CN120128289B - Power carrier channel simulation method and system based on dynamic multi-dimensional interference model - Google Patents
Power carrier channel simulation method and system based on dynamic multi-dimensional interference modelInfo
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Abstract
本发明涉及载波通信技术领域，具体公开了一种基于动态多维度干扰模型的电力载波信道仿真方法及系统，基于动态多维度干扰模型的电力载波信道仿真方法包括：采集电力线噪声样本，提取周期性干扰、暂态噪声和背景噪声的特征参数，按照噪声类型及对应的电网工况进行分类标注，建立包含噪声特征与工况映射关系的噪声数据库；基于噪声数据库，分别建立周期性干扰模型、暂态噪声模型和背景噪声模型，形成多维度噪声干扰模型。本发明实施例的基于动态多维度干扰模型的电力载波信道仿真方法通过多维度噪声建模、动态环境适配，显著提升了电力载波通信仿真的精度、实时性和场景覆盖能力，解决了现有技术在复杂电网环境下的仿真瓶颈。
The present invention relates to the field of carrier communication technology, and specifically discloses a power carrier channel simulation method and system based on a dynamic multi-dimensional interference model. The power carrier channel simulation method based on the dynamic multi-dimensional interference model includes: collecting power line noise samples, extracting characteristic parameters of periodic interference, transient noise and background noise, classifying and labeling according to noise type and corresponding power grid operating conditions, and establishing a noise database containing a mapping relationship between noise characteristics and operating conditions; based on the noise database, respectively establishing a periodic interference model, a transient noise model and a background noise model to form a multi-dimensional noise interference model. The power carrier channel simulation method based on the dynamic multi-dimensional interference model of the embodiment of the present invention significantly improves the accuracy, real-time and scene coverage capability of power carrier communication simulation through multi-dimensional noise modeling and dynamic environment adaptation, and solves the simulation bottleneck of the prior art in a complex power grid environment.
Description
Technical Field
The invention relates to the technical field of carrier communication, in particular to a power carrier channel simulation method and system based on a dynamic multidimensional interference model.
Background
The power line carrier communication (PLC) technology utilizes a power line network to transmit data, and has important application in the fields of smart grids, new energy access and the like. However, the power line channel has complex noise interference, including periodic interference (e.g., switching power supply noise), transient noise (e.g., pulse burst), and background noise (steady-state gaussian noise). In the prior art, the PLC channel simulation method has the following defects:
The traditional PLC channel interference model generally simplifies noise into a single type (such as additive Gaussian white noise), complex and changeable interference characteristics in the actual environment of a power line cannot be accurately reflected, and the traditional method mostly adopts a static modeling mode and cannot adapt to the influence of dynamic factors such as power grid load change, topological structure adjustment and the like on a communication channel.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems in the related art to some extent. Therefore, the invention aims to provide a power carrier channel simulation method and a system based on a dynamic multidimensional interference model so as to establish the multidimensional interference model and realize dynamic simulation of channel characteristics.
To achieve the above objective, an embodiment of a first aspect of the present invention provides a power carrier channel simulation method based on a dynamic multidimensional interference model, the method comprising the following steps:
S1, collecting a power line noise sample, extracting characteristic parameters of periodic interference, transient noise and background noise, classifying and labeling according to noise types and power grid working conditions, and establishing a database of noise characteristics and working condition mapping;
s2, constructing a multidimensional noise interference model comprising a periodic interference model, a transient noise model and a background noise model based on the database;
S3, inputting topological parameters and real-time operation data of the power grid, establishing a power grid structure model, and dynamically updating a channel transfer function according to power grid line structure change, load state change or environmental temperature overrun;
s4, analyzing the influence of load change on noise characteristics by combining the historical load data of the power grid and the multidimensional noise interference model to generate a load change curve;
s5, dynamically synthesizing periodic interference, transient noise and background noise according to the updated channel transfer function and load change curve, and adding the synthesized noise to the power carrier signal to generate a simulation signal;
S6, calculating communication performance indexes, evaluating the effect of the anti-interference algorithm, and calibrating the parameters of the multidimensional noise interference model.
In some embodiments of the present invention, in step S1, the characteristic parameters of the periodic interference are extracted by using Fast Fourier Transform (FFT), the characteristic parameters of the transient noise are extracted by using wavelet transform in combination with a pulse detection algorithm, and the characteristic parameters of the background noise are extracted by using a statistical analysis method.
In some embodiments of the present invention, in step S2, a non-gaussian distribution is introduced when a background noise model is established, and a noise characteristic offset caused by a power grid load change is adapted by dynamically adjusting weight parameters of the gaussian distribution and the α -stable distribution.
In some embodiments of the present invention, in step S3, the channel transfer function is updated based on the following three dynamic update triggering conditions, which specifically includes:
the power grid line structure change triggers the update, namely, the branch connection/disconnection is identified through sensor monitoring or load current fluctuation analysis, and the delay and attenuation parameters of the signal transmission path are dynamically adjusted;
When the load impedance variation amplitude exceeds a preset threshold value, updating the attenuation degree and the phase shift characteristic of signal transmission;
When the environmental temperature change exceeds a preset range, the signal transmission loss and the phase parameter are corrected based on the association relation between the temperature and the impedance of the lead.
