Transmission tower sensor layout method based on dung beetle optimization algorithm

Abstract

The invention discloses a power transmission tower sensor layout method based on a dung beetle optimization algorithm. The method specifically comprises the following steps: establishing a finite element model of the transmission tower; performing modal analysis on the finite element model of the transmission tower to obtain modal shape vectors corresponding to modal orders of the finite element model of the transmission tower; calculating an ROC value changing along with a modal order based on a Fisher information matrix 2-norm method, and selecting the modal order; according to the modal shape vector under the selected modal order, utilizing a dung beetle optimization algorithm to carry out optimization arrangement on different numbers of sensors; and comparing arrangement effects of different numbers of sensors, determining the number of the sensors, and giving a sensor arrangement scheme. According to the method, the transmission tower sensor optimization arrangement problem can be efficiently solved, a scientific and systematic decision support tool is provided for power grid transmission tower state monitoring, the monitoring cost can be saved, the data collection redundancy problem can be avoided, and good social and economic benefits can be brought.

Description

Technical Field

[0001] The present invention relates to the field of sensor optimization layout, and in particular to a method for arranging power transmission tower sensors based on a dung beetle optimization algorithm. Background Art

[0002] With the rapid development of new power system construction, the importance of overhead transmission lines as long-distance power transmission channels for interconnection between power load centers cannot be underestimated. Once an abnormal situation occurs and affects power transmission, it will cause huge economic losses and directly affect the safety and reliability of the power grid. In recent years, there have been frequent and continuous heavy rainfalls in South China, and rainwater cannot be discharged in time. The hidden dangers of landslides in the foundations of towers located on hillsides are frequent. At the same time, the overall or local vibration of the transmission towers will cause fatigue damage to the tower materials, tearing of bolt holes, loosening or even falling off of node bolts, reducing the bearing capacity of the towers, causing local damage to the towers at the least, and causing the towers to overturn as a whole at the worst, seriously affecting the safe operation of the power system. Landslides occur frequently not only in mountainous and hilly areas, but also with climate change and the intensification of human activities, their frequency and intensity have also increased. In addition, operation and maintenance personnel often have little experience in the prevention and treatment of foundation landslides, making it difficult to effectively judge and prevent them. How to monitor and maintain the condition of the tower economically and efficiently has become an urgent problem to be solved. At the same time, the frequent occurrence of extreme weather events and the complex and changeable natural environment have also brought more challenges to the condition monitoring and safety assessment of the tower. Vibration monitoring of transmission towers is one of the important methods to study the structural state characteristics of towers. Among the problems that need to be solved in tower vibration monitoring, the first one is the optimal arrangement of sensors. In order to reduce costs and improve monitoring efficiency, it is necessary to study how to find the reasonable location for sensor installation on transmission towers and how to measure more effective structural state information with as few sensors as possible. Therefore, it is of great engineering significance to study the optimal arrangement of sensors on transmission towers and explore the issues of sensor arrangement points and sensor quantity (Wang Jian. Optimal arrangement of transmission tower vibration monitoring sensors and structural state parameter analysis [D]. North China Electric Power University (Beijing), 2017).

[0003] Mathematically, sensor optimization layout is a statistical optimal experimental design problem. Existing methods mainly include effective independence method, modal kinetic energy method, and effective independent driving point residual method, which are classic algorithms for solving such problems in the field of structural dynamics. They have a strict theoretical basis and good computational stability, but they are highly dependent on grid division and tend to ignore the spatial structural characteristics of transmission towers. Heuristic algorithms such as genetic algorithms and particle swarm algorithms iteratively seek optimal solutions by simulating certain natural or human social behaviors. They are widely used to solve various complex problems due to their good robustness and applicability, but their solutions have certain instability. In the prior art, particle swarm algorithms are used to optimize sensor layout tasks (acceleration sensor layout method, device, and medium based on particle swarm algorithm (CN116522710A)), but particle swarm algorithms tend to quickly approach the global optimal solution. This method may lead to convergence to the local optimum when there are multiple local optimal solutions in the search space of the problem. Therefore, particle swarm algorithms usually need to introduce other mechanisms (such as mutation operations) to enhance the ability to jump out of the local optimum. At the same time, the performance of the particle swarm algorithm depends on the balance between multiple parameters (such as inertia weight, cognitive coefficient and social coefficient). The tuning of these parameters is often a complex process, especially for different types of problems, which often require readjustment. Summary of the invention

[0004] The purpose of the present invention is to propose a method for arranging sensors on transmission towers based on the dung beetle optimization algorithm, so as to solve the problem of how to effectively improve the arrangement effect and reduce the cost of monitoring the status of transmission towers while ensuring the calculation efficiency.

