Photovoltaic power station communication equipment interference assessment method, medium and system

Abstract

The invention provides a photovoltaic power station communication equipment interference assessment method, medium and system, and belongs to the technical field of communication equipment interference, and the method comprises the following steps: establishing a communication equipment interference basic database by collecting communication equipment operation data and environmental electromagnetic data in a photovoltaic power station; performing time domain analysis on the data, converting the data into frequency domain characteristic parameters, establishing an interference evaluation index system, and constructing a neural network model; and collecting operation data of to-be-evaluated communication equipment, extracting characteristic parameters, inputting the characteristic parameters into the model, obtaining an interference evaluation result, and determining an anti-interference level. Decomposing a stable component and a variable component of the interference signal time domain characteristic parameter, and establishing an equation set through an interference source influence coefficient matrix to calculate an interference source influence coefficient matrix and a communication equipment anti-interference coefficient matrix. And finally, correcting the evaluation result according to the anti-interference coefficient matrix to obtain a corrected interference evaluation value, and establishing an early warning threshold value for real-time monitoring and early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of communication equipment interference, and in particular, relates to a method, medium and system for evaluating interference of communication equipment in a photovoltaic power station. Background Art

[0002] With the rapid development of photovoltaic power generation technology, photovoltaic power stations have become an important part of the development of clean energy. In the operation of photovoltaic power stations, the communication system undertakes key functions such as data transmission, equipment monitoring, and operation scheduling. However, communication equipment in photovoltaic power station environments often face complex electromagnetic interference problems. These interferences come not only from the electromagnetic radiation generated by photovoltaic components, but also from the combined influence of surrounding environmental factors.

[0003] At present, the interference assessment of photovoltaic power station communication equipment mainly adopts the following methods: the first is the assessment method based on field measurement, which collects electromagnetic environment data for analysis by arranging measurement equipment on site; the second is the assessment method based on simulation model, which predicts interference by establishing an electromagnetic field simulation model; the third is the assessment method based on empirical statistics, which determines the interference law through statistical analysis of historical data. These traditional methods have obvious limitations in practical applications: the field measurement method requires a lot of manpower and material resources, and the measurement process is greatly affected by external conditions; the simulation model method is often too idealized and difficult to accurately reflect the complex interference situation in the actual operating environment; the empirical statistical method lacks theoretical support, and the reliability and accuracy of the assessment results are low.

[0004] Especially in large-scale photovoltaic power stations, due to the wide distribution of equipment and complex and changeable environmental conditions, traditional interference assessment methods are difficult to meet actual needs. Existing technologies cannot effectively conduct systematic correlation analysis between the operating status of communication equipment, environmental factors and interference characteristics, resulting in a large deviation between the interference assessment results and the actual situation. Summary of the invention

[0005] In view of this, the present invention provides a photovoltaic power station communication equipment interference assessment method, medium and system, which can solve the technical problem that the prior art cannot effectively and systematically correlate the operating status, environmental factors and interference characteristics of the communication equipment.

[0006] The present invention is achieved in that:

[0007] A first aspect of the present invention provides a method for evaluating interference of communication equipment in a photovoltaic power station, comprising the following steps:

[0008] S10, collecting communication equipment operation data and environmental electromagnetic data in the photovoltaic power station, the communication equipment operation data including communication equipment operation time, communication equipment maintenance time, communication equipment fault record, the environmental electromagnetic data including photovoltaic module electromagnetic radiation intensity, ambient air humidity value, ambient temperature value, and establishing a communication equipment interference basic database;

[0009] S20, performing time domain analysis on the data in the communication device interference basic database, extracting time domain characteristic parameters of interference signals, and performing frequency domain conversion on the time domain characteristic parameters of interference signals to obtain frequency domain characteristic parameters of interference signals;

[0010] S30, establishing an interference assessment index system according to the interference signal time domain characteristic parameters and the interference signal frequency domain characteristic parameters, constructing a neural network model based on the interference assessment index system, and training to obtain an interference assessment model;

[0011] S40, collecting operation data of the communication device to be evaluated, and extracting time domain characteristic parameters and frequency domain characteristic parameters of the interference signal of the communication device to be evaluated;

[0012] S50, inputting the interference signal time domain characteristic parameters and the interference signal frequency domain characteristic parameters of the communication device to be evaluated into the interference evaluation model to obtain an interference evaluation result;

[0013] S60, determining the anti-interference level of the communication equipment according to the interference evaluation result;

[0014] S70, decomposing the time domain characteristic parameter of the interference signal into a stable component and a variable component to obtain a stable component of the interference signal and a variable component of the interference signal;

[0015] S80, according to the interference signal stable component and the interference signal variable component, establish an equation group through an interference source influence coefficient matrix to calculate the interference source influence coefficient matrix, and calculate the communication device anti-interference coefficient matrix based on the interference source influence coefficient matrix;

[0016] S90. According to the anti-interference coefficient matrix of the communication equipment, the interference assessment result is corrected to obtain a corrected interference assessment value, wherein the corrected interference assessment value is the original interference assessment value multiplied by the anti-interference coefficient matrix of the communication equipment, and based on the corrected interference assessment value, a communication equipment interference warning threshold is established to perform real-time monitoring and warning.

[0017] On the basis of the above technical solution, a photovoltaic power station communication equipment interference assessment method of the present invention can also be improved as follows:

[0018] Wherein, the step S10 specifically includes:

[0019] Step 101: collect operation time data, maintenance time data and fault record data of each communication device in the photovoltaic power station, and arrange the collected operation time data, maintenance time data and fault record data in a manner that each row corresponds to one communication device to form a device operation data matrix;

[0020] Step 102: Collect the electromagnetic radiation intensity data of photovoltaic modules, the ambient air humidity value data and the ambient temperature value data around each communication device in the photovoltaic power station, and arrange the collected electromagnetic radiation intensity data of photovoltaic modules, the ambient air humidity value data and the ambient temperature value data in a manner that each row corresponds to one communication device, to form an environmental electromagnetic data matrix;

[0021] Step 103, storing the equipment operation data matrix and the environmental electromagnetic data matrix in a communication equipment interference basic database respectively;

[0022] The step S20 specifically includes:

[0023] Step 201: Use a high-speed data acquisition card to sample data in the communication equipment interference basic database, set the sampling frequency to 2 kHz, and set the sampling time to 48 hours;

[0024] Step 202: extract interference signal amplitude data, interference duration data, interference period data and signal strength data from the sampled data, perform weighted calculation on the extracted interference signal amplitude data, interference duration data, interference period data and signal strength data to obtain time domain feature parameters;

[0025] Step 203, performing fast Fourier transform processing on the time domain characteristic parameters, setting a Hanning window with an overlap rate of 50%;

[0026] Step 204: extract frequency point amplitude data, frequency point phase data and frequency point phase difference data from the fast Fourier transform result, and arrange the extracted frequency point amplitude data, frequency point phase data and frequency point phase difference data into a frequency domain feature parameter matrix.

[0027] Furthermore, the step S30 specifically includes:

[0028] Step 301: Use the interference signal time domain characteristic parameter and the interference signal frequency domain characteristic parameter as input parameters to construct a neural network input layer;

[0029] Step 302: construct a neural network hidden layer, wherein a plurality of neurons are arranged in the neural network hidden layer, and each neuron adopts a rectified linear unit function as an activation function;

[0030] Step 303: construct a neural network output layer, and set a plurality of neurons in the neural network output layer;

[0031] Step 304: Establish a weight matrix from the input layer to the hidden layer, wherein the weight matrix is ​​used to characterize the connection relationship between each neuron in the input layer and each neuron in the hidden layer;

[0032] Step 305: Establish a weight matrix from the hidden layer to the output layer, wherein the weight matrix is ​​used to characterize the connection relationship between each neuron in the hidden layer and each neuron in the output layer;

[0033] Step 306: Adopt an adaptive moment estimation optimization algorithm to train the neural network, set the learning rate to 0.001, and set the number of training rounds to 500, to obtain an interference assessment model.

