Online Monitoring Method for Fatigue Degree of Closing Spring in High-Voltage Circuit Breaker Based on Energy Storage Action Model

Abstract

This invention discloses an online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model, belonging to the field of high-voltage circuit breaker monitoring technology. The specific steps are as follows: Step S1: Construct an energy storage action model; Step S2: Collect the voltage and current values ​​of the energy storage motor and the angular displacement signal of the crank of the operating mechanism; Step S3: Determine the fitness function; Step S4: Solve the energy storage action model using the Zunhaishao swarm optimization algorithm and the fitness function to obtain the closing spring stiffness K; Step S5: Calculate the current fatigue degree of the closing spring based on the closing spring stiffness K. Using the above-mentioned online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model, an energy storage action model including parameters such as crank angular displacement, closing spring stiffness, energy storage motor voltage, and energy storage motor current is constructed. The Zunhaishao swarm optimization algorithm is used to solve the model, achieving accurate online measurement of the closing spring stiffness.

Description

Technical Field

[0001] This invention relates to the field of high-voltage circuit breaker monitoring technology, and in particular to an online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model. Background Technology

[0002] As crucial protection and control electrical appliances in power transmission and transformation systems, the operating status of high-voltage circuit breakers directly impacts the safe and stable operation of the power grid. Spring-operated mechanisms are widely used in power grids due to their low pollution, low cost, and ease of use. However, with the increasing number of opening and closing operations, the closing spring is frequently subjected to stretching and compression, and remains in a state of stretching and compression for extended periods. This leads to stress relaxation due to material fatigue, resulting in reduced stiffness and a continuous decrease in closing speed, or even complete failure to operate, directly affecting the safe operation of the power grid. Therefore, real-time monitoring of the fatigue level of high-voltage circuit breaker closing springs is of great significance for ensuring the safe operation of the power grid.

[0003] For fatigue analysis and online monitoring of high-voltage circuit breaker springs, existing methods mainly focus on analyzing the vibration and sound signal characteristics of the circuit breaker. Since vibration and sound signals are indirect signals generated by changes in the circuit breaker spring operating mechanism, resulting in internal impacts that are transmitted through a complex process to the circuit breaker casing, accurate diagnosis of spring fatigue faults based on vibration and sound signal characteristics is difficult. Furthermore, existing literature primarily uses pattern recognition to qualitatively diagnose spring fatigue based on vibration and sound feature vectors, failing to quantitatively detect the degree of spring fatigue. Existing technologies also include methods for evaluating high-voltage circuit breaker springs based on energy storage current analysis. For example, patent application number 201911053115.6 discloses a method for assessing the health status of high-voltage circuit breaker energy storage devices based on energy storage current analysis. This method involves attaching an open-type current sensor to the power line of the energy storage motor to collect the current throughout the motor's operation. The current value collected by the open-type current sensor is transmitted to a mechanical characteristic tester, which records the motor current waveform. The online monitoring method involves directly mounting the measuring device on the circuit breaker's energy storage device without removing the spring. The device is installed during circuit breaker operation without affecting its normal operation. Real-time monitoring of the circuit breaker's energy storage motor current ensures the integrity and continuity of the data, enabling analysis and judgment of trends in energy storage device defects. However, analysis using a single parameter results in low monitoring accuracy and fails to reflect changes in the closing spring stiffness. Summary of the Invention

[0004] The purpose of this invention is to provide an online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model, thereby solving the above-mentioned technical problems.

[0005] To achieve the above objectives, this invention provides an online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model. The specific steps are as follows:

[0006] Step S1: Obtain the fixed parameters of the high-voltage circuit breaker and construct the energy storage operation model;

[0007] Step S2: Deploy the data acquisition components, which include a voltage sensor, a current sensor, and an angle displacement sensor. The voltage sensor and the current sensor acquire the voltage and current signals of the energy storage motor, respectively, and the angle displacement sensor is used to acquire the angle displacement signal of the crank of the operating mechanism in the high-voltage circuit breaker.

[0008] Step S3: Determine the fitness function based on the energy storage action model in Step S1 and the signals collected in Step S2;

[0009] Step S4: Solve the energy storage action model using the tunic group algorithm and the fitness function obtained in step S3 to obtain the closing spring stiffness K;

[0010] Step S5: Calculate the current fatigue level of the closing spring based on the closing spring stiffness K using the fatigue level quantification formula based on the closing spring stiffness value.

[0011] Preferably, in step S1, the energy storage action model includes a voltage module, an energy storage motor module, a crank angular displacement module, and a load module; the voltage module is connected to the input terminal of the energy storage motor module.

[0012] Preferably, the energy storage motor module includes a winding section transfer function submodule, a mechanical section transfer function submodule, a first gain submodule, and a second gain submodule. The winding section transfer function submodule describes the winding section of the energy storage motor, and the mechanical section transfer function submodule describes the mechanical section of the energy storage motor. The winding section transfer function submodule and the mechanical section transfer function submodule are connected in parallel. The first gain submodule and the second gain submodule describe the electromagnetic torque coefficient and back electromotive force coefficient of the energy storage motor. The first gain submodule, the mechanical section transfer function submodule, and the second gain submodule are connected in series. The first gain submodule and the second gain submodule are respectively connected to the output terminal and the input terminal of the winding section transfer function submodule.