[0005] The present invention is based on the problem of optimizing the layout of acceleration sensors of transmission towers. Taking the DungBeetle Optimizer algorithm (DBO) as a basic theoretical tool, a transmission tower sensor layout method based on the DungBeetle Optimizer algorithm is proposed. Under the premise of ensuring the calculation efficiency, the layout effect can be effectively improved, the cost of transmission tower status monitoring can be reduced, and the problems of traditional methods being difficult to handle complex constraints, poor calculation efficiency and unsuitable for dynamic systems can be solved.

[0006] The purpose of the present invention is achieved by at least one of the following technical solutions.

[0007] A method for arranging sensors on a transmission tower based on a dung beetle optimization algorithm comprises the following steps:

[0008] S1. Establish a finite element model of the transmission tower;

[0009] S2. Performing modal analysis on the finite element model of the transmission tower to obtain modal vibration vectors corresponding to each modal order of the finite element model of the transmission tower;

[0010] S3, calculate the ROC value that changes with the modal order based on the Fisher information matrix 2-norm method, and select the modal order;

[0011] S4. According to the modal vibration shape vectors under the selected modal order, the dung beetle optimization algorithm is used to optimize the arrangement of different numbers of sensors;

[0012] S5. Compare the layout effects of different numbers of sensors, determine the number of sensors and provide a sensor layout plan.

[0013] Furthermore, in step S1, in the workbench module of the finite element analysis technology software ANSYS, a pair of transmission towers are modeled at a 1:1 scale, four tower feet are fixedly constrained, and a transmission line load is applied to the tower head, thereby obtaining a finite element model of the transmission tower.

[0014] Furthermore, in step S2, all positions for setting sensors in the transmission tower are defined as nodes, and the finite element model of the transmission tower is subjected to modal analysis through APDL of ANSYS software, so as to obtain the modal vibration shape vector corresponding to each node under each order mode, and the modal vibration shape vector is used to characterize the modal information of each node in the transmission tower, thereby obtaining the modal vibration shape vector corresponding to each order mode.

[0015] Furthermore, in step S3, the Fisher information matrix Q of the i-th order mode is i The expression is:

[0016]

[0017] In the formula, φ i represents the mode shape vector of the i-th mode, as follows:

[0018] φ i =[φ i1 ,φ i2 ,...,φ in ,...,φ iN ] T

[0019] Among them, φ in is the modal vibration vector corresponding to the nth node under the i-th order mode, and N is the number of nodes included in the transmission tower;

[0020] 2-norm rate of change (ROC) of the i-th mode ROC i Calculated by the following formula:

[0021]

[0022] In the formula, Qi represents the Fisher information matrix of the i-th mode, ||Q i || 2 Indicates Q i 2-norm; As the modal order i increases, ROC i The value will change when ROC i When the value is less than 0.1, it can be considered that the vibration shape vectors of the first i-order modes can basically cover all the modal information, and then the analysis can be carried out on the first i-order modes.

[0023] Furthermore, step S4 includes the following steps:

[0024] S4.1. Execute parameter initialization:

[0025] Different dimensional parameters D are set according to different numbers of sensors, and the value of the dimensional parameter D is consistent with the number of sensors, so as to fully characterize the acceleration sensor and facilitate the initialization of the dung beetle population information in the subsequent steps;

[0026] S4.2, perform population initialization;

[0027] S4.3, iteratively execute the dung beetle optimization algorithm, update the position of each dung beetle, reach the maximum number of iterations, and output the global optimal solution corresponding to the current dimension parameter D and its fitness value;

[0028] S4.4. After the calculation of the global optimal solution for each corresponding number of sensors is completed, the number of sensors is modified, and the dimension parameter D is changed according to the number of sensors, and step S4.2 is returned and re-executed until the calculation of the global optimal solution corresponding to all numbers of sensors is completed.

[0029] Furthermore, in step S4.1, the parameter initialization is specifically as follows:

[0030] Set the dimension, maximum number of iterations, population size, percentage of rolling dung beetles, percentage of brooding dung beetles, percentage of stealing dung beetles, reference history weight, reference optimal position weight, scaling factor, and search upper and lower bounds;

[0031] Furthermore, in step S4.2, the population is initialized as follows:

[0032] Each dung beetle individual is randomly initialized within the search upper and lower bounds, and each dung beetle individual corresponds to a layout scheme, that is, each dung beetle individual randomly selects D nodes as the initial position, D is the dimension parameter, and then the MAC matrix is ​​calculated according to the modal vibration shape vectors corresponding to the nodes included in the layout scheme corresponding to each dung beetle individual, and then the fitness value of each initial position of each dung beetle individual is calculated and sorted using the element with the largest absolute value in the MAC matrix to obtain the current global best position, the global worst position and the local best position of each dung beetle; among them, the global best position refers to the best solution found in the entire population, that is, the position with the highest fitness value, the global worst position refers to the worst solution found in the entire population, that is, the position with the lowest fitness value, and the local best position of each dung beetle refers to the best solution among all positions experienced by each dung beetle individual.

