Optimization processing method for flame discharge signal under 10kV overhead line

Abstract

The invention discloses a flame discharge signal optimization processing method for a 10kV overhead line, which relates to the technical field of electrical signal processing and specifically comprises the following steps: 1, obtaining a flame discharge signal under a 10kV overhead line, and classifying, arranging and storing the data; 2, improving a walrus optimization algorithm; 3, setting initial parameters of a variational modal decomposition algorithm, including a modal number K and a penalty factor α, and adjusting the modal number K and the penalty factor α of the variational modal decomposition algorithm by using the improved walrus optimization algorithm; 4, obtaining an optimized variational modal decomposition algorithm, and using the optimized variational modal decomposition algorithm obtained in step 3 to perform denoising processing on the flame discharge signal under the 10kV overhead line in step 1, thereby achieving flame discharge signal optimization. The method of the invention improves the analysis accuracy of flame discharge signal processing under the 10kV overhead line.

Description

Technical Field

[0001] The present 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 flame discharge signals under 10kV overhead lines is an effective signal analysis method. Flame discharge signals have complex frequency components and are often contaminated by a significant amount of noise. The VMD algorithm decomposes the signal into a series of sub-signals with specific frequencies and bandwidths, extracting useful information and removing noise. However, the performance and effectiveness of the VMD algorithm depend heavily on the proper setting of the mode number K and the penalty factor α. Appropriate parameter selection ensures that the VMD-derived mode number neither over-decomposes nor misses important components. Intelligent optimization algorithms can help achieve this.

[0004] The Walrus Optimization (WO) algorithm simulates the social behaviors of walruses in their natural environment, including migration, foraging, roosting, resource gathering, and escape. Walruses determine their course of action by receiving key signals, such as danger and safety signals. However, the WO algorithm relies on the diversity of the initial population. If the initial population is low in diversity, it may be difficult to cover the entire search space in the early stages of the search, thus affecting the final convergence. Summary of the Invention

[0005] To solve the problems existing in the prior art, the purpose of the present invention is to provide a method for optimizing the processing of flame discharge signals under 10kV overhead lines. The present invention improves the walrus optimization algorithm to enhance the optimization capability 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 to obtain the optimal parameters, performs denoising 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 present invention adopts a technical solution: a flame discharge signal optimization processing method for a 10kV overhead line, 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: Set the initial parameters of the variational modal decomposition algorithm, including the mode number K and the penalty factor α, and use the improved walrus optimization algorithm to adjust the mode number K and the penalty factor α of the variational modal decomposition algorithm to obtain the optimized variational modal decomposition algorithm;

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

[0011] As a further improvement of the present invention, the step 2 specifically includes 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 to 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 perturbation into the population diversity factor to 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] Where t represents the current number of iterations, w max Indicates the maximum initial weight, w min represents the minimum weight, T max Indicates 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] Where, 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 includes the following steps:

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

[0022]

[0023] Where 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, update the walrus position using the adaptive weight factor w and population diversity factor D of the walrus optimization algorithm;

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

[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 minimum 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 to 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] Where, 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, 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 perturbation is introduced to update the position of the walrus. The updated position formula is as follows:

[0034]

[0035] Where, 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, is the vector position of the i-th 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] Where 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 as follows:

[0042]

[0043] Where 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 flame discharge signal data points 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 walrus's search intensity at different iteration stages, enhances the algorithm's global search and balances local search, and improves the algorithm's adaptability. The improved walrus optimization algorithm is used to adjust the mode number K and penalty factor α of the variational mode decomposition algorithm, thereby improving the performance and effect of the variational mode decomposition algorithm. The optimized variational mode decomposition algorithm is used to denoise the flame discharge signal under a 10kV overhead line 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 This is a flow chart of adjusting the mode number K and penalty factor α of the variational mode decomposition algorithm using the improved walrus optimization algorithm in an embodiment of the present invention. 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. Dynamically adjust the search capabilities of different search stages through the improved adaptive weight factor, thereby balancing the global search and local optimization capabilities.

[0054] Step 2.2, improve the population diversity factor D of the walrus optimization algorithm, introduce chaotic perturbation into the population diversity factor, and obtain the improved population diversity factor D;

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

[0056] Step 4: Using the optimized variational mode decomposition algorithm obtained in step 3, denoising the flame discharge signal under the 10 kV overhead line in step 1 is performed 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 formula of the improved adaptive weight factor w of the walrus optimization algorithm is:

[0058]

[0059] Where t represents the current number of iterations, w max Indicates the maximum initial weight, w min represents the minimum weight, T max Indicates 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 perturbation 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] Where, 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. An appropriate K value accurately captures the different frequency components of the flame discharge signal under a 10kV overhead line, allowing each mode to focus on a specific frequency range. If the K value is too small, the decomposition effect will be insufficient, and some key components may not be separated, resulting in loss of signal characteristics. If the K value is too large, the variational mode decomposition will generate too many modes, increasing the computational complexity and easily overfitting the noise components, making it difficult to remove the noise during the decomposition.

