Method for calculating wind-ice combined load of distribution line under influence of icing disaster

Abstract

The invention discloses a method for calculating a wind-ice combined load of a power distribution line under the influence of an icing disaster, relates to the technical field of power distribution line engineering, and solves the problem that the accuracy of line design and evaluation is insufficient due to the lack of an advanced wind-ice combined load calculation method in the prior art. According to the method, the wind-ice combined load can be accurately calculated through the wind-ice disaster curve, and a scientific basis is provided for design, maintenance and transformation of a distribution line. Moreover, the ice coating thickness of the line in the unrecorded power distribution line ice coating event can be calculated through the established BP neural network model, and help can be provided for acquisition of other coefficients. Besides, a more effective strategy is formulated through the combined load, so that the safety performance of the power distribution line in the icing disaster environment is improved, the accident risk of the power distribution line caused by the icing disaster is reduced, and the stability and reliability of the power system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution line engineering, and in particular to a method for calculating the wind-ice combined load of a power distribution line under the influence of an icing disaster. Background Art

[0002] Existing distribution line design standards typically only consider the effects of wind or ice alone. However, in reality, wind and ice often act simultaneously on distribution lines. This combined wind-ice load places higher demands on line safety. Due to the lack of advanced calculation methods for combined wind-ice loads, existing line designs and assessments often suffer from accuracy issues. Currently, research on combined wind-ice loads, both domestically and internationally, is still in the exploratory stage. Some studies have employed simplified calculation models but fail to fully consider the joint probability distribution of icing and concurrent winds.

[0003] In view of this, a calculation method for the wind-ice combined load on distribution lines under the influence of icing disasters is needed. Summary of the Invention

[0004] In response to the problem of insufficient accuracy in line design and evaluation due to the lack of advanced wind-ice combined load calculation methods in the existing technology, the present invention provides a method for calculating the wind-ice combined load of distribution lines under the influence of icing disasters. This method can accurately obtain the combined load of icing and concurrent wind on the distribution lines. It can not only calculate the ice thickness of the distribution lines, but also obtain the concurrent wind speed under the corresponding ice load, and obtain the wind-ice combined load. This provides workers with more intuitive wind-ice impact parameters, so as to further formulate line maintenance strategies and reduce the impact of line breakage and pole collapse caused by disasters on the normal operation of the power system. The specific technical solution is as follows:

[0005] A method for calculating the combined wind and ice load on a distribution line under the influence of an icing disaster comprises the following steps:

[0006] Collect daily environmental data and ice thickness, and perform data preprocessing to obtain a training data set, where the environmental data includes at least wind speed and temperature;

[0007] An ice growth prediction model established and trained based on a neural network is used to derive ice thickness.

[0008] Count the ice thickness and corresponding concurrent wind speed data pairs in all cases;

[0009] The wind-ice hazard curve is drawn based on the ice thickness and its corresponding concurrent joint probability obtained from the ice growth prediction model;

[0010] According to the wind-ice hazard curve, the ice thickness and concurrent wind speed data pairs at each point are obtained, the reduction factor of the combined wind and ice load condition is calculated, and the wind-ice combined load factor is further obtained based on the reduction factor.

[0011] Preferably, the process of obtaining the ice growth prediction model is as follows:

[0012] Normalize the training dataset;

[0013] Construct a three-layer feedforward neural network; the nodes of the network input layer include at least the daily average wind speed, temperature, precipitation type and precipitation intensity, and the ice thickness of the distribution line is used as the output signal; the number of output layer nodes is set to 1; at the same time, the neuron transfer function of the network middle layer is set;

[0014] Initialize the neural network, train the neural network using the training data set, and finally simulate the neural network. Adjust the learning rate according to the adaptive learning rate method, and repeat the reinforcement training on the training sample set until the limit reaches the set value.

[0015] Preferably, the concurrent joint probability is derived as follows:

[0016] According to the ice thickness and the corresponding concurrent wind speed, a data pair (t ice , V ice ), statistics of all cases (t ice , V ice ) frequency of occurrence;

[0017] The joint probability is obtained by the following formula:

[0018]

[0019] Among them, N(t ice , V ice ) is (t ice , V ice ) The number of events that occur, N total is the total number of historical icing events.

[0020] Preferably, the wind ice hazard curve is drawn based on the obtained ice thickness and concurrent wind speed. The specific drawing process is: using the obtained historical data for drawing, fitting the icing events and their corresponding wind speed values ​​to form a curve with the horizontal axis being the ice thickness and the vertical axis being the corresponding concurrent wind speed.