In some embodiments of the present invention, the power grid line structure change triggering update further includes specific handling means of branch in or branch out:
When detecting that the type of the branch accessed in the power grid is a predefined type, directly calling the stored signal transmission parameters corresponding to the branch, including signal delay time and attenuation degree, and updating the signal transmission parameters into a channel model;
when the type of the accessed branch is not predefined, sending a test signal covering a frequency range of 10kHz-10MHz to a power grid, fitting measured signal strength and phase data by a least square method by measuring the strength change and the phase difference of a signal at a receiving end, reversely calculating the signal delay time and the attenuation degree of the branch, and storing new parameters for subsequent calling;
and (3) branch disconnection processing, namely when a certain branch line is detected to be disconnected, removing signal transmission path information corresponding to the branch from the channel model, marking the path as an invalid state, and ignoring the signal contribution of the path in subsequent simulation.
In some embodiments of the present invention, in step S4, the influence of the load variation on the noise characteristic specifically includes at least one of:
a. Load variation causes noise intensity variation;
b. load variation causes noise frequency distribution variation;
c. Load variation causes noise time characteristic variation;
d. The load change causes the power ratio among background noise, transient noise and periodic interference to change, and different noise combination forms are formed under different load working conditions.
In some embodiments of the present invention, in step S5, the dynamic synthesized periodic interference, transient noise, and background noise are embodied as:
According to the real-time load impedance, the harmonic amplitude of periodic interference, the pulse generation probability of transient noise and the power spectrum density of background noise are dynamically adjusted to form a dynamic noise combination, wherein the combination is to adjust the relative intensity and characteristics of various types of noise according to the real-time condition of an analog power line so as to generate an overall noise signal, and the composition and intensity of the overall noise signal dynamically change along with the analog time.
In some embodiments of the present invention, the specific operation of adding the synthetic noise to the power carrier signal to generate the simulation signal is:
and superposing the total noise signal with the value of the original power carrier communication signal at each point of the simulation time to obtain a simulation signal carrying communication data and superposed with the synthetic noise.
In order to achieve the above objective, a second aspect of the present invention provides a power carrier channel simulation system based on a dynamic multidimensional interference model, including:
The parameter acquisition module is used for acquiring real-time parameters of the power grid and at least comprises line load information;
the noise generation module is internally stored or connected with a multidimensional 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 synthesized noise signal;
The signal superposition module is used for superposing the synthesized noise signal and the original power carrier signal to generate a simulation signal with simulation noise;
and the performance evaluation module is used for receiving the simulation signals, calculating and evaluating communication performance indexes.
To achieve the above objective, an embodiment of a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory, where the computer program, when executed by the processor, implements the above power carrier channel simulation method based on a dynamic multidimensional interference model.
According to the power carrier channel simulation method and system based on the dynamic multi-dimensional interference model, through multi-dimensional noise modeling, dynamic environment adaptation and system level optimization, the precision, instantaneity and scene coverage capability of power carrier communication simulation are remarkably improved, the simulation bottleneck of the prior art in a complex power grid environment is solved, important engineering values are provided for scenes such as intelligent power grid equipment research and development, new energy access and the like, equipment research and development period is shortened, and remarkable technical progress and industry application prospect are provided.
Drawings
Fig. 1 is a process schematic diagram of a power carrier channel simulation method based on a dynamic multidimensional interference model according to an embodiment of the present invention;
FIG. 2 is a time-frequency two-dimensional thermodynamic diagram of the present invention;
FIG. 3 is a graph showing the relationship between noise intensity and load in the present invention;
FIG. 4 is a graph showing the relationship between noise frequency distribution and load in the present invention;
FIG. 5 is a graph showing the relationship between noise time characteristics and load in the present invention;
FIG. 6 is a schematic diagram of the relationship between the noise type power ratio and the load in the present invention;
Fig. 7 is a schematic structural diagram of a power carrier channel simulation system based on a dynamic multidimensional interference model according to another embodiment of the present invention;
Fig. 8 is a schematic structural diagram of an electronic device according to another embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The following describes a power carrier channel simulation method and system based on a dynamic multidimensional interference model and electronic equipment according to an embodiment of the invention with reference to the accompanying drawings.
Fig. 1 is a flow chart of a power carrier channel simulation method based on a dynamic multidimensional interference model according to an embodiment of the present invention.
As shown in fig. 1, the power carrier channel simulation method based on the dynamic multidimensional interference model includes the following steps:
A power carrier channel simulation method based on a dynamic multidimensional interference model comprises the following steps:
S1, a high-speed analog-to-digital converter (ADC) is utilized to collect power line noise signals in real time at a proper sampling frequency, so that the collected noise signals can accurately reflect actual conditions. In order to improve the comprehensiveness of data, the method can be used for carrying out multiple collection under different power grid working conditions in different time periods, extracting characteristic parameters of periodic interference, transient noise and background noise, carrying out classification labeling according to noise types and corresponding power grid working conditions, wherein the power grid working conditions comprise load size and power grid topological structure, and finally establishing a noise database containing the mapping relation between noise characteristics and working conditions, so that the establishment and inquiry of a follow-up model are facilitated.
