Apparatus and method for performance evaluation of a substation service steel structure

Abstract

The application discloses a kind of performance evaluation device and method of substation service steel frame, including vibration excitation equipment, data acquisition equipment, data analysis equipment;Vibration excitation equipment includes vibration generation component and force sensor, vibration generation component generates exciting force acting on service steel frame according to predetermined signal, and force sensor detects exciting force acting on service steel frame;Data acquisition equipment detects the three-dimensional acceleration of all steel structure connecting nodes of service steel frame, and receives the exciting force detected by force sensor;Data analysis equipment is connected with data acquisition equipment, and according to the three-dimensional acceleration and exciting force of detection, obtains the performance evaluation result of service steel frame.The application does not destroy substation steel frame structure, does not affect the normal service of structure, can quickly and accurately evaluate the global performance of substation steel frame, and host computer does not need to store the data of data acquisition equipment, and the storage and operation ability requirement of host computer is low.

Description

Technical Field

[0001] This invention belongs to the technical field of substation structure testing, specifically relating to a performance evaluation device and method for steel structures in service at substations. Background Technology

[0002] Steel frames are critical structural components of substations, supporting electrical equipment and protecting it from external factors such as wind, rain, and earthquakes. These structures provide essential support and protection for various substation components, including conductors, transformers, circuit breakers, and switches. However, like any other infrastructure, substation steel frames are subject to various degradation mechanisms that affect their performance and service life. The unique operating environment of substations presents various challenges to steel structures, including corrosive environments, temperature fluctuations, humidity, and air pollutants. Furthermore, mechanical loads from heavy equipment, earthquakes, and wind further accelerate the degradation of steel frame performance. Accurate assessment of substation steel frame performance and maintenance of the safety and structural integrity of these critical assets are essential. If performance tests are conducted on a steel frame, some structural components must be disassembled, which will affect the normal service life of the steel frame. If X-ray flaw detectors, ultrasonic flaw detectors, magnetic particle flaw detectors, penetrant detectors, etc., are used to detect damage to steel structures, only local information about the detected area is often obtained. For example, patent CN211528254U discloses a transmitter support device for X-ray flaw detection of GIS equipment, including a transmitter and a mounting frame for fixing the transmitter, and a rotating mechanism fixedly connected to the bottom of the mounting frame. The transmitter rotates around the axis of the rotating mechanism. A lifting mechanism for adjusting the height of the transmitter is rotatably connected to the bottom of the rotating mechanism. The lifting mechanism includes a top lifting rod connected to the rotating mechanism and a bottom lifting rod connected to a base mechanism. A pitch mechanism is used to adjust the tilt angle of the transmitter relative to the horizontal plane, and the pitch mechanism connects the lifting mechanism and the mounting frame. A base mechanism is also included for moving or fixing the lifting mechanism, pitch mechanism, rotating mechanism, mounting frame, and transmitter as a whole relative to the ground. For steel frame structures of substations in service, because there are high-voltage transmission lines above, construction personnel cannot conduct performance tests on the upper part of the steel frame, otherwise it would cause safety accidents such as casualties. Therefore, it is impossible to obtain global information about the steel frame.

[0003] Therefore, how to provide a performance evaluation device for steel structures in service with substations, to quickly, comprehensively, and accurately evaluate the performance of steel structures in service with substations, without affecting the normal use of the structure, is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a performance evaluation device and method for steel structures in service at substations. It can perform a comprehensive performance evaluation of the entire steel structure in service at substations, and the evaluation process does not damage the structure of the steel structure or affect its normal service.

[0005] In a first aspect, the present invention provides a performance evaluation device for a steel structure in service at a substation, comprising: a vibration excitation device, a data acquisition device, and a data analysis device;

[0006] The vibration excitation device includes a vibration generating component and a force sensor. The vibration generating component generates an excitation force acting on the service steel frame according to a predetermined signal, and the force sensor detects the excitation force acting on the service steel frame.

[0007] The data acquisition equipment detects the triaxial acceleration of all steel structure connection nodes of the service steel frame and receives the excitation force detected by the force sensor;

[0008] The data analysis equipment communicates with the data acquisition equipment and obtains the performance evaluation results of the in-service steel frame based on the detected triaxial acceleration and excitation force.

[0009] Furthermore, the vibration generating assembly includes a signal generator, a power amplifier, an electromagnetic exciter, and a force transmission component connected in sequence;

[0010] The signal generator produces white noise;

[0011] The power amplifier amplifies the white noise electrical signal generated by the signal generator and inputs the amplified white noise electrical signal into the electromagnetic exciter;

[0012] The electromagnetic exciter generates excitation force based on the input white noise electrical signal;

[0013] The force transmission component is connected to an electromagnetic vibrator and a force sensor at both ends, and the force sensor is fixed to a connection node on the steel frame in service.

[0014] Furthermore, the data acquisition equipment includes a triaxial accelerometer and a data acquisition unit with communication connectivity;

[0015] Triaxial acceleration sensors are installed on all steel structure connection nodes of the service steel frame and detect the triaxial acceleration of all steel structure connection nodes of the service steel frame.

[0016] The data acquisition unit receives data detected by the triaxial accelerometer and force sensor, and transmits the received data to the data analysis equipment.

[0017] Furthermore, the data analysis equipment obtains performance evaluation results for the in-service steel frame based on the detected triaxial acceleration and excitation force, including:

[0018] Obtain the approximate finite element model corresponding to the steel frame in service; wherein, the approximate finite element model is composed of multiple connected structural elements;

[0019] A finite element energy model is constructed based on the approximate finite element model.