[0033] Furthermore, the fitness corresponding to each dung beetle is calculated based on the modal assurance criterion; the modal assurance criterion refers to a method in the related art of determining the MAC modal matrix through the modal vibration vector to determine the optimal position; the fitness is used to guide the optimal arrangement of sensors on the transmission tower, and the entire sensor optimization arrangement model is optimized towards the goal of the highest arrangement effect; the calculation method of the MAC matrix is ​​as follows:

[0034]

[0035] Among them, MAC ij is the corresponding value of the i-th row and j-th column in the MAC modal matrix; They represent the modal vibration shape vectors of the i-th and j-th modes corresponding to the specific layout schemes, as follows:

[0036]

[0037] in, is the modal vibration vector corresponding to the mth node under the i-th mode of the specific layout scheme, N is the number of nodes included in the specific layout scheme, The modal vibration shape vectors corresponding to the nodes included in the specific arrangement scheme are recoded;

[0038] The calculation method of fitness f is as follows:

[0039] f=1-max(|MAC ij |) / c,i≠j;

[0040] Among them, |MAC ij | is the absolute value of the corresponding value in the i-th row and j-th column in the MAC modality matrix, and c is a constant.

[0041] Furthermore, in step S4.3, each dung beetle population is divided into rolling dung beetles, brooding dung beetles, small dung beetles, and stealing dung beetles, and the position update method of each dung beetle is different (Xue, Jiankai, Shen, et al. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization [J]. The Journal of Supercomputing, 2023, 79 (7): 7305-7336.);

[0042] During the iteration of the dung beetle optimization algorithm, each dung beetle will calculate the fitness function value according to its current position and record its own optimal fitness and position. Its own optimal fitness is the local optimal fitness. If the fitness of the current position is better than the previously recorded local optimal fitness, the current position will be updated to the current local optimal position.

[0043] The position update of the rolling dung beetle can be calculated as follows:

[0044]

[0045] Among them, t represents the number of iterations, X a (t) represents the position information of the a-th ball-rolling dung beetle at the t-th iteration; k∈{0,1}, the ball-rolling behavior is divided into an obstacle mode and an obstacle-free mode, k=1 represents the obstacle-free mode, and k=0 represents the obstacle mode; b is a constant, b∈(0,1); β is a constant, β∈(0,0.2); α is a natural coefficient, which takes 1 or -1, α=-1 represents deviation from the original direction, and α=1 represents no deviation; θ is a randomly generated number in a uniform distribution in the range of [0,π], representing the deflection angle of the ball-rolling dung beetle; X w is the current global worst position, which refers to the worst solution found in the entire population;

[0046] The position update of the brooding dung beetle can be calculated by the following formula:

[0047]

[0048] Among them, X * is the current local best position. For each dung beetle, the current local best position will be updated according to its fitness value; Lb and Ub represent the search lower bound and search upper bound respectively, Lb * and Ub * are the lower and upper limits of the spawning area respectively; R = 1-t / T max , T max Indicates the maximum number of iterations; B q(t) is the position information of the qth egg ball at the tth iteration, b 1 and b 2 Respectively represent two independent random vectors of size 1×D, where D represents dimension information;

[0049] The position update of a small dung beetle can be calculated as follows:

[0050]

[0051] Among them, X b represents the current global best feeding position, that is, the current global best position, which refers to the best solution found in the entire population; Lb b and Ub b are the lower and upper bounds of the optimal foraging area respectively; x A (t) represents the position information of the Ath small dung beetle at the tth iteration, C 1 represents a random number that follows a normal distribution, C 2 represents a random vector belonging to (0,1);

[0052] The position update of the stealing dung beetle can be calculated as follows:

[0053] T V (t+1)=X b +S×g×(|T V (t)-X * |+|T V (t)-X b |);

[0054] Among them, T V (t) represents the position information of the Vth thieving dung beetle at the tth iteration; g is a random vector of size 1×D, which obeys the normal distribution; S represents a constant value;

[0055] After each iterative calculation, determine whether the position of each dung beetle exceeds the boundary. If it exceeds the boundary, reset the current dung beetle position to the boundary value. Then, by comparing the fitness of all dung beetle individuals, select the position of the dung beetle individual with the highest fitness value and update it as the current global best position; select the position of the dung beetle individual with the lowest fitness value and update it as the current global worst position; each dung beetle individual updates its corresponding local best position;

[0056] Repeat the update of the position of each dung beetle until the maximum number of iterations is reached. When the predetermined maximum number of iterations is reached, the global optimal solution corresponding to the current dimension parameter D is output, that is, the global optimal position and its fitness value obtained by the last iterative calculation.