[0034] Furthermore, the step S40 specifically includes:

[0035] Step 401: Collecting operation time data, maintenance time data and fault record data of the communication equipment to be evaluated;

[0036] Step 402: Collecting the electromagnetic radiation intensity data, ambient air humidity value data and ambient temperature value data of the photovoltaic components around the communication device to be evaluated;

[0037] Step 403: extract the time domain characteristic parameters and frequency domain characteristic parameters of the interference signal from the collected data.

[0038] Furthermore, the step S50 specifically includes:

[0039] Step 501: Arrange the time domain characteristic parameters of the interference signal of the communication device to be evaluated into a time domain characteristic parameter matrix in a manner that each row corresponds to a time point;

[0040] Step 502: Arrange the frequency domain characteristic parameters of the interference signal of the communication device to be evaluated into a frequency domain characteristic parameter matrix in a manner that each row corresponds to one frequency point;

[0041] Step 503: input the time domain characteristic parameter matrix and the frequency domain characteristic parameter matrix into the interference assessment model to obtain an interference assessment result;

[0042] The step S60 specifically includes:

[0043] Step 601: setting an anti-interference level evaluation standard, dividing the anti-interference level into multiple levels, and setting a corresponding interference evaluation result range for each level;

[0044] Step 602: Compare the interference assessment result with the anti-interference level evaluation standard;

[0045] Step 603: Determine the anti-interference level of the communication device according to the comparison result, and use the anti-interference level as an evaluation index of the anti-interference capability of the communication device.

[0046] Furthermore, the step S70 specifically includes:

[0047] Step 701: perform an integration operation on the time domain characteristic parameter of the interference signal according to the observation period to obtain a stable component of the interference signal;

[0048] Step 702: Subtract the interference signal stable component from the interference signal time domain characteristic parameter to obtain the interference signal variable component.

[0049] Furthermore, the step S80 specifically includes:

[0050] Step 801: Calculate the degree of influence of the environment on the communication device according to the electromagnetic radiation intensity data of the photovoltaic module, the ambient air humidity value data and the ambient temperature value data;

[0051] Step 802: Calculate the anti-interference capability of the communication device in the current operation state according to the device operation time data, the maintenance time data and the failure number data;

[0052] Step 803: Calculate the impact intensity of the interference on the communication device according to the interference occurrence frequency data, the interference duration data and the communication interruption duration data;

[0053] Step 804: Combine the environmental impact degree, the anti-interference capability and the impact strength in a block diagonal manner to form an interference source impact coefficient matrix;

[0054] Step 805: Calculate the inverse matrix of the interference source influence coefficient matrix, multiply the inverse matrix with the normalization coefficient matrix and the correction coefficient matrix to obtain the communication equipment anti-interference coefficient matrix.

[0055] Furthermore, the step S90 specifically includes:

[0056] Step 901: multiply the interference assessment result by the communication device anti-interference coefficient matrix and the assessment weight diagonal matrix to obtain a corrected interference assessment value;

[0057] Step 902: Calculate the standard deviation of the corrected interference assessment value, and establish a warning threshold according to the corrected interference assessment value and the standard deviation;

[0058] Step 903: Perform real-time monitoring on the communication equipment, and issue a warning signal when the real-time monitoring value exceeds the warning threshold.

[0059] The interference source influence coefficient matrix establishes an equation group including an environmental influence equation, an equipment state equation, an interference characteristic equation and a comprehensive evaluation equation;

[0060] The environmental impact equation is used to evaluate the degree of influence of the surrounding environment on the communication equipment of the photovoltaic power station. The input includes the electromagnetic radiation intensity of the photovoltaic module, the current air humidity value and the current temperature value. The output is the interference influence coefficient of the environment on the communication equipment.

[0061] The device state equation is used to evaluate the impact of the communication device's own operating state on its anti-interference capability. The input includes the communication device's operating time, the communication device's most recent maintenance time, and the cumulative number of communication device failures. The output is the anti-interference capability coefficient of the device in its current state.

[0062] The interference characteristic equation is used to evaluate the impact characteristics of interference signals on communication equipment. The input includes the number of occurrences of interference signals in a continuous monitoring period, the duration of each interference signal, and the duration of communication interruption caused by each interference. The output is the impact intensity coefficient of the type of interference.

[0063] The comprehensive evaluation equation is used to perform weighted calculation on the above three coefficients to obtain the final result. The input includes the environmental impact degree coefficient, the equipment anti-interference ability coefficient and the interference impact intensity coefficient. The output is the final interference source impact coefficient matrix.

[0064] The calculations or equations involved in the present invention are described in detail below:

[0065] 1. Establishment of interference basic database (step S10):

[0066] The equipment operation data matrix D is specifically expressed as follows:

[0067]

[0068] In the formula, t i1 is the running time of the i-th device; m i1 is the maintenance time of the i-th device; f i1 is the number of failures of the i-th device; n is the number of devices.

[0069] The environmental electromagnetic data matrix E is specifically expressed as follows:

[0070]

[0071] In the formula, r i1 is the electromagnetic radiation intensity at the ith position; h i1 is the air humidity at the ith position; t i1 is the temperature value at the ith position.

[0072] 2. Time domain analysis process (step S20):

[0073] Time domain characteristic parameter vector F t The specific expressions are as follows:

[0074] F t ={f t1 ,f t2 ,...,f tn};

[0075] f ti =α i ·A i +β i ·T i +γ i ·P i +δ i ·S i +ε t ;

[0076] In the formula, f ti is the i-th time domain characteristic parameter; A i is the interference signal amplitude; T i is the duration of interference; P i is the interference period; S i is the signal strength; α i ,β i ,γ i ,δ i is the weight coefficient; ε t is the time domain error term.

[0077] 3. Frequency domain conversion process:

[0078] Frequency domain feature parameter matrix F f The specific expressions are as follows:

[0079]

[0080] In the formula, a i1 is the amplitude of the ith frequency point; p i1 is the phase of the ith frequency point; φ i1 is the phase difference of the ith frequency point; m is the number of frequency points.

[0081] Frequency domain conversion calculation process:

[0082]

[0083] In the formula, f fi is the i-th frequency domain characteristic parameter; f tk is the time domain sequence; N is the number of sampling points; ε f is the frequency domain error term.

[0084] 4. Interference signal decomposition process (step S70):

[0085] Stable component extraction:

[0086]

[0087] In the formula, S c is the stable component; s(t) is the original signal; T is the observation period; ε s is the stable component error term.

[0088] Extraction of variable components:

[0089] D c =s(t)-S c +ε d ;

[0090] Where D c is the variable component; ε d is the variable component error term.

[0091] 5. Neural network model (step S30):

[0092] The input layer to hidden layer weight matrix W is specifically expressed as follows:

[0093]

[0094] In the formula, w ij is the connection weight between the i-th hidden layer neuron and the j-th input neuron; h is the number of hidden layer neurons; n is the number of input layer neurons.

[0095] The weight matrix V from the hidden layer to the output layer is specifically expressed as follows:

[0096]

[0097] In the formula, v ij is the connection weight between the ith output neuron and the jth hidden layer neuron; o is the number of neurons in the output layer.

[0098] Neural network calculation process:

[0099]

[0100] In the formula, H l is the output of the lth hidden layer neuron; σ is the activation function, using the ReLU function; x i is the input feature; b l is the hidden layer bias; O k is the output of the kth output neuron; c k is the output layer bias; ε n is the network error term.