[0020] Based on the finite element energy model, the energy of the corresponding in-service steel frame is obtained according to the triaxial acceleration of all steel structure connection nodes.

[0021] The actual input energy is obtained based on the triaxial acceleration at the position where the excitation force is applied and the detected excitation force.

[0022] An energy correction model is constructed, and the model is iteratively processed based on the energy of the in-service steel frame and the energy of the actual input until the predetermined conditions are met, thus giving the current stiffness and mass of the in-service steel frame.

[0023] Furthermore, an approximate finite element model corresponding to the in-service steel frame is obtained, including:

[0024] Finite element analysis was performed on the in-service steel frame to obtain an approximate finite element model.

[0025] Furthermore, an approximate finite element model is constructed through steps such as structural discretization, defining material models, setting connections, and defining boundary conditions. The elastic modulus and density of the material model in the approximate finite element model are defined through a key information matrix.

[0026] Furthermore, based on the approximate finite element model, a finite element energy model is constructed, including:

[0027] Obtain the parameters of velocity, stiffness, displacement, mass, and acceleration of the approximate finite element model;

[0028] Based on the velocity, stiffness, and displacement parameters of the approximate finite element model, a strain energy quantum model is given.

[0029] Based on the parameters of velocity, mass, and acceleration from the approximate finite element model, a kinetic energy sub-model is given;

[0030] A finite element energy model is derived based on the strain energy quantum model and the kinetic energy quantum model.

[0031] Furthermore, the finite element energy model satisfies the following relationship:

[0032]

[0033]

[0034]

[0035] In the formula, The energy of the steel structure in service, i.e., the finite element energy model. It is the strain energy of the structural unit, i.e., the strain energy sub-model; The kinetic energy of the structural unit is represented by the kinetic energy sub-model; Δt represents the time step between adjacent detections; m represents the number of degrees of freedom of the in-service steel frame; n represents the number of structural units of the in-service steel frame; V g It is an (m×n)×n velocity matrix; v is an m×1 velocity vector, i.e., v = [v1, v2, ..., v] i ,…,v m ] T T is the matrix transpose, v i It is the velocity of the i-th degree of freedom of the service steel frame; It is the stiffness matrix of the steel frame in service, which is an (m×n)×m matrix; U is the n×n stiffness matrix of the i-th structural element in the service steel frame; u is the m×1 displacement vector of all degrees of freedom of the service steel frame. i Let be the displacement of the i-th degree of freedom of the steel frame in service; It is the (m×n)×m mass matrix of the steel frame in service. is the m×m mass matrix of the i-th structural unit in the service steel frame; is the m×1 acceleration vector of the service steel frame, a i It is the acceleration of the i-th degree of freedom of the steel frame in service.

[0036] Furthermore, the velocity and displacement of the steel frame in service are obtained by integrating the acceleration once and twice, respectively.

[0037] Furthermore, based on the finite element energy model, the energy of the corresponding service steel frame is obtained according to the triaxial acceleration of all steel structure connection nodes, including:

[0038] Based on the triaxial acceleration detected at all steel structure connection nodes, the number of degrees of freedom of the in-service steel frame is given;

[0039] Based on the triaxial acceleration and number of degrees of freedom of all steel structure connection nodes, the acceleration vector, velocity vector and displacement vector of the service steel frame in each degree of freedom are obtained;

[0040] Based on the approximate finite element model, the acceleration vector, displacement vector and velocity vector of the in-service steel frame in each degree of freedom are analyzed and processed to obtain the strain energy and kinetic energy of the in-service steel frame.

[0041] The energy of the in-service steel frame is obtained from its strain energy and kinetic energy.

[0042] Furthermore, based on the triaxial acceleration at the position where the excitation force acts and the detected excitation force, the actual input energy is obtained, including:

[0043] Based on the detected excitation force and acceleration information of the excitation nodes, the actual energy input to the service steel frame by the exciter is obtained.

[0044] Furthermore, the constructed energy correction model includes a gain sub-model, a performance parameter sub-model, and an error covariance sub-model;

[0045] The construction of the energy correction model includes:

[0046] Determine the parameters of the prior key information matrix, prior error covariance, prior measurement covariance, and energy of the steel structure in service;

[0047] Based on the parameters of prior measurement covariance, prior error covariance, and energy of the service steel frame, a gain sub-model is obtained;

[0048] Based on the gain sub-model and the parameters of the energy and prior key information matrix of the service steel frame, the performance parameter sub-model is obtained;

[0049] Based on the gain sub-model and the parameters of the energy and prior error covariance of the service steel frame, the error covariance sub-model is obtained.

[0050] Furthermore, the gain sub-model satisfies the following relationship:

[0051]

[0052] In the formula, K k R is the gain of the k-th detection, i.e., the gain sub-model; R is the prior measurement covariance. It is the energy of the service steel frame output by an approximate finite element model. It is the prior error covariance corresponding to the k-th detection;

[0053] The performance parameter sub-model satisfies the following relationship:

[0054]

[0055] In the formula, It is the prior key information matrix of the k-th detection. It is the key information matrix corresponding to the k-th detection, i.e., the performance parameter sub-model; It is the actual energy of the steel structure input during the kth service cycle;

[0056] The error covariance sub-model satisfies the following relationship:

[0057]

[0058] In the formula, P kIt is the error covariance corresponding to the k-th detection, i.e., the error covariance sub-model, and I is the identity matrix.