As an example, the above-mentioned suitable sampling frequency should be not lower than the nyquist frequency.
As an example, when extracting the characteristic parameters of the periodic interference, the collected time domain signal is converted into the frequency domain by using Fast Fourier Transform (FFT), and the characteristic parameters of the periodic interference, such as frequency, period, amplitude, etc., are extracted by analyzing information such as spectral line distribution, peak frequency, amplitude, etc., in the frequency domain;
Extracting characteristic parameters of transient noise by adopting wavelet transformation and a pulse detection algorithm;
when the characteristic parameters of the background noise are extracted, statistical analysis methods, such as mean value calculation, variance calculation, probability density function calculation and the like, are used for extracting the characteristic parameters of the background noise so as to describe the statistical characteristics of the background noise.
S2, respectively establishing a periodic interference model, a transient noise model and a background noise model based on a noise database, and combining the established periodic interference model, the transient noise model and the background noise model together to form a multidimensional noise interference model, wherein the model can comprehensively describe various characteristics of power line noise.
S3, inputting topological parameters of the power grid, such as the number of nodes, a line connection relation and line parameters, wherein the line parameters comprise resistance, inductance, capacitance and the like, establishing a power grid structure model by using a circuit theory and a network analysis method, wherein the model can describe the transmission path and characteristics of a power carrier signal in the power grid, and then acquiring operation data of the power grid, such as information of load size, voltage, current and the like, reflecting real-time working conditions of the power grid, and dynamically updating a channel transmission function according to the real-time working conditions. If the load changes, the parameters of attenuation, delay, phase and the like of the channel are updated to reflect the influence of the change of the working condition of the power grid on the signal transmission.
S4, predicting a load fluctuation trend according to historical load data of the power grid and a multidimensional noise interference model, analyzing the influence of load change on the characteristics of periodic interference, transient noise and background noise, generating a load change curve, and optimizing anti-interference algorithm parameters.
As one example, load prediction may be performed using an autoregressive integrated moving average model (ARIMA) or a long term memory network (LSTM).
As an example, the interference immunity of the communication system under different load and noise conditions is improved by adjusting parameters of the adaptive filter, changing the modulation and demodulation modes, and the like.
And S5, according to the updated channel transfer function and the load change curve, parameters of the multidimensional noise interference model are adjusted in real time to generate time-varying periodic interference, transient noise and background noise components, then the periodic interference, the transient noise and the background noise are dynamically combined, the synthesized noise is superimposed on the power carrier communication signal through superposition operation, and an interference-containing simulation signal is output, wherein the simulation signal can truly reflect the transmission condition of the power carrier communication in an actual noise environment.
As one example, dynamic combining is to determine weights of noise components at different times according to occurrence probabilities and time distributions of different noises, and then perform weighted summation.
S6, calculating communication performance indexes, such as Bit Error Rate (BER), signal-to-noise ratio (SNR), throughput and the like, evaluating the effect of the anti-interference algorithm, comparing the calculated communication performance indexes with measured data, and calibrating parameters of the multi-dimensional noise interference model according to comparison results.
As an example, if the simulated bit error rate has a larger deviation from the actually measured bit error rate, the parameters of the noise model are adjusted to be more in line with the actual situation.
The power carrier channel simulation method based on the dynamic multidimensional interference model can accurately simulate the transmission condition of power carrier communication in an actual noise environment through comprehensive collection and modeling of power line noise and real-time tracking and analysis of power grid working conditions, timely find out the defects of problems and an anti-interference algorithm of a communication system, calibrate model parameters according to actual measurement data, continuously improve the accuracy and reliability of the multidimensional noise interference model, enable simulation results to be closer to the actual condition, and provide powerful support for design and optimization of the power carrier communication system.
In some embodiments of the present invention, the feature parameter extracting step of the transient noise specifically includes:
S11, preprocessing the original power line noise signal, namely preprocessing the original power line noise signal, wherein the purpose of the preprocessing is to eliminate DC offset and retain transient noise components in a target frequency band, such as 10kHz-1MHz, and suppress power frequency and low frequency interference through a band-pass filter to retain transient noise components in the target frequency band, and the purpose of eliminating the DC component is to eliminate the original signal Calculating the mean valueThe signal after removing the direct current component is: , wherein, For the number of sample points,,In order to sample the period of time,The direct current component mean value of the original signal;
band-pass filtering, namely, a Butterworth band-pass filter is adopted, and the transfer function is as follows:
Wherein, the For the range of the pass-band,Low/high cut-off frequency for band-pass filters, e.g.,,For the filter order, usually 2-4 is taken;
Filtered signal WhereinRepresenting the fourier transform.
S12, time-frequency analysis, namely performing continuous wavelet transformation on the preprocessed signals, and analyzing energy distribution of the signals in different time and frequency ranges by adjusting the scale and translation parameters of the wavelets to generate a time-frequency domain coefficient matrix;
wavelet basis function selection, adopting Morlet wavelet:
Wherein, the For the center angular frequency, 5rad/s is typically taken to balance the time and frequency domain resolutions.