[0101] 6. Environmental impact equation (step S80):

[0102]

[0103] Where, E is the environmental impact coefficient; R, H, T are the electromagnetic radiation intensity, air humidity value, and ambient temperature value respectively; R0, H0, T0 are standard reference values; k1, k2, k3 are weight coefficients; α, β, γ are sensitivity indexes; λ1, λ2, λ3 are attenuation coefficients; ε e is the environmental error term.

[0104] 7. Equipment state equation:

[0105]

[0106] Where S is the equipment status coefficient; t is the operating time; τ is the time since the last maintenance; f is the number of failures; λ1, λ2, λ3 are weight coefficients; μ1, μ2, μ3 are performance attenuation coefficients; v1, v2, v3 are stability coefficients; ε s is the state error term.

[0107] 8. Interference characteristic equation:

[0108]

[0109] Where I is the interference characteristic coefficient; N is the number of interference occurrences; D is the duration of interference; B is the duration of communication interruption; N0, D0, B0 are reference thresholds; ω1, ω2, ω3 are weight coefficients; δ, θ, φ are influence indexes; η1, η2, η3 are suppression coefficients; ε i is the characteristic error term.

[0110] 9. Interference source influence coefficient matrix calculation:

[0111]

[0112] Where M is the interference source influence coefficient matrix; ρ i ,σ i ,τ i is the comprehensive weight coefficient; ε m is the comprehensive error term.

[0113] 10. Anti-interference coefficient matrix calculation:

[0114] R=M -1 ·K·C+ε r ;

[0115]

[0116] Where R is the anti-interference coefficient matrix; K is the standardization coefficient matrix; C is the correction coefficient matrix; c i is the correction coefficient of each dimension; ε r is the anti-interference error term.

[0117] 11. Calculation of the corrected interference assessment value:

[0118] V modified =V original ·R·W+ε v ;

[0119] W = diag(w1,w2,w3);

[0120] Where V modified is the revised evaluation value; V original is the original evaluation value; W is the evaluation weight diagonal matrix; w i Evaluate the weights for each dimension; ε v To correct the error term.

[0121] 12. Establishment of warning thresholds:

[0122] T warning =α·V modified +β·σ V +γ;

[0123] Where, T warning is the warning threshold; σ v is the standard deviation of the evaluation value; α, β are weight coefficients; γ is the benchmark threshold.

[0124] Parameter acquisition method:

[0125] 1. Time domain parameters: high-speed data acquisition card is used for sampling, sampling frequency is 2kHz, and sampling time is 48 hours;

[0126] 2. Frequency domain parameters: FFT transformation is used, Hanning window is selected as the window function, and the overlap rate is 50%;

[0127] 3. Weight coefficient: determined by combining expert scoring method with fuzzy hierarchical analysis method;

[0128] 4. Error term: determined through Monte Carlo simulation and historical data statistical analysis, controlled within ±3%;

[0129] 5. Neural network parameters: Adam optimizer, learning rate 0.001, training rounds 500 times;

[0130] 6. Correction coefficient: obtained by fitting the experimental data and optimized using the least squares method.

[0131] Explanation of the equation construction principle:

[0132] 1. The time domain characteristic equation adopts a multivariable linear weighted model, taking into account the basic characteristics of the interference signal;

[0133] 2. The frequency domain conversion uses the FFT algorithm to fully describe the signal characteristics through parameters such as amplitude and phase;

[0134] 3. The environmental impact equation adopts a power exponential composite function to reflect the nonlinear impact and saturation characteristics of environmental factors;

[0135] 4. The equipment state equation adopts a double exponential model to reflect the attenuation and stabilization process of equipment performance;

[0136] 5. The interference characteristic equation adopts the form of power exponential combination to describe the cumulative effect and inhibitory effect of interference;

[0137] 6. Matrix calculations use block diagonalization to reduce the amount of calculations and improve numerical stability.

[0138] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned photovoltaic power station communication equipment interference assessment method.

[0139] A third aspect of the present invention provides a photovoltaic power station communication equipment interference assessment system, which includes the above-mentioned computer-readable storage medium.

[0140] Compared with the prior art, the interference assessment method, medium and system of photovoltaic power station communication equipment provided by the present invention have the following significant advantages: First, the present invention adopts a multi-dimensional data acquisition strategy to comprehensively collect communication equipment operation data and environmental electromagnetic data, and establishes a complete interference basic database. Through the dual feature extraction of time domain and frequency domain, the essential characteristics of the interference signal are deeply excavated, and a more abundant and accurate feature input is provided for the subsequent evaluation model, which significantly improves the accuracy of interference assessment. Secondly, the present invention innovatively introduces a neural network model, and establishes an interference assessment model with adaptive ability through learning and training of a large amount of historical data. The model can automatically identify and learn the interference characteristics under different working conditions, effectively overcome the limitations of human experience judgment in traditional evaluation methods, and make the evaluation results more objective and reliable. Thirdly, the present invention realizes accurate identification and quantitative analysis of interference sources by introducing the stable component and variable component decomposition technology of the interference signal, combined with the interference source influence coefficient matrix. This method can not only accurately assess the current interference status, but also predict potential interference risks, solve the technical problem that the prior art cannot effectively perform systematic correlation analysis on the operating status, environmental factors and interference characteristics of communication equipment, and provide a scientific basis for the preventive maintenance of communication equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0141] Figure 1 A flow chart of the method provided by the present invention. DETAILED DESCRIPTION

[0142] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0143] like Figure 1 FIG. 1 is a flow chart of a method for evaluating interference of communication equipment in a photovoltaic power station provided by the present invention. The method comprises the following steps:

[0144] S10, collect the communication equipment operation data and environmental electromagnetic data in the photovoltaic power station, the communication equipment operation data includes the communication equipment operation time, the communication equipment maintenance time, and the communication equipment failure record, and the environmental electromagnetic data includes the electromagnetic radiation intensity of the photovoltaic module, the ambient air humidity value, and the ambient temperature value, and establish a basic database of communication equipment interference;

[0145] S20, performing time domain analysis on the data in the communication equipment interference basic database, extracting time domain characteristic parameters of the interference signal, and performing frequency domain conversion on the time domain characteristic parameters of the interference signal to obtain frequency domain characteristic parameters of the interference signal;

[0146] S30, establishing an interference assessment index system according to the time domain characteristic parameters of the interference signal and the frequency domain characteristic parameters of the interference signal, constructing a neural network model based on the interference assessment index system, and training to obtain an interference assessment model;

[0147] S40, collecting operation data of the communication device to be evaluated, and extracting time domain characteristic parameters and frequency domain characteristic parameters of the interference signal of the communication device to be evaluated;

[0148] S50, inputting the interference signal time domain characteristic parameters and the interference signal frequency domain characteristic parameters of the communication device to be evaluated into the interference evaluation model to obtain the interference evaluation result;

[0149] S60, determining the anti-interference level of the communication equipment according to the interference assessment result;

[0150] S70, decomposing the time domain characteristic parameters of the interference signal into stable components and variable components to obtain a stable component of the interference signal and a variable component of the interference signal;

[0151] S80, according to the interference signal stable component and the interference signal variable component, establish an equation group through the interference source influence coefficient matrix to calculate the interference source influence coefficient matrix, and calculate the communication equipment anti-interference coefficient matrix based on the interference source influence coefficient matrix;

[0152] S90. According to the anti-interference coefficient matrix of the communication equipment, the interference assessment result is corrected to obtain a corrected interference assessment value, which is the original interference assessment value multiplied by the anti-interference coefficient matrix of the communication equipment. Based on the corrected interference assessment value, a communication equipment interference warning threshold is established to perform real-time monitoring and warning.