[0059] Furthermore, the energy correction model is iteratively processed based on the energy of the in-service steel frame and the energy of the actual input until predetermined conditions are met, giving the current stiffness and mass of the in-service steel frame, including:

[0060] Initial values ​​are assigned to the prior error covariance and the prior measurement covariance;

[0061] The gain is obtained by analyzing and processing the energy, prior measurement covariance, and prior error covariance of the service steel frame corresponding to the first test through the gain sub-model.

[0062] Initial values ​​are assigned to the prior key information matrix of the steel structure in service;

[0063] The performance parameter sub-model is used to analyze and process the gain, the energy of the service steel frame corresponding to the first detection, the actual energy of the input service steel frame corresponding to the first detection, and the prior key information matrix to obtain the processed key information matrix.

[0064] The gain, the energy of the service steel frame corresponding to the first test, and the prior error covariance are analyzed and processed by the error covariance sub-model to obtain the processed error covariance.

[0065] Based on the time sequence of detection, the prior error covariance in the gain sub-model and the error covariance sub-model is updated using the processed error covariance obtained before each detection. The prior key information matrix in the performance parameter sub-model is updated using the processed key information matrix obtained before each detection. The covariance is calculated by averaging the multiple triaxial accelerations detected before each detection, and the calculated covariance is used to update the prior measurement covariance in the gain sub-model.

[0066] The energy of the remaining detected service steel frame and the input real energy are processed sequentially by the energy correction model after each update until the output of the performance parameter sub-model is the same key information matrix in two consecutive times.

[0067] The key information matrix of the last output of the performance parameter sub-model is processed to obtain the current stiffness and mass of the steel frame in service.

[0068] Furthermore, initial values ​​are assigned to the prior error covariance and the prior measurement covariance, including:

[0069] Choose a value from the arbitrary error covariance and measurement covariance as the prior error covariance after assigning the initial value;

[0070] Initial values ​​are assigned to the prior key information matrix of the service steel frame, including:

[0071] Select one matrix from any set of key information matrices as the prior key information matrix after initialization.

[0072] Furthermore, the key information matrix includes the elastic modulus and density information of all structural units;

[0073] Processing the key information matrix from the last output of the performance parameter sub-model yields the current stiffness and mass of the in-service steel frame, including:

[0074] Obtain the stiffness matrix corresponding to the structural unit, and obtain the actual size of the structural unit based on the in-service steel frame;

[0075] The moment of inertia of the structural unit is obtained based on its actual size and shape.

[0076] Based on the stiffness matrix, the stiffness of all structural elements is obtained through the actual dimensions, moment of inertia, and elastic modulus of the structural elements.

[0077] Based on the actual dimensions of the structural unit, give the volume of the structural unit;

[0078] The mass of all structural elements is obtained based on their volume and density.

[0079] Furthermore, the key information matrix satisfies the following relationship:

[0080] Y = {E; ρ

[0081] E = [E1,...,E] i ,...,E n ] T

[0082] ρ=[ρ1,...,ρ i ,...,ρ n ] T

[0083] In the formula, Y is the key information matrix, E is the elastic modulus matrix, and E i Let ρ be the elastic modulus of the i-th structural element, and ρ be the density matrix. i It is the density of the i-th structural unit, and T is the matrix transpose.

[0084] Furthermore, the stiffness matrix of the structural element satisfies the following relationship:

[0085]

[0086] In the formula, K eLet be the stiffness matrix of the structural element, E be the elastic modulus of the structural element, G be the moment of inertia of the cross section of the structural element, A be the cross-sectional area of ​​the structural element, and l be the length of the structural element.

[0087] Based on the volume and density of the structural elements, the masses of all structural elements are obtained, including:

[0088] The mass of a structural unit is obtained by multiplying its density and volume.

[0089] Secondly, the present invention also provides a performance evaluation method for steel structures in service at substations, employing the aforementioned performance evaluation device for steel structures in service at substations, the performance evaluation method comprising:

[0090] The data acquisition equipment was set at all steel structure connection nodes and vibration force application locations of the steel frame to be tested in service.

[0091] The vibration excitation force is generated by the vibration excitation equipment and applied to the steel frame under test.

[0092] The triaxial acceleration and excitation force measured multiple times were analyzed and processed using data analysis equipment to obtain the performance evaluation results of the steel frame in service.

[0093] The present invention provides a performance evaluation device and method for steel structures in service at substations, which has at least the following beneficial effects:

[0094] (1) The present invention uses vibration excitation equipment, data acquisition equipment and data analysis equipment to analyze the structural vibration response of the steel frame in service, thereby realizing the performance evaluation of the overall structure of the steel frame in service, without causing damage to the steel frame structure of the substation or affecting the normal service of the steel frame structure of the substation.

[0095] (2) The data analysis equipment of the present invention adopts the energy correction theory, which can obtain the sum of the strain energy and kinetic energy of all structural units, that is, obtain the stiffness and mass information of all structural units. With this stiffness and mass information, the global performance of the substation steel frame can be evaluated.

[0096] (3) The data analysis device of the present invention can detect the stiffness and mass of the structural unit through updates and iterations. It only needs the vibration data of the previous step and the current step, and does not need historical data. Therefore, the calculation speed is fast and the requirements for the storage and computing power of the host computer are low. Attached Figure Description

[0097] Figure 1 This is a schematic diagram of a performance evaluation device for a steel structure in service at a substation according to the present invention;

[0098] Figure 2A schematic diagram of a vibration generating component according to one embodiment of the present invention;

[0099] Figure 3 A flowchart illustrating the performance evaluation results of one embodiment of the present invention;

[0100] Figure 4 A flowchart of an iterative process provided in one embodiment of the present invention;

[0101] Figure 5 A schematic diagram illustrating the process of updating the elastic modulus and density of a single component according to a certain embodiment of the present invention;

[0102] Figure 6 A schematic diagram illustrating the elastic modulus update process of a service steel frame according to a certain embodiment of the present invention;

[0103] Figure 7 This is a schematic diagram of the density update process of a service steel frame according to one embodiment of the present invention.