[0057] Further, in step S5, the fitness value corresponding to the global optimal solution obtained by running the dung beetle optimization algorithm in each dimension is compared with the fitness value f max According to the formula maxMAC=1-f max Calculate the layout effect value maxMAC corresponding to the global optimal solution of each dimension;

[0058] The sensor layout scheme with the least number of sensors under the premise that the layout effect value maxMAC is less than 0.25 is selected as the optimal layout scheme.

[0059] The beneficial effects brought by the technical solution of the present invention are as follows:

[0060] (1) Theoretical innovation. This paper proposes for the first time a sensor optimization layout method based on the dung beetle optimization algorithm. The proposed method simulates the natural behavior of dung beetles to perform optimization solutions, effectively solving the problems of poor adaptability and low global search capabilities of other algorithms, while ensuring excellent fitness values ​​and achieving efficient solution of the model;

[0061] (2) Social and economic benefits. The present invention can efficiently solve the problem of optimal layout of transmission tower sensors, and provides a scientific and systematic decision support tool for the status monitoring of transmission towers in power grids, which is conducive to saving monitoring costs and avoiding data collection redundancy problems, thereby bringing good social and economic benefits.

[0062] Compared with the prior art, the advantages of the present invention are:

[0063] The present invention aims at the problem of optimal placement of sensors and proposes a placement method based on the dung beetle optimization algorithm. The content studied is closely related to actual needs, and its significance is self-evident. Compared with the existing sensor optimization placement methods, the present invention has the following advantages: starting from the dung beetle optimization algorithm, the present invention adopts a probability model to describe the uncertainty of the objective function, and optimizes by dynamically sampling and updating the model, which can better find the optimal placement scheme in the global scope and avoid falling into the local optimum; it does not require the objective function to have a specific form, and it can be easier to consider multiple objectives at the same time by designing a multi-objective optimization strategy, which is particularly suitable for dynamic systems or complex scenarios where the objective function is difficult to describe. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 The present invention is a flowchart of a method for arranging sensors on a transmission tower based on a dung beetle optimization algorithm in an embodiment of the present invention.

[0065] Figure 2 The present invention is an algorithm flow chart of a method for arranging sensors on a transmission tower based on a dung beetle optimization algorithm in an embodiment of the present invention.

[0066] Figure 3 In the embodiment of the present invention, based on Figure 2 The program flow chart of the algorithm construction.

[0067] Figure 4 It is a schematic diagram of a finite element model of a transmission tower established in an embodiment of the present invention.

[0068] Figure 5 It is a schematic diagram of the calculation result of the arrangement effect value in the embodiment of the present invention.

[0069] Figure 6 It is a schematic diagram of the arrangement result in the embodiment of the present invention. DETAILED DESCRIPTION

[0070] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and embodiments. The same or similar symbols and annotations mentioned in the description of this specification represent the same or similar physical meanings or have the same or similar functions, and the legends used in this specification are only for better explanation of the present invention, and the applicability of the present invention is not limited thereto.

[0071] Example:

[0072] A method for arranging sensors on transmission towers based on the dung beetle optimization algorithm, such as Figure 1 As shown, the following steps are included:

[0073] S1. Establish a finite element model of the transmission tower;

[0074] In one embodiment, in the workbench module of the finite element analysis technology software ANSYS, a pair of transmission towers are modeled at a 1:1 scale, four tower feet are fixedly constrained, and a transmission line load is applied to the tower head, thereby obtaining a finite element model of the transmission tower.

[0075] S2. Performing modal analysis on the finite element model of the transmission tower to obtain modal vibration vectors corresponding to each modal order of the finite element model of the transmission tower;

[0076] All locations for setting sensors in the transmission tower are defined as nodes. The finite element model of the transmission tower is subjected to modal analysis through the APDL of ANSYS software to obtain the modal vibration shape vector corresponding to each node under each order mode. The modal vibration shape vector is used to characterize the modal information of each node in the transmission tower, and then the modal vibration shape vector corresponding to each order mode is obtained.

