Flame discharge signal optimization processing method for 10kV overhead line

Abstract

The invention discloses a flame discharge signal optimization processing method used under a 10kV overhead line, and relates to the technical field of electric signal processing, and the method specifically comprises the steps: 1, obtaining a flame discharge signal under the 10kV overhead line, and carrying out the classification arrangement and storage of data; step 2, improving a sea elephant optimization algorithm; 3, setting initial parameters of a variational mode decomposition algorithm, including a mode number K and a penalty factor alpha, and setting the mode number K and the penalty factor alpha of the variational mode decomposition algorithm by using an improved sea image optimization algorithm; 4, obtaining an optimized variational mode decomposition algorithm, and performing denoising processing on the flame discharge signal under the 10kV overhead line in the step 1 by adopting the optimized variational mode decomposition algorithm obtained in the step 3 to realize optimization of the flame discharge signal; according to the method, the analysis precision of flame discharge signal processing under the 10kV overhead line is improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric signal processing, in particular to a flame discharge signal optimization processing method for a 10kV overhead line. Background Art

[0002] In recent years, due to the continuous occurrence of 10kV line insulation breakdown and tripping accidents caused by forest and grassland fires, how to scientifically, accurately and quickly discover and process the discharge signals caused by early wildfire flames on 10kV overhead lines is of great significance for early warning and prevention of 10kV line insulation breakdown and tripping accidents caused by wildfires and for analyzing and determining the causes of accidents.

[0003] Applying the variational mode decomposition (VMD) algorithm to the processing of flame discharge signals under 10kV overhead lines is an effective signal analysis method. Flame discharge signals have complex frequency components and are often mixed with a lot of noise. The VMD algorithm can decompose the signal into a series of sub-signals with specific frequencies and bandwidths, extract useful information from them and remove noise. However, the performance and effect of the VMD algorithm depend to a large extent on the reasonable setting of the mode number K and the penalty factor α. Appropriate parameter selection can ensure that the mode number obtained by VMD is neither over-decomposed nor misses important components. Intelligent optimization algorithms can help achieve this process.

[0004] The Walrus Optimization (WO) algorithm simulates the social behavior of walruses in the natural environment, including migration, foraging, roosting, collecting resources, and escaping. Walruses decide on their actions by receiving key signals (such as danger signals and safety signals). However, the walrus optimization algorithm relies on the diversity of the initial population. If the diversity of the initial population is low, it may make it difficult to cover the entire search space in the early search stage, thus affecting the final convergence effect. Summary of the invention

[0005] In order to solve the problems existing in the prior art, the purpose of the present invention is to provide a flame discharge signal optimization processing method for 10kV overhead lines. The present invention improves the walrus optimization algorithm to enhance the optimization ability of the walrus optimization algorithm, uses the improved walrus optimization algorithm to adjust the mode number K and penalty factor α of the variational mode decomposition algorithm, obtains the optimal parameters, performs denoising processing on the flame discharge signal under the 10kV overhead line, and improves the efficiency of analyzing the flame discharge signal under the 10kV overhead line.

[0006] To achieve the above object, the technical solution adopted by the present invention is: a flame discharge signal optimization processing method for 10kV overhead lines, comprising the following steps:

[0007] Step 1: Collect the flame discharge signal under the 10kV overhead line, and classify and store the data;

[0008] Step 2: Improve the walrus optimization algorithm;

[0009] Step 3, setting the initial parameters of the variational modal decomposition algorithm, including the modal number K and the penalty factor α, and using the improved walrus optimization algorithm to adjust the modal number K and the penalty factor α of the variational modal decomposition algorithm to obtain an optimized variational modal decomposition algorithm;

[0010] Step 4: using the optimized variational mode decomposition algorithm obtained in step 3 to perform denoising on the flame discharge signal under the 10 kV overhead line in step 1, so as to achieve optimized processing of the flame discharge signal;

[0011] As a further improvement of the present invention, the step 2 specifically comprises the following steps:

[0012] Step 2.1, improve the adaptive weight factor w of the walrus optimization algorithm, dynamically adjust the search capabilities of different search stages through the improved adaptive weight factor, and balance the global search and local optimization capabilities;

[0013] Step 2.2, improve the population diversity factor D of the walrus optimization algorithm, introduce chaotic disturbance into the population diversity factor, and improve the position update model of the walrus optimization algorithm.