[0104] Explanation of reference numerals in the attached drawings: 1-Service steel frame, 2-Vibration excitation equipment, 21-Vibration generating component, 211-Signal generator, 212-Power amplifier, 213-Electromagnetic exciter, 214-Force transmission component, 22-Force sensor, 3-Data acquisition equipment, 31-Triaxial accelerometer, 32-Data acquisition instrument, 4-Data analysis equipment. Detailed Implementation

[0105] To better understand the above technical solutions, a detailed description of the solutions will be provided below in conjunction with the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0106] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, and “multiple” generally includes at least two unless the context clearly indicates otherwise.

[0107] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that an article or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the article or device that includes said element.

[0108] See Figure 1 As shown, the present invention provides a performance evaluation device for steel structure in service in a substation, comprising: a vibration excitation device 2, a data acquisition device 3, and a data analysis device 4;

[0109] The vibration excitation device 2 includes a vibration generating component 21 and a force sensor 22. The vibration generating component 21 generates an excitation force acting on the service steel frame 1 according to a predetermined signal, and the force sensor 22 detects the excitation force acting on the service steel frame 1.

[0110] Data acquisition device 3 detects the triaxial acceleration of all steel structure connection nodes of the service steel frame 1 and receives the excitation force detected by force sensor 22;

[0111] The data analysis device 4 is connected to the data acquisition device 3 and obtains the performance evaluation results of the in-service steel frame 1 based on the detected triaxial acceleration and excitation force.

[0112] In practical application scenarios, see Figure 2 As shown, the vibration generating assembly 21 may include a signal generator 211, a power amplifier 212, an electromagnetic exciter 213, and a force transmission component 214 connected in sequence.

[0113] Signal generator 211 generates white noise, and the wide spectrum characteristics of white noise can fully excite the modal vibration of the steel frame structural unit of the substation;

[0114] The power amplifier 212 amplifies the white noise electrical signal generated by the signal generator 211 and inputs the amplified white noise electrical signal into the electromagnetic exciter 213;

[0115] The electromagnetic exciter 213 generates excitation force based on the input white noise electrical signal;

[0116] The force transmission component 214 is connected to the electromagnetic vibrator 213 and the force sensor 22 at both ends, and the force sensor 22 is fixed on a certain steel structure connection node of the service steel frame 1.

[0117] Specifically, the force transmission component 214 is a circular cross-section steel rod with threads at both ends. The thread at one end of the force transmission component 214 is connected to the threaded hole of the electromagnetic vibrator 213, and the thread at the other end is connected to the threaded hole of the force sensor 22 on the steel frame.

[0118] When the vibration excitation device 2 is working, it generally generates bandwidth-limited white noise (0-200Hz) to excite the steel frame. The acceleration in all three directions at each node is measured by the data acquisition device 3.

[0119] Data acquisition device 3 includes a triaxial accelerometer 31 and a data acquisition instrument 32 connected by communication;

[0120] The triaxial acceleration sensor 31 is installed on all steel structure connection nodes of the service steel frame 1 and detects the triaxial acceleration of all steel structure connection nodes of the service steel frame 1.

[0121] The data acquisition unit 32 receives data detected by the triaxial acceleration sensor 31 and the force sensor 22, and transmits the received data to the data analysis device 4.

[0122] The sampling frequency of the data acquisition equipment is generally not less than 256Hz. When the data analysis equipment is working, it first performs a time-domain analysis of the structural unit's response under vibration excitation. This is done by placing the structural model into a 0-200Hz bandwidth-limited white noise input signal for 3 seconds to generate the response. Then, the energy of the structure is calculated based on the substation steel frame 1 structural response at each time step, and the unknown parameters (elastic modulus and density) are estimated using energy correction theory. The sampling time in the simulation is generally 1 / 256 second. The elastic modulus and density of the approximate finite element model of the steel frame are approximate and can be chosen to have any value.

[0123] See Figure 3 As shown, the data analysis equipment obtains the performance evaluation results of the in-service steel frame based on the detected triaxial acceleration and excitation force, which may include:

[0124] Obtain the approximate finite element model corresponding to the steel frame in service; wherein, the approximate finite element model is composed of multiple connected structural elements;

[0125] A finite element energy model is constructed based on the approximate finite element model.

[0126] Based on the finite element energy model, the energy of the corresponding in-service steel frame is obtained according to the triaxial acceleration of all steel structure connection nodes.

[0127] The actual input energy is obtained based on the triaxial acceleration at the position where the excitation force is applied and the detected excitation force.

[0128] An energy correction model is constructed, and the model is iteratively processed based on the energy of the in-service steel frame and the energy of the actual input until the predetermined conditions are met, thus giving the current stiffness and mass of the in-service steel frame.

[0129] Among them, obtaining the approximate finite element model corresponding to the service steel frame includes:

[0130] Finite element analysis was performed on the in-service steel frame to obtain an approximate finite element model.

