A method for predicting mountainous lightning point distribution based on zero bit lines

Abstract

The application discloses a mountainous area lightning point distribution prediction method based on zero position line. First, a real lightning image or video is collected, and is identified and extracted, and an angle difference and probability distribution function relationship is fitted and counted. Second, based on the lightning development characteristics under the mountainous area terrain, the concept of zero position line is proposed, and is drawn by using the super relaxation iteration method through the space electric field calculation method. Finally, the zero position line is combined with the angle difference probability distribution fitting function to establish a lightning channel model, the initial point position of the leader is changed, a large number of simulations are carried out, and a lightning point distribution histogram is obtained, and the lightning point distribution is predicted. The prediction method provided by the application can predict the distribution of the lightning point under the mountainous area terrain, and provides a reference for mountainous area lightning warning and mountainous area differentiated lightning protection.

Description

Technical Field

[0001] This invention belongs to the field of power system overvoltage technology, specifically relating to a prediction method for lightning strike point distribution in mountainous areas based on the zero-position line. Background Technology

[0002] Lightning is a common atmospheric discharge phenomenon. Due to its instantaneous, high-voltage, and high-current characteristics, its hazards are often unpredictable. Lightning disasters have a significant impact on various sectors, especially the power industry, where lightning protection is essential. Economic efficiency is also a crucial factor to consider in engineering design. Accurate lightning strike distribution prediction is of guiding significance for the lightning protection design of power lines and buildings. Since mountainous terrain accounts for more than 60% of my country's land area, and many transmission lines pass through mountainous regions, lightning strike distribution prediction in mountainous terrain is of great importance.

[0003] Predicting lightning strike distribution in specific terrains requires simulating the development path of lightning leaders under those terrains. Existing lightning path simulations primarily rely on fractal simulations. While fractal lightning development models use methods that calculate spatial electric fields to simulate lightning leader development paths, they suffer from high computational complexity and slow processing speed. Predicting lightning strike distribution requires thousands of simulations, and fractal simulations are even more time-consuming, making them unsuitable for accurate lightning strike distribution prediction.

[0004] Therefore, a new method for predicting lightning strike distribution in mountainous terrain is needed. Summary of the Invention

[0005] In order to solve the technical problems existing in the background art, the present invention aims to provide a method for predicting the distribution of lightning strike points in mountainous areas based on the zero-position line.

[0006] To solve the technical problem, the technical solution of the present invention is as follows:

[0007] A method for predicting lightning strike point distribution in mountainous areas based on zero-position lines, the method comprising:

[0008] S1: Preprocess real lightning images and identify lightning channels, extracting lightning channels from the images;

[0009] S2: Based on the lightning channel image, the angle difference between the development direction of each step of the lightning leader and the development direction of the previous step is divided into seventeen states. Statistical analysis of the lightning channel is performed to obtain the angle difference and probability fitting curve function.

[0010] S3: Calculate the direction of the strongest electric field from the initial point of lightning leader development to the mountain body under mountainous terrain using MATLAB programming, and form the zero potential line;

[0011] S4: Using MATLAB programming, input the step size, angle difference, probability fitting curve function, and zero line to simulate the lightning channel path under mountainous terrain. Combined with the Monte Carlo method, a large number of lightning strike paths are simulated to obtain the lightning distribution.

[0012] Furthermore, step S1 specifically includes:

[0013] The weighted average method is used to convert real lightning images to grayscale, thereby increasing the difference in grayscale values ​​between the background and the lightning channel pixels.

[0014] The lightning channel was identified and extracted using the Canny operator based on Otsu adaptive thresholding, resulting in a lightning channel map.

[0015] Furthermore, a weighted average method is used, as shown in the following formula:

[0016]

[0017] In the formula w R w G w B The weights for R, G, and B are respectively, and different values ​​can produce different grayscale images.

[0018] Furthermore, step S2 specifically includes:

[0019] Lightning leader simulation is performed using the angle difference between adjacent paths. In each step interval of the lightning channel, straight lines are used to replace the original irregular curves.