[0013] Preferably, the crank angular displacement module includes a delay submodule, a third gain submodule, and an integral submodule connected in series. The input terminal of the delay submodule is located between the second gain submodule and the mechanical part transfer function submodule.

[0014] Preferably, the load module includes a custom function submodule and a fourth gain submodule connected in series. The output of the fourth gain submodule is located between the first gain submodule and the mechanical part transmission subfunction submodule. The input of the custom function submodule is connected to the output of the integral submodule.

[0015] Preferably, in step S3,

[0016] Step S31: Determine the objective function;

[0017] The integral squared error between the online measured current signal and the current signal output by the energy storage action model is used to describe the deviation between the energy storage action model and the actual current. The objective function to be solved is as follows:

[0018]

[0019] Among them, o ISE Let be the integral squared error, and e(t) be the difference between the current value of the energy storage action model and the current value measured online. Let i(t) be the current value measured online at time t, and i(t) be the current value of the energy storage action model at time t.

[0020]

[0021] Among them, L and L -1 G1(s) and G2(s) are the Laplace transform and inverse Laplace transform, respectively, and the transfer functions of the energy storage motor winding section and mechanical section are the transfer functions, respectively.

[0022]

[0023] B and J are the equivalent damping coefficient and equivalent moment of inertia referred to the motor shaft, respectively, and L T For the equivalent winding inductance, R T k is the equivalent winding resistance. e k is the back electromotive force coefficient. t ρ is the electromagnetic torque coefficient, g is the reduction ratio of the gearbox, and s is the closing spring vector. This is a function of the load torque value. and These are the crank angular displacement signal and voltage signal measured online, respectively; K is the closing spring stiffness; and F0 is the spring preload.

[0024] Step S32: Based on the objective function, derive the individual fitness function f(X) in the tunic swarm algorithm. The individual fitness function f(X) is as follows:

[0025]

[0026] Where X is the position vector of an individual in the tunicate group.

[0027] Preferably, in step S5, the fatigue degree quantification formula based on the closing spring stiffness value is as follows:

[0028]

[0029] Where D is the fatigue damage degree, K0 is the initial stiffness value of the closing spring when there is no fatigue damage, and K is the stiffness of the closing spring.

[0030] Therefore, the present invention employs the above-mentioned online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model, which has the following beneficial effects:

[0031] (1) Taking into account all the influencing factors of the input and output of the energy storage motor, an energy storage action model of the energy storage motor was constructed to accurately depict the steady-state behavior of the energy storage motor when the magnetic circuit is saturated.

[0032] (2) The effect of using the tunic group algorithm to solve the parameters of the energy storage action model is that the optimization accuracy is higher, and the closing spring stiffness value of the high voltage circuit breaker can be effectively monitored online. It can reflect the changing trend of the closing spring stiffness very significantly. By combining the mapping relationship between the closing spring stiffness and the fatigue degree of the high voltage circuit breaker with the online monitoring method of closing spring stiffness, the online effective monitoring of the fatigue degree of the closing spring can be realized.

[0033] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0034] Figure 1 This is a flowchart of an online monitoring method for the fatigue degree of closing springs of high-voltage circuit breakers based on an energy storage action model, according to the present invention.

[0035] Figure 2 This is a schematic diagram of the spring operating mechanism according to an embodiment of the present invention;

[0036] Figure 3 This is a diagram illustrating the energy storage process of the spring operating mechanism according to an embodiment of the present invention;

[0037] Figure 4 This is a simplified load model diagram of the energy storage motor's energy storage operation process according to the present invention;

[0038] Figure 5 This is a load change curve of the energy storage motor during its energy storage operation.

[0039] Figure 6 This is the equivalent circuit diagram of the energy storage motor;

[0040] Figure 7 This is a block diagram of a single-input single-output system.

[0041] Figure 8This is a current curve diagram of the energy storage motor during its energy storage operation.

[0042] Figure 9 This is a diagram illustrating the energy storage operation model of the present invention;

[0043] Figure 10 The current curve is derived from the energy storage operation model of the energy storage motor of this invention.

[0044] Figure 11 This is a graph showing the voltage, current, and crank angular displacement signals of the energy storage motor in Experiment 1 of this invention.

[0045] Figure 12 This is a graph showing the voltage, current, and crank angular displacement signals of the energy storage motor in Experiment 2 of this invention.

[0046] Figure 13 This is a graph showing the voltage, current, and crank angular displacement signals of the energy storage motor in Experiment 3 of this invention.

[0047] Figure 14 A comparison graph of the current signal generated by the algorithm detection results and the current signal measured experimentally.

[0048] Figure 15 The curve showing the change in stiffness of the closing spring;

[0049] Figure 16 This is a curve showing the change in fatigue level of the closing spring.