Wavelet transform computation for preprocessing signalsCWT is performed to generate a time-frequency domain coefficient matrix:
Wherein, the For the scale parameters, the frequency resolution is controlled,,For the translation parameters, the time positioning is controlled,Is a complex conjugate number.
Time-frequency diagram generation, namely calculating coefficient amplitude matrixGenerating a time-scale (frequency) two-dimensional thermodynamic diagram with time on the horizontal axisThe vertical axis is equivalent frequency,Is the wavelet center frequency.
As shown in FIG. 2, the shades of color at different locations in the graph represent the energy density of the signal at that particular time and frequency point, with brighter/warmer colors (e.g., yellow, red) indicating higher energy for that time frequency point and darker/colder colors (e.g., dark blue) indicating lower energy. The figure clearly visualizes a transient pulse event occurring at about 3ms, the pulse having a broadband frequency characteristic, the energy being distributed over a very wide frequency range (10 kHz-1 MHz), and the pulse being transient, the energy being concentrated over a very short time window.
S13, pulse judgment and positioning, namely setting a dynamic threshold value, such as a proportional threshold value of a global maximum value, based on the amplitude value of the time-frequency domain coefficient, identifying an energy concentration area exceeding the threshold value, and determining the starting and ending time points of the transient pulse;
Dynamic threshold setting, namely taking the global maximum value of the time-frequency coefficient amplitude Setting a threshold valueWhereinIs a scale factor, e.g.And optimizing through actual measurement data.
Energy region identification, marking all in a time-frequency diagramCombining the continuous time intervals to obtain candidate pulse segments。
Time-positioning optimization, namely, for each candidate segment, in the time domain signalSlope abrupt points (first derivative peaks) are found, and the corrected pulse start/stop times are:
Within the candidate segment(s), (Is a pulse duration estimate);
s14, waveform interception, namely intercepting a time domain waveform segment corresponding to the transient pulse from the preprocessing signal according to the positioning result, wherein the method specifically comprises the following steps of:
According to positioning AndCutting out pulse waveform fragments from the preprocessed signal:
Wherein, the As a rectangular window function, whenAnd 1 is taken when the time is 1, otherwise, the time is 0.
And S15, modeling and fitting the waveform, namely assuming that the transient pulse waveform accords with the double-exponential decay characteristics, such as fast rise and slow decay, carrying out parameterization fitting on the intercepted waveform by adopting a least square method, adjusting model parameters to minimize fitting errors, and calculating to obtain the characteristic parameters related to the transient noise.
The model assumes that the transient pulse waveform meets the double exponential decay characteristic:
,
wherein: for the pulse peak amplitude (V), Is a fast decay time constantCorresponding to the rising edge),Is slow decay time constant%Corresponding decaying edge), and。
Least square fitting, defining a fitting error function:
Wherein, the In order to be able to estimate the parameters,Sampling the number of the pulse fragments;
Solving the optimal solution of the parameters by a gradient descent method or a matrix inversion method to minimize The method comprises the following steps: Thereby obtaining the optimal parameters ;
Output of characteristic parameters, pulse amplitudeTime constant of riseDecay time constantDuration of pulseEnergy of。
In the prior art, background noise is generally assumed to follow Gaussian distribution, but actual power line background noise often contains impulse type low-frequency noise, such as random narrow pulses generated by poor contact, and has heavy tail distribution characteristics (non-Gaussian), a Probability Density Function (PDF) of the background noise has long tail effect, and a traditional Gaussian model can underestimate the occurrence probability of extreme noise events, so that error rate simulation deviation is caused in a low signal-to-noise ratio scene.
In some embodiments of the present invention, in step S2, a non-gaussian distribution is introduced when the background noise model is built, specifically including:
S21, analyzing noise characteristics, namely carrying out statistical analysis on the collected background noise characteristic parameters and identifying the distribution rule of asymmetric waveforms or bursty pulse components of the background noise characteristic parameters, wherein the method specifically comprises the following steps:
Collecting background noise data, wherein the data comprise amplitude information of noise at different moments;
And (3) carrying out statistical analysis on the collected noise data. Calculating the mean value of noise, which can reflect the average level of noise, calculating variance, wherein the variance represents the dispersion degree of noise energy, calculating skewness, wherein the skewness is not 0 and indicates that the noise distribution is asymmetric, and calculating kurtosis, wherein the kurtosis is larger than 0, which means that the noise has the characteristics of peak and thick tail, namely burst pulse;
Pulse detection using a sliding window (window length ) Short-time energy is calculated:
When (when) When it is determined as an emergency and the time is recorded。
The short-term energy of the noise is calculated using a sliding window method, and when the short-term energy exceeds a certain threshold, here set to 2.5 times the noise variance, the burst is considered to be detected and the moment at which the burst occurs is recorded.
S22, constructing a combined distribution model, namely modeling the background noise into a combined model with multiple distribution types according to analysis results, wherein the model framework adopts a mixed model of Gaussian distribution and alpha-stable distribution, and describes the superposition characteristics of steady-state noise and impulse noise.
