Power carrier channel simulation method and system based on dynamic multi-dimensional interference model

Abstract

The invention relates to the technical field of carrier communication, and particularly discloses a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model.The power line carrier channel simulation method based on the dynamic multi-dimensional interference model comprises the steps that a power line noise sample is collected, and characteristic parameters of periodic interference, transient noise and background noise are extracted; classifying and marking according to noise types and corresponding power grid working conditions, and establishing a noise database containing a mapping relation between noise characteristics and working conditions; and based on the noise database, respectively establishing a periodic interference model, a transient noise model and a background noise model, and forming a multi-dimensional noise interference model. According to the power line carrier channel simulation method based on the dynamic multi-dimensional interference model, through multi-dimensional noise modeling and dynamic environment adaptation, the precision, the real-time performance and the scene coverage capability of power line carrier communication simulation are remarkably improved, and the simulation bottleneck in a complex power grid environment in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of carrier communication, and particularly to a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model. Background Art

[0002] Power line carrier communication (PLC) technology uses the power line network to transmit data and has important applications in fields such as smart grids and new energy access. However, the power line channel has complex noise interference, including periodic interference (such as switching power supply noise), transient noise (such as impulse groups), and background noise (steady-state Gaussian noise). In the prior art, the PLC channel simulation method has the following defects: Traditional PLC channel interference models usually simplify the noise to a single type (such as additive white Gaussian noise) and cannot accurately reflect the complex and variable interference characteristics in the actual power line environment; moreover, existing methods mostly adopt static modeling methods and cannot adapt to the influence of dynamic factors such as grid load changes and topology structure adjustments on the communication channel. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to some extent. For this purpose, the object of the present invention is to propose a power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model to establish a multi-dimensional interference model and realize the dynamic simulation of channel characteristics.

[0004] To achieve the above object, a power line carrier channel simulation method based on a dynamic multi-dimensional interference model according to the first aspect embodiment of the present invention includes the following steps: S1. Collect power line noise samples, extract the characteristic parameters of periodic interference, transient noise, and background noise, classify and label them according to the noise type and grid operating conditions, and establish a database mapping noise characteristics to operating conditions; S2. Based on the database, construct a multi-dimensional noise interference model including a periodic interference model, a transient noise model, and a background noise model; S3. Input grid topology parameters and real-time operation data, establish a grid structure model, and dynamically update the channel transfer function according to changes in the grid line structure, load status, or environmental temperature exceeding the limit; S4. Combine the grid historical load data and the multi-dimensional noise interference model to analyze the influence of load changes on noise characteristics and generate a load change curve; S5. Dynamically synthesize periodic interference, transient noise, and background noise according to the updated channel transfer function and the load change curve, and superimpose the synthesized noise on the power line carrier signal to generate a simulation signal; S6. Calculate communication performance indicators, evaluate the effect of anti-interference algorithms, and calibrate the parameters of the multi-dimensional noise interference model.

[0005] In some embodiments of the present invention, in step S1, the fast Fourier transform (FFT) is used to extract the characteristic parameters of periodic interference, the wavelet transform combined with the pulse detection algorithm is used to extract the characteristic parameters of transient noise, and the statistical analysis method is used to extract the characteristic parameters of background noise.

[0006] In some embodiments of the present invention, in step S2, a non-Gaussian distribution is introduced when establishing the background noise model, and the weight parameters of the Gaussian distribution and the α-stable distribution are dynamically adjusted to adapt to the noise characteristic offset caused by the change of the power grid load.

[0007] In some embodiments of the present invention, in step S3, the channel transfer function is updated based on the following three dynamic update trigger conditions, specifically including: Power grid line structure change trigger update: The branch access / disconnection is identified by sensor monitoring or load current fluctuation analysis, and the delay and attenuation parameters of the signal transmission path are dynamically adjusted; When the change amplitude of the load impedance exceeds the preset threshold, the attenuation degree and phase shift characteristics of the signal transmission are updated; When the environmental temperature change exceeds the preset range, the signal transmission loss and phase parameters are corrected based on the correlation between temperature and wire impedance.

[0008] In some embodiments of the present invention, the power grid line structure change trigger update further includes the specific processing methods for branch access or branch disconnection: Known type branch access processing: When it is detected that the branch type accessed in the power grid is a predefined type, the stored signal transmission parameters corresponding to this branch, including the signal delay time and attenuation degree, are directly called and updated to the channel model; Unknown type branch access processing: When it is detected that the accessed branch type is not predefined, a test signal covering the frequency range of 10 kHz - 10 MHz is sent to the power grid. By measuring the intensity change and phase difference of the received signal, the least squares method is used to fit the measured signal intensity and phase data, and the signal delay time and attenuation degree of this branch are inversely calculated and the new parameters are stored for subsequent calls; Branch disconnection processing: When it is detected that a certain branch line is disconnected, the signal transmission path information corresponding to this branch is removed from the channel model, and this path is marked as an invalid state, and the signal contribution of this path is ignored in subsequent simulations.

[0009] In some embodiments of the present invention, in step S4, the influence of the load change on the noise characteristics specifically includes at least one of the following: a. The load change causes the noise intensity to change; b. The load change causes the noise frequency distribution to change; c. The change in load causes the change in the time characteristics of the noise; d. The change in load causes the change in the power ratio among the background noise, transient noise, and periodic interference, forming different noise combination forms under different load conditions.

[0010] In some embodiments of the present invention, in step S5, the dynamic synthesis of periodic interference, transient noise, and background noise is specifically manifested as: According to the real-time load impedance, dynamically adjust the harmonic amplitude of the periodic interference, the pulse occurrence probability of the transient noise, and the power spectral density of the background noise to form a dynamic noise combination. The combination adjusts the relative intensity and characteristics of various noises according to the simulated real-time condition of the power line to generate an overall noise signal, and the composition and intensity of the overall noise signal change dynamically with the simulated time.