[0014] As a further improvement of the present invention, in step 2.1, the adaptive weight factor w of the improved walrus optimization algorithm is as follows:

[0015]

[0016] In the formula, t represents the current iteration number, w max Indicates the maximum initial weight, w min represents the minimum weight, T max represents the maximum number of iterations, w i (t) is the weight of the i-th walrus in the t-th iteration.

[0017] As a further improvement of the present invention, in step 2.2, the mathematical model of the population diversity factor D of the improved walrus optimization algorithm is as follows:

[0018]

[0019] In the formula, represents the position of the i-th walrus in the t-th iteration, represents the average position of all walruses, N represents the population size, and D(t) represents the diversity factor of the population at the tth iteration.

[0020] As a further improvement of the present invention, the step 3 specifically comprises the following steps:

[0021] Step 3.1: The modal number K and penalty factor α of the variational modal decomposition algorithm are used as the solution vector. The mathematical model formula of the modal number K and penalty factor α is:

[0022]

[0023] In the formula, K i represents the variational mode decomposition mode number corresponding to the i-th walrus, α i represents the variational mode decomposition penalty factor corresponding to the i-th walrus, represents the solution vector of the i-th walrus;

[0024] Step 3.2: Initialize the parameters of the improved walrus optimization algorithm, including: the maximum size of the walrus population N, the maximum number of iterations T max , problem dimension E, upper bound ub and lower bound lb of variational mode decomposition parameters K and α;

[0025] Step 3.3: Check whether the current number of iterations t is equal to or greater than the maximum number of iterations T max , if yes, go to step 3.7; otherwise, go to step 3.4 to step 3.6;

[0026] Step 3.4, using the adaptive weight factor w of the walrus optimization algorithm and the population diversity factor D to update the position of the walrus;

[0027] Step 3.5: In each iteration, calculate the population diversity factor D. If D is equal to or greater than the threshold D threshold , continue normal search, otherwise, introduce chaotic disturbance to update the position of the walrus;

[0028] Step 3.6, calculate the fitness value of each walrus position according to the objective function G, and retain the walrus position with the smallest fitness in the current iteration as the global optimal solution;

[0029] Step 3.7, resolve the walrus position corresponding to the minimum global fitness into the mode number and penalty factor solution set of the variational mode algorithm, and obtain the mode number and penalty factor of the optimal variational mode decomposition algorithm.

[0030] As a further improvement of the present invention, in step 3.4, the updated walrus position formula is as follows:

[0031]

[0032] In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random perturbation factor, and rand is a random value in [0,1]. is the vector position of the i-th walrus at the t+1th iteration.

[0033] As a further improvement of the present invention, in step 3.5, chaotic disturbance is introduced to update the position of the walrus, and the updated position formula is as follows:

[0034]

[0035] In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random disturbance factor, is the vector position of the ith walrus in the t+1th iteration, chaos(t) is a chaotic sequence, and the chaotic mapping formula is:

[0036] x n+1 = r·x n ·(1-x n )

[0037] In the formula, x n represents the current chaos value, r represents the chaos parameter, x n+1 Indicates the next chaos value generated.

[0038] As a further improvement of the present invention, in step 3.5, the threshold D threshold The calculation method is as follows:

[0039]

[0040] Where N represents the population size, E represents the problem dimension, and δ represents the adjustment coefficient.

[0041] As a further improvement of the present invention, in step 3.6, the signal-to-noise ratio SNR is used as the objective function, and the mathematical model formula of the objective function G is specifically as follows:

[0042]

[0043] In the formula, x signal represents the signal component of the flame discharge signal point under the i-th 10kV overhead line, x noise represents the noise component of the flame discharge signal point under the i-th 10kV overhead line, and M represents the total number of data points of the flame discharge signal under the 10kV overhead line.

[0044] The beneficial effects of the present invention are:

[0045] The present invention improves the walrus optimization algorithm by introducing an adaptive weight factor and a population diversity factor, dynamically adjusts the search intensity of the walrus in different iteration stages, enhances the global search of the algorithm and balances the local search, and enhances the adaptability of the algorithm; uses the improved walrus optimization algorithm to adjust the mode number K and the penalty factor α of the variational mode decomposition algorithm, and improves the performance and effect of the variational mode decomposition algorithm; uses the optimized variational mode decomposition algorithm to denoise the flame discharge signal under the 10kV overhead line, so as to obtain purer signal data, thereby improving the efficiency of analyzing the flame discharge signal under the 10kV overhead line. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 is a flow chart of an embodiment of the present invention;