Gaussian distribution-a steady-state portion used to describe the background noise, just like a steady-flowing water flow under calm water. The method is determined by two parameters, namely a mean value and a standard deviation, wherein the mean value represents the center position, and the standard deviation represents the dispersion degree of data.
Alpha-stable distribution-mainly used to describe the burst component in noise, similar to occasional spray on water. The method has two important parameters, wherein the characteristic index alpha determines the pulse characteristic of the distribution, the closer alpha is to 0, the stronger the pulse characteristic is, and the scale parameter gamma controls the diffusion range of noise energy.
Basic distribution definition:
gaussian distribution (stationary noise):
Probability Density Function (PDF):
Wherein, the Is the mean value of the gaussian distribution,Is the standard deviation.
Symmetrical with each otherSteady distribution (SaS, impulse noise):
characteristic function: ;
Wherein, the Is characterized by index%The smaller the value the stronger the pulse characteristics) is degraded to a gaussian distribution,Is a scale parameter (controlling noise energy spread).
Hybrid model expression:
Wherein the method comprises the steps of In order to distribute the weight of the weight,For the gaussian component weights,Is that-Stabilizing the component weights.
S23, dynamic parameter adjustment, namely, noise characteristics are offset due to the change of the power grid load, for example, when the load is increased, burst pulses may be increased. Therefore, weight parameters of each distribution in the combined model are automatically adjusted according to the real-time load condition, so that the model can adapt to noise characteristic deviation caused by power grid load change;
the adjusting method comprises the following steps:
weight adjustment the weight of the gaussian distribution decreases with increasing load power. At no load, the initial value of the Gaussian distribution weight is set to be 0.7, and the Gaussian distribution weight is reduced by 0.006 when the load power is increased by 1 kW.
The characteristic index alpha decreases as the total harmonic distortion of the load increases. The initial characteristic index is set to 1.6, and the larger the total harmonic distortion is, the smaller α is, which means that the pulse characteristics are stronger.
Parameter optimization, namely continuously updating model parameters by using a minimum mean square error algorithm to minimize the error between a model predicted value and an actual noise value.
S24, sparse pulse decomposition and compensation, namely separating burst pulse components from background noise, and reducing interference of the burst pulse components to an integral noise model, wherein the sparse pulse decomposition and compensation specifically comprises the following steps:
Pulse separation by setting a threshold (here 3 times the standard deviation of the noise) when the noise amplitude exceeds this threshold, it is considered a burst. Extracting the pulses individually to form a pulse train;
Pulse modeling, namely establishing independent sub-models for separated pulses. The pulse frequency is in accordance with poisson distribution, the time base occurrence rate is 5 times per second when the pulse is empty, and the higher the load current is, the higher the occurrence rate is;
And (3) steady-state noise processing, namely subtracting the pulse sequence from the original background noise to obtain steady-state noise. The previously constructed combined distribution model is applied to the stationary noise so that the impulse components can be avoided from interfering with the model parameter estimation.
S25, model verification and calibration, namely checking the accuracy of the model, ensuring that the model can truly reflect the actual background noise condition, and specifically comprising the following steps:
The verification method comprises the following steps:
K-S test, comparing the background noise generated by the model simulation with the cumulative distribution function of the actually measured noise. If the maximum deviation between the two exceeds a preset threshold value, the maximum deviation is set to be 0.1, and the model is required to be adjusted;
root mean square error checking, namely calculating root mean square error between the simulation value and the actual measured value of the model. Calibration of the model is also required when the root mean square error exceeds 0.3 times the noise standard deviation.
And when the model verification fails, the model parameters are adjusted by adopting a particle swarm optimization algorithm. The algorithm will continually look for a combination of parameters that minimizes the objective function (here the sum of the K-S test deviation and the root mean square error), and after 50 iterations, search with 40 particles until the model meets the accuracy requirements.
Through the combination of Gaussian distribution and alpha-stable distribution, the problem that a traditional Gaussian model cannot accurately describe long tail pulse is solved, weight parameters of the model can be automatically adjusted according to power grid load power, the dynamic switching of pulse components is enhanced when Gaussian steady-state noise is taken as a main component in light load and the noise characteristics of nonlinear load in an actual power grid are more met.
Because the power grid environment is complex and changeable, the channel transmission characteristics can be affected by a plurality of factors. Therefore, the invention provides three dynamic update triggering conditions, namely the power grid line structure change, the load state mutation and the environmental temperature overrun, and the channel transfer function is updated and calibrated in time by monitoring and responding to the conditions so as to ensure that the model can accurately reflect the actual signal transmission condition. In step S3, the channel transfer function is updated based on the following three dynamic update triggering conditions, which specifically includes:
s31, the change of the power grid line structure triggers updating, and the change of the power grid line structure directly affects the transmission path and the characteristics of signals, so that the channel transmission function needs to be updated in time.
Real-time sensor monitoring, namely installing sensors at key nodes and branches of a power grid, wherein the sensors can sense the connection state of a line in real time, such as whether a new branch is connected or whether an existing branch is disconnected. The sensor feeds back the monitored information to the control system in time so as to make corresponding adjustment;
Load current fluctuation analysis, namely by monitoring and analyzing load current in a power grid in real time, when abnormal current fluctuation occurs, the change of a line structure can be indicated. For example, when a new branch is taken in, the total load current will increase accordingly, and when the branch is taken off, the load current will decrease. By analyzing the characteristics of the current fluctuation, such as amplitude, frequency and the like, whether the line structure is changed can be judged.