[0011] In some embodiments of the present invention, the specific operation of superimposing the synthesized noise on the power carrier signal to generate a simulation signal is: Superimpose the overall noise signal on the value of the original power carrier communication signal at each point in the simulated time to obtain a simulation signal carrying communication data and superimposed with the synthesized noise.

[0012] To achieve the above object, the embodiment of the second aspect of the present invention proposes a power carrier channel simulation system based on a dynamic multi-dimensional interference model, including: A parameter acquisition module that acquires real-time grid parameters, including at least line load information; A noise generation module that stores or connects to a multi-dimensional noise interference model internally and is configured to: generate a time-varying noise component according to the real-time parameters, dynamically combine periodic interference, transient noise, and background noise, and generate a synthesized noise signal; A signal superimposing module that superimposes the synthesized noise signal on the original power carrier signal to generate a simulation signal with simulated noise; A performance evaluation module that receives the simulation signal, calculates, and evaluates communication performance indicators.

[0013] To achieve the above object, the embodiment of the third aspect of the present invention proposes an electronic device, including a memory, a processor, and a computer program stored on the memory. When the computer program is executed by the processor, it implements the above-mentioned power carrier channel simulation method based on a dynamic multi-dimensional interference model.

[0014] The power line carrier channel simulation method and system based on a dynamic multi-dimensional interference model according to the embodiments of the present invention significantly improve the accuracy, real-time performance, and scenario coverage ability of power line carrier communication simulation through multi-dimensional noise modeling, dynamic environment adaptation, and system-level optimization. It solves the simulation bottleneck of the prior art in complex power grid environments, has important engineering value for scenarios such as the research and development of smart grid equipment and the access of new energy, shortens the equipment research and development cycle, and has significant technological progress and industry application prospects. Description of the Drawings

[0015] Figure 1 is a process schematic diagram of the power line carrier channel simulation method based on a dynamic multi-dimensional interference model according to an embodiment of the present invention; Figure 2 is a two-dimensional time-frequency heat map in the present invention; Figure 3 is a schematic diagram of the relationship between noise intensity and load in the present invention; Figure 4 is a schematic diagram of the relationship between noise frequency distribution and load in the present invention; Figure 5 is a schematic diagram of the relationship between noise time characteristics and load in the present invention; Figure 6 is a schematic diagram of the relationship between the power ratio of noise types and load in the present invention; Figure 7 is a schematic structural diagram of the power line carrier channel simulation system based on a dynamic multi-dimensional interference model according to another embodiment of the present invention; Figure 8 is a schematic structural diagram of an electronic device according to another embodiment of the present invention. Detailed Embodiments

[0016] The embodiments of the present invention will be described in detail below. The examples of the embodiments are shown in the drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0017] The power line carrier channel simulation method, system, and electronic device based on a dynamic multi-dimensional interference model according to the embodiments of the present invention will be described below with reference to the drawings.

[0018] Figure 1 is a flowchart of the power line carrier channel simulation method based on a dynamic multi-dimensional interference model according to an embodiment of the present invention.

[0019] As Figure 1 shown, the power line carrier channel simulation method based on a dynamic multi-dimensional interference model includes the following steps: A power line carrier channel simulation method based on a dynamic multi-dimensional interference model, comprising the following steps: S1. Use a high-speed analog-to-digital converter (ADC) to collect power line noise signals in real time at an appropriate sampling frequency to ensure that the collected noise signals can accurately reflect the actual situation. To improve the comprehensiveness of the data, multiple collections can be performed at different time periods and under different power grid operating conditions; subsequently, extract the characteristic parameters of periodic interference, transient noise, and background noise, classify and label them according to the noise type and the corresponding power grid operating conditions, where the power grid operating conditions include load size and power grid topology structure, and finally establish a noise database containing the mapping relationship between noise characteristics and operating conditions for facilitating the subsequent establishment and query of the model.

[0020] As an example, the above appropriate sampling frequency should not be lower than the Nyquist frequency.

[0021] As an example, when extracting the characteristic parameters of periodic interference, use the fast Fourier transform (FFT) to convert the collected time-domain signal to the frequency domain, and extract the characteristic parameters of periodic interference, such as frequency, period, amplitude, etc., by analyzing information such as spectral line distribution, peak frequency, and amplitude in the frequency domain; Adopt wavelet transform combined with a pulse detection algorithm to extract the characteristic parameters of transient noise; When extracting the characteristic parameters of background noise, use statistical analysis methods, such as calculating the mean, variance, probability density function, etc., to extract the characteristic parameters of background noise to describe its statistical characteristics.

[0022] S2. Based on the noise database, establish a periodic interference model, a transient noise model, and a background noise model respectively, and combine the established periodic interference model, transient noise model, and background noise model together to form a multi-dimensional noise interference model, which can comprehensively describe various characteristics of power line noise.

[0023] S3. Input power grid topology parameters such as the number of nodes, line connection relationships, and line parameters, where the line parameters include resistance, inductance, capacitance, etc., and use circuit theory and network analysis methods to establish a power grid structure model, which can describe the transmission path and characteristics of power line carrier signals in the power grid; subsequently, obtain the operating data of the power grid in real time, such as load size, voltage, current, etc. information, which reflects the real-time operating conditions of the power grid, and dynamically update the channel transfer function according to the real-time operating conditions. For example, when the load changes, update parameters such as the attenuation, delay, and phase of the channel to reflect the impact of power grid operating condition changes on signal transmission.

[0024] S4. Predict the load fluctuation trend based on the power grid historical load data and the multi-dimensional noise interference model, analyze the impact of load changes on the characteristics of periodic interference, transient noise, and background noise, generate a load change curve, and optimize the anti-interference algorithm parameters.