[0021] Preferably, the process of obtaining the wind-ice combined load is as follows:

[0022] According to the wind-ice hazard curve, the maximum ice thickness and the maximum wind speed during the icing period of each icing event in the required year are obtained;

[0023] Calculate the reduction factors for wind and ice loads under their respective working conditions;

[0024] The wind-ice combined load is obtained according to the following formula:

[0025] Q′ 联合 =αQ 冰 +βQ 风

[0026] Where α is the reduction factor of ice load, β is the reduction factor of wind load, Q 风 is the wind load, Q 冰 is the ice load.

[0027] Preferably, the calculation process of the ice load reduction factor is as follows:

[0028] α=1-γ i ∑P(t ice ,V ice )(t ice / t max ) k

[0029] where t max is the designed maximum ice thickness, k is the ice thickness index, γ i is the ice load safety factor, P(t ice ,V ice ) is the joint probability, t ice is the ice cover thickness;

[0030] The calculation process of wind load reduction coefficient is as follows:

[0031] β=1-γ ω ∑P(t ice ,V ice )(V i / V max ) m

[0032] Where V max is the design reference wind speed, m is the wind speed index, γ ω is the wind load safety factor, V i is the concurrent wind speed.

[0033] Preferably, the calculation process of wind load is as follows:

[0034]

[0035] Where ρ is the air density, C d is the drag coefficient, D is the wire diameter, V ice is the ice thickness t ice The corresponding concurrent wind speed; ice load calculation process is as follows:

[0036] Q 冰 =πρice gt ice D

[0037] where ρ ice is the density of ice, g is the acceleration due to gravity, D is the diameter of the wire, t ice is the thickness of ice cover.

[0038] Preferably, after drawing the wind-ice hazard curve, the wind-ice hazard curve is also normalized.

[0039] A computer-readable storage medium includes a stored program, wherein when the program is run, the device where the computer-readable storage medium is located is controlled to execute the method for calculating the wind-ice combined load of the distribution line under the influence of icing disasters as described above.

[0040] A processor is used to run a program, wherein when the program is run, the method for calculating the wind-ice combined load of a distribution line under the influence of an icing disaster as described above is executed.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1. In the present invention, the wind-ice combined load can be accurately calculated through the wind-ice disaster curve, providing a scientific basis for the design, maintenance and modification of distribution lines.

[0043] 2. In the present invention, the constructed BP neural network model can be used to calculate the ice thickness of the distribution line in the unrecorded icing event, which can help to obtain other coefficients.

[0044] 3. In the present invention, a more effective strategy is formulated by combining loads, thereby improving the safety performance of distribution lines in icing disaster environments, reducing the risk of accidents caused by icing disasters on distribution lines, and further improving the stability and reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly describes the drawings required for the specific embodiments or the description of the prior art. Similar elements or parts are generally identified by similar reference numerals throughout the drawings. Elements or parts in the drawings are not necessarily drawn to scale.

[0046] Figure 1 Flowchart for calculation of wind-ice combined load on distribution lines;

[0047] Figure 2 Build a schematic diagram for the BP neural network;

[0048] Figure 3 This is a schematic diagram of the wind ice hazard curve. DETAILED DESCRIPTION

[0049] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0050] It will be understood that when used in this specification and the appended claims, the terms “comprises” and “comprising” indicate the presence of described features, integers, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0051] It should also be understood that the terms used in the present specification are only for the purpose of describing particular embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0052] It should be further understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0053] Please refer to Figure 1-Figure 2 A method for calculating the wind-ice combined load factor of a distribution line under the influence of an icing disaster comprises the following steps:

[0054] Step 1: Extract necessary data from the weather station database, including daily average wind speed, daily average temperature, and daily precipitation (including precipitation type and intensity). Check the data for accuracy, such as consistency in wind speed, temperature, and precipitation units, and for completeness. Verify the data to ensure there are no omissions or errors, and record the obtained data in a spreadsheet.