[0153] The specific implementation methods of the above steps are described in detail below:

[0154] The specific implementation method of step S10 is to establish a basic database of communication equipment interference, collect and store the operation data and environmental electromagnetic data of communication equipment in the photovoltaic power station. First, the operation time data, maintenance time data and fault record data of all communication equipment are collected, and the collected data are preprocessed and standardized. The data is arranged in a manner that each row corresponds to a communication device, and the equipment operation data matrix is ​​constructed. Each row in the matrix contains data of three dimensions: the operation time, maintenance time and fault record of the equipment. Then, the electromagnetic radiation intensity data, ambient air humidity value data and ambient temperature value data of the photovoltaic components around each communication device in the photovoltaic power station are collected, and the collected data are preprocessed and standardized. The data is arranged in a manner that each row corresponds to a communication device, and the environmental electromagnetic data matrix is ​​constructed. Each row in the matrix contains data of three dimensions: electromagnetic radiation intensity, air humidity and temperature value at the location. Finally, the equipment operation data matrix and the environmental electromagnetic data matrix are respectively stored in the basic database of communication equipment interference, and the database is managed by a relational database management system to ensure the integrity and consistency of the data. Among them, the standard reference value of electromagnetic radiation intensity is 100 microwatts per square centimeter, the standard reference value of air humidity is 60%, and the standard reference value of temperature is 25 degrees Celsius.

[0155] The specific implementation method of step S20 is to perform time domain analysis and frequency domain conversion on the data in the communication equipment interference basic database to extract the characteristic parameters of the interference signal. A high-speed data acquisition card is used to sample the data, and the sampling frequency is set to 2 kHz to meet the requirements of the Nyquist sampling theorem, and the sampling time is set to 48 hours to ensure that enough data samples are obtained. Interference signal amplitude data, interference duration data, interference period data and signal strength data are extracted from the sampled data, and the extracted data is weighted and calculated using a multivariate linear weighted model to obtain time domain characteristic parameters. Among them, the amplitude weight coefficient is 0.3, the duration weight coefficient is 0.25, the period weight coefficient is 0.25, and the signal strength weight coefficient is 0.2. Fast Fourier transform is performed on the time domain characteristic parameters, and the Hanning window function is selected for windowing processing, and the window function overlap rate is set to 50% to reduce spectrum leakage. Frequency point amplitude data, frequency point phase data and frequency point phase difference data are extracted from the fast Fourier transform results, and the extracted data are arranged into a frequency domain characteristic parameter matrix.

[0156] The specific implementation method of step S30 is to establish an interference assessment index system and construct a neural network model. The time domain characteristic parameters and frequency domain characteristic parameters of the interference signal are used as input parameters to construct the neural network input layer, and the number of input layer neurons is determined according to the characteristic parameter dimension. Construct a neural network hidden layer, set multiple neurons in the hidden layer, each neuron uses a modified linear unit function as an activation function, and the number of hidden layer neurons is determined by a cross-validation method, which is generally 1.5 times the number of input layer neurons. Construct a neural network output layer, set multiple neurons in the output layer, and the number of output layer neurons is determined according to the evaluation index dimension. Establish a weight matrix from the input layer to the hidden layer, the weight matrix is ​​used to characterize the connection relationship between each neuron in the input layer and each neuron in the hidden layer, and the initial weight value uses a random decimal. Establish a weight matrix from the hidden layer to the output layer, the weight matrix is ​​used to characterize the connection relationship between each neuron in the hidden layer and each neuron in the output layer, and the initial weight value uses a random decimal. Use an adaptive moment estimation optimization algorithm to train the neural network, set the learning rate to 0.001, and set the training round to 500 rounds to obtain an interference assessment model.

[0157] The specific implementation method of step S40 is to collect data of the communication equipment to be evaluated and extract characteristic parameters. Collect the operation time data, maintenance time data and fault record data of the communication equipment to be evaluated, and preprocess and standardize the collected data. Collect the electromagnetic radiation intensity data of the photovoltaic components around the communication equipment to be evaluated, the ambient air humidity value data and the ambient temperature value data, and preprocess and standardize the collected data. Extract the time domain characteristic parameters of the interference signal from the collected data, including parameters such as signal amplitude, duration, period and signal strength. Perform frequency domain conversion on the time domain characteristic parameters to obtain frequency domain characteristic parameters, including parameters such as frequency point amplitude, phase and phase difference.

[0158] The specific implementation method of step S50 is to input the characteristic parameters of the communication device to be evaluated into the interference assessment model. The time domain characteristic parameters of the interference signal of the communication device to be evaluated are arranged into a time domain characteristic parameter matrix in a manner that each row corresponds to a time point, and the number of rows of the matrix is ​​equal to the number of sampling points, and the number of columns is equal to the time domain characteristic parameter dimension. The frequency domain characteristic parameters of the interference signal of the communication device to be evaluated are arranged into a frequency domain characteristic parameter matrix in a manner that each row corresponds to a frequency point, and the number of rows of the matrix is ​​equal to the number of frequency points, and the number of columns is equal to the frequency domain characteristic parameter dimension. The time domain characteristic parameter matrix and the frequency domain characteristic parameter matrix are input into the interference assessment model, and the interference assessment result is obtained by the forward propagation calculation of the neural network.

[0159] The specific implementation method of step S60 is to determine the anti-interference level of the communication equipment. Set the anti-interference level judgment standard, divide the anti-interference level into 5 levels, namely excellent level, good level, general level, poor level and bad level, and set the corresponding interference evaluation result range for each level. The range of the excellent level is above 90 points, the range of the good level is 80 to 90 points, the range of the general level is 70 to 80 points, the range of the poor level is 60 to 70 points, and the range of the poor level is below 60 points. Compare the interference evaluation result with the anti-interference level judgment standard, determine the anti-interference level of the communication equipment according to the comparison result, and use the anti-interference level as the evaluation indicator of the anti-interference ability of the communication equipment.

[0160] The specific implementation method of step S70 is to decompose the interference signal into components. According to the observation period, the time domain characteristic parameters of the interference signal are integrated, and the integration interval is the entire observation period. The trapezoidal integration method is used for numerical calculation to obtain the stable component of the interference signal. The stable component of the interference signal is subtracted from the time domain characteristic parameter of the interference signal to obtain the variable component of the interference signal. Among them, the stable component reflects the long-term trend and baseline level of the interference signal, and the variable component reflects the fluctuation and instantaneous change characteristics of the interference signal.

[0161] The specific implementation of step S80 is to calculate the interference source influence coefficient matrix and the anti-interference coefficient matrix. According to the electromagnetic radiation intensity data of the photovoltaic module, the ambient air humidity value data and the ambient temperature value data, the power exponential composite function model is used to calculate the degree of influence of the environment on the communication equipment, and the sensitivity index and attenuation coefficient in the function are obtained by fitting the experimental data. According to the equipment operation time data, maintenance time data and fault number data, the double exponential model is used to calculate the anti-interference ability of the communication equipment under the current operation state, and the performance attenuation coefficient and stability coefficient in the model are determined by historical data analysis. According to the interference occurrence number data, the interference duration data and the communication interruption duration data, the power exponential combination model is used to calculate the influence intensity of the interference on the communication equipment, and the influence index and suppression coefficient in the model are determined by expert experience and data analysis. The environmental influence degree, anti-interference ability and influence intensity are combined in a block diagonal manner to form an interference source influence coefficient matrix. Calculate the inverse matrix of the interference source influence coefficient matrix, use Gaussian Jordan elimination method to perform matrix inversion operation, multiply the inverse matrix with the standardized coefficient matrix and the correction coefficient matrix to obtain the communication equipment anti-interference coefficient matrix.

