularization coefficient, and the default value is 0.01. is the predicted value at time t. The change coefficient matrix M reflects the influence degree of each eigenvalue on the change of the carrier signal, and provides an important basis for the subsequent comprehensive evaluation.

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

[0102]

[0103] Among them, I comp is the change comprehensive index, w ij is the weight coefficient, γ is the adjustment coefficient, and the default value is 0.5. The weight coefficient w ij is obtained by using the analytic hierarchy process (AHP). The specific steps include: 1) Construct the judgment matrix A, and the element a ij represents the importance degree of the i-th factor relative to the j-th factor; 2) Calculate the eigenvector W and the maximum eigenvalue λ max ; 3) Conduct the consistency test. When the consistency ratio CR < 0.1, the normalized eigenvector W is used as the weight vector w ij . The change comprehensive index I comp comprehensively reflects the change characteristics of the carrier signal and provides a basis for the 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 change comprehensive index to judge the stability state of the carrier concentrator to be measured. The specific calculation formula is as follows:

[0105]

[0106] In the formula, S state is the stability state index, L rate is the load rate data. The determination of the stable state judgment threshold adopts the method combining expert scoring and statistical analysis: 1) Collect the stability scores of 50 power system experts under different working conditions; 2) Standardize the scoring data; 3) Calculate the probability density distribution of each scoring interval; 4) Determine the threshold according to the inflection point of the probability density curve. When S state < 0.6, it is judged as a stable state. When 0.6 ≤ S state < 0.8, it is judged as a metastable state. When S state ≥ 0.8, it is judged as an unstable state. By comprehensively considering the load situation and the change characteristics, the actual working state of the carrier concentrator to be measured can be judged more accurately.

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

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

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

[0110] First, the method collects the load rate data of the carrier concentrator to be measured in real time, as well as the carrier signal characteristic data collected by the signal acquisition units set at the upstream and downstream communication interfaces of the concentrator, including carrier signal waveforms, intensities, frequencies, phases, error rates, etc. At the same time, the operation parameter data of the power grid where the concentrator is located, such as voltage, current, power factor, etc., are also obtained. These data provide a basis 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 changing interference components. Harmonic decomposition can reflect the spectral characteristics of the carrier signal, while wavelet decomposition can further distinguish between low-frequency stable components and high-frequency changing components.

[0112] Next, the method establishes a multivariate non-linear regression model between the changing components of the carrier signal and the power grid parameters to explore the internal relationship between the two. This modeling method greatly improves the calculation efficiency compared with the traditional one-by-one calculation method and reduces the consumption of the concentrator chip resources. At the same time, a regularization term is introduced to improve the stability and generalization performance of the model.

[0113] Based on the above regression model, the method constructs an evaluation index that comprehensively reflects the stability of the carrier concentrator. This index not only considers the influence degree of each eigenvalue on the change of the carrier signal, but also introduces the maximum eigenvalue of the matrix, which can more comprehensively describe the working state of the concentrator.

[0114] Finally, the method uses the load rate data and the comprehensive index as the basis for judging the stability of the concentrator. By combining the expert scoring method and statistical analysis, the thresholds of three stable states are determined, so as to accurately judge whether the concentrator to be measured is in a stable state, a metastable state or an unstable state.

[0115] The following provides a specific embodiment 1 of the method of the present invention. The specific implementation manners of each step in this embodiment 1 are described in detail as follows: The specific implementation manner of step S10 is as follows:

[0116] First, the load rate data L of the carrier concentrator to be measured is collected in real time through a current sensor connected to the load end. rate The load rate is an important indicator reflecting the system load condition and is of great significance for evaluating the working performance of the carrier concentrator.

[0117] Secondly, dedicated signal acquisition units are respectively set at the upstream communication end and the downstream communication end of the carrier concentrator to be measured, and the following carrier signal characteristic parameters are collected in real time:

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

[0119] Carrier signal intensity S amp : representing the amplitude size of the carrier signal;

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

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

[0122] Carrier signal bit error rate S ber : representing the proportion of bit errors detected at 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 state analysis and evaluation.