[0131] The process of obtaining an approximate finite element model by performing finite element analysis on a service steel frame is a routine analysis process. Specifically, this involves steps such as discretizing the service steel frame, defining the material model, setting connections, and defining boundary conditions to construct an approximate finite element model. The elastic modulus and density of the material model in the approximate finite element model are then defined using a key information matrix.

[0132] More specifically, structural discretization involves dividing the simulation model of the in-service steel frame into multiple structural units. This division can be based on the size and type of the structural units, and the units can be the smallest possible components. Defining the material model involves inputting different property parameters into the divided structural units. For example, for general linear metallic materials (such as structural steel), this includes elastic modulus, density, and Poisson's ratio; for materials requiring consideration of nonlinearity, a corresponding nonlinear model needs to be defined. Connection settings involve connecting multiple structural units. For example, in actual in-service steel frames, connections may be welded or bolted, and factors such as preload, friction, and changes in contact state should be considered. The connection relationships are defined based on the structural mechanical response. Defining boundary conditions involves defining constraints and loads on the simulation model. By performing structural discretization, defining the material model, setting connections, and defining boundary conditions for the in-service steel frame, the overall stiffness matrix of the in-service steel frame is determined.

[0133] Constructing a finite element energy model based on an approximate finite element model can include:

[0134] Obtain the parameters of velocity, stiffness, displacement, mass, and acceleration of the approximate finite element model;

[0135] Based on the velocity, stiffness, and displacement parameters of the approximate finite element model, a strain energy quantum model is given.

[0136] Based on the parameters of velocity, mass, and acceleration from the approximate finite element model, a kinetic energy sub-model is given;

[0137] A finite element energy model is derived based on the strain energy quantum model and the kinetic energy quantum model.

[0138] The finite element energy model satisfies the following relationship:

[0139]

[0140]

[0141]

[0142] In the formula, The energy of the steel structure in service, i.e., the finite element energy model. It is the strain energy of the structural unit, i.e., the strain energy sub-model; The kinetic energy of the structural unit is represented by the kinetic energy sub-model; Δt represents the time step between adjacent detections; m represents the number of degrees of freedom of the in-service steel frame; n represents the number of structural units of the in-service steel frame; V g It is an (m×n)×n velocity matrix; v is an m×1 velocity vector, i.e., v = [v1, v2, ..., v] i ,…,v m ] T T is the matrix transpose, v i It is the velocity of the i-th degree of freedom of the service steel frame; It is the stiffness matrix of the steel frame in service, which is an (m×n)×m matrix; U is the n×n stiffness matrix of the i-th structural element in the service steel frame; u is the m×1 displacement vector of all degrees of freedom of the service steel frame. i Let be the displacement of the i-th degree of freedom of the steel frame in service; It is the (m×n)×m mass matrix of the steel frame in service. is the m×m mass matrix of the i-th structural unit in the service steel frame; is the m×1 acceleration vector of the service steel frame, a i It is the acceleration of the i-th degree of freedom of the steel frame in service.

[0143] Based on the finite element energy model, the energy of the corresponding in-service steel frame is obtained according to the triaxial acceleration of all steel structure connection nodes, including:

[0144] Based on the triaxial acceleration detected at all steel structure connection nodes, the number of degrees of freedom of the in-service steel frame is given;

[0145] Based on the triaxial acceleration and number of degrees of freedom of all steel structure connection nodes, the acceleration vector, velocity vector and displacement vector of the service steel frame in each degree of freedom are obtained;

[0146] Based on the approximate finite element model, the acceleration vector, displacement vector and velocity vector of the in-service steel frame in each degree of freedom are analyzed and processed to obtain the strain energy and kinetic energy of the in-service steel frame.

[0147] The energy of the in-service steel frame is obtained from its strain energy and kinetic energy.

[0148] Based on the triaxial acceleration at the location where the excitation force is applied and the detected excitation force, the actual input energy is obtained, including:

[0149] The actual energy input to the service steel structure by the vibrator is obtained based on the detected excitation force and the triaxial acceleration at the corresponding node. The displacement is obtained by quadratic integration of the triaxial acceleration, and the actual energy is derived from the excitation force and displacement.

[0150] The constructed energy correction model includes a gain sub-model, a performance parameter sub-model, and an error covariance sub-model;

[0151] The construction of the energy correction model includes:

[0152] Determine the parameters of the prior key information matrix, prior error covariance, prior measurement covariance, and energy of the steel structure in service;

[0153] Based on the parameters of prior measurement covariance, prior error covariance, and energy of the service steel frame, a gain sub-model is obtained;

[0154] Based on the gain sub-model and the parameters of the energy and prior key information matrix of the service steel frame, the performance parameter sub-model is obtained;

[0155] Based on the gain sub-model and the parameters of the energy and prior error covariance of the service steel frame, the error covariance sub-model is obtained.

[0156] The gain sub-model satisfies the following relationship:

[0157]

[0158] In the formula, K k R is the gain of the k-th detection, i.e., the gain sub-model; R is the prior measurement covariance. It is the energy of the service steel frame output by an approximate finite element model. It is the prior error covariance corresponding to the k-th detection;

[0159] The performance parameter sub-model satisfies the following relationship:

[0160]

[0161] In the formula, It is the prior key information matrix of the k-th detection. It is the key information matrix corresponding to the k-th detection, i.e., the performance parameter sub-model; It is the actual energy of the steel structure input during the kth service cycle;

[0162] The error covariance sub-model satisfies the following relationship:

[0163]

[0164] In the formula, P k It is the error covariance corresponding to the k-th detection, i.e., the error covariance sub-model, and I is the identity matrix.