[0025] As an example, an autoregressive integrated moving average model (ARIMA) or a long short-term memory network (LSTM) can be used for load prediction.

[0026] As an example, by adjusting the parameters of the adaptive filter, changing the modulation and demodulation method, etc., the anti-interference ability of the communication system under different loads and noise conditions can be improved.

[0027] S5. According to the updated channel transfer function and the load change curve, adjust the parameters of the multi-dimensional noise interference model in real time to generate time-varying periodic interference, transient noise, and background noise components; then dynamically combine the periodic interference, transient noise, and background noise, and superimpose the synthesized noise on the power line communication signal through superposition operation to output a simulated signal with interference, which can truly reflect the transmission situation of power line communication in the actual noise environment.

[0028] As an example, the dynamic combination is to determine the weights of each noise component at different times according to the occurrence probability and time distribution of different noises, and then perform weighted summation.

[0029] S6. Calculate communication performance indicators, such as bit error rate (BER), signal-to-noise ratio (SNR), throughput, etc., to evaluate the effect of the anti-interference algorithm, and compare the calculated communication performance indicators with the measured data, and calibrate the parameters of the multi-dimensional noise interference model according to the comparison results.

[0030] As an example, if there is a large deviation between the bit error rate obtained by simulation and the measured bit error rate, adjust the parameters of the noise model to make it more in line with the actual situation.

[0031] The power line channel simulation method based on the dynamic multi-dimensional interference model can accurately simulate the transmission situation of power line communication in the actual noise environment through the comprehensive acquisition and modeling of power line noise, and the real-time tracking and analysis of the grid operating conditions, timely discover the problems existing in the communication system and the deficiencies of the anti-interference algorithm, calibrate the model parameters according to the measured data, continuously improve the accuracy and reliability of the multi-dimensional noise interference model, make the simulation results closer to the actual situation, and provide strong support for the design and optimization of the power line communication system.

[0032] In some embodiments of the present invention, the specific steps for extracting the characteristic parameters of the transient noise specifically include: S11. Signal preprocessing: Preprocess the original power line noise signal, the purpose of which is to eliminate the DC offset and retain the target frequency band, such as the transient noise components in the range of 10 kHz - 1 MHz, and suppress the power frequency and low-frequency interference through a band-pass filter, and retain the transient noise components within the target frequency band; DC component elimination: Calculate the mean value of the original signal Calculate the mean , the signal after removing the DC component is: , where is the number of sampling points, , is the sampling period, is the mean value of the DC component of the original signal; Band-pass filtering: A Butterworth band-pass filter is used, and the transfer function is: where is the passband range, are the low / high cut-off frequencies of the band-pass filter, such as , , is the order of the filter, usually taken as 2 - 4; The filtered signal , where represents the Fourier transform.

[0033] S12, Time-frequency analysis: Perform continuous wavelet transform on the preprocessed signal, and analyze the energy distribution of the signal in different time and frequency ranges by adjusting the scale and translation parameters of the wavelet to generate a time-frequency domain coefficient matrix; Wavelet basis function selection: Morlet wavelet is used: where is the central angular frequency, usually taken as 5 rad / s to balance the time domain and frequency domain resolutions.

[0034] Wavelet transform calculation: Perform CWT on the preprocessed signal to generate a time-frequency domain coefficient matrix :

[0035] where is the scale parameter, which controls the frequency resolution, , is the translation parameter, which controls the time localization, is the conjugate complex number.

[0036] Time-frequency diagram generation: Calculate the coefficient amplitude matrix to generate a two-dimensional heat map of time-scale (frequency), with the horizontal axis being time , and the vertical axis being the equivalent frequency , is the wavelet center frequency.

[0037] Such as Figure 2As shown, the depth of color at different positions in the figure represents the energy density of the signal at that specific time and specific frequency point. The brighter / warmer the color (e.g., yellow, red), the higher the energy at that time-frequency point; the darker / colder the color (e.g., dark blue), the lower the energy. This figure clearly visualizes a transient pulse event that occurs at approximately 3 ms. This pulse has broadband frequency characteristics, with energy distributed over a wide frequency range (10 kHz - 1 MHz), and this pulse is instantaneous, with energy concentrated within a very short time window.

[0038] S13. Pulse determination and location: Based on the amplitude of the time-frequency domain coefficients, set a dynamic threshold, such as a proportional threshold of the global maximum value, identify the energy concentration regions that exceed the threshold, and determine the start and end time points of the transient pulse. Dynamic threshold setting: Take the global maximum value of the amplitude of the time-frequency coefficients , and set the threshold , where is the proportional factor, such as , which needs to be optimized through measured data.

[0039] Energy region identification: Mark all regions in the time-frequency diagram, merge consecutive time intervals, and obtain the candidate pulse segments .

[0040] Time location optimization: For each candidate segment, find the slope mutation point (peak of the first derivative) in the time-domain signal , and correct the pulse start / end time to:

[0041] Within the candidate segment, ( is the estimated value of the pulse duration); S14. Waveform interception: According to the positioning result, intercept the time-domain waveform segment corresponding to the transient pulse from the preprocessed signal, specifically including: According to the positioned and , intercept the pulse waveform segment from the preprocessed signal:

[0042] where, is the rectangular window function, which takes 1 when , and 0 otherwise.

[0043] S15. Waveform modeling and fitting: Assume that the transient pulse waveform conforms to the double-exponential decay characteristic, such as fast rise and slow decay, use the least squares method to parametrically fit the intercepted waveform, adjust the model parameters to minimize the fitting error, and calculate the characteristic parameters related to the transient noise.