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

[0125] Dedicated grid parameter monitoring equipment is installed on the grid equipment in the area where the carrier concentrator to be measured is located, and the following grid operation data is collected in real time:

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

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

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

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

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

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

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

[0133]

[0134] where V0 is the reference value of the voltage, A k is the amplitude of the k-th 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] where I0 is the reference value of the current, B k is the amplitude of the k-th harmonic component, θ k is the phase angle, ∈ i (t) is the measurement error.

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

[0139]

[0140] where PF0 is the reference value of the power factor, C k is the amplitude of the k-th 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 variation 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, perform harmonic decomposition on the collected carrier signal s(t) using Fourier transform 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 varying harmonic components.

[0146] Secondly, perform wavelet decomposition on the carrier signal using the Mallat algorithm to further obtain the low-frequency stable component c j,j and the high-frequency varying 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] In the formula, h n and g n are the coefficients of the orthogonal wavelet basis functions.

[0150] Through harmonic decomposition and wavelet decomposition, the stable basic components and variable 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] Adopt the method of multiple nonlinear regression to establish the functional relationship between the variable component Y t of the carrier signal and the eigenvalue X ti of the grid parameters:

[0153]

[0154] where 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 internal relationship between the variability of the carrier signal and the grid parameters can be reflected, providing a basis for subsequent state 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 variable characteristics of the carrier signal can be constructed:

[0158]

[0159] The calculation formula for the matrix element m ij is:

[0160]

[0161] where λ is the regularization coefficient, defaulting to 0.01, is the predicted value at time t.

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

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

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

[0165]

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

[0167] The weight coefficient w ij is obtained by using the analytic hierarchy process (AHP). The specific steps include:

[0168] 1) Construct a judgment matrix A, and the element a ij represents the importance degree 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) Conduct a consistency test. When the consistency ratio CR < 0.1, the normalized eigenvector W is used as the weight vector w ij .

[0171] The variation comprehensive index I comp comprehensively reflects the variation characteristics of the carrier signal, providing a basis for the subsequent stability judgment.

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

[0173] Taking into account the load rate L rate and the variation comprehensive index I comp , an index S reflecting the stability state of the to-be-tested carrier concentrator 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 the stability scores of 50 power system experts under different working conditions;

[0177] 2) Standardize the scoring data:

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

[0179] 4) Determine the threshold according to the inflection point of the probability density curve: When \(S\) state \(< 0.6\), it is determined to be in a stable state; when \(0.6\leq S\) state \(< 0.8\), it is determined to be in a metastable state; when \(S\) state \(\geq 0.8\), it is determined to be in an unstable state.

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

[0181] To better understand and implement the present invention, the following provides Embodiment 2 of a specific application scenario of the present invention: A certain power enterprise is responsible for the remote automatic meter reading work in a certain area. There are a total of 10 carrier concentrators in this area, which are responsible for collecting the meter data of more than 100 acquisition terminals and transmitting the data to the master station system through carrier communication. In order to comprehensively evaluate the working states of these carrier concentrators, the power enterprise decides to use the detection method proposed by the present invention for diagnosis.

[0182] First, the power enterprise installs dedicated signal acquisition units on the 10 carrier concentrators respectively to collect the load rate data and carrier signal characteristic data of each concentrator in real time. At the same time, grid parameter monitoring devices are also installed on the main distribution lines in this area to collect index data such as grid voltage, current, and power factor in real time. All these data are transmitted to the monitoring center of the power enterprise through optical fiber communication to provide a basis for subsequent analysis and evaluation.

[0183] In the monitoring center, the staff first preprocess and sort out the collected data. Taking the carrier concentrator numbered Concentrator A as an example, part of the data collected within 24 hours is shown in Table 1.

[0184] Table 1 - Operating data of Carrier Concentrator A

[0185]

[0186] As Figure 2 shown, it is the 24-hour operating parameter change trend chart of Carrier Concentrator A. This chart uses a hyperbola to show the change trends of the load rate and signal strength of Carrier Concentrator A within 24 hours. The red curve represents the change of the load rate, and the blue curve represents the change of the signal strength. The abscissa is time (hours), and the ordinate is the parameter value. It can be seen from the figure that the load rate fluctuates around 65%, and the fluctuation range is about ±5%; the signal strength fluctuates around 36 dB, and the fluctuation range is relatively small, about ±1 dB. Both parameters show some periodic change characteristics.