[0165] See Figure 4 As shown, the energy correction model is iteratively processed based on the energy of the in-service steel frame and the energy of the actual input until a predetermined condition is met, giving the current stiffness and mass of the in-service steel frame, including:

[0166] Initial values ​​are assigned to the prior error covariance and the prior measurement covariance;

[0167] The gain is obtained by analyzing and processing the energy, prior measurement covariance, and prior error covariance of the service steel frame corresponding to the first test through the gain sub-model.

[0168] Initial values ​​are assigned to the prior key information matrix of the steel structure in service;

[0169] The performance parameter sub-model is used to analyze and process the gain, the energy of the service steel frame corresponding to the first detection, the actual energy of the input service steel frame corresponding to the first detection, and the prior key information matrix to obtain the processed key information matrix.

[0170] The gain, the energy of the service steel frame corresponding to the first test, and the prior error covariance are analyzed and processed by the error covariance sub-model to obtain the processed error covariance.

[0171] Based on the time sequence of detection, the prior error covariance in the gain sub-model and the error covariance sub-model is updated using the processed error covariance obtained before each detection. The prior key information matrix in the performance parameter sub-model is updated using the processed key information matrix obtained before each detection. The covariance is calculated by averaging the multiple triaxial accelerations detected before each detection, and the calculated covariance is used to update the prior measurement covariance in the gain sub-model.

[0172] The energy of the remaining detected service steel frame and the input real energy are processed sequentially by the energy correction model after each update until the output of the performance parameter sub-model is the same key information matrix in two consecutive times.

[0173] The key information matrix of the last output of the performance parameter sub-model is processed to obtain the current stiffness and mass of the steel frame in service.

[0174] The system's state can be detected through an iterative detection process. After a very short time, the approximate parameters of stiffness and mass will stabilize and converge to the true values.

[0175] In the process of processing the first detection data using the energy correction model, the prior key information matrix, prior measurement covariance, and prior error covariance in the energy correction model are assigned initial values. In subsequent processing of the remaining detection data using the energy correction model, the prior key information matrix and prior error covariance are both based on the processed key information matrix and error covariance from the previous output. The prior measurement covariance is calculated using the covariance of the triaxial accelerations from the processed detection data. Specifically, the average value of all triaxial accelerations in each detection of the processed data is calculated, and the average value of each triaxial acceleration from the processed data is processed using existing covariance calculation methods. In other words, the covariance is calculated based on all triaxial accelerations before the current detection. That is, the prior key information matrix, prior measurement covariance, and prior error covariance for the subsequent processing are updated using the key information matrix, measurement covariance, and error covariance from the previous processing. After the update is completed, the detection data processing is implemented.

[0176] The key information matrix of this invention includes the elastic modulus and density information of all structural units. Specifically, the key information matrix satisfies the following relationship:

[0177] Y = {E; ρ}

[0178] E = [E1,...,E] i ,...,E n ] T

[0179] ρ=[ρ1,...,ρ i ,...,ρ n ] T

[0180] In the formula, Y is the key information matrix, E is the elastic modulus matrix, and E i Let ρ be the elastic modulus of the i-th structural element, and ρ be the density matrix. i It is the density of the i-th structural unit, and T is the matrix transpose;

[0181] Processing the key information matrix from the last output of the performance parameter sub-model yields the current stiffness and mass of the in-service steel frame, including:

[0182] Obtain the stiffness matrix corresponding to the structural unit, and obtain the actual size of the structural unit based on the in-service steel frame;

[0183] The moment of inertia of the structural unit is obtained based on its actual size and shape.

[0184] Based on the stiffness matrix, the stiffness of all structural elements is obtained through the actual dimensions, moment of inertia, and elastic modulus of the structural elements.

[0185] Based on the actual dimensions of the structural unit, give the volume of the structural unit;

[0186] The mass of all structural elements is obtained based on their volume and density.

[0187] The stiffness matrix corresponding to the structural unit can be obtained during finite element analysis; or it can be the stiffness matrix provided by the manufacturer when the steel frame in service leaves the factory. The stiffness matrix is ​​an inherent property of the steel frame in service, so it can also be obtained by fitting after actual testing.

[0188] The stiffness matrix of the structural element satisfies the following relationship:

[0189]

[0190] In the formula, K e Let be the stiffness matrix of the structural element, E be the elastic modulus of the structural element, G be the moment of inertia of the cross section of the structural element, A be the cross-sectional area of ​​the structural element, and l be the length of the structural element.

[0191] After obtaining the stiffness matrix of the structural elements, the stiffness matrix of the in-service steel frame can be represented by the stiffness matrix formed by superimposing the stiffness matrices of all structural elements. In practical applications, the stiffness of the in-service steel frame is represented by the specific stiffness matrix obtained.

[0192] The calculation and testing processes for all the above models can be determined in the host computer finite element software.