And updating the channel transfer function, namely, after detecting the change of the line structure, dynamically adjusting the delay parameter and the attenuation parameter of the signal transmission path in the model. Signals, when transmitted in different line structures, experience different delays and attenuations, and therefore require re-determination of these parameters according to the new line structure. The channel transfer function is then recalculated based on the adjusted delay parameter and attenuation parameter. The channel transfer function describes the transfer characteristics of the signal in the channel, including amplitude attenuation, phase change, etc. of the signal, and thus needs to be updated according to the actual situation.
S32, triggering updating by sudden change of a load state, wherein the sudden change of the load state can cause the attenuation degree and the phase shift characteristic of a signal in a transmission path to change, so that the channel model parameters need to be adaptively adjusted.
The method for judging the abrupt change of the load state comprises the steps of acquiring impedance information of a load through real-time load data monitoring. When the load impedance change amplitude exceeding 20% is detected, it is determined as a significant change. For example, the start-up or shut-down of a high power device can result in a large change in load impedance.
And updating the channel transfer function, namely triggering the self-adaptive adjustment of the channel model parameters after judging that the load state is changed significantly. According to the change condition of the load impedance, the attenuation degree and the phase shift characteristic of the signal in the transmission path are adjusted, and the channel transfer function is updated to reflect the influence of the load state change on signal transmission.
S33, triggering and calibrating when the ambient temperature exceeds the limit, wherein the change of the ambient temperature can influence the resistance and the capacitance of the lead, so that the loss parameter and the phase parameter in the signal transmission process are changed, and therefore, the channel transmission function needs to be globally calibrated.
The method for monitoring the change of the environmental temperature comprises the steps of installing a temperature sensor at a key position of a power grid and monitoring the change of the environmental temperature in real time. When the ambient temperature changes by more than 10 ℃, the temperature change is considered to be beyond the allowable range.
And calibrating the channel transfer function, namely dynamically correcting loss parameters and phase parameters in the signal transmission process according to the association relation between the temperature and the resistance and capacitance of the lead. Generally, an increase in temperature results in an increase in wire resistance and a decrease in capacitance, thereby increasing signal loss and phase shift.
And carrying out 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 condition and improve the performance of the communication system.
In some embodiments of the invention, the grid line structure change triggering update further includes specific handling means of branch in or branch out:
The known type branch access processing comprises that when the branch type accessed in the power grid is detected to be a predefined type, as the signal transmission characteristics of the branch are researched and recorded, the stored signal transmission parameters corresponding to the branch, including signal delay time and attenuation degree, can be directly called and updated into a channel model. Therefore, the channel transfer function can be updated rapidly and accurately, and the response speed of the system is improved;
and (3) carrying out unknown type branch access processing, namely sending a test signal covering a frequency range of 10kHz-10MHz to a power grid when the type of the accessed branch is not predefined, fitting measured signal strength and phase data by adopting a least square method by measuring the strength change and the phase difference of a signal at a receiving end, reversely calculating the signal delay time and the attenuation degree of the branch, and storing newly obtained parameters for subsequent calling. Thus, when the same type of branch access is encountered again, the stored parameters can be directly used, so that the efficiency of the system is improved;
And (3) branch disconnection processing, namely when a branch line is detected to be disconnected, removing signal transmission path information corresponding to the branch from a channel model, marking the path as an invalid state, ignoring signal contribution of the path in subsequent simulation, and eliminating influence of the path when global parameters are re-fitted, so that the accuracy of a result is prevented from being influenced by residual error paths in subsequent simulation.
In some embodiments of the present invention, in step S4, the influence of the load variation on the noise characteristic specifically includes at least one of:
a. The load change results in a noise intensity change, as shown in fig. 3, in which the horizontal axis represents the load percentage (0% -100%), the vertical axis represents the noise intensity (RMS value), and the curve in the graph shows a quadratic function rising trend, and the noise intensity increases significantly when the load increases, for example, when the load increases from 20% to 80%, and the noise intensity increases from about 2.5 RMS to about 8.5 RMS.
B. The load change causes the noise frequency distribution change, as shown in fig. 4, the horizontal axis is frequency (0-1000 Hz), the vertical axis is power spectrum density, the green curve (light load 20%) is higher in high frequency band (> 500 Hz) energy, the peak is shifted right, the red curve (heavy load 80%) is dominant in low frequency band (< 500 Hz) energy, and the peak is shifted left.
C. The load change causes the noise time characteristic to change, as shown in fig. 5, the upper graph (30% of medium load) shows sparsely distributed high-amplitude pulses (such as 0.2 seconds and 0.6 seconds) in the time domain waveform, the background noise fluctuation is gentle, the lower graph (70% of high load) shows the pulse interval to be shortened (such as 0.1 seconds, 0.3 seconds and 0.7 seconds), and the background noise amplitude is increased and the fluctuation is severe.