[0187] Meanwhile, the power grid parameter monitoring devices in this area have also collected corresponding power grid operation data, and some of the data are shown in Table 2.

[0188] Table 2 - Power 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 will be specifically implemented as follows:

[0191] Step S10: Real-time obtain the load rate data of the carrier concentrator to be measured and the collected carrier signal characteristic data. Taking concentrator A as an example, its load rate data and carrier signal characteristic data within 24 hours are shown in Table 1. These data provide a basis for subsequent analysis and evaluation.

[0192] Step S20: Obtain the power grid operation data of the area where the carrier concentrator to be measured is located. Similarly, taking it as an example, the power grid voltage, current, and power factor data of its location area are shown in Table 2. There is a certain correlation between these power grid parameter data and the working state of the carrier concentrator, which is very helpful for analyzing the internal relationship between the two.

[0193] As Figure 3 shown, it is a heat map for power grid parameter correlation analysis. This map shows the correlation between the three power grid parameters of voltage, current, and power factor in the form of a heat map. The redder the color, the stronger the positive correlation, and the closer the value is to 1, the stronger the correlation. It can be seen from the figure that the correlation coefficient between voltage and current is 0.85, indicating a strong positive correlation; the correlation coefficient between voltage and power factor is 0.72, and the correlation coefficient between current and power factor is 0.68, both showing a medium degree of positive correlation.

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

[0195]

[0196] where V0 is the reference value of the voltage, taking 10.5 kV; A k is the amplitude of the k-th harmonic component, f k is the harmonic frequency, taking [100, 200, 300,..., 1300] Hz respectively; φ k is the phase angle; ∈ v (t) is the measurement error. Similarly, similar mathematical models can also be established for the power grid current I(t) and the power factor PF(t). By fitting the data within 24 hours, a mathematical curve reflecting the change trend of power grid parameters over time can be obtained.

[0197] As shown Figure 4 in the figure, it is a graph of the harmonic analysis results of the carrier signal. This graph shows the amplitude distribution of the carrier signal at different frequencies in the form of a bar graph. The abscissa is the frequency (Hz), ranging from 0 to 1300 Hz; the ordinate is the amplitude, with the unit of V·s-1. It can be seen from the figure that as the frequency increases, the amplitude of the harmonic components shows an exponential decay trend, indicating that the main energy of the carrier signal is concentrated in the low-frequency band.

[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 varying harmonic components.

[0201] Secondly, use the Mallat algorithm to perform wavelet decomposition on the carrier signal to further obtain the low-frequency stable component c j,k and the high-frequency varying 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] In the formula, h n and g n are the coefficients of the orthogonal wavelet basis functions. Through harmonic decomposition and wavelet decomposition, the stable basic components and the varying interference components in the carrier signal can be effectively distinguished.

[0205] Step S50: Establish the functional relationship between the varying component of the carrier signal and the grid parameters. Taking concentrator A as an example, taking its carrier signal varying component Y t as the dependent variable, and the grid voltage X 1t , the grid current X 2t and the grid power factor X 3t as the independent variables, establish the following multiple nonlinear regression model:

[0206]

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

[0208] Step S60: Construct a coefficient matrix that reflects the variability characteristics of the carrier signal. Taking concentrator A as an example, according to the regression model established in step S50, construct the following variability coefficient matrix M:

[0209]

[0210] where the matrix element m ij is calculated by the formula:

[0211]

[0212] This variability coefficient matrix M reflects the influence degree of each grid parameter on the variability of the carrier signal.

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

[0214]

[0215] where w ij is the weight coefficient, determined to be [0.45, 0.35, 0.20] through the Analytic Hierarchy Process (AHP). This I comp index comprehensively reflects the variability characteristics of the carrier signal and provides a basis for subsequent stability judgment.

[0216] Step S80: Judge the stability state of the carrier concentrator based on the load rate data and the variability comprehensive index. Taking concentrator A as an example, its load rate data L rate and the variability comprehensive index I comp are 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, it is judged as the stable state, when 0.6 ≤ Sstate When it is less than 0.8, it is determined to be in a metastable state. When S state ≥ 0.8, it is determined to be in an unstable state. Therefore, the stability state evaluation result of "Concentrator A" is unstable.