[0064] In step 3, the penalty factor α is used to control the bandwidth constraint in variational mode decomposition. Larger values ​​of α impose stricter bandwidth constraints, making the decomposed modes smoother and reducing the impact of noise, but may result in the loss of some detailed information. Smaller values ​​of α relax the bandwidth constraint, allowing the decomposed modes to contain more detail, making them suitable for detecting subtle signal changes. Selecting an appropriate α value can achieve a balance between signal reconstruction accuracy and noise suppression. A moderate α value can effectively reduce noise interference while preserving the key signal features, enhancing the accuracy of signal reconstruction and the reliability of the decomposition results, making variational mode decomposition more effective in denoising and decomposing 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. This allows the variational mode algorithm to 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 as follows:

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

[0067]

[0068] Where 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, update the walrus position using the adaptive weight factor w and population diversity factor D of the walrus optimization algorithm;

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

[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 minimum 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 to 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] Where, 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, 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 greater than the threshold D threshold , continue normal search, otherwise, introduce chaotic perturbation to update the walrus position, and the updated position formula is:

[0079]

[0080] Where, 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, is the vector position of the i-th 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] Where x n represents the current chaos value, r represents the chaos parameter, which is 4, x n+1 Indicates the next chaos value generated;

[0083] In step 3, 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 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. The mathematical model formula of the objective function G is:

[0087]

[0088] Where 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 flame discharge signal data points under the 10kV overhead line.

[0089] In this embodiment, flame discharge signals from a 10kV overhead line are first obtained, and the data is classified, organized, and stored. The adaptive weight factor w and population diversity factor D of the walrus optimization algorithm are improved, and chaotic perturbations are introduced into the population diversity factor. Initial parameters for the variational modal decomposition algorithm, including the mode number K and penalty factor α, are then set. The improved walrus optimization algorithm is used to adjust the mode number K and penalty factor α of the variational modal decomposition algorithm, resulting in an optimized variational modal decomposition algorithm. The optimized variational modal decomposition algorithm is then used to denoise the flame discharge signal from the 10kV overhead line, achieving optimized flame discharge signal processing. This improves the analytical accuracy of flame discharge signal processing from 10kV overhead lines.

[0090] This embodiment has the following advantages: in practice, when denoising the flame discharge signal under a 10kV overhead line containing various complex noise signals, it is possible to effectively avoid the interference of the noise signal on the experimental results; using the improved walrus optimization algorithm to adjust the penalty mode number K and factor α of the variational mode decomposition algorithm can effectively avoid over-decomposition and noise overfitting, and balance the smoothness of the signal and the retention of details; the flame discharge signal under the 10kV overhead line after denoising is very convenient for subsequent signal processing.

[0091] The above-described embodiments merely represent specific implementations of the present invention. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, and all such variations and improvements fall within the scope of protection 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; 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 to balance the global search and local optimization capabilities. Step 2.2, improve the population diversity factor D of the walrus optimization algorithm, introduce chaotic perturbation, and improve the position update model of the walrus optimization algorithm; In step 2.2, the mathematical model of the population diversity factor D of the improved walrus optimization algorithm is as follows: Where, 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; Step 3: Set the initial parameters of the variational modal decomposition algorithm, including the mode number K and the penalty factor α, and use the improved walrus optimization algorithm to adjust the mode number K and the penalty factor α of the variational modal decomposition algorithm to obtain the optimized variational modal decomposition algorithm; The step 3 specifically includes the following steps: Step 3.1, the mode number K and penalty factor α of the variational mode decomposition algorithm are used as the solution vector; 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, update the walrus position using the adaptive weight factor w and population diversity factor D of the walrus optimization algorithm; Step 3.5: In each iteration, calculate the population diversity factor D. If D is greater than or equal to the threshold D threshold , continue normal search, otherwise, introduce chaotic perturbation to update the walrus's position; In step 3.5, chaotic perturbation is introduced to update the position of the walrus. The updated position formula is as follows: Where, 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, is the vector position of the i-th walrus in the t+1th iteration, chaos(t) is a chaotic sequence, w i (t) is the weight of the i-th walrus in the t-th iteration; Step 3.6: Calculate the fitness value of each walrus position according to the objective function G, and retain the walrus position with the minimum 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; Step 4: Use the optimized variational mode decomposition algorithm obtained in step 3 to perform denoising on 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, characterized in that: In step 2.1, the adaptive weight factor w of the improved walrus optimization algorithm is as follows: Where t represents the current number of iterations, w max Indicates the maximum initial weight, w min represents the minimum weight, T max Indicates the maximum number of iterations, w i (t) is the weight of the i-th walrus in the t-th iteration.

3. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 1, characterized in that: In step 3.1, the mathematical model formula of the modal number K and the penalty factor α is: Where 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 for the i-th walrus.

4. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 1, characterized in that: In step 3.5, the chaotic mapping formula is: x n+1 =r·x n ·(1-x n ) Where x n represents the current chaos value, r represents the chaos parameter, x n+1 Indicates the next chaos value generated.

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

6. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 1, 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.

7. The flame discharge signal optimization processing method for 10kV overhead lines according to claim 1, characterized in that: In step 3.6, the signal-to-noise ratio (SNR) is used as the objective function. The mathematical model formula of the objective function G is as follows: Where 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 flame discharge signal data points under the 10kV overhead line.

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