[0077] S3, calculate the ROC value that changes with the modal order based on the Fisher information matrix 2-norm method, and select the modal order;

[0078] The Fisher information matrix Q of the i-th mode i The expression is:

[0079]

[0080] In the formula, φ i represents the mode shape vector of the i-th mode, as follows:

[0081] φ i =[φ i1 ,φ i2 ,...,φ in ,...,φ iN ] T

[0082] Among them, φ in is the modal vibration vector corresponding to the nth node under the i-th order mode, and N is the number of nodes included in the transmission tower;

[0083] 2-norm rate of change (ROC) of the i-th mode ROC i Calculated by the following formula:

[0084]

[0085] In the formula, Q i represents the Fisher information matrix of the i-th mode, ||Q i || 2 Indicates Q i 2-norm; As the modal order i increases, ROC i The value will change when ROC i When the value is less than 0.1, it can be considered that the vibration shape vectors of the first i-order modes can basically cover all the modal information, and then the analysis can be carried out on the first i-order modes.

[0086] S4. According to the modal vibration vectors under the selected modal order, the dung beetle optimization algorithm is used to optimize the arrangement of different numbers of sensors, including the following steps:

[0087] S4.1. Execute parameter initialization:

[0088] Different dimensional parameters D are set according to different numbers of sensors, and the value of the dimensional parameter D is consistent with the number of sensors, so as to fully characterize the acceleration sensor and facilitate the initialization of the dung beetle population information in the subsequent steps;

[0089] The parameter initialization is as follows:

[0090] Set the dimension, maximum number of iterations, population size, percentage of rolling dung beetles, percentage of brooding dung beetles, percentage of stealing dung beetles, reference history weight, reference optimal position weight, scaling factor, and search upper and lower bounds;

[0091] In one embodiment, taking the dry-shaped iron tower as an example, the population size is set to 30, the maximum number of iterations is 200, the proportion of rolling dung beetles is 0.2, the proportion of brooding dung beetles is 0.2, the proportion of small dung beetles is 0.2, the proportion of stealing dung beetles is 0.4, the reference history weight is 0.1, the reference optimal position weight is 0.3, the scaling factor is 0.5, the search upper bound is 1.0, and the search lower bound is 0.001.

[0092] S4.2. Perform population initialization, as follows:

[0093] Each dung beetle individual is randomly initialized within the search upper and lower bounds, and each dung beetle individual corresponds to a layout scheme, that is, each dung beetle individual randomly selects D nodes as the initial position, D is the dimension parameter, and then the MAC matrix is ​​calculated according to the modal vibration shape vectors corresponding to the nodes included in the layout scheme corresponding to each dung beetle individual, and then the fitness value of each initial position of each dung beetle individual is calculated and sorted using the element with the largest absolute value in the MAC matrix to obtain the current global best position, the global worst position and the local best position of each dung beetle; among them, the global best position refers to the best solution found in the entire population, that is, the position with the highest fitness value, the global worst position refers to the worst solution found in the entire population, that is, the position with the lowest fitness value, and the local best position of each dung beetle refers to the best solution among all positions experienced by each dung beetle individual.

[0094] The fitness of each dung beetle is calculated based on the modal assurance criterion; the modal assurance criterion refers to a method in the related art of determining the MAC modal matrix through the modal vibration vector to determine the optimal position; the fitness is used to guide the optimal arrangement of sensors on the transmission tower, and the entire sensor optimization arrangement model is optimized towards the goal of the highest arrangement effect; the calculation method of the MAC matrix is ​​as follows:

[0095]

[0096] Among them, MAC ij is the corresponding value of the i-th row and j-th column in the MAC modal matrix; They represent the modal vibration shape vectors of the i-th and j-th modes corresponding to the specific layout schemes, as follows:

[0097]

[0098] in, is the modal vibration vector corresponding to the mth node under the i-th mode of the specific layout scheme, N is the number of nodes included in the specific layout scheme, The modal vibration shape vectors corresponding to the nodes included in the specific arrangement scheme are recoded;

[0099] The calculation method of fitness f is as follows:

[0100] f=1-max(|MAC ij |) / c,i≠j;

[0101] Among them, |MAC ij | is the absolute value of the corresponding value of the i-th row and j-th column in the MAC modality matrix, and c is a constant, which is 1 in this embodiment.

[0102] S4.3, iteratively execute the dung beetle optimization algorithm, update the position of each dung beetle, reach the maximum number of iterations, and output the global optimal solution corresponding to the current dimension parameter D and its fitness value;

[0103] Each dung beetle population is divided into ball-rolling dung beetles, brooding dung beetles, small dung beetles, and thieving dung beetles, and each dung beetle has a different method of updating its position;

[0104] During the iteration of the dung beetle optimization algorithm, each dung beetle will calculate the fitness function value according to its current position and record its own optimal fitness and position. Its own optimal fitness is the local optimal fitness. If the fitness of the current position is better than the previously recorded local optimal fitness, the current position will be updated to the current local optimal position.