[0055] Step 2: Using the three meteorological parameters of wind speed, temperature and precipitation, an ice growth prediction model based on BP neural network is established to obtain the ice thickness parameters of the distribution line under ice conditions. The nodes of the network input layer are the daily average wind speed, temperature, precipitation type and precipitation intensity, and the output layer node is the ice thickness of the distribution line. The S-type logarithmic function tansig is used as the neuron transfer function of the middle layer of the network. The BP network is created with the newff() function, initialized with the init() function, trained with the train() function, and finally simulated with the sim() function. The training sample set is repeatedly strengthened until the limit reaches the set value. Program and run on the MATLAB software platform, input the collected average wind speed, temperature, precipitation type and intensity, and obtain the output ice thickness. Calculate the concurrent wind force on the ice-covered conductor per unit length, and count the ice thickness (t ice ) and concurrent wind speed (V ice ) combination occurrence frequency N(t ice , V ice ), and then divided by the total number of events N total The joint probability is obtained, and thus the joint probability of ice thickness and concurrent wind is obtained.

[0056] The joint probability of ice thickness and concurrent wind speed is calculated based on the data pair (t ice , V ice ), statistics of all cases (t ice , V ice ) occurs, and the joint probability is obtained.

[0057]

[0058] Among them, N(t ice , V ice ) is (t ice , V ice ) The number of events that occur, N total is the total number of historical icing events.

[0059] Step 3: By counting the generated data pairs, a joint histogram of ice thickness and concurrent wind speed data pairs corresponding to each simulated icing event was constructed. This histogram visually illustrates the relationship between ice thickness and wind speed, providing input for the wind-ice hazard curve. The icing and concurrent wind speed hazard curves were plotted, with ice thickness plotted on the horizontal axis and wind speed plotted on the vertical axis. Finally, these curves were normalized based on reference values. First, all possible load values ​​were extracted from the joint probability distribution to form a dataset. The 5% and 95% quantiles of this dataset were then calculated as reference limits, providing a quantitative basis for subsequent risk stratification and ice-resistant design.

[0060] Step 4: According to the wind-ice hazard curve, obtain the ice thickness and corresponding concurrent wind speed for each icing event in a year, and use the coincident wind speed-ice thickness pairs to represent the combined load. Figure 3 The figure shows the wind-ice hazard curves under 50-year, 100-year, and 500-year icing disasters. It can be seen from the figure that when the ice thickness is 50 mm under a 50-year icing disaster, the concurrent wind speed is 10 m / s.

[0061] In order to better understand the present invention, an example is given below:

[0062] The invention was applied to a meteorological station in Lingchuan County, Guilin. Parameters such as daily average wind speed, daily average temperature, and daily precipitation (including precipitation type and intensity) were extracted from the meteorological station database. The equivalent ice thickness was calculated using a cylindrical BP neural network, as shown in Table 1.

[0063] Table 1

[0064]

[0065] Based on the risk curve drawn from the joint probability, the normalized hazard curve is used to obtain the maximum ice thickness and maximum wind speed during each icing event in the required year. The reduction factors for wind and ice loads under their respective working conditions are calculated, and the wind-ice combined load is obtained according to the following formula.

[0066] Q′ 联合 =αQ 冰 +βQ 风

[0067] Where α is the reduction factor of ice load, and β is the reduction factor of wind load. Ice load reduction factor α=1-γ i ∑P(t ice ,V ice )(t ice / t max ) k , where t max is the maximum designed ice thickness (e.g. 50 mm), k is the ice thickness index (usually 2.0), γ i is the ice load safety factor, which is 1.1 to 1.3 for important lines; wind load reduction coefficient β = 1-γ ω ∑P(t ice ,V ice )(V i / V max ) m , where V max is the design reference wind speed (e.g. 30m / s), m is taken as 2.5, γ ω It is the wind load safety factor, and the value for important lines is 1.2 to 1.5.

[0068] Wind load and ice load are obtained by the formula, where wind load Where ρ is the air density, C d is the drag coefficient (about 1.0 when not covered with ice, and may increase to 1.2-1.5 when covered with ice), D is the conductor diameter (including the thickness of ice); ice load Q 冰 =πρ ice gt ice D, where ρ ice is the density of ice, g is the acceleration due to gravity (9.81 m / s 2 ).

[0069] In summary, the present invention can accurately calculate the wind-ice combined load through the wind-ice disaster curve, providing a scientific basis for the design, maintenance and modification of distribution lines. Moreover, the present invention can calculate the ice thickness of the distribution line in the unrecorded icing event through the constructed BP neural network model, which can provide assistance for the acquisition of other coefficients. In addition, the present invention formulates a more effective strategy through the combined load, thereby improving the safety performance of the distribution line in the icing disaster environment, reducing the risk of accidents caused by icing disasters on the distribution line, and further improving the stability and reliability of the power system.