[0193] To evaluate the performance of a steel structure in service at a substation, the vibration generator in the vibration excitation device of this invention generates a bandwidth-limited white noise signal of 0–200 Hz. This signal is amplified by a power amplifier and input into an electromagnetic vibrator, which applies vibration excitation to the steel structure. Triaxial accelerometers of the data acquisition device are installed at all connection nodes of the steel structure, and the sampling frequency of the data acquisition instrument is generally not less than 256 Hz. An approximate finite element model of the steel structure is established in the data analysis device, where the stiffness and mass are unknown, but all other model parameters are known. Before starting the update, the initial elastic modulus in the prior key information matrix can be set to 1.0 × 10⁻⁶. 10 N / m 2 ,Right now The initial density in the prior key information matrix is ​​set to 1.0 × 10⁻⁶. 3kg / m 3 ,Right now The response was generated by placing the structural model under a 0-200Hz bandwidth-limited white noise input signal for 3 seconds. Extensive calculations revealed that the results always converged when the initial values ​​varied. For example, the modulus of elasticity changed from an initial value of 1.0 × 10⁻⁶. 10 N / m 2 Updated to the actual value of 7.0 × 10. 10 N / m 2 The density starts from an initial value of 1.0 × 10⁻⁶. 3 kg / m 3 Updated to the actual value of 2700 kg / m 3 The elastic modulus of component 20 of the service steel frame converged to 7.0 × 10⁻⁶ within 0.9 seconds (approximately 230 detection time steps). 10 N / m 2 The true value, such as Figure 5 As shown in a, the density of component 45 of the service steel frame converges to 2700 kg / m³ within 1.1 seconds (approximately 280 detection time steps). 3 The true value, such as Figure 5 As shown in b. In addition to individual components (structural units) of the service steel frame, the overall health status of the service steel frame can also be identified for the global structure. The energy correction principle can quickly estimate the status of the service steel frame.

[0194] This invention illustrates a scenario where three components of a serviced steel frame suffer severe damage. Specifically, three components of the serviced steel frame have experienced severe damage, macroscopically manifested as follows: in the upper diagonal component (component number 3), stiffness loss is 20% and mass loss is 10%; in the middle crossbar component (component number 64), stiffness loss is 50% and mass loss is 60%; and in the bottom diagonal component (component number 76), stiffness loss is 80% and density loss is 90%. The performance of this structure was evaluated using the substation serviced steel frame performance evaluation device provided by this invention. The updated elastic modulus value of the substation serviced steel frame is as follows: Figure 6 As shown in the figure, the elastic modulus of all undamaged components converges to E0 = 7.0 × 10⁻⁶ within approximately 1.1 seconds (280 detection time steps). 10 N / m 2 The true values ​​are as follows. For damaged component 45, the elastic modulus converges to the true value of 0.8E0; the elastic modulus of component 64 does indeed converge to the true value of 0.5E0; the elastic modulus of component 76 converges to the true value of 0.2E0, all of which are consistent with the actual stiffness loss.

[0195] The model updates for all component density values, similar to those for the elastic modulus, converged to the true values ​​within approximately 1.1 seconds (280 detection time steps). Figure 7 As shown in the figure, the density of all undamaged components converges from the initial value to ρ0 = 2700 kg / m³ within approximately 1.1 seconds (280 detection time steps). 3 The true values ​​of the densities of damaged components 45, 64, and 76 all converge to the true values.

[0196] In summary, the performance evaluation device for substation steel frame provided by this invention can identify the stiffness and mass loss of all components, thus enabling a global evaluation of the structure's performance.

[0197] This invention also provides a performance evaluation method for steel structures in service at substations, employing the aforementioned performance evaluation device for steel structures in service at substations. The performance evaluation method includes:

[0198] The data acquisition equipment was set at all steel structure connection nodes and vibration force application locations of the steel frame to be tested in service.

[0199] The vibration excitation force is generated by the vibration excitation equipment and applied to the steel frame under test.

[0200] The triaxial acceleration and excitation force measured multiple times were analyzed and processed using data analysis equipment to obtain the performance evaluation results of the steel frame in service.

[0201] This invention also provides a performance evaluation method for steel structures in service at substations, including...

[0202] S1. Using arbitrary stiffness and mass as the initial stiffness and mass of the steel frame in service at the substation, establish an approximate finite element model of the steel frame in service.

[0203] S2. The data acquisition equipment detects the triaxial acceleration and excitation force of all steel frame connection nodes;

[0204] S3. Based on the detected triaxial acceleration and the approximate finite element model of the steel frame in service at the substation, the strain energy and kinetic energy of the steel frame are given.

[0205] S4. Based on the pre-built energy correction model, and the strain energy, kinetic energy and excitation force of the steel frame, the gain corresponding to the detection time is given;

[0206] S5. Based on the gain, obtain the updated stiffness, mass, and error covariance;

[0207] S6. Iterate through S2 to S5 until the stiffness and mass of adjacent iterations do not change, and output the corresponding stiffness and mass values.