[0105] The position update of the rolling dung beetle can be calculated as follows:

[0106]

[0107] Among them, t represents the number of iterations, X a (t) represents the position information of the a-th ball-rolling dung beetle at the t-th iteration; k∈{0,1}, the ball-rolling behavior is divided into an obstacle mode and an obstacle-free mode, k=1 represents the obstacle-free mode, and k=0 represents the obstacle mode; b is a constant, b∈(0,1); β is a constant, β∈(0,0.2); α is a natural coefficient, which takes 1 or -1, α=-1 represents deviation from the original direction, and α=1 represents no deviation; θ is a randomly generated number in a uniform distribution in the range of [0,π], representing the deflection angle of the ball-rolling dung beetle; X w is the current global worst position, which refers to the worst solution found in the entire population;

[0108] The position update of the brooding dung beetle can be calculated by the following formula:

[0109]

[0110] Among them, X * is the current local best position. For each dung beetle, the current local best position will be updated according to its fitness value; Lb and Ub represent the search lower bound and search upper bound respectively, Lb* and Ub * are the lower and upper limits of the spawning area respectively; R = 1-t / T max , T max Indicates the maximum number of iterations; B q (t) is the position information of the qth egg ball at the tth iteration, b 1 and b 2 Respectively represent two independent random vectors of size 1×D, where D represents dimension information;

[0111] The position update of a small dung beetle can be calculated as follows:

[0112]

[0113] Among them, X b represents the current global best feeding position, that is, the current global best position, which refers to the best solution found in the entire population; Lb b and Ub b are the lower and upper bounds of the optimal foraging area respectively; x A (t) represents the position information of the Ath small dung beetle at the tth iteration, C 1 represents a random number that follows a normal distribution, C 2 represents a random vector belonging to (0,1);

[0114] The position update of the stealing dung beetle can be calculated as follows:

[0115] T V (t+1)=X b +S×g×(|T V (t)-X * |+|T V (t)-X b |);

[0116] Among them, T V (t) represents the position information of the Vth thieving dung beetle at the tth iteration; g is a random vector of size 1×D, which obeys the normal distribution; S represents a constant value;

[0117] After each iterative calculation, determine whether the position of each dung beetle exceeds the boundary. If it exceeds the boundary, reset the current dung beetle position to the boundary value. Then, by comparing the fitness of all dung beetle individuals, select the position of the dung beetle individual with the highest fitness value and update it as the current global best position; select the position of the dung beetle individual with the lowest fitness value and update it as the current global worst position; each dung beetle individual updates its corresponding local best position;

[0118] Repeat the update of the position of each dung beetle until the maximum number of iterations is reached. When the predetermined maximum number of iterations is reached, the global optimal solution corresponding to the current dimension parameter D is output, that is, the global optimal position and its fitness value obtained by the last iterative calculation.

[0119] S4.4. After the calculation of the global optimal solution for each corresponding number of sensors is completed, the number of sensors is modified, and the dimension parameter D is changed according to the number of sensors, and step S4.2 is returned and re-executed until the calculation of the global optimal solution corresponding to all numbers of sensors is completed.

[0120] S5. Compare the layout effects of different numbers of sensors, determine the number of sensors and provide a sensor layout plan;

[0121] Compare the fitness values ​​corresponding to the global optimal solution obtained by running the dung beetle optimization algorithm in each dimension. max According to the formula maxMAC=1-f max Calculate the layout effect value maxMAC corresponding to the global optimal solution of each dimension;

[0122] The sensor layout scheme with the least number of sensors under the premise that the layout effect value maxMAC is less than 0.25 is selected as the optimal layout scheme.

[0123] In one embodiment, the steps of the present invention are constructed as follows: Figure 2 The algorithm shown and the one constructed according to the algorithm are Figure 3 The procedure shown;

[0124] In one embodiment, the transmission tower finite element model is established as follows: Figure 4 As shown; take the transmission tower as the research model, the operation is as follows Figure 3 The program shown in the figure obtains the calculation results of the arrangement effect values ​​when the number of sensors is 3, 4, 5, 6, 7, 8, 9, and 10, respectively. Figure 5 shown.