[0044] Model assumption: The transient pulse waveform conforms to the double-exponential decay characteristic: , Where: is the peak amplitude of the pulse (V), is the fast decay time constant ( , corresponding to the rising edge), is the slow decay time constant ( , corresponding to the decay edge), and .

[0045] Least squares fitting: Define the fitting error function:

[0046] Where, are the parameters to be estimated, is the number of sampling points of the pulse segment; Solve for the optimal solution of the parameters: Minimize through the gradient descent method or the matrix inversion method, satisfying: , and then obtain the optimal parameters ; Characteristic parameter output: Pulse amplitude , rise time constant , decay time constant , pulse duration , energy .

[0047] In the prior art, it is generally assumed that the background noise follows a Gaussian distribution. However, the actual power line background noise often contains impulse-type low-frequency noise, such as random narrow pulses generated by poor contact, presenting a heavy-tailed distribution characteristic (non-Gaussian). Its probability density function (PDF) has a long-tail effect. The traditional Gaussian model will underestimate the occurrence probability of extreme noise events, resulting in a deviation in the bit error rate simulation in low signal-to-noise ratio scenarios.

[0048] In some embodiments of the present invention, in step S2, when establishing the background noise model, a non-Gaussian distribution is introduced, specifically including: S21. Analyze the noise characteristics: Statistically analyze the characteristic parameters of the collected background noise to identify the distribution law of its asymmetric waveform or burst pulse components, specifically including: Data collection: First, collect background noise data, which contains the amplitude information of the noise at different times; Statistical analysis: Statistically analyze the collected noise data. Calculate the mean of the noise, which can reflect the average level of the noise; calculate the variance, and the magnitude of the variance reflects the degree of dispersion of the noise energy; calculate the skewness, and a non-zero skewness indicates that the noise distribution is asymmetric; calculate the kurtosis, and a kurtosis greater than 0 means that the noise has the characteristics of sharp peaks and heavy tails, that is, there are burst pulses; Pulse detection: Use a sliding window (window length ), and calculate the short-time energy:

[0049] When it is, it is determined as a burst pulse event and the time is recorded.

[0050] Use the sliding window method to calculate the short-time energy of the noise. When the short-time energy exceeds a certain threshold, which is set here to 2.5 times the noise variance, it is considered that a burst pulse has been detected, and the time when the pulse appears is recorded.

[0051] S22. Construct a combined distribution model: According to the analysis results, model the background noise as a combined model of multiple distribution types. The model architecture uses a mixture model of Gaussian distribution and α-stable distribution to describe the superposition characteristics of steady-state noise and pulse noise.

[0052] Gaussian distribution: Used to describe the steady-state part in the background noise, just like the steadily flowing water under a calm water surface. It is determined by two parameters, the mean and the standard deviation. The mean represents the central position, and the standard deviation represents the degree of data dispersion.

[0053] α-stable distribution: Mainly used to describe the burst pulse component in the noise, similar to the occasional waves on the water surface. It has two important parameters. The characteristic exponent α determines the pulse characteristics of the distribution. The closer α is to 0, the stronger the pulse characteristics; the scale parameter γ controls the diffusion range of the noise energy.

[0054] Definition of the basic distribution: Gaussian distribution (steady-state noise): Probability density function (PDF):

[0055] Among them, is the mean of the Gaussian distribution, is the standard deviation.

[0056] Symmetric -stable distribution (SaS, pulse noise): Characteristic function: ; Among them, is the characteristic exponent ( Degenerate into a Gaussian distribution. The smaller the value, the stronger the impulse characteristic), is the scale parameter (controlling the diffusion of noise energy).

[0057] Mixture model expression:

[0058] where is the distribution weight, is the Gaussian component weight, is -stable component weight.

[0059] S23. Dynamic parameter adjustment: Changes in the power grid load will cause the noise characteristics to shift. For example, when the load increases, the number of burst pulses may increase. Therefore, according to the real-time load situation, automatically adjust the weight parameters of each distribution in the combined model so that the model can adapt to the noise characteristic shift caused by the change of the power grid load; Adjustment method: Weight adjustment: The weight of the Gaussian distribution will decrease as the load power increases. When the load is unloaded, the initial value of the Gaussian distribution weight is set to 0.7; for every 1 kW increase in the load power, the Gaussian distribution weight decreases by 0.006.

[0060] α-stable distribution parameter adjustment: The characteristic exponent α will decrease as the total harmonic distortion of the load increases. The initial characteristic exponent is set to 1.6. The greater the total harmonic distortion, the smaller α, which means the stronger the impulse characteristic.

[0061] Parameter optimization: Use the least mean square error algorithm to continuously update the model parameters to minimize the error between the model prediction value and the actual noise value.

[0062] S24. Sparse pulse decomposition and compensation: Separate the burst pulse component from the background noise to reduce its interference with the overall noise model, specifically including: Pulse separation: By setting a threshold (here it is 3 times the noise standard deviation), when the noise amplitude exceeds this threshold, it is considered a burst pulse. Extract these pulses separately to form a pulse sequence; Pulse modeling: Establish an independent sub-model for the separated pulses. The occurrence frequency of the pulses follows a Poisson distribution. The basic occurrence rate is 5 times per second when the load is unloaded, and the higher the load current, the higher the occurrence rate; the amplitude of the pulses follows a lognormal distribution, and the parameters of the distribution are obtained through the analysis of the actual pulse data; Steady-state noise processing: Subtract the pulse sequence from the original background noise to obtain the steady-state noise. Apply the combined distribution model constructed above to the steady-state noise, which can avoid the interference of the pulse component on the model parameter estimation.