[0208] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A device for performance assessment of a substation service steel structure, characterized in that, The application relates to a vibration excitation device, a data acquisition device and a data analysis device. The vibration excitation device comprises a vibration generating assembly and a force sensor, the vibration generating assembly generates an exciting force acting on a service steel structure according to a predetermined signal, and the force sensor detects the exciting force acting on the service steel structure. The data acquisition device detects three-direction accelerations of all steel structure connecting nodes of the service steel structure and receives the exciting force detected by the force sensor. The data analysis device is in communication connection with the data acquisition device, and obtains a performance evaluation result of the service steel structure according to the detected three-direction accelerations and the exciting force, which comprises the following steps: Performing finite element analysis on the service steel structure to obtain an approximate finite element model; wherein the approximate finite element model is composed of multiple connected structure units; Obtaining parameters of velocity, stiffness, displacement, mass and acceleration of the approximate finite element model; giving a strain energy submodel based on the parameters of velocity, stiffness and displacement of the approximate finite element model; giving a kinetic energy submodel based on the parameters of velocity, mass and acceleration of the approximate finite element model; and obtaining a finite element energy model based on the strain energy submodel and the kinetic energy submodel; Based on the finite element energy model, the energy of the corresponding service steel structure is obtained according to the three-direction accelerations of all steel structure connecting nodes; The real input energy is obtained according to the three-direction accelerations of the position where the exciting force acts and the detected exciting force; An energy correction model is constructed, and the energy correction model is iteratively processed based on the energy of the service steel structure and the real input energy until a predetermined condition is met, so as to give the current stiffness and mass of the service steel structure; The energy correction model comprises a gain submodel, a performance parameter submodel and an error covariance submodel; the construction of the energy correction model comprises the following steps: determining a priori key information matrix, a priori error covariance, a priori measurement covariance and parameters of the energy of the service steel structure corresponding to the service steel structure; obtaining the gain submodel based on the priori measurement covariance, the priori error covariance and the parameters of the energy of the service steel structure; obtaining the performance parameter submodel based on the gain submodel and the parameters of the energy of the service steel structure and the priori key information matrix; and obtaining the error covariance submodel based on the gain submodel and the parameters of the energy of the service steel structure and the priori error covariance. The vibration generating assembly comprises a signal generator, a power amplifier, an electromagnetic exciter and a force transmission component which are connected in sequence; 2. The performance evaluation apparatus of claim 1, wherein The signal generator generates white noise; The power amplifier amplifies the white noise electrical signal generated by the signal generator and inputs the amplified white noise electrical signal into the electromagnetic exciter; The electromagnetic exciter generates an exciting force according to the input white noise electrical signal; The force transmission component is connected with the electromagnetic exciter and the force sensor at two ends, and the force sensor is fixed on a certain steel structure connecting node of the service steel structure. The data acquisition device comprises three-direction acceleration sensors and a data acquisition instrument which are in communication connection; 3. The performance evaluation apparatus of claim 1, wherein The three-direction acceleration sensors are arranged on all steel structure connecting nodes of the service steel structure and detect three-direction accelerations of all steel structure connecting nodes of the service steel structure; The data acquisition instrument receives data detected by the three-direction acceleration sensors and the force sensor and transmits the received data to the data analysis device. ​ 4. The performance evaluation apparatus of claim 1, wherein Based on the finite element energy model, the energy of the corresponding in-service steel frame is obtained according to the triaxial acceleration of all steel structure connection nodes, including: Based on the triaxial acceleration detected at all steel structure connection nodes, the number of degrees of freedom of the in-service steel frame is given; Based on the triaxial acceleration and number of degrees of freedom of all steel structure connection nodes, the acceleration vector, velocity vector and displacement vector of the service steel frame in each degree of freedom are obtained; Based on the approximate finite element model, the acceleration vector, displacement vector and velocity vector of the in-service steel frame in each degree of freedom are analyzed and processed to obtain the strain energy and kinetic energy of the in-service steel frame. The energy of the in-service steel frame is obtained from its strain energy and kinetic energy.

5. The performance evaluation apparatus of claim 4, wherein The energy correction model is iteratively processed based on the energy of the in-service steel frame and the energy input from the actual source until predetermined conditions are met, thus providing the current stiffness and mass of the in-service steel frame, including: Initial values ​​are assigned to the prior error covariance and the prior measurement covariance; The gain is obtained by analyzing and processing the energy, prior measurement covariance, and prior error covariance of the service steel frame corresponding to the first test through the gain sub-model. Initial values ​​are assigned to the prior key information matrix of the steel structure in service; The performance parameter sub-model is used to analyze and process the gain, the energy of the service steel frame corresponding to the first detection, the actual energy of the input service steel frame corresponding to the first detection, and the prior key information matrix to obtain the processed key information matrix. The gain, the energy of the service steel frame corresponding to the first test, and the prior error covariance are analyzed and processed by the error covariance sub-model to obtain the processed error covariance. Based on the time sequence of detection, the prior error covariance in the gain sub-model and the error covariance sub-model is updated using the processed error covariance obtained before each detection. The prior key information matrix in the performance parameter sub-model is updated using the processed key information matrix obtained before each detection. The covariance is calculated by averaging the multiple triaxial accelerations detected before each detection, and the calculated covariance is used to update the prior measurement covariance in the gain sub-model. The energy of the remaining detected service steel frame and the input real energy are processed sequentially by the energy correction model after each update until the output of the performance parameter sub-model is the same key information matrix in two consecutive times. The key information matrix of the last output of the performance parameter sub-model is processed to obtain the current stiffness and mass of the steel frame in service.

6. The performance evaluation device of claim 5, wherein, The key information matrix includes the elastic modulus and density information of all structural elements; Processing the key information matrix from the last output of the performance parameter sub-model yields the current stiffness and mass of the in-service steel frame, including: Obtain the stiffness matrix corresponding to the structural unit, and obtain the actual size of the structural unit based on the in-service steel frame; The moment of inertia of the structural unit is obtained based on its actual size and shape. Based on the stiffness matrix, the stiffness of all structural elements is obtained through the actual dimensions, moment of inertia, and elastic modulus of the structural elements. Based on the actual dimensions of the structural unit, give the volume of the structural unit; The mass of all structural elements is obtained based on their volume and density.

7. A method of performance assessment of a substation service steel structure, characterized in that, The performance evaluation device for substation service steel structures as described in any one of claims 1 to 6 is used, and the performance evaluation method includes: The data acquisition equipment was set at all steel structure connection nodes and vibration force application locations of the steel frame to be tested in service. The vibration excitation force is generated by the vibration excitation equipment and applied to the steel frame under test. The triaxial acceleration and excitation force measured multiple times were analyzed and processed using data analysis equipment to obtain the performance evaluation results of the steel frame in service.

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