[0047] Figure 2 The present invention is a flowchart of using the improved walrus optimization algorithm to adjust the mode number K and penalty factor α of the variational mode decomposition algorithm. DETAILED DESCRIPTION

[0048] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0049] Example

[0050] like Figure 1 As shown, a flame discharge signal optimization processing method for a 10kV overhead line includes the following steps:

[0051] Step 1: Collect the flame discharge signal under the 10kV overhead line, and classify and store the data;

[0052] Step 2: Improve the Walrus Optimization Algorithm (WO), the specific steps include:

[0053] Step 2.1, improve the adaptive weight factor w of the walrus optimization algorithm, and dynamically adjust the search capabilities of different search stages through the improved adaptive weight factor, so as to balance the global search and local optimization capabilities;

[0054] Step 2.2, improving the population diversity factor D of the walrus optimization algorithm, introducing chaotic disturbance into the population diversity factor, and obtaining the improved population diversity factor D;

[0055] Step 3, setting the initial parameters of the variational modal decomposition algorithm, including the modal number K and the penalty factor α, and using the improved walrus optimization algorithm to adjust the modal number K and the penalty factor α of the variational modal decomposition algorithm to obtain an optimized variational modal decomposition algorithm;

[0056] Step 4: using the optimized variational mode decomposition algorithm obtained in step 3 to perform denoising on the flame discharge signal under the 10 kV overhead line in step 1, so as to achieve optimized processing of the flame discharge signal;

[0057] In step 2, the adaptive weight factor w of the walrus optimization algorithm is improved to balance the global search and local optimization capabilities; the adaptive weight factor w formula of the improved walrus optimization algorithm is:

[0058]

[0059] In the formula, t represents the current iteration number, w max Indicates the maximum initial weight, w min represents the minimum weight, T max represents the maximum number of iterations, w i (t) is the weight of the i-th walrus in the t-th iteration;

[0060] In step 2, the population diversity factor D of the walrus optimization algorithm is improved. Chaotic disturbance is introduced into the population diversity factor to improve the position update model of the walrus optimization algorithm. The mathematical model of the population diversity factor D of the improved walrus optimization algorithm is:

[0061]

[0062] In the formula, represents the position of the i-th walrus at the t-th iteration, represents the average position of all walruses, N represents the population size, and D(t) represents the diversity factor of the population at the tth iteration;

[0063] In step 3, the modal number K determines the number of sub-signals (intrinsic mode functions, IMFs) obtained after signal decomposition. The appropriate K value can accurately capture the different frequency components in the flame discharge signal under the 10kV overhead line, so that each mode focuses on a frequency range. If the K value is too small, the decomposition effect is insufficient, which may cause some key components to fail to be separated, resulting in the loss of signal characteristics; if the K value is set too large, the variational mode decomposition will generate too many modes, resulting in an increase in computational complexity, and it is easy to overfit the noise component, making the noise difficult to remove during decomposition.

[0064] In step 3, the penalty factor α is used to control the bandwidth constraint in variational mode decomposition. A larger α value will impose stricter constraints on the bandwidth, making the decomposed mode smoother and reducing the impact of noise, but may cause the loss of some detailed information; a smaller α value relaxes the bandwidth restriction, and the decomposed mode contains more details, which is suitable for detecting subtle changes in the signal. Selecting a suitable α value can achieve a balance between signal reconstruction accuracy and noise suppression. A moderate α value can effectively reduce noise interference while maintaining the key features of the signal, enhance the accuracy of signal reconstruction and the reliability of the decomposition results, and make variational mode decomposition perform better in the denoising and decomposition of complex signals.

[0065] In step 3, the improved walrus optimization algorithm is used to adjust the mode number and penalty factor of the variational mode algorithm, and the improved walrus optimization algorithm is used to adjust the mode number K and penalty factor α of the variational mode decomposition algorithm, so that the variational mode algorithm can better adapt to the flame discharge signal characteristics under different 10kV overhead lines, and achieve better flame discharge signal decomposition and denoising effects under 10kV overhead lines. The specific steps are:

[0066] Step 3.1: The modal number K and penalty factor α of the variational modal decomposition algorithm are used as the solution vector. The mathematical model formula of the modal number K and penalty factor α is:

[0067]

[0068] In the formula, K i represents the variational mode decomposition mode number corresponding to the i-th walrus, α i represents the variational mode decomposition penalty factor corresponding to the i-th walrus, represents the solution vector of the i-th walrus;