[0020] The angle difference between the next step size and the previous step size is divided into 17 states with an interval of 10 degrees ± 5 degrees, and the angle difference of each step size of multiple lightning channels is statistically calculated.

[0021] Finally, all the data are integrated, and the angle difference is fitted with its corresponding probability of occurrence to obtain the angle difference versus probability fitting curve function.

[0022] Furthermore, the fitting formula is as follows:

[0023] y = 7.59 + 2.59 × 10 -2 x + 4.21 × 10 -3 x 2 -2.15×10 -5 x 3 -3.29×10 -6 x 4 +5.65×10 -9 x 5 +6.44×10 -10 x 6-4.35×10 -13 x 7 -4.01×10 -14 x 8

[0024] In the formula, x and y represent the angle difference and its corresponding probability, respectively.

[0025] Furthermore, step S3 specifically includes:

[0026] Using the over-relaxation iterative method and MATLAB programming, the field strength at various points in the space surrounding the mountainous terrain was calculated. The direction of the maximum field strength at the head of the lightning leader channel during the lightning leader process was found, forming the zero-position line under typical terrain.

[0027] Furthermore, the zero-potential line is defined as the path with the strongest electric field strength between the initial point of lightning leader development and the mountain in mountainous terrain. The space between the thundercloud and the mountain is discretized into a series of equally spaced grids. It is determined that the charge distribution in the entire space remains unchanged, and the spatial electric field corresponding to each step of lightning leader development is a static field. Then, the potential of the discretized spatial grid satisfies the Poisson equation. The upper and lower boundary conditions satisfy the first type of boundary conditions. The upper boundary is set as the potential of the thundercloud, and the lower boundary, including the mountain, is set to 0. The left and right boundaries of the grid are far enough away from the mountain, that is, the left and right boundaries satisfy the Neumann boundary conditions.

[0028] The potential φ of each grid in the above spatial grid i,j The discrete calculation formula is obtained by solving the discrete Laplace equation:

[0029] 4φ i,j -φ i+1,j -φ i,j+1 -φ i,j-1 -φ i-1,j =0

[0030] Solving the above equation using the over-relaxation iterative method yields the following equation:

[0031]

[0032] The optimal relaxation factor ω0 in the above formula is calculated by the following formula:

[0033]

[0034] In the formula, n and m are the number of grids in the length and width of the grid space, respectively.

[0035] Compared with the prior art, the advantages of the present invention are as follows:

[0036] The prediction method proposed in this invention can simulate the development path of lightning leaders in mountainous terrain, further predict the distribution of lightning strikes in mountainous terrain, provide guidance for differentiated lightning protection of power lines in mountainous areas, and also provide a reference for lightning early warning in mountainous areas. Attached Figure Description

[0037] Figure 1 Real lightning images;

[0038] Figure 2 Lightning channel diagram;

[0039] Figure 3 The result of processing by substituting straight lines for curves;

[0040] Figure 4 Schematic diagram of angle θ;

[0041] Figure 5 Distribution and fitting curve of angle difference Δθ and corresponding probability P;

[0042] Figure 6 Flowchart of zero-position line simulation based on spatial electric field calculation;

[0043] Figure 7 Simulation results of the zero-position line based on spatial electric field calculation;

[0044] Figure 8 Flowchart of lightning path simulation in typical mountainous terrain;

[0045] Figure 9 Simulation diagram of lightning path under typical mountainous terrain;

[0046] Figure 10 Histogram of ground lightning strike distribution in typical mountainous terrain when the initial position of the leader appears randomly on the upper plane. Detailed Implementation

[0047] The specific implementation of the present invention is described below with reference to embodiments:

[0048] It should be noted that the structures, proportions, sizes, etc. shown in this specification are only used to complement the content disclosed in the specification for those skilled in the art to understand and read, and are not intended to limit the conditions under which the present invention can be implemented. Any modifications to the structure, changes in the proportions, or adjustments to the size, without affecting the effects and objectives that the present invention can produce, should still fall within the scope of the technical content disclosed in the present invention.

[0049] Furthermore, the terms such as "upper," "lower," "left," "right," "middle," and "one" used in this specification are merely for clarity of description and are not intended to limit the scope of the invention. Any changes or adjustments to their relative relationships, without substantially altering the technical content, should also be considered within the scope of the invention.