[0162] The specific implementation method of step S90 is to correct the interference assessment result and establish an early warning mechanism. The interference assessment result is multiplied by the anti-interference coefficient matrix of the communication equipment and the evaluation weight diagonal matrix to obtain a corrected interference assessment value, and the evaluation weight is determined by the hierarchical analysis method. The standard deviation of the corrected interference assessment value is calculated, and the sample standard deviation calculation method is adopted. The early warning threshold is established according to the corrected interference assessment value and the standard deviation. The early warning threshold is set to the corrected assessment value plus 2 times the standard deviation. The communication equipment is monitored in real time, and the real-time assessment value is calculated by the sliding window method. When the real-time monitoring value exceeds the early warning threshold, an early warning signal is issued. The early warning signal includes two modes: sound and light alarm and system prompt.

[0163] This implementation method achieves effective evaluation and early warning of interference of photovoltaic power station communication equipment through reasonable data collection, feature extraction, model construction and early warning mechanism setting, and has strong practicality and reliability. The various algorithms and models used in this method have been fully analyzed theoretically and verified experimentally to ensure the accuracy and credibility of the evaluation results. By performing dual analysis of the interference signal in the time domain and frequency domain, combined with the influence of environmental factors and equipment status, a complete evaluation index system is constructed, which provides strong technical support for the operation and maintenance management of photovoltaic power station communication equipment.

[0164] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are run in a computer, they are used to execute the above-mentioned photovoltaic power station communication equipment interference assessment method.

[0165] A third aspect of the present invention provides a photovoltaic power station communication equipment interference assessment system, which includes the above-mentioned computer-readable storage medium.

[0166] Specifically, the principle of the present invention is:

[0167] Principle of multi-dimensional data collection and feature extraction: The data collected by the present invention includes two categories: communication equipment operation data and environmental electromagnetic data. Communication equipment operation data reflects the usage status and performance changes of the equipment, while environmental electromagnetic data reflects the characteristics of external interference sources. The amplitude, duration and other characteristics of the interference signal are extracted through time domain analysis, and then the frequency domain is converted through Fourier transform to obtain characteristics such as spectrum distribution and energy density. This time domain-frequency domain dual feature extraction method can comprehensively describe the characteristics of the interference signal and provide complete feature information for subsequent modeling.

[0168] Neural network modeling principle: The present invention uses a multi-layer perceptron neural network to construct an interference assessment model. The network can learn and fit the relationship between complex interference features and assessment results through nonlinear mapping of hidden layer neurons. During the training process, the network weights are continuously adjusted through the back propagation algorithm, and an accurate interference assessment model is finally established. The adaptive learning ability of the neural network enables the model to be continuously optimized and the assessment accuracy to be improved.

[0169] Interference component decomposition and influence coefficient calculation principle: The present invention innovatively proposes a method for decomposing the stable component and variable component of the interference signal. The stable component reflects the long-term continuous interference, while the variable component reflects the sudden and temporary interference. By establishing the environmental impact equation, the equipment state equation, the interference characteristic equation and the comprehensive evaluation equation, a complete interference source influence coefficient matrix equation group is formed. This equation group can accurately quantify the influence of different factors on the interference.

[0170] Principle of dynamic correction and early warning mechanism: The present invention corrects the original evaluation results by calculating the anti-interference coefficient matrix of the communication equipment. The correction process takes into account the actual anti-interference capability of the equipment, making the evaluation results more in line with the actual situation. At the same time, a dynamic early warning threshold is established based on the corrected evaluation value, realizing real-time monitoring and early warning of interference risks.

[0171] The organic combination of these technical principles has formed a complete interference assessment and early warning system. This system conforms to the basic principles of system engineering, has a clear theoretical basis and strict logical relationship. Through the comprehensive analysis of multi-dimensional data, accurate assessment of interference conditions is achieved; through the adaptive learning of neural networks, continuous optimization of the assessment model is guaranteed; through the decomposition of interference components and the influence coefficient matrix, accurate identification of interference sources is achieved; through dynamic correction and early warning mechanisms, the real-time effectiveness of the assessment results is ensured.

[0172] A specific embodiment 1 of the present invention is provided below. The specific implementation of each step in this embodiment 1 is described in detail as follows: The specific implementation of step S10 is as follows:

[0173] Step 101 is to collect the operation time data, maintenance time data and fault record data of each communication device in the photovoltaic power station, and arrange them into a device operation data matrix D, which is expressed as follows:

[0174]

[0175] Among them, t i1 is the running time of the i-th device, m i1 is the maintenance time of the i-th device, f i1is the number of failures of the ith device, and n is the number of devices. The purpose of this step is to record the usage history of each communication device.

[0176] Step 102 is to collect electromagnetic radiation intensity data, ambient air humidity value data and ambient temperature value data around each communication device in the photovoltaic power station, and arrange them into an environmental electromagnetic data matrix E, which is expressed as follows:

[0177]

[0178] Among them, r i1 is the electromagnetic radiation intensity at the ith position, h i1 is the air humidity at the ith position, t i1 is the temperature value of the ith position. The purpose of this step is to obtain data such as electromagnetic interference, humidity and temperature of the environment where the communication equipment is located.

[0179] Step 103 is to store the equipment operation data matrix D and the environmental electromagnetic data matrix E in the communication equipment interference basic database. The purpose of this step is to organize and summarize the various types of relevant data collected to provide support for subsequent interference analysis and evaluation.

[0180] The specific implementation of step S20 is as follows:

[0181] Step 201 is to use a high-speed data acquisition card to sample the data in the communication equipment interference basic database, and set the sampling frequency to f s =2000Hz, and the sampling time is set to T = 48h. The purpose of this step is to obtain the original interference data with high time resolution.

[0182] Step 202 is to extract the interference signal amplitude A from the sampled data. i , Interference duration T i , interference period P i and signal strength S i The time domain characteristic parameters are calculated by weighted calculation to obtain the time domain characteristic parameter vector F t , expressed as follows:

[0183] f ti =α i ·A i +β i ·T i +γ i ·P i +δ i ·S i +ε t ;

[0184] Among them, α i ,β i ,γi ,δ i is the weight coefficient, ε t is the time domain error term. The purpose of this step is to quantify the key time domain characteristics of the interference signal.

[0185] Step 203 is to calculate the time domain characteristic parameter vector F t Perform Fast Fourier Transform (FFT) processing, set the Hanning window and the overlap rate to 50%. The purpose of this step is to convert the time domain feature parameters to the frequency domain.

[0186] Step 204 is to extract the frequency point amplitude a from the FFT result. i1 , frequency point phase p i1 The phase difference between the frequency points i1 Equal frequency domain characteristic parameters are arranged into a frequency domain characteristic parameter matrix F f , expressed as follows:

[0187]

[0188] Where m is the number of frequency points. The purpose of this step is to fully describe the characteristics of the interference signal in the frequency domain.

[0189] The specific implementation of step S30 is as follows:

[0190] Step 301 is to transform the time domain feature parameter vector F t and frequency domain characteristic parameter matrix F f The input layer of the neural network is constructed as input parameters.

[0191] Step 302 is to construct a hidden layer of a neural network, set h neurons, and each neuron uses a rectified linear unit (ReLU) function as an activation function, which is expressed as follows:

[0192]

[0193] Among them, H l is the output of the lth hidden layer neuron, σ is the ReLU activation function, x i is the input feature, w li is the weight, b l The purpose of this step is to design the hidden layer structure and improve the expressiveness of the model.

[0194] Step 303 is to construct the output layer of the neural network and set o neurons. The purpose of this step is to design the output layer structure and determine the dimension and form of the final output.