[0063] S25. Model Verification and Calibration: Verify the accuracy of the model to ensure that the model can truly reflect the actual background noise situation, specifically including: Verification Method: K - S Test: Compare the cumulative distribution functions of the background noise generated by model simulation and the actually measured noise. If the maximum deviation between the two exceeds a preset threshold, which is set to 0.1 here, it indicates that the model needs to be adjusted; Root Mean Square Error Test: Calculate the root mean square error between the model simulation value and the actually measured value. When the root mean square error exceeds 0.3 times the standard deviation of the noise, the model also needs to be calibrated.

[0064] Calibration Method: When the model verification fails, the particle swarm optimization algorithm is used to adjust the model parameters. This algorithm continuously searches for the parameter combination that minimizes the objective function (here it is the sum of the K - S test deviation and the root mean square error). After 50 iterations and using 40 particles for the search, until the model meets the accuracy requirements.

[0065] By combining the Gaussian distribution and the α - stable distribution, the problem that the traditional Gaussian model cannot accurately describe long - tailed pulses is solved, and the weight parameters of the model will be automatically adjusted according to the grid load power, realizing the dynamic switching with mainly Gaussian steady - state noise under light load and enhanced pulse components under heavy load, which is more in line with the noise characteristics of nonlinear loads in the actual power grid.

[0066] Due to the complex and changeable power grid environment, the channel transmission characteristics will be affected by various factors. Therefore, the present invention proposes three dynamic update trigger conditions, namely, the change of the power grid line structure, the sudden change of the load state, and the over - limit of the environmental temperature. By monitoring and responding to these conditions, the timely update and calibration of the channel transmission function are realized to ensure that the model can accurately reflect the actual signal transmission situation. In step S3, the channel transmission function is updated based on the following three dynamic update trigger conditions, specifically including: S31. Update Triggered by the Change of the Power Grid Line Structure. The change of the power grid line structure will directly affect the signal transmission path and characteristics. Therefore, it is necessary to update the channel transmission function in a timely manner.

[0067] Real - time Sensor Monitoring: Install sensors at key nodes and branches of the power grid. These sensors can sense the connection status of the lines in real time, such as whether there is a new branch access or an existing branch disconnection. The sensors timely feedback the monitored information to the control system for corresponding adjustments; Analysis of load current fluctuations: By monitoring and analyzing the real-time load current in the power grid, when abnormal current fluctuations occur, it may mean that the line structure has changed. For example, when a new branch is connected, the total load current will increase accordingly; when a branch is disconnected, the load current will decrease. By analyzing the characteristics such as the amplitude and frequency of the current fluctuations, it can be determined whether the line structure has changed.

[0068] Steps to update the channel transfer function: After detecting a change in the line structure, it is first necessary to dynamically adjust the delay parameters and attenuation parameters of the signal transmission path in the model. When the signal is transmitted in different line structures, it will experience different delays and attenuations, so these parameters need to be re-determined according to the new line structure. Then, according to the adjusted delay parameters and attenuation parameters, the channel transfer function is recalculated. The channel transfer function describes the transmission characteristics of the signal in the channel, including the amplitude attenuation and phase change of the signal, so it needs to be updated according to the actual situation.

[0069] S32. Updates are triggered by sudden changes in the load state. Sudden changes in the load state will cause changes in the attenuation degree and phase shift characteristics of the signal in the transmission path, so it is necessary to adaptively adjust the channel model parameters.

[0070] Method for determining sudden changes in the load state: By monitoring real-time load data, the impedance information of the load is obtained. When it is detected that the change amplitude of the load impedance exceeds 20%, it is determined as a significant change. For example, the startup or shutdown of high-power equipment will cause a large change in the load impedance.

[0071] Steps to update the channel transfer function: After determining that a significant change has occurred in the load state, trigger the adaptive adjustment of the channel model parameters. According to the change in the load impedance, adjust the attenuation degree and phase shift characteristics of the signal in the transmission path, and update the channel transfer function to reflect the impact of the load state change on signal transmission.

[0072] S33. Calibration is triggered when the ambient temperature exceeds the limit. Changes in the ambient temperature will affect the resistance and capacitance of the wire, resulting in changes in the loss parameters and phase parameters during signal transmission, so it is necessary to globally calibrate the channel transfer function.

[0073] Method for monitoring changes in the ambient temperature: Install temperature sensors at key positions in the power grid to monitor the changes in the ambient temperature in real time. When the change in the ambient temperature exceeds 10°C, it is considered that the temperature change exceeds the allowable range.

[0074] Steps to calibrate the channel transfer function: According to the correlation between temperature and wire resistance and capacitance, dynamically correct the loss parameters and phase parameters during signal transmission. Generally speaking, an increase in temperature will cause an increase in wire resistance and a decrease in capacitance, resulting in an increase in signal loss and phase shift.

[0075] Perform global calibration on the channel transfer function to ensure the accuracy of the model at different temperatures. By calibrating the channel transfer function, the model can better reflect the actual signal transmission situation and improve the performance of the communication system.

[0076] In some embodiments of the present invention, the trigger for updating due to the change in the power grid line structure further includes specific processing methods for branch access or branch disconnection: Known type branch access processing: When it is detected that the type of the branch accessed in the power grid is a predefined type, since the signal transmission characteristics of this branch have been studied and recorded, the stored signal transmission parameters corresponding to this branch, including the signal delay time and attenuation degree, can be directly called and updated to the channel model. This can quickly and accurately update the channel transfer function and improve the response speed of the system; Unknown type branch access processing: When it is detected that the type of the accessed branch is not predefined, a test signal covering the frequency range of 10 kHz - 10 MHz is sent to the power grid. By measuring the intensity change and phase difference of the received signal, the least squares method is used to fit the measured signal intensity and phase data, and the signal delay time and attenuation degree of this branch are inversely calculated and the newly obtained parameters are stored for subsequent calls. In this way, when the same type of branch access is encountered again, the stored parameters can be directly used to improve the efficiency of the system; Branch disconnection processing: When it is detected that a certain branch line is disconnected, the signal transmission path information corresponding to this branch is removed from the channel model, and this path is marked as an invalid state. The signal contribution of this path is ignored in subsequent simulations, and its influence is excluded during global parameter refitting to avoid the residual error path affecting the result accuracy in subsequent simulations.