[0050] Example 1:

[0051] A method for simulating lightning leader development paths and predicting their strike point distribution suitable for mountainous terrain. It includes the following steps:

[0052] Step 1: From such Figure 1 The lightning channel is extracted from the actual lightning images. First, the collected real lightning images are converted to grayscale. Here, we use a weighted average method, the formula of which is as follows:

[0053]

[0054] In the formula w R w G w B The weights for R, G, and B are respectively, and different values ​​can produce different grayscale images.

[0055] A Canny operator-based method using Otsu adaptive thresholding is employed to identify lightning channels. This method performs well even with low contrast in preprocessed images, producing clearer depictions of lightning channel edges. The identification results are as follows: Figure 2 As shown.

[0056] Step 2: From the processed lightning channel Figure 2 Extract the angle difference and probability fitting curve between adjacent paths. For each step interval in the image, perform straight-line substitution, replacing the curve within the step interval with a straight line segment. Different step intervals are replaced with line segments of different colors. The processing result is as follows: Figure 3 As shown.

[0057] Let θ be the angle between each step of the path and the vertical line, such as... Figure 4 As shown, the angle difference Δθ between adjacent paths is divided into 17 states at 10° intervals. If the angle between the next path and the previous path in a clockwise direction is Δθ, then the state of this step is recorded as -Δθ; otherwise, it is recorded as +Δθ. The number of steps with angle differences of 0° (±5°), 10° (±5°), ..., 80° (±5°) is then statistically analyzed to obtain the probability of the angle difference. Figure 3 The statistical results are shown in Table 1.

[0058] Table 1: Probability statistics of angle difference Δθ. A scatter plot was drawn, and a polynomial fit was performed to plot the fitted curve, as shown below. Figure 5As shown.

[0059]

[0060] according to Figure 5 The fitting formula is as follows:

[0061] y = 7.59 + 2.59 × 10 -2 x + 4.21 × 10 -3 x 2 -2.15×10 -5 x 3 -3.29×10 -6 x 4

[0062] +5.65×10 -9 x 5 +6.44×10 -10 x 6 -4.35×10 -13 x 7

[0063] -4.01×10 -14 x 8

[0064] In the formula, x and y represent the angle difference and its corresponding probability, respectively.

[0065] Step 3: After obtaining the angle difference probability, it is also necessary to find the zero line of the mountainous terrain that represents the deterministic characteristics.

[0066] The zero-potential line is defined as the path with the strongest electric field strength between the initial point of lightning leader development and the mountain in mountainous terrain. To facilitate the calculation of the spatial electric field, the space between the thundercloud and the mountain is discretized into a series of equally spaced grids. Several assumptions are made: since lightning development is instantaneous, the charge distribution throughout the space can be considered constant; the spatial electric field corresponding to each step of lightning leader development is a static field, and the potential of the discretized spatial grid satisfies the Poisson equation. The upper and lower boundary conditions satisfy the first type of boundary conditions; the upper boundary is set to the thundercloud potential, and the lower boundary, including the mountain, is set to 0 potential. The left and right boundaries of the grid are sufficiently far from the mountain, i.e., the left and right boundaries satisfy the Neumann boundary conditions. At this point, the potential φ of each grid in this spatial grid is... i,j Both can be obtained by solving the discrete Laplace equation, and the discretized calculation formula is as follows:

[0067] 4φ i,j -φ i+1,j -φ i,j+1 -φ i,j-1 -φ i-1,j =0

[0068] Solving the above equation using the over-relaxation iterative method yields the following equation.

[0069]

[0070] The optimal relaxation factor ω0 in the above formula is calculated by the following formula.

[0071]

[0072] In the formula, n and m are the number of grids in the length and width of the grid space, respectively.

[0073] Using the principle of the super-relaxation algorithm, the zero-position line was obtained through simulation using MATLAB programming. The flowchart is as follows. Figure 6 The result is as follows Figure 7 As shown.