[0195] Step 304 and step 305 respectively establish the weight matrix W from the input layer to the hidden layer and the weight matrix V from the hidden layer to the output layer, which are expressed as follows:

[0196]

[0197] The purpose of these two steps is to initialize the parameters of the neural network model and lay the foundation for subsequent model training.

[0198] Step 306 is to train the neural network using the adaptive moment estimation (Adam) optimization algorithm, with the learning rate set to η = 0.001 and the training rounds set to N = 500, to obtain an interference assessment model. The purpose of this step is to train the neural network model through the optimization algorithm so that it can effectively learn the interference assessment law.

[0199] The specific implementation of step S40 is as follows:

[0200] Step 401 is to collect the operation time data t, maintenance time data τ and fault record data f of the communication equipment to be evaluated. The purpose of this step is to obtain the usage history data of the equipment to be evaluated itself.

[0201] Step 402 is to collect electromagnetic radiation intensity data R, ambient air humidity data H and ambient temperature data T around the communication device to be evaluated. The purpose of this step is to obtain relevant data about the environment in which the device to be evaluated is located.

[0202] Step 403 is to extract the time domain characteristic parameters and frequency domain characteristic parameters of the interference signal from the collected data. The purpose of this step is to extract characteristic parameters from the actual operation data of the device to be evaluated according to the method of the aforementioned step S20, laying the foundation for the subsequent interference evaluation model input.

[0203] The specific implementation of step S50 is as follows:

[0204] Step 501 is to arrange the time domain characteristic parameters of the device to be evaluated into a matrix F′ according to time points t .

[0205] Step 502 is to arrange the frequency domain characteristic parameters of the device to be evaluated into a matrix F′ according to the frequency points f .

[0206] Step 503 is to convert F′ t and F′ f Input into the interference assessment model constructed above to obtain the interference assessment result V original The purpose of this step is to use the trained neural network model to conduct a comprehensive analysis and evaluation of the actual situation of the equipment to be evaluated.

[0207] The specific implementation of step S60 is as follows:

[0208] Step 601 is to set the anti-interference level evaluation criteria, divide the anti-interference level into k levels, and set the corresponding interference evaluation result range for each level. The purpose of this step is to determine reasonable anti-interference level classification standards.

[0209] Step 602 is to convert the interference evaluation result V original Compare with the anti-interference level evaluation criteria.

[0210] Step 603 is to determine the anti-interference level of the device to be evaluated according to the comparison result, as the final anti-interference capability evaluation index. The purpose of this step is to determine the specific anti-interference level of the device to be evaluated.

[0211] The specific implementation of step S70 is as follows:

[0212] Step 701 is to calculate the time domain characteristic parameter vector F according to the observation period T. t Perform integration operation to obtain the stable component S of the interference signal c :

[0213]

[0214] Among them, s(t) is the original signal, ε s is the stable component error term. The purpose of this step is to extract the stable component in the interference signal.

[0215] Step 702 is to transform the time domain feature parameter vector F t Subtract the stable component S c , get the interference signal variation component D c :

[0216] D c =s(t)-S c +ε d ;

[0217] Among them, ε d is the variable component error term. The purpose of this step is to separate the variable component of the interference signal.

[0218] The specific implementation of step S80 is as follows:

[0219] Step 801 is to establish an environmental impact equation E according to the electromagnetic radiation intensity R, the ambient humidity H and the ambient temperature T, which is expressed as follows:

[0220]

[0221] Among them, R0, H0, T0 are standard reference values, k1, k2, k3 are weight coefficients, α, β, γ are sensitivity indexes, λ1, λ2, λ3 are attenuation coefficients, ε e is the environmental error term. The purpose of this step is to evaluate the impact of environmental factors on communication equipment.

[0222] Step 802 is to establish the equipment state equation S according to the operation time t, maintenance time τ and the number of failures f, which is expressed as follows:

[0223]

[0224] Among them, λ1,λ2,λ3 are weight coefficients, μ1,μ2,μ3 are performance attenuation coefficients, ν1,ν2,ν3 are stability coefficients, ε s is the state error term. The purpose of this step is to evaluate the impact of the device's own state on its ability to resist interference.

[0225] Step 803 is to establish an interference characteristic equation I according to the number of interference occurrences N, the duration of interference D and the duration of communication interruption B, which is expressed as follows:

[0226]

[0227] Among them, N0, D0, B0 are reference thresholds, ω1, ω2, ω3 are weight coefficients, δ, θ, φ are influence indexes, η1, η2, η3 are suppression coefficients, ε i is the characteristic error term. The purpose of this step is to evaluate the impact characteristics of the interference signal on the communication equipment.

[0228] Step 804 is to combine the environmental impact E, the device status S and the interference feature I in a diagonal matrix to form an interference source influence coefficient matrix M:

[0229]

[0230] Among them, i ,σ i ,τ i is the comprehensive weight coefficient, ε m The purpose of this step is to construct a matrix model that describes the impact of interference sources.

[0231] Step 805 is to calculate the inverse matrix M of the interference source influence coefficient matrix M -1 , and multiplied by the normalization coefficient matrix K and the correction coefficient matrix C, the communication equipment anti-interference coefficient matrix R is obtained:

[0232] R=M -1 ·K·C+ε r ;

[0233]

[0234] Among them, c i is the correction coefficient of each dimension, ε r The purpose of this step is to further calculate the anti-interference capability coefficient matrix of the communication equipment itself based on the interference source influence coefficient matrix.

[0235] The specific implementation of step S90 is as follows:

[0236] Step 901 is to convert the original interference evaluation result v original Multiply the communication equipment anti-interference coefficient matrix R and the evaluation weight diagonal matrix W to obtain the corrected interference evaluation value V modified :

[0237] V modified =V original ·R·W+ε v ;

[0238]

[0239] Among them, w i Evaluate the weights for each dimension, ε v The purpose of this step is to correct and revise the original interference assessment results.

[0240] Step 902 is to calculate the corrected evaluation value V modified The standard deviation σ v , and according to V modified and σ V Establish warning threshold T warning :

[0241] T warning =α·V modified +β·σ V +γ;

[0242] Among them, α and β are weight coefficients, and γ is the benchmark threshold. The purpose of this step is to set a reasonable warning threshold to provide a basis for subsequent real-time monitoring and warning.

[0243] Step 903 is to monitor the communication equipment in real time. When the real-time monitoring value exceeds the warning threshold T warning The purpose of this step is to use the warning threshold established above to continuously monitor the communication equipment and issue warnings in a timely manner.

[0244] In order to better understand and implement the present invention, the following provides Example 2 of a specific application scenario of the present invention: A photovoltaic power station adopts a centralized layout, mainly composed of photovoltaic arrays, junction boxes, transformers, inverters, monitoring systems, etc. Among them, the monitoring system is responsible for the real-time monitoring and remote control of various equipment inside the power station, and plays a vital role. The communication equipment of the monitoring system mainly includes industrial Ethernet switches, remote terminal units, wireless communication modules, etc. Due to the complex environment of the power station, these communication devices are often subject to electromagnetic interference from photovoltaic arrays, inverters and other equipment, resulting in frequent communication failures, which seriously affects the automated operation of the power station.

[0245] In response to this problem, the power station operation and maintenance personnel decided to use the communication equipment interference assessment and early warning method proposed in this invention to conduct a comprehensive analysis and dynamic monitoring of the communication equipment inside the power station. The following is the specific implementation process and effect of this method in the power station.

[0246] Data collection phase:

[0247] First, the power station operation and maintenance personnel collected operating data of communication equipment and environmental electromagnetic data for a week.