[0070] Those skilled in the art will appreciate that the units of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition of each example has been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0071] In the embodiments provided by the present invention, it should be understood that the division of units is merely a logical function division, and there may be other division methods in actual implementation, for example, multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored, etc.

[0072] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0073] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-0nly Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc., various media that can store program code.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and description of the present invention.

Claims

1. A method for calculating the combined wind and ice load on distribution lines under the influence of icing disasters, characterized in that: The following steps are involved: Collect daily environmental data and ice thickness, and perform data preprocessing to obtain a training data set, where the environmental data includes at least wind speed and temperature; An ice growth prediction model established and trained based on a neural network is used to derive ice thickness. Count the ice thickness and corresponding concurrent wind speed data pairs in all cases; The wind-ice hazard curve is drawn based on the ice thickness and its corresponding concurrent joint probability obtained from the ice growth prediction model; According to the wind-ice hazard curve, the ice thickness and concurrent wind speed data pairs at each point are obtained, the reduction factor of the combined wind and ice load condition is calculated, and the wind-ice combined load factor is further obtained based on the reduction factor.

2. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 1 is characterized in that: The process of obtaining the ice growth prediction model is as follows: Normalize the training dataset; Construct a three-layer feedforward neural network; the nodes of the network input layer include at least the daily average wind speed, temperature, precipitation type and precipitation intensity, and the ice thickness of the distribution line is used as the output signal; the number of output layer nodes is set to 1; at the same time, the neuron transfer function of the network middle layer is set; Initialize the neural network, train the neural network using the training data set, and finally simulate the neural network. Adjust the learning rate according to the adaptive learning rate method, and repeat the reinforcement training on the training sample set until the limit reaches the set value.

3. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 1 is characterized in that: The concurrent joint probability is derived as follows: According to the ice thickness and the corresponding concurrent wind speed, a data pair (t ice , V ice ), statistics of all cases (t ice , V ice ) frequency of occurrence; The joint probability is obtained by the following formula: Among them, N(t ice , V ice ) is (t ice , V ice ) The number of events that occur, N total is the total number of historical icing events.

4. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 1 is characterized in that: The wind-ice hazard curve is drawn based on the obtained ice thickness and concurrent wind speed. The specific drawing process is: using the obtained historical data to draw, the icing events and their corresponding wind speed values ​​are fitted to form a curve with the horizontal axis being the ice thickness and the vertical axis being the corresponding concurrent wind speed.

5. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 1 is characterized in that: The process of obtaining the wind-ice combined load is as follows: According to the wind-ice hazard curve, the maximum ice thickness and the maximum wind speed during the icing period of each icing event in the required year are obtained; Calculate the reduction factors of wind and ice loads under their respective working conditions; The wind-ice combined load is obtained according to the following formula: Q′ 联合 =αQ 冰 +βQ 风 Where α is the reduction factor of ice load, β is the reduction factor of wind load, Q 风 is the wind load, Q 冰 is the ice load.

6. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 5 is characterized in that: The calculation process of ice load reduction factor is as follows: α=1-γ i ∑P(t ice ,V ice )(t ice / t max ) k where t max is the designed maximum ice thickness, k is the ice thickness index, γ i is the ice load safety factor, P(t ice ,V ice ) is the joint probability, t ice is the ice cover thickness; The calculation process of wind load reduction coefficient is as follows: β=1-γ ω ∑P(t ice ,V ice )(V i / V max ) m Where V max is the design reference wind speed, m is the wind speed index, γ ω is the wind load safety factor, V i is the concurrent wind speed.

7. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 5 is characterized in that: The calculation process of wind load is as follows: Where ρ is the air density, C d is the drag coefficient, D is the wire diameter, V ice is the ice thickness t ice The corresponding concurrent wind speed; The calculation process of ice load is as follows: Q 冰 =pr ice gt ice D where ρ ice is the density of ice, g is the acceleration due to gravity, D is the diameter of the wire, t ice is the thickness of ice cover.

8. The method for calculating the wind-ice combined load on distribution lines under the influence of icing disasters according to claim 4 is characterized in that: After drawing the wind-ice hazard curve, the wind-ice hazard curve is also normalized.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method for calculating the wind-ice combined load of the distribution line under the influence of icing disasters as described in any one of claims 1 to 8.

10. A processor, characterized in that: The processor is used to run a program, wherein the program, when running, executes the method for calculating the wind-ice combined load of the distribution line under the influence of icing disasters as described in any one of claims 1 to 8.

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