[0074] Step 4: Combine the obtained angle difference with the probability fitting curve and the zero-position line, and use MATLAB to write a program to generate the lightning leader path under mountainous terrain. The flowchart is as follows. Figure 8 The simulation effect diagram is as follows Figure 9 As shown. Then, by changing the initial pilot point position and conducting numerous simulations, the distribution of lightning strike points was statistically analyzed, as shown in the diagram. Figure 10 .

[0075] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

[0076] Many other changes and modifications can be made without departing from the concept and scope of this invention. It should be understood that this invention is not limited to the specific embodiments, and the scope of this invention is defined by the appended claims.

Claims

1. A method for predicting lightning strike point distribution in mountainous areas based on zero-position lines, characterized in that, The method includes: S1: Preprocess real lightning images and identify lightning channels, extracting lightning channels from the images; S2: Divide the angle difference between the development direction of each step of the lightning leader and the development direction of the previous step into seventeen states, perform statistical analysis on the lightning channel, and obtain the angle difference and probability fitting curve function. S3: Calculate the direction of the strongest electric field from the initial point of lightning leader development to the mountain body under mountainous terrain using MATLAB programming, and form the zero potential line; S4: Using MATLAB programming, input the step size, angle difference, probability fitting curve function, and zero line to simulate the lightning channel simulation path under mountainous terrain. Combined with the Monte Carlo method, a large number of lightning strike paths are simulated to obtain the lightning distribution. Step S3 specifically includes: Using the over-relaxation iterative method and MATLAB programming, the field strength at various points in the space surrounding the mountainous terrain was calculated, and the direction of the maximum field strength at the head of the lightning leader channel was found during the lightning leader process, forming the zero-position line under typical terrain. The zero-potential line is defined as the path with the strongest electric field strength between the initial point of lightning leader development and the mountain in mountainous terrain. The space between the thundercloud and the mountain is discretized into a series of equally spaced grids. It is determined that the charge distribution in the entire space remains unchanged, and the spatial electric field corresponding to each step of lightning leader development is a static field. Then, the potential of the discretized spatial grid satisfies the Poisson equation. The upper and lower boundary conditions satisfy the first type of boundary conditions. The upper boundary is set as the potential of the thundercloud, and the lower boundary, including the mountain, is set to 0 potential. The left and right boundaries of the grid are far enough away from the mountain, that is, the left and right boundaries satisfy the Neumann boundary conditions. Potential of each grid in the above spatial grid The discrete calculation formula is obtained by solving the discrete Laplace equation: ， Solving the above equation using the over-relaxation iterative method yields the following equation: ， The optimal relaxation factor in the above formula The value is calculated by the following formula: ， In the formula, n and m are the number of grids in the length and width of the grid space, respectively.

2. The method for predicting lightning strike point distribution in mountainous areas based on zero-position lines according to claim 1, characterized in that, Step S1 specifically includes: The weighted average method is used to convert real lightning images to grayscale, thereby increasing the difference in grayscale values ​​between the background and the lightning channel pixels. The lightning channel was identified and extracted using the Canny operator based on Otsu adaptive thresholding, resulting in a lightning channel map.

3. The method for predicting lightning strike point distribution in mountainous areas based on the zero-position line according to claim 2, characterized in that, The weighted average method is used, and the formula is as follows: ， In the formula , , The weights for R, G, and B are respectively, and different values ​​can produce different grayscale images.

4. The method for predicting lightning strike point distribution in mountainous areas based on zero-position lines according to claim 1, characterized in that, Step S2 specifically includes: Lightning leader simulation is performed using the angle difference between adjacent paths. In each step interval of the lightning channel, straight lines are used to replace the original irregular curves. The angle difference between the next step size and the previous step size is divided into 17 states with an interval of 10 degrees ± 5 degrees, and the angle difference of each step size of multiple lightning channels is statistically calculated. Finally, all the data are integrated, and the angle difference is fitted with its corresponding probability of occurrence to obtain the angle difference versus probability fitting curve function.

5. The method for predicting lightning strike point distribution in mountainous areas based on the zero-position line according to claim 4, characterized in that, The fitting formula is as follows: ， In the formula, x and y represent the angle difference and its corresponding probability, respectively.

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