D. The load change causes the power ratio among background noise, transient noise and periodic interference to change, and different noise combination forms are formed under different load working conditions, as shown in fig. 6, and the horizontal axis is the load percentage (0% -100%), which is consistent with clause a. The vertical axis is the power ratio (%), and the sum of the three types of noise power is 100%, wherein:
blue region (background noise) from 50% to 10% as load increases, reflecting that background noise (e.g. thermal noise) dominates at light load and decreases in proportion due to other noise enhancement at heavy load;
the red area (periodic disturbance) is increased from 20% to 60%, and the periodic disturbance (such as a switching power supply harmonic wave) is obviously enhanced along with the increase of the load;
Green area (transient noise) is that the transient pulse is increased when the equipment is started and stopped frequently during medium load simulation and the transient pulse is reduced during full load due to system stability when the equipment is in the rising and falling state (the peak value is about 30% at 50% load).
As one example, a machine learning algorithm, such as a neural network, decision tree, etc., may be utilized to learn and analyze a large amount of historical data to establish a mapping relationship between the channel transfer function and various influencing factors. When the dynamic update triggering condition is monitored, the change of the channel transfer function is rapidly predicted through a machine learning model, and corresponding update is carried out.
As one example, the computation and update tasks of the channel transfer function are distributed over the cloud computing center and edge devices. The cloud computing center can perform deep analysis and processing on a large amount of global data, and more accurate channel transfer function updating is realized. Therefore, the advantages of cloud computing and edge computing can be fully exerted, and the performance and response speed of the system are improved.
It should be noted that in a power carrier communication system, the real-time load impedance is a dynamically changing parameter, which has a significant influence on various types of noise in the system. The invention monitors the real-time load impedance and pertinently adjusts the correlation characteristics of the periodic interference, the transient noise and the background noise, thereby generating the time-varying noise component which accords with the actual situation. In some embodiments of the present invention, generating the time-varying noise component in real time in step S5 is embodied as:
And (3) adjusting the amplitude of the periodic interference harmonic wave, namely establishing a mapping relation between the real-time load impedance and the amplitude of the periodic interference harmonic wave. An empirical formula or look-up table can be obtained from a large number of experimental data and theoretical analyses. The harmonic amplitude of certain specific frequencies may decrease, for example, when the real-time load impedance increases, whereas the harmonic amplitude may increase when the load impedance decreases. The system can monitor the change of the load impedance in real time, and dynamically adjust the harmonic amplitude of the periodic interference according to the pre-established mapping relation;
The probability of occurrence of a transient noise is adjusted, and the transient noise is usually caused by sudden events such as switch operation, lightning stroke and the like in a power system. The change in load impedance affects the stability of the power system, thereby changing the probability of pulse generation of transient noise. During adjustment, the relation between the real-time load impedance and the transient noise pulse occurrence probability can be established through experiments and analysis. Such as when the load impedance changes significantly, the voltage and current of the power system may fluctuate, which may increase the probability of occurrence of transient noise pulses. The system dynamically adjusts the pulse generation probability of transient noise according to the change of the real-time load impedance;
Power spectral density adjustment of background noise is noise that persists in an electrical power system and its power spectral density is related to the overall operating state of the electrical power system. The change in real-time load impedance affects the electromagnetic environment of the power system, thereby changing the power spectral density of the background noise. By monitoring the real-time load impedance, its effect on the background noise power spectral density is analyzed. Spectral analysis techniques can be employed to monitor the power spectral density variation of background noise in real time. According to the change condition of load impedance, dynamically regulating power spectrum density of background noise to make it more fit actual condition
By the above-described adjustment and combination, an overall noise signal is generated. The composition and strength of this overall noise signal will vary dynamically over analog time, mainly as represented by:
The composition dynamic changes, namely, the relative intensity and characteristics of periodic interference, transient noise and background noise are continuously adjusted at different simulation time points due to the change of real-time load impedance and power line conditions, so that the composition of the overall noise signal is also changed. For example, at one point in time, periodic disturbances may dominate, while at another point in time, transient noise may become a major component.
Intensity dynamics-the intensity of the overall noise signal will also vary dynamically with analog time. The intensity of the overall noise signal may suddenly increase when the real-time load impedance changes greatly or the power system is suddenly involved, and may be relatively small when the power system is stably operated.
Further refining the scheme, the specific operation of superposing the synthesized noise to the power carrier communication signal is as follows:
and superposing the total noise signal with the value of the original power carrier communication signal without noise at each point of the simulation time to obtain a simulation signal carrying communication data and superposed with synthetic noise.
Because the power carrier communication signals and the noise signals are all electric signals, the signals can be regarded as signals in a linear system in the transmission process, and the linear superposition principle is satisfied. By superposing the noise signals, the situation that the signals are interfered by the noise in the actual power carrier communication can be simulated.