[0069] Step 3.2: Initialize the parameters of the improved walrus optimization algorithm, including: the maximum size of the walrus population N, the maximum number of iterations T max , problem dimension E, upper bound ub and lower bound lb of variational mode decomposition parameters K and α;

[0070] Step 3.3: Check whether the current number of iterations t is equal to or greater than the maximum number of iterations T max , if yes, go to step 3.7; otherwise, go to step 3.4 to step 3.6;

[0071] Step 3.4, using the adaptive weight factor w of the walrus optimization algorithm and the population diversity factor D to update the position of the walrus;

[0072] Step 3.5: In each iteration, calculate the population diversity factor D. If D is equal to or greater than the threshold D threshold , continue normal search, otherwise, introduce chaotic disturbance to update the position of the walrus;

[0073] Step 3.6, calculate the fitness value of each walrus position according to the objective function G, and retain the walrus position with the smallest fitness in the current iteration as the global optimal solution;

[0074] Step 3.7, resolve the walrus position corresponding to the minimum global fitness into the mode number and penalty factor solution set of the variational mode algorithm, and obtain the mode number and penalty factor of the optimal variational mode decomposition algorithm.

[0075] In step 3.4, the adaptive weight factor w of the walrus optimization algorithm is used to update the walrus position. The updated position formula is:

[0076]

[0077] In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random perturbation factor, and rand is a random value in [0,1]. is the vector position of the i-th walrus at the t+1th iteration;

[0078] Step 3.5 In each iteration, calculate the population diversity factor D. If D is equal to or greater than the threshold D threshold , continue normal search, otherwise, introduce chaotic disturbance to update the position of the walrus, and the updated position formula is:

[0079]

[0080] In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random disturbance factor, is the vector position of the ith walrus in the t+1th iteration, chaos(t) is a chaotic sequence, and the chaotic mapping formula is:

[0081] x n+1 = r·x n ·(1-x n )

[0082] In the formula, x n represents the current chaos value, r represents the chaos parameter, the value is 4, x n+1 Indicates the next chaotic value generated;

[0083] In step 3, in each iteration, the population diversity factor D is calculated. If D is equal to or greater than the threshold D threshold , continue normal search, otherwise introduce chaotic disturbance to update the position of the walrus, and calculate the threshold D according to the following formulathreshold :

[0084]

[0085] Where N represents the population size, E represents the problem dimension, and δ represents the adjustment coefficient, which ranges from [0.5 to 1.0].

[0086] In step 3.6, the signal-to-noise ratio (SNR) is used as the objective function, and the mathematical model formula of the objective function G is:

[0087]

[0088] In the formula, x signal represents the signal component of the flame discharge signal point under the i-th 10kV overhead line, x noise represents the noise component of the flame discharge signal point under the i-th 10kV overhead line, and M represents the total number of data points of the flame discharge signal under the 10kV overhead line.

[0089] In this embodiment, the flame discharge signal under the 10kV overhead line is first obtained, and the data is classified, sorted and stored; the adaptive weight factor w and population diversity factor D of the walrus optimization algorithm are improved, and chaotic disturbance is introduced into the population diversity factor. Then the initial parameters of the variational modal decomposition algorithm are set, including the modal number K and the penalty factor α, and the modal number K and the penalty factor α of the variational modal decomposition algorithm are adjusted using the improved walrus optimization algorithm to obtain an optimized variational modal decomposition algorithm; then the flame discharge signal under the 10kV overhead line is denoised using the optimized variational modal decomposition algorithm to achieve optimized processing of the flame discharge signal. The analysis accuracy of the flame discharge signal processing under the 10kV overhead line is improved.

[0090] This embodiment has the following advantages: in practice, when reducing the noise of the flame discharge signal under the 10kV overhead line containing various complex noise signals, the interference of the noise signal on the experimental results can be effectively avoided; the penalty mode number K and factor α of the variational mode decomposition algorithm are adjusted by the improved walrus optimization algorithm, which can effectively avoid over-decomposition and noise overfitting, and balance the smoothness and detail retention of the signal; the flame discharge signal under the 10kV overhead line after noise reduction is very convenient for subsequent signal processing.