[0222] Through the above analysis process, the power enterprise comprehensively diagnosed the working states of 10 carrier concentrators, and the results are shown in Table 3.

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

[0224]

[0225]

[0226] It can be seen from Table 3 that 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] As Figure 5 shown, it is the comprehensive index distribution diagram of the changes of the carrier concentrator. This diagram uses a bar chart to show the comprehensive index distribution of the changes of 10 carrier concentrators (A - J). The abscissa is the concentrator number, and the ordinate is the comprehensive index of change I comp . The color of the bar chart also reflects the stability state: green indicates stable (I comp < 5.5), yellow indicates metastable (5.5 ≤ I comp ≤ 6.5), and red indicates unstable (I comp > 6.5). It can be seen from the figure that the comprehensive index of change of Concentrator F is the highest, reaching 7.08, while the comprehensive index of change of Concentrator G is the lowest, being 4.56.

[0228] The power enterprise can take corresponding maintenance measures for concentrators in different states: for unstable concentrators, emergency repairs need to be carried out as soon as possible to eliminate possible hardware failures; for concentrators in a metastable state, monitoring and maintenance need to be strengthened to detect and eliminate potential hidden dangers in a timely manner; for stable concentrators, the inspection intensity can be appropriately relaxed to save maintenance costs on the premise of ensuring normal operation.

[0229] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. A method for detecting the status of a remote meter reading carrier concentrator for electricity meters, characterized in that, Including: Real-time acquisition of the load rate data of the carrier concentrator to be measured and the carrier data collected by the signal acquisition units set at the upstream communication end and the downstream communication end of the carrier concentrator to be measured; Acquire the power grid data collected by the power grid parameter monitoring equipment installed in the area where the carrier concentrator to be measured is located; establish the change curve of the power grid data over time, denoted as the power grid parameter change curve; decompose and calculate the carrier signal data to obtain the stable component and the variable component of the carrier signal; establish the functional relationship among the stable component of the carrier signal, the power grid parameter change curve, and the variable component of the carrier signal; construct the carrier signal variable coefficient matrix based on the functional relationship; calculate the variable comprehensive index according to the carrier signal variable coefficient matrix; judge the stability state of the carrier concentrator to be measured based on the load rate data and the variable comprehensive index.

2. The method for detecting the status of the remote meter reading carrier concentrator of the electric meter according to claim 1, characterized in that The carrier data includes carrier signal data, carrier signal strength, carrier signal frequency, carrier signal phase, and carrier signal error rate.

3. The method for detecting the status of an electric meter remote reading carrier concentrator according to claim 2, wherein, The power grid data includes power grid voltage, power grid current, power factor, and power grid harmonics.

4. The method for detecting the status of the remote meter reading carrier concentrator of an electric meter according to claim 3, characterized in that, The acquisition period of the power grid parameter change curve is 24 hours.

5. The method for detecting the state of an electric meter remote reading carrier concentrator according to claim 4, wherein, For the decomposition of the carrier signal data, harmonic decomposition and wavelet decomposition calculations are adopted.

6. The method for detecting the status of the remote meter reading carrier concentrator of an electric meter according to claim 5, wherein The wavelet decomposition calculation adopts the Mallat algorithm.

7. The method for detecting the state of the remote meter reading carrier concentrator of an electric meter according to claim 6, characterized in that, The variable comprehensive index is obtained by weighted averaging the relevant coefficients in the carrier signal variable coefficient matrix.

8. The method for detecting the status of a remote meter reading carrier concentrator of an electric meter according to claim 7, wherein, The types of stability states include stable state, metastable state, and unstable state.

9. A computer-readable storage medium, characterized in that, Program instructions are stored in the computer-readable storage medium, and when the program instructions run in the computer, they are used to execute a method for detecting the state of an electric meter remote reading carrier concentrator according to any one of claims 1-8.

10. A remote meter reading carrier concentrator status detection system for electric meters, characterized in that, Including the computer-readable storage medium according to claim 9.

Images