The specific superposition implementation steps are as follows:
① The synchronization of the overall noise signal and the power carrier communication signal is required to ensure accurate superposition at each point in analog time. Clock synchronization techniques may be employed to provide both signals with the same time reference. For example, using a high precision clock source to synchronize the sampling and processing of the signal, ensuring that signal values acquired at the same point in time are corresponding;
② And adding the value of the overall noise signal to the value of the power carrier communication signal at each point in analog time on the basis of synchronization. May be implemented by hardware circuits or software algorithms. For example, in a hardware implementation, two signals may be added using an adder circuit; in a software implementation, a programming language may be used to write an algorithm to add the sample values of the two signals point by point;
③ And obtaining the simulation signal carrying the communication data and superimposed with the synthetic noise through the superposition operation. The simulation signal can be used for performance evaluation and testing of a subsequent power carrier communication system, such as analyzing indexes of bit error rate, signal-to-noise ratio and the like of the signal so as to evaluate the communication performance of the system in an actual noise environment.
Based on the above method, as shown in fig. 7, a power carrier channel simulation system based on a dynamic multidimensional interference model includes:
The system comprises a parameter acquisition module, a parameter acquisition module and a power carrier channel acquisition module, wherein the parameter acquisition module is used for acquiring real-time power grid parameters related to the power carrier channel, the real-time power grid parameters at least comprise line load information, and the core task of the parameter acquisition module is to collect the real-time power grid parameters closely related to the power carrier channel, wherein the line load information is one of the most critical parameters. The change of the line load information can have a significant influence on the transmission characteristics of the power carrier signal, and can also influence the characteristics of various noises. In addition, the module may acquire other important parameters, such as power grid voltage, frequency, line topology, and the like.
And the noise generation module is internally stored or connected with the multidimensional noise interference model and is configured to respond 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 multidimensional noise interference model and dynamically combine the periodic interference, the transient noise and the background noise to generate a synthesized noise signal.
And the signal superposition module is used for superposing the synthesized noise signal generated by the noise generation module and the input original power carrier communication signal, for example, by adding operation, so as to generate a simulation signal with simulation noise.
Signal superposition there are two implementations of hardware implementation in which the superposition of signals can be implemented using adder circuits. And inputting the synthesized noise signal and the original power carrier communication signal into an adder, wherein the signal output by the adder is the simulation signal with the simulation noise.
In the digital signal processing system, programming language algorithm may be used to add the sampling values of the synthesized noise signal and the original power carrier communication signal point by point to obtain the simulating signal.
The performance evaluation module is used for receiving the simulation signal and calculating and evaluating the simulation signal according to preset communication performance indexes such as error rate, signal-to-noise ratio, signal strength and the like. The performance evaluation module may evaluate the simulation signal by using various methods, such as statistical analysis, spectrum analysis, and the like. Through analysis of the evaluation result, the performance of the power carrier communication system under different noise environments can be known, and a basis is provided for optimization and improvement of the system.
Corresponding to the embodiment, the invention also provides electronic equipment.
Referring to fig. 8, an electronic device 200 according to the present invention includes a processor 201 and a memory 203. Wherein the processor 201 is coupled to the memory 203, such as via a bus 202. Optionally, the electronic device 200 may also 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 is not limited to the embodiment of the present invention.
The Processor 201 may be a CPU (Central Processing Unit ), general purpose Processor, DSP (DIGITAL SIGNAL Processor, data signal Processor), ASIC (Application SPECIFIC INTEGRATED Circuit), FPGA (Field Programmable GATE ARRAY ) or other programmable logic device, transistor logic device, hardware component, or any combination thereof. Which may implement or perform the various exemplary logical blocks, modules, and circuits described in connection with the present disclosure. The processor 201 may also be a combination that implements computing functionality, e.g., comprising one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 202 may include a path to transfer information between the aforementioned components. Bus 202 may be a PCI (PERIPHERAL COMPONENT INTERCONNECT, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, or the like. The bus 202 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
The memory 203 is used for storing a computer program corresponding to the power carrier channel simulation method based on the dynamic multidimensional interference model according to the above embodiment of the present invention, and the computer program is controlled to be executed by the processor 201. The processor 201 is arranged to execute computer programs stored in the memory 203 for implementing what is shown in the foregoing method embodiments.
Among them, the electronic device 200 includes, but is not limited to, a mobile terminal such as a notebook computer, a PAD (tablet computer), etc., and a fixed terminal such as a desktop computer, etc. The electronic device 200 shown in fig. 8 is only an example and should not be construed as limiting the functionality and scope of use of the embodiments of the invention.
According to the electronic equipment 200 provided by the embodiment of the invention, the high-precision dynamic simulation of the power line carrier communication channel interference is realized through dynamic multidimensional interference modeling, real-time parameter updating, intelligent optimization algorithm and modularized system architecture, the technical bottleneck of dynamic interference modeling in the power line carrier communication is solved, an efficient and reliable tool is provided for the research and development and performance evaluation of the communication equipment, and the robustness and generalization capability of the model in practical application are ensured through closed-loop calibration of simulation and measured data.
It should be noted that the logic and/or steps represented in the flowcharts or otherwise described herein, for example, may be considered as a ordered listing of executable instructions for implementing logical functions, and may be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include an electrical connection (an electronic device) having one or more wires, a portable computer diskette (a 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, the computer readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of techniques known in the art, discrete logic circuits with logic gates for implementing logic functions on data signals, application specific integrated circuits with appropriate combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present invention, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
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