[0077] In some embodiments of the present invention, in step S4, the influence of load change on the noise characteristics specifically includes at least one of the following: a. The load change causes the noise intensity to change. As Figure 3 shown, the horizontal axis is the load percentage (0% - 100%), and the vertical axis is the noise intensity (RMS value). The curve in the figure shows a quadratic function rising trend. When the load increases, the noise intensity increases significantly. For example, when the load increases from 20% to 80%, the noise intensity increases from about 2.5 RMS to about 8.5 RMS.

[0078] b. The load change causes the noise frequency distribution to change. As Figure 4 shown, the horizontal axis is the frequency (0 - 1000 Hz), and the vertical axis is the power spectral density. The green curve (light load 20%): The energy in the high frequency band (>500 Hz) is higher, showing a right shift of the peak. The red curve (heavy load 80%): The energy in the low frequency band (<500 Hz) dominates, and the peak shifts to the left.

[0079] c. The change in load causes a change in the temporal characteristics of the noise. For example, Figure 5 As shown, the upper half graph (medium load of 30%): High-amplitude pulses are sparsely distributed in the time-domain waveform (such as at 0.2 seconds and 0.6 seconds), and the background noise fluctuates smoothly; the lower half graph (high load of 70%): The pulse interval shortens (such as at 0.1 seconds, 0.3 seconds, and 0.7 seconds), and the amplitude of the background noise increases and the fluctuation is intense.

[0080] d. The change in load causes a change in the power ratio among the background noise, transient noise, and periodic interference, forming different noise combination forms under different load conditions. For example, Figure 6 As shown, the horizontal axis: load percentage (0% - 100%), which is consistent with clause a. The vertical axis: power ratio (%). The sum of the power ratios of the three types of noise is 100%. Among them: The blue area (background noise): Decreases from 50% to 10% as the load increases, reflecting that the background noise (such as thermal noise) dominates under light load, and the ratio decreases due to the enhancement of other noises under heavy load; The red area (periodic interference): Increases from 20% to 60%, indicating that the periodic interference (such as the harmonics of the switching power supply) increases significantly as the load increases; The green area (transient noise): First increases and then decreases (the peak is about 30% at 50% load), simulating that the transient pulses increase due to frequent start and stop of the device under medium load, and decrease due to the system stability under full load.

[0081] As an example, machine learning algorithms such as neural networks and decision trees can be used to learn and analyze a large amount of historical data to establish the mapping relationship between the channel transfer function and various influencing factors. When the dynamic update trigger condition is detected, the change in the channel transfer function is quickly predicted through the machine learning model, and corresponding updates are made.

[0082] As an example, the calculation and update tasks of the channel transfer function are assigned to the cloud computing center and edge devices. The edge device can collect and process local data in real time for preliminary analysis and judgment; the cloud computing center can perform in-depth analysis and processing on a large amount of global data to achieve more accurate update of the channel transfer function. This can give full play to the advantages of cloud computing and edge computing and improve the performance and response speed of the system.

[0083] It should be noted that in a power line carrier communication system, the real-time load impedance is a dynamically changing parameter, which can have a significant impact on various types of noise in the system. By monitoring the real-time load impedance, the present invention specifically adjusts the relevant characteristics of periodic interference, transient noise, and background noise, thereby generating a time-varying noise component that conforms to the actual situation. In some embodiments of the present invention, the specific manifestation of generating the time-varying noise component in step S5 is as follows: Harmonic amplitude adjustment of periodic interference: Establish a mapping relationship between the real-time load impedance and the harmonic amplitude of periodic interference. An empirical formula or a look-up table can be obtained through a large amount of experimental data and theoretical analysis. For example, when the real-time load impedance increases, the harmonic amplitude of certain specific frequencies may decrease; conversely, when the load impedance decreases, the harmonic amplitude may increase. The system will monitor the change of the load impedance in real time and dynamically adjust the harmonic amplitude of periodic interference according to the pre-established mapping relationship; Pulse occurrence probability adjustment of transient noise: Transient noise is usually caused by sudden events such as switch operations and lightning strikes in the power system. The change of the load impedance will affect the stability of the power system, thereby changing the pulse occurrence probability of transient noise. When adjusting, the relationship between the real-time load impedance and the pulse occurrence probability of transient noise can also be established through experiments and analysis. For example, when the load impedance changes greatly, the voltage and current of the power system will fluctuate, which may increase the pulse occurrence probability of transient noise. The system will dynamically adjust the pulse occurrence probability of transient noise according to the change of the real-time load impedance; Power spectral density adjustment of background noise: Background noise is the noise that persists in the power system, and its power spectral density is related to the overall operating state of the power system. The change of the real-time load impedance will affect the electromagnetic environment of the power system, thereby changing the power spectral density of background noise. By monitoring the real-time load impedance, analyze its influence on the power spectral density of background noise. Spectral analysis technology can be used to monitor the change of the power spectral density of background noise in real time. According to the change of the load impedance, dynamically adjust the power spectral density of background noise to make it more in line with the actual situation Through the above adjustments and combinations, an overall noise signal is generated. The composition and intensity of this overall noise signal will change dynamically with the simulation time, mainly manifested in: Dynamically changing composition: At different simulation time points, due to the changes in the real-time load impedance and the power line conditions, the relative intensities and characteristics of periodic interference, transient noise, and background noise will be continuously adjusted, resulting in changes in the composition of the overall noise signal. For example, at a certain time point, periodic interference may dominate; while at another time point, transient noise may become the main component.