[0248] Specifically include:

[0249] 1. Operation data collection: A total of 20 communication devices were installed, and the operation time, the last maintenance time, and the cumulative number of failures of each device were recorded. The collection period is one week, and the statistical results are shown in Table 1.

[0250] Table 1 Communication equipment operation data

[0251] Equipment No. Running time (h) Maintenance time (d) Number of failures (times) 1 4320 180 3 2 4680 210 2 3 4800 240 1 4 5040 270 2 5 4560 150 4 6 4440 240 3 7 4920 210 2 8 5280 270 1 9 4800 180 2 10 4680 240 3 11 5040 180 2 12 4800 210 1 13 4560 240 2 14 4320 270 3 15 4800 150 2 16 4440 210 1 17 4920 240 2 18 5280 180 1 19 4680 210 2 20 4560 240 3

[0252] 2. Environmental data collection: A total of 20 environmental monitoring points were installed inside the power station, and the electromagnetic radiation intensity of the photovoltaic modules, ambient air humidity and ambient temperature of each monitoring point were recorded. The collection period was also one week, and the statistical results are shown in Table 2.

[0253] Table 2 Environmental electromagnetic data

[0254]

[0255]

[0256] Through the above-mentioned week of data collection, the power station operation and maintenance personnel have initially established a basic database of communication equipment interference.

[0257] Time-frequency domain analysis phase:

[0258] Next, the power station operation and maintenance personnel conducted time domain analysis and frequency domain conversion on the collected data. The specific steps are as follows:

[0259] 1. Time domain feature parameter extraction:

[0260] The data in the interference basic database are sampled continuously for 48 hours at a sampling frequency of 2000 Hz.

[0261] The time domain characteristic parameters such as interference signal amplitude, duration, period and signal strength are extracted from the sampled data.

[0262] These time domain characteristic parameters are weighted and calculated to obtain the time domain characteristic parameter vector F t .

[0263] 2. Frequency domain feature parameter extraction:

[0264] Perform FFT transformation on the time domain characteristic parameter vector, use Hanning window with an overlap rate of 50%.

[0265] Frequency domain characteristic parameters such as frequency point amplitude, frequency point phase and frequency point phase difference are extracted from the FFT results.

[0266] Arrange these frequency domain feature parameters into a frequency domain feature parameter matrix F f .

[0267] Through the above analysis, the power station operation and maintenance personnel obtained the key parameters that describe the time-frequency characteristics of the interference signal. Next, these parameters are input into the interference assessment model.

[0268] Interference Assessment Modeling Phase:

[0269] The power station operation and maintenance personnel built a disturbance assessment model based on a neural network. The specific process is as follows:

[0270] 1. Model input:

[0271] The time domain characteristic parameter vector F t and frequency domain characteristic parameter matrix F f As the input layer of the neural network.

[0272] 2. Model structure:

[0273] There are 100 neurons in the hidden layer, and each neuron uses the ReLU activation function.

[0274] Five neurons are set in the output layer, corresponding to five different anti-interference levels.

[0275] 3. Model training:

[0276] The Adam optimization algorithm is used to train the neural network, the learning rate is set to 0.001, and the training rounds are 500. During the training process, the network will adaptively learn the interference rules contained in the time-frequency characteristics and continuously optimize the internal weights and bias parameters.

[0277] After the training was completed, the power plant operation and maintenance personnel obtained a neural network model that can evaluate the anti-interference performance of communication equipment.

[0278] Interference impact analysis phase:

[0279] After having the interference assessment model, the power station operation and maintenance personnel further analyzed the impact of environmental factors and equipment status on the anti-interference ability of communication equipment. The details are as follows:

[0280] 1. Environmental impact analysis:

[0281] According to the environmental data in Table 2, the environmental impact equation E is established to quantitatively evaluate the interference effect of electromagnetic radiation intensity, humidity and temperature on communication equipment.

[0282] The calculated environmental impact coefficient E is 0.78, indicating that environmental factors cause great interference to communication equipment.

[0283] 2. Equipment status analysis:

[0284] According to the equipment operation data in Table 1, the equipment state equation S is established to evaluate the anti-interference ability of the communication equipment itself.

[0285] The calculated equipment status coefficient S is 0.71, which indicates that the performance of the equipment itself also has a certain impact on the anti-interference ability.

[0286] 3. Interference feature analysis:

[0287] According to the number of interference occurrences, duration and communication interruption duration actually monitored, the interference characteristic equation I is established to evaluate the impact intensity of the interference signal on the communication equipment.

[0288] The calculated interference influence coefficient I is 0.82, indicating that the characteristics of the interference signal are also an important factor affecting the anti-interference ability.

[0289] The above three influencing factors are combined into the interference source influence coefficient matrix M, and then its inverse matrix M is calculated. - 1, the anti-interference coefficient matrix R of the communication equipment can be obtained. In this way, the comprehensive impact of the environment, equipment and interference on the anti-interference performance of the communication equipment is fully quantified.

[0290] Real-time monitoring and early warning stage:

[0291] With the interference assessment model and anti-interference coefficient matrix, the power plant operation and maintenance personnel finally established a real-time monitoring and early warning mechanism:

[0292] 1. Calculation of corrected interference assessment value:

[0293] Multiply the original interference assessment result by the anti-interference coefficient matrix R and the assessment weight diagonal matrix W to obtain the corrected interference assessment value V modified .

[0294] Calculate V modified The standard deviation σ V .

[0295] 2. Warning threshold setting:

[0296] According to V modified and σ V, establish the warning threshold T w arning=0.8*V m odified+0.2*σ V +50.

[0297] 3. Real-time monitoring and early warning:

[0298] Real-time monitoring of communication equipment is performed. When the monitoring value exceeds the warning threshold T w Arning, immediately send out an early warning signal.

[0299] The early warning signal will be sent to the power station control center through the remote terminal unit to remind the operation and maintenance personnel to take interference suppression measures in time.

[0300] Through the implementation of the above stages, the power station has successfully established a complete communication equipment interference assessment and early warning system. The following is a detailed analysis of the application effect of this solution:

[0301] 1. Analysis of interference assessment results:

[0302] The 20 communication devices were input into the trained neural network model respectively, and the evaluation results of their anti-interference levels were obtained as follows: 3 devices were at level 5 (highest); 5 devices were at level 4; 7 devices were at level 3; 4 devices were at level 2; and 1 device was at level 1 (lowest);

[0303] From the results, we can see that most devices have strong anti-interference capabilities, but there are still a few devices with weak anti-interference performance, which need special attention.

[0304] 2. Analysis of interference source impact:

[0305] According to calculations, the environmental impact coefficient E is 0.78, the equipment status coefficient S is 0.71, the interference characteristic coefficient I is 0.82, and the final result of the comprehensive impact coefficient matrix M is 0.64.

[0306] This shows that environmental factors, the equipment's own status and the characteristics of the interference signal have reduced the anti-interference ability of communication equipment to a certain extent, especially the impact of the interference signal itself is the most significant.

[0307] 3. Analysis of early warning effect:

[0308] In real-time monitoring, the monitoring values ​​of 2 devices exceeded the warning threshold T w arning.

[0309] After receiving the early warning signal, the operation and maintenance personnel immediately took measures such as shielding and filtering, successfully suppressed the interference, and restored the communication equipment to normal operation.

[0310] The establishment of the early warning mechanism has greatly improved the power station's response speed to interference with communication equipment and effectively avoided serious communication failures.

[0311] It should be noted that the variables involved in the present invention are explained in detail as shown in Table 3 below.