[0125] like Figure 5 As shown in the figure, the operating results under different numbers of sensors are shown. It can be seen from the figure that the index is the best when the number of sensors is 8, that is, the layout effect is the best at this time. According to the measurement point positions given by the operating results under the preset number, a specific layout plan is given, such as Figure 6 As shown: sensors are arranged at nodes numbered 28, 49, 69, 96, 120, 187, 288, and 321 respectively.

[0126] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for arranging sensors on transmission towers based on a dung beetle optimization algorithm, characterized in that: The steps include: S1. Establish a finite element model of the transmission tower; S2. Performing modal analysis on the finite element model of the transmission tower to obtain modal vibration vectors corresponding to each modal order of the finite element model of the transmission tower; S3, calculate the ROC value that changes with the modal order based on the Fisher information matrix 2-norm method, and select the modal order; S4. According to the modal vibration shape vectors under the selected modal order, the dung beetle optimization algorithm is used to optimize the arrangement of different numbers of sensors; S5. Compare the layout effects of different numbers of sensors, determine the number of sensors and provide a sensor layout plan.

2. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 1 is characterized in that: In step S1, in the workbench module of the finite element analysis technology software ANSYS, a transmission tower is modeled at a 1:1 scale, four tower feet are fixedly constrained, and a transmission line load is applied to the tower head, thereby obtaining a finite element model of the transmission tower.

3. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 1 is characterized in that: In step S2, all positions for setting sensors in the transmission tower are defined as nodes, and the finite element model of the transmission tower is subjected to modal analysis through APDL of ANSYS software, so as to obtain the modal vibration shape vector corresponding to each node under each order mode, and the modal vibration shape vector is used to characterize the modal information of each node in the transmission tower, thereby obtaining the modal vibration shape vector corresponding to each order mode.

4. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 1 is characterized in that: In step S3, the Fisher information matrix Q of the i-th mode is i The expression is: In the formula, φ i represents the mode shape vector of the i-th mode, as follows: f i =[φ i1 ,f i2 ,...,f in ,...,f iN ] T Among them, φ in is the modal vibration vector corresponding to the nth node under the i-th order mode, and N is the number of nodes included in the transmission tower; 2-norm rate of change (ROC) of the i-th mode ROC i Calculated by the following formula: In the formula, Q i represents the Fisher information matrix of the i-th mode, ||Q i ||2 means Q i 2-norm; As the modal order i increases, ROC i The value will change when ROC i When the value is less than 0.1, it can be considered that the vibration shape vectors of the first i-order modes can basically cover all the modal information, and then the analysis can be carried out on the first i-order modes.

5. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 1 is characterized in that: Step S4 includes the following steps: S4.

1. Execute parameter initialization: Different dimensional parameters D are set according to different numbers of sensors, and the value of the dimensional parameter D is consistent with the number of sensors, so as to fully characterize the acceleration sensor and facilitate the initialization of the dung beetle population information in the subsequent steps; S4.2, perform population initialization; S4.3, iteratively execute the dung beetle optimization algorithm, update the position of each dung beetle, reach the maximum number of iterations, and output the global optimal solution corresponding to the current dimension parameter D and its fitness value; S4.

4. After the calculation of the global optimal solution for each corresponding number of sensors is completed, the number of sensors is modified, and the dimension parameter D is changed according to the number of sensors, and step S4.2 is returned and re-executed until the calculation of the global optimal solution corresponding to all numbers of sensors is completed.

6. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 5 is characterized in that: In step S4.1, the parameters are initialized as follows: Set the dimension, maximum number of iterations, population size, percentage of rolling dung beetles, percentage of brooding dung beetles, percentage of stealing dung beetles, reference history weight, reference optimal position weight, scaling factor, and search upper and lower bounds.

7. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 5 is characterized in that: In step S4.2, the population is initialized as follows: Each dung beetle individual is randomly initialized within the search upper and lower bounds, and each dung beetle individual corresponds to a layout scheme, that is, each dung beetle individual randomly selects D nodes as the initial position, D is the dimension parameter, and then the MAC matrix is ​​calculated according to the modal vibration shape vectors corresponding to the nodes included in the layout scheme corresponding to each dung beetle individual, and then the fitness value of each initial position of each dung beetle individual is calculated and sorted using the element with the largest absolute value in the MAC matrix to obtain the current global best position, the global worst position and the local best position of each dung beetle; among them, the global best position refers to the best solution found in the entire population, that is, the position with the highest fitness value, the global worst position refers to the worst solution found in the entire population, that is, the position with the lowest fitness value, and the local best position of each dung beetle refers to the best solution among all positions experienced by each dung beetle individual.

8. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 7 is characterized in that: The fitness of each dung beetle is calculated based on the modal assurance criterion; the modal assurance criterion refers to a method in the related art of determining the MAC modal matrix through the modal vibration vector to determine the optimal position; the fitness is used to guide the optimal arrangement of sensors on the transmission tower, and the entire sensor optimization arrangement model is optimized towards the goal of the highest arrangement effect; the calculation method of the MAC matrix is ​​as follows: Among them, MAC ij is the corresponding value of the i-th row and j-th column in the MAC modal matrix; They represent the modal vibration shape vectors of the i-th and j-th modes corresponding to the specific layout schemes, as follows: in, is the modal vibration vector corresponding to the mth node under the i-th mode of the specific layout scheme, N is the number of nodes included in the specific layout scheme, The modal vibration shape vectors corresponding to the nodes included in the specific arrangement scheme are recoded; The calculation method of fitness f is as follows: f=1-max(|MAC ij |) / c,i≠j; Among them, |MAC ij | is the absolute value of the corresponding value in the i-th row and j-th column in the MAC modality matrix, and c is a constant.

9. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 5, characterized in that: In step S4.3, each dung beetle population is divided into rolling dung beetles, brooding dung beetles, small dung beetles, and stealing dung beetles, and the position updating method of each dung beetle is different; During the iteration of the dung beetle optimization algorithm, each dung beetle will calculate the fitness function value according to its current position and record its own optimal fitness and position. Its own optimal fitness is the local optimal fitness. If the fitness of the current position is better than the previously recorded local optimal fitness, the current position will be updated to the current local optimal position. The position update of the rolling dung beetle can be calculated as follows: Where t represents the number of iterations, X a (t) represents the position information of the a-th ball-rolling dung beetle at the t-th iteration; k∈{0,1}, the ball-rolling behavior is divided into an obstacle mode and an obstacle-free mode, k=1 represents the obstacle-free mode, and k=0 represents the obstacle mode; b is a constant, b∈(0,1); β is a constant, β∈(0,0.2); α is a natural coefficient, which takes 1 or -1, α=-1 represents deviation from the original direction, and α=1 represents no deviation; θ is a randomly generated number in a uniform distribution in the range of [0,π], representing the deflection angle of the ball-rolling dung beetle; X w is the current global worst position, which refers to the worst solution found in the entire population; The position update of the brooding dung beetle can be calculated by the following formula: Among them, X * is the current local best position; for each dung beetle, the current local best position will be updated according to its fitness value; Lb and Ub represent the search lower bound and search upper bound respectively, Lb * and Ub * are the lower and upper limits of the spawning area respectively; R = 1-t / T max , T max Indicates the maximum number of iterations; B q (t) is the position information of the qth egg ball at the tth iteration, b1 and b2 represent two independent random vectors of size 1×D, and D represents the dimension information; The position update of a small dung beetle can be calculated as follows: Among them, X b represents the current global best feeding position, that is, the current global best position, which refers to the best solution found in the entire population; Lb b and Ub b are the lower and upper bounds of the optimal foraging area respectively; x A (t) represents the position information of the Ath small dung beetle at the tth iteration, C1 represents a random number that obeys the normal distribution, and C2 represents a random vector belonging to (0,1); The position update of the stealing dung beetle can be calculated as follows: T V (t+1)=X b +S×g×(|T V (t)-X * |+|T V (t)-X b |); Among them, T V (t) represents the position information of the Vth thieving dung beetle at the tth iteration; g is a random vector of size 1×D, which obeys the normal distribution; S represents a constant value; After each iterative calculation, determine whether the position of each dung beetle exceeds the boundary. If it exceeds the boundary, reset the current dung beetle position to the boundary value. Then, by comparing the fitness of all dung beetle individuals, select the position of the dung beetle individual with the highest fitness value and update it as the current global best position; select the position of the dung beetle individual with the lowest fitness value and update it as the current global worst position; each dung beetle individual updates its corresponding local best position; Repeat the update of the position of each dung beetle until the maximum number of iterations is reached. When the predetermined maximum number of iterations is reached, the global optimal solution corresponding to the current dimension parameter D is output, that is, the global optimal position and its fitness value obtained by the last iterative calculation.

10. The method for arranging transmission tower sensors based on the dung beetle optimization algorithm according to claim 1, characterized in that: In step S5, the fitness value corresponding to the global optimal solution obtained by running the dung beetle optimization algorithm in each dimension is compared. max According to the formula maxMAC = 1-f max Calculate the layout effect value maxMAC corresponding to the global optimal solution of each dimension; The sensor layout scheme with the least number of sensors under the premise that the layout effect value maxMAC is less than 0.25 is selected as the optimal layout scheme.

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