[0084] Intensity dynamic change: The intensity of the overall noise signal also changes dynamically with the simulation time. When there are large changes in the real-time load impedance or power system emergencies occur, the intensity of the overall noise signal may suddenly increase; while when the power system is operating stably, the intensity of the overall noise signal may be relatively small.

[0085] Further refine the above scheme. The specific operation of superimposing the synthetic noise on the power line carrier communication signal is as follows: Superimpose the overall noise signal on the value of the original power line carrier communication signal without noise at each point in the simulation time to obtain a simulation signal carrying communication data and superimposed with synthetic noise.

[0086] Since both the power line carrier communication signal and the noise signal are electrical signals, they can be regarded as signals in a linear system during the transmission process and satisfy the principle of linear superposition. By superimposing the noise signal, the situation of signal being interfered by noise in actual power line carrier communication can be simulated.

[0087] Specific steps for superposition implementation: ① Signal synchronization: To ensure accurate superposition at each point in the simulation time, it is necessary to synchronize the overall noise signal and the power line carrier communication signal. Clock synchronization technology can be used to make the two signals have the same time reference. For example, use a high-precision clock source to synchronize the sampling and processing processes of the signals to ensure that the signal values obtained at the same time point are corresponding; ② Numerical superposition: On the basis of synchronization, at each point in the simulation time, add the value of the overall noise signal to the value of the power line carrier communication signal. This can be achieved through hardware circuits or software algorithms. For example, in hardware implementation, an adder circuit can be used to add the two signals; in software implementation, an algorithm can be written in a programming language to add the sampling values of the two signals point by point; ③ Simulation signal generation: Through the above superposition operation, a simulation signal carrying communication data and superimposed with synthetic noise is obtained. This simulation signal can be used for subsequent performance evaluation and testing of the power line carrier communication system, such as analyzing indicators such as the bit error rate and signal-to-noise ratio of the signal to evaluate the communication performance of the system in an actual noise environment. Based on the above method, as Figure 7 Disclosed is a power line carrier channel simulation system based on a dynamic multi-dimensional interference model, including: A parameter acquisition module is used to acquire real-time power grid parameters related to the power line carrier channel. The real-time power grid parameters at least include line load information. The core task of the parameter acquisition module is to collect real-time power grid parameters closely related to the power line carrier channel, and the line load information is one of the most critical parameters. Changes in the line load information will have a significant impact on the transmission characteristics of the power line carrier signal and also affect the characteristics of various noises. In addition, this module may also acquire other important parameters, such as grid voltage, frequency, line topology, etc.

[0088] A noise generation module internally stores or connects to a multi-dimensional noise interference model and is configured to: in response to the real-time power grid parameters acquired by the parameter acquisition module, generate a time-varying noise component in real time according to the multi-dimensional noise interference model, and dynamically combine periodic interference, transient noise, and background noise to generate a synthetic noise signal.

[0089] A signal superposition module is used to superpose the synthetic noise signal generated by the noise generation module with the input original power line carrier communication signal, such as through an addition operation, to generate a simulated signal with analog noise.

[0090] There are two implementation methods for signal superposition: Hardware implementation: An adder circuit can be used to implement signal superposition. The synthetic noise signal and the original power line carrier communication signal are input into the adder, and the signal output by the adder is the simulated signal with analog noise.

[0091] Software implementation: In a digital signal processing system, an algorithm can be written in a programming language to add the sampled values of the synthetic noise signal and the original power line carrier communication signal point by point to obtain the simulated signal.

[0092] A performance evaluation module is used to receive the simulated signal and calculate and evaluate the simulated signal according to preset communication performance indicators, such as bit error rate, signal-to-noise ratio, signal strength, etc. The performance evaluation module can use various methods to evaluate the simulated signal, such as statistical analysis, spectrum analysis, etc. By analyzing the evaluation results, the performance of the power line carrier communication system in different noise environments can be understood, providing a basis for system optimization and improvement. Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0093] As Figure 8 shown is a schematic structural diagram of an electronic device in the present invention. The electronic device 200 includes: a processor 201 and a memory 203. Among them, the processor 201 and the memory 203 are connected, such as through a bus 202. Optionally, the electronic device 200 may further include a transceiver 204. It should be noted that in practical applications, the transceiver 204 is not limited to one, and the structure of the electronic device 200 does not constitute a limitation to the embodiments of the present invention.

[0094] The processor 201 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in connection with the disclosure of the present invention. The processor 201 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0095] The bus 202 can include a path for transmitting information between the above components. The bus 202 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, etc. The bus 202 can be divided into an address bus, a data bus, a control bus, etc. For the sake of representation, Figure 8 only a thick line is used to represent it in the figure, but it does not mean that there is only one bus or one type of bus.

[0096] The memory 203 is used to store a computer program corresponding to the power line carrier channel simulation method based on the dynamic multi-dimensional interference model in the above embodiments of the present invention, and this computer program is controlled and executed by the processor 201. The processor 201 is used to execute the computer program stored in the memory 203 to implement the content shown in the foregoing method embodiments.

[0097] Among them, the electronic device 200 includes but is not limited to: mobile terminals such as laptop computers, PADs (tablet computers), etc., and fixed terminals such as desktop computers, etc. Figure 8 The illustrated electronic device 200 is only an example and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0098] The electronic device 200 according to the embodiment of the present invention realizes high-precision dynamic simulation of the power line carrier communication channel interference through dynamic multi-dimensional interference modeling, real-time parameter update, intelligent optimization algorithm and modular system architecture, solves the technical bottleneck of dynamic interference modeling in power line carrier communication, provides an efficient and reliable tool for the research and development and performance evaluation of communication devices, and ensures the robustness and generalization ability of the model in practical applications through the closed-loop calibration of simulation and measured data.