[0091] The above-mentioned embodiments only express the specific implementation of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A flame discharge signal optimization processing method for 10kV overhead lines, characterized in that: The following steps are involved: Step 1: Collect the flame discharge signal under the 10kV overhead line, and classify and store the data; Step 2: Improve the walrus optimization algorithm; Step 3, setting the initial parameters of the variational modal decomposition algorithm, including the modal number K and the penalty factor α, and using the improved walrus optimization algorithm to adjust the modal number K and the penalty factor α of the variational modal decomposition algorithm to obtain an optimized variational modal decomposition algorithm; Step 4: Use the optimized variational mode decomposition algorithm obtained in step 3 to denoise the flame discharge signal under the 10kV overhead line in step 1 to achieve optimized processing of the flame discharge signal.

2. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 1 is characterized in that: The step 2 specifically includes the following steps: Step 2.1, improve the adaptive weight factor w of the walrus optimization algorithm, dynamically adjust the search capabilities of different search stages through the improved adaptive weight factor, and balance the global search and local optimization capabilities; Step 2.2, improve the population diversity factor D of the walrus optimization algorithm, introduce chaotic disturbance into the population diversity factor, and improve the position update model of the walrus optimization algorithm.

3. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 2 is characterized in that: In step 2.1, the adaptive weight factor w of the improved walrus optimization algorithm is as follows: In the formula, t represents the current iteration number, w max Indicates the maximum initial weight, w min represents the minimum weight, T max represents the maximum number of iterations, w i (t) is the weight of the i-th walrus in the t-th iteration.

4. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 3 is characterized in that: In step 2.2, the mathematical model of the population diversity factor D of the improved walrus optimization algorithm is as follows: In the formula, represents the position of the i-th walrus in the t-th iteration, represents the average position of all walruses, N represents the population size, and D(t) represents the diversity factor of the population at the tth iteration.

5. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 4 is characterized in that: The step 3 specifically comprises the following steps: Step 3.1: The modal number K and penalty factor α of the variational modal decomposition algorithm are used as the solution vector. The mathematical model formula of the modal number K and penalty factor α is: In the formula, K i represents the variational mode decomposition mode number corresponding to the i-th walrus, α i represents the variational mode decomposition penalty factor corresponding to the i-th walrus, represents the solution vector of the i-th walrus; Step 3.2: Initialize the parameters of the improved walrus optimization algorithm, including: the maximum size of the walrus population N, the maximum number of iterations T max , problem dimension E, upper bound ub and lower bound lb of variational mode decomposition parameters K and α; Step 3.3: Check whether the current number of iterations t is equal to or greater than the maximum number of iterations T max , if yes, go to step 3.7; otherwise, go to step 3.4 to step 3.6; Step 3.4, using the adaptive weight factor w of the walrus optimization algorithm and the population diversity factor D to update the position of the walrus; Step 3.5: In each iteration, calculate the population diversity factor D. If D is equal to or greater than the threshold D threshold , continue normal search, otherwise, introduce chaotic disturbance to update the position of the walrus; Step 3.6, calculate the fitness value of each walrus position according to the objective function G, and retain the walrus position with the smallest fitness in the current iteration as the global optimal solution; Step 3.7, resolve the walrus position corresponding to the minimum global fitness into the mode number and penalty factor solution set of the variational mode algorithm, and obtain the mode number and penalty factor of the optimal variational mode decomposition algorithm.

6. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 5 is characterized in that: In step 3.4, the updated walrus position formula is as follows: In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random perturbation factor, and rand is a random value in [0,1]. is the vector position of the i-th walrus at the t+1th iteration.

7. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 5, characterized in that: In step 3.5, chaotic perturbation is introduced to update the position of the walrus. The updated position formula is as follows: In the formula, represents the vector position of the i-th walrus in the t-th iteration, represents the global optimal position of the current population, β is the random disturbance factor, is the vector position of the ith walrus in the t+1th iteration, chaos(t) is a chaotic sequence, and the chaotic mapping formula is: x n+1 =r·x n ·(1-x n ) In the formula, x n represents the current chaos value, r represents the chaos parameter, x n+1 Indicates the next chaos value generated.

8. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 5 is characterized in that: In step 3.5, the threshold D threshold The calculation method is as follows: Where N represents the population size, E represents the problem dimension, and δ represents the adjustment coefficient.

9. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 5, characterized in that: In step 3.6, the signal-to-noise ratio SNR is used as the objective function, and the mathematical model formula of the objective function G is as follows: In the formula, x signal represents the signal component of the flame discharge signal point under the i-th 10kV overhead line, x noise represents the noise component of the flame discharge signal point under the i-th 10kV overhead line, and M represents the total number of data points of the flame discharge signal under the 10kV overhead line.

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