[0312] Table 3 Variable explanation table

[0313]

[0314]

[0315] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for evaluating interference of communication equipment in a photovoltaic power station, characterized in that: The following steps are involved: S10, collecting communication equipment operation data and environmental electromagnetic data in the photovoltaic power station, the communication equipment operation data including communication equipment operation time, communication equipment maintenance time, communication equipment fault record, the environmental electromagnetic data including photovoltaic module electromagnetic radiation intensity, ambient air humidity value, ambient temperature value, and establishing a communication equipment interference basic database; S20, performing time domain analysis on the data in the communication device interference basic database, extracting time domain characteristic parameters of interference signals, and performing frequency domain conversion on the time domain characteristic parameters of interference signals to obtain frequency domain characteristic parameters of interference signals; S30, establishing an interference assessment index system according to the interference signal time domain characteristic parameters and the interference signal frequency domain characteristic parameters, constructing a neural network model based on the interference assessment index system, and training to obtain an interference assessment model; S40, collecting operation data of the communication device to be evaluated, and extracting time domain characteristic parameters and frequency domain characteristic parameters of the interference signal of the communication device to be evaluated; S50, inputting the interference signal time domain characteristic parameters and the interference signal frequency domain characteristic parameters of the communication device to be evaluated into the interference evaluation model to obtain an interference evaluation result; S60, determining the anti-interference level of the communication equipment according to the interference evaluation result; S70, decomposing the time domain characteristic parameter of the interference signal into a stable component and a variable component to obtain a stable component of the interference signal and a variable component of the interference signal; S80, according to the interference signal stable component and the interference signal variable component, establish an equation group through an interference source influence coefficient matrix to calculate the interference source influence coefficient matrix, and calculate the communication device anti-interference coefficient matrix based on the interference source influence coefficient matrix; S90. According to the anti-interference coefficient matrix of the communication equipment, the interference assessment result is corrected to obtain a corrected interference assessment value, wherein the corrected interference assessment value is the original interference assessment value multiplied by the anti-interference coefficient matrix of the communication equipment, and based on the corrected interference assessment value, a communication equipment interference warning threshold is established to perform real-time monitoring and warning.

2. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 1, characterized in that: The step S10 specifically includes: Step 101: collect operation time data, maintenance time data and fault record data of each communication device in the photovoltaic power station, and arrange the collected operation time data, maintenance time data and fault record data in a manner that each row corresponds to one communication device to form a device operation data matrix; Step 102: Collect the electromagnetic radiation intensity data of photovoltaic modules, the ambient air humidity value data and the ambient temperature value data around each communication device in the photovoltaic power station, and arrange the collected electromagnetic radiation intensity data of photovoltaic modules, the ambient air humidity value data and the ambient temperature value data in a manner that each row corresponds to one communication device, to form an environmental electromagnetic data matrix; Step 103, storing the equipment operation data matrix and the environmental electromagnetic data matrix in a communication equipment interference basic database respectively; The step S20 specifically includes: Step 201: Use a high-speed data acquisition card to sample data in the communication equipment interference basic database, set the sampling frequency to 2 kHz, and set the sampling time to 48 hours; Step 202: extract interference signal amplitude data, interference duration data, interference period data and signal strength data from the sampled data, perform weighted calculation on the extracted interference signal amplitude data, interference duration data, interference period data and signal strength data to obtain time domain feature parameters; Step 203, performing fast Fourier transform processing on the time domain characteristic parameters, setting a Hanning window with an overlap rate of 50%; Step 204: extract frequency point amplitude data, frequency point phase data and frequency point phase difference data from the fast Fourier transform result, and arrange the extracted frequency point amplitude data, frequency point phase data and frequency point phase difference data into a frequency domain feature parameter matrix.

3. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 2, characterized in that: The step S30 specifically includes: Step 301: Use the interference signal time domain characteristic parameter and the interference signal frequency domain characteristic parameter as input parameters to construct a neural network input layer; Step 302: construct a neural network hidden layer, wherein a plurality of neurons are arranged in the neural network hidden layer, and each neuron adopts a rectified linear unit function as an activation function; Step 303: construct a neural network output layer, and set a plurality of neurons in the neural network output layer; Step 304: Establish a weight matrix from the input layer to the hidden layer, wherein the weight matrix is ​​used to characterize the connection relationship between each neuron in the input layer and each neuron in the hidden layer; Step 305: Establish a weight matrix from the hidden layer to the output layer, wherein the weight matrix is ​​used to characterize the connection relationship between each neuron in the hidden layer and each neuron in the output layer; Step 306: Adopt an adaptive moment estimation optimization algorithm to train the neural network, set the learning rate to 0.001, and set the number of training rounds to 500, to obtain an interference assessment model.

4. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 3, characterized in that: The step S40 specifically includes: Step 401: Collecting operation time data, maintenance time data and fault record data of the communication equipment to be evaluated; Step 402: Collecting the electromagnetic radiation intensity data, ambient air humidity value data and ambient temperature value data of the photovoltaic components around the communication device to be evaluated; Step 403: extract the time domain characteristic parameters and frequency domain characteristic parameters of the interference signal from the collected data.

5. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 4, characterized in that: The step S50 specifically includes: Step 501: Arrange the time domain characteristic parameters of the interference signal of the communication device to be evaluated into a time domain characteristic parameter matrix in a manner that each row corresponds to a time point; Step 502: Arrange the frequency domain characteristic parameters of the interference signal of the communication device to be evaluated into a frequency domain characteristic parameter matrix in a manner that each row corresponds to one frequency point; Step 503: input the time domain characteristic parameter matrix and the frequency domain characteristic parameter matrix into the interference assessment model to obtain an interference assessment result; The step S60 specifically includes: Step 601: setting an anti-interference level evaluation standard, dividing the anti-interference level into multiple levels, and setting a corresponding interference evaluation result range for each level; Step 602: Compare the interference assessment result with the anti-interference level evaluation standard; Step 603: Determine the anti-interference level of the communication device according to the comparison result, and use the anti-interference level as an evaluation index of the anti-interference capability of the communication device.

6. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 5, characterized in that: The step S70 specifically includes: Step 701: perform an integration operation on the time domain characteristic parameter of the interference signal according to the observation period to obtain a stable component of the interference signal; Step 702: Subtract the interference signal stable component from the interference signal time domain characteristic parameter to obtain the interference signal variable component.

7. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 6, characterized in that: The step S80 specifically includes: Step 801: Calculate the degree of influence of the environment on the communication device according to the electromagnetic radiation intensity data of the photovoltaic module, the ambient air humidity value data and the ambient temperature value data; Step 802: Calculate the anti-interference capability of the communication device in the current operation state according to the device operation time data, the maintenance time data and the failure number data; Step 803: Calculate the impact intensity of the interference on the communication device according to the interference occurrence frequency data, the interference duration data and the communication interruption duration data; Step 804: Combine the environmental impact degree, the anti-interference capability and the impact strength in a block diagonal manner to form an interference source impact coefficient matrix; Step 805: Calculate the inverse matrix of the interference source influence coefficient matrix, multiply the inverse matrix with the normalization coefficient matrix and the correction coefficient matrix to obtain the communication equipment anti-interference coefficient matrix.

8. A method for evaluating interference of communication equipment in a photovoltaic power station according to claim 7, characterized in that: The step S90 specifically includes: Step 901: multiply the interference assessment result by the communication device anti-interference coefficient matrix and the assessment weight diagonal matrix to obtain a corrected interference assessment value; Step 902: Calculate the standard deviation of the corrected interference assessment value, and establish a warning threshold according to the corrected interference assessment value and the standard deviation; Step 903: Perform real-time monitoring on the communication equipment, and issue a warning signal when the real-time monitoring value exceeds the warning threshold.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, the method for evaluating interference of communication equipment in a photovoltaic power station according to any one of claims 1 to 8 is used to execute.

10. A photovoltaic power station communication equipment interference assessment system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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