[0099] It should be noted that the logic and / or steps represented in the flowchart or described in other ways herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus or device), or in combination with these instruction execution systems, apparatus or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, carry a channel, propagate or transmit a program for use by or in combination with an instruction execution system, apparatus or device. More specific examples (non-exhaustive list) of computer-readable media include the following: an electrical connection portion with one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, a computer-readable medium can even be paper or other suitable media on which a program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpretation or other appropriate processing as necessary, and then stored in a computer memory.

[0100] It should be understood that each part of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following well-known technologies in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application-specific integrated circuits with appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0101] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0102] In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0103] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A power carrier channel simulation method based on a dynamic multi-dimensional interference model, characterized in that: The following steps are involved: S1. Collect power line noise samples, extract characteristic parameters of periodic interference, transient noise and background noise, classify and label them according to noise type and grid working condition, and establish a database mapping noise characteristics and working conditions; S2. Based on the database, construct a multidimensional noise interference model including a periodic interference model, a transient noise model and a background noise model; S3, input grid topology parameters and real-time operation data, establish a grid structure model, and dynamically update the channel transfer function according to changes in grid line structure, load status or excessive ambient temperature; S4. Analyze the impact of load changes on noise characteristics by combining historical load data of the power grid and the multi-dimensional noise interference model, and generate a load change curve; S5. According to the updated channel transfer function and load change curve, dynamically synthesize periodic interference, transient noise and background noise, and superimpose the synthesized noise on the power carrier signal to generate a simulation signal; S6. Calculate communication performance indicators, evaluate the effectiveness of the anti-interference algorithm, and calibrate the parameters of the multi-dimensional noise interference model.

2. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 1 is characterized in that: In step S1, fast Fourier transform (FFT) is used to extract characteristic parameters of periodic interference, wavelet transform combined with pulse detection algorithm is used to extract characteristic parameters of transient noise, and statistical analysis method is used to extract characteristic parameters of background noise.

3. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 1 is characterized in that: In step S2, a non-Gaussian distribution is introduced when establishing a background noise model, and the noise characteristic deviation caused by the change of the power grid load is adapted by dynamically adjusting the weight parameters of the Gaussian distribution and the α-stable distribution.

4. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 1 is characterized in that: In step S3, the channel transfer function is updated based on the following three dynamic update triggering conditions, specifically including: Grid line structure changes trigger updates: Identify branch access / disconnection through sensor monitoring or load current fluctuation analysis, and dynamically adjust the delay and attenuation parameters of the signal transmission path; When the load impedance variation exceeds a preset threshold, the attenuation degree and phase shift characteristics of the signal transmission are updated; When the ambient temperature changes beyond a preset range, the signal transmission loss and phase parameters are corrected based on the correlation between temperature and wire impedance.

5. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 4 is characterized in that: The grid line structure change triggering update further includes a specific processing method for branch access or branch disconnection: Known type branch access processing: When it is detected that the type of branch accessed in the power grid is a pre-defined type, the stored signal transmission parameters corresponding to the branch, including signal delay time and attenuation degree, are directly called and updated to the channel model; Unknown type branch access processing: When it is detected that the accessed branch type is not pre-defined, a test signal covering the frequency range of 10kHz-10MHz is sent to the power grid. By measuring the strength change and phase difference of the receiving end signal, the least squares method is used to fit the measured signal strength and phase data, and the signal delay time and attenuation degree of the branch are reversely calculated, and the new parameters are stored for subsequent calls; Branch disconnection processing: When a branch line is detected to be disconnected, the signal transmission path information corresponding to the branch is removed from the channel model, and the path is marked as invalid. The signal contribution of the path is ignored in subsequent simulations.

6. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 1 is characterized in that: In step S4, the influence of the load change on the noise characteristics specifically includes at least one of the following: a. Load changes lead to changes in noise intensity; b. Load changes lead to changes in noise frequency distribution; c. Load changes cause changes in noise time characteristics; d. Load changes cause changes in the power ratio between background noise, transient noise and periodic interference, forming different noise combinations under different load conditions.

7. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 1 is characterized in that: In step S5, the dynamic synthesis of periodic interference, transient noise and background noise is specifically manifested as: The harmonic amplitude of periodic interference, the pulse occurrence probability of transient noise, and the power spectrum density of background noise are dynamically adjusted according to the real-time load impedance to form a dynamic noise combination. The combination adjusts the relative intensity and characteristics of various types of noise according to the real-time conditions of the simulated power lines to generate an overall noise signal, the composition and intensity of which change dynamically with the simulation time.

8. The power carrier channel simulation method based on the dynamic multi-dimensional interference model according to claim 7 is characterized in that: The specific operation of adding the synthetic noise to the power carrier signal to generate the simulation signal is: The overall noise signal is superimposed on the value of the original power carrier communication signal at each point in the simulation time to obtain a simulation signal that carries the communication data and is superimposed on the synthetic noise.

9. A power carrier channel simulation system based on a dynamic multi-dimensional interference model, characterized in that: include: A parameter acquisition module, which acquires real-time parameters of the power grid, including at least line load information; A noise generation module, which internally stores or is connected to a multi-dimensional noise interference model and is configured to: generate a time-varying noise component according to the real-time parameters, dynamically combine periodic interference, transient noise and background noise, and generate a synthetic noise signal; A signal superposition module, which superimposes the synthetic noise signal with the original power carrier signal to generate a simulation signal with simulated noise; The performance evaluation module receives the simulation signal, calculates and evaluates the communication performance index.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, the method for simulating a power carrier channel based on a dynamic multi-dimensional interference model as described in any one of claims 1 to 8 is implemented.

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