[0050] Figure Labels

[0051] 1. Closing spring; 2. Crank; 3. Gearbox; 4. Cam; 5. Energy storage shaft; 6. Pull rod; 7. Connecting rod; 8. Switch spindle; 9. Energy storage motor. Detailed Implementation

[0052] Example

[0053] In the description of this invention, it should be noted that the terms "upper," "lower," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. In the description of this invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," and "connect" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0054] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0055] like Figure 1 As shown, an online monitoring method for the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model is described, with the following specific steps:

[0056] Step S1: Obtain the fixed parameters of the high-voltage circuit breaker and construct the energy storage operation model.

[0057] In this embodiment, the spring operating mechanism is as follows: Figure 2 As shown, one end of the closing spring 1 is connected to a fixed pin, and the other end is connected to the crank 2. The energy storage shaft 5 passes through the reduction gearbox 3 and is connected to the worm gear in the reduction gearbox 3 through a bushing. The crank 2 is installed at the left end of the energy storage shaft 5, and the right end of the energy storage shaft 5 is equipped with a cam 4.

[0058] The energy storage process is as follows:

[0059] like Figure 3As shown, before the closing signal is issued, crank 2 is in the highest stretched position, and closing spring 1 is in a stretched state. After the closing signal is issued, the iron core of the closing coil strikes the tripping device to unlock the closing latch. Subsequently, closing spring 1 is released, crank 2 is pulled back to the lowest point, driving the energy storage shaft 5 to rotate. Cam 4 drives the switch main shaft 8 to rotate through a set of connecting rods 7, ultimately driving the pull rod 6 to move upward and contact the stationary contact. The opening latch is limited to the switch main shaft 8 after rotating a certain angle, completing the closing action. After closing, the mechanism performs an energy storage action. The energy storage motor 9 is energized and rotates, driving the closing spring 1 to stretch through the reduction gearbox 3 and crank 2. When the closing spring 1 is stretched to the highest point, the closing latch limits crank 2, the power to the energy storage motor 9 is cut off, crank 2 returns to the stretched position, and the energy storage action is completed. During the energy storage action, the closing spring 1 is stretched as crank 2 rotates, applying a changing torque to the energy storage shaft 5, which is ultimately loaded onto the energy storage motor 9 through the worm gear. If the influence of resistance is ignored, the load condition of the energy storage motor 9 can be described as follows: taking the center of the energy storage shaft 5 as the origin, the line connecting the center of the energy storage shaft 5 and the fixed end of the closing spring 1 as the baseline, the direction pointing towards the fixed end of the closing spring 1 as the positive direction, and the angular displacement direction of the crank 2 as the positive angle direction. For example... Figure 4 As shown, the length of crank 2 is denoted as r0 and remains constant throughout the process. The initial angle between crank 2 and the baseline is denoted as θ0. The length from the axis of crank 2 to the fixed end of the closing spring 1 is denoted as b. The original length of the closing spring 1 is denoted as l0. The angle through which crank 2 rotates from its initial position at any given time is denoted as θ. The vector of crank 2 at any given time is denoted as c, with its direction pointing from the origin to the end of crank 2 and its length being r0. The vector of the closing spring 1 at any given time is denoted as s, with its direction pointing from the moving end of the closing spring 1 to the fixed end and its length changing with the motion.

[0060] The simplified model yields an expression for the initial length.

[0061]

[0062] The crank vector c is determined solely by the crank length and the rotation angle.

[0063] c=(r0cos(θ+θ0),r0sin(θ+θ0)) (2)

[0064] The crank position and the fixed end position of the spring define the closing spring vector s.

[0065] s=(b-r0cos(θ+θ0),-r0sin(θ+θ0)) (3)

[0066] Ignoring complex nonlinear cases, the magnitude of the restoring force generated by the closing spring is determined by the spring elongation Δl and the spring stiffness K.

[0067] F=KΔl=K(|s|-l0) (4)

[0068] Considering that the initial length of the closing spring before it is stretched is not necessarily its original length, meaning that there is a spring preload before the energy storage motor stretches the spring to do work, we introduce F0 to describe the spring preload. Therefore, the magnitude of the restoring force is:

[0069]

[0070] The restoring force generated by the closing spring is represented by vector F, with magnitude F and direction in the same direction as s.

[0071]

[0072] The load torque M is the outer product of the crank vector c and the restoring force vector F of the closing spring. Its magnitude is:

[0073]

[0074] Since the crank length r0 and the length b from the crankshaft center to the fixed end of the closing spring can be directly measured and remain constant for the same assembled high-voltage circuit breaker, the initial crank angle θ0 and angular displacement (measured by the same angular displacement sensor in actual testing, and can be uniformly described by the function θ(t)) are given. Therefore, the load torque M can be simply represented as a function of the angular displacement function θ(t), the closing spring stiffness K, and the spring preload F0.

[0075] |M|=m[θ(t),K,F0] (8)

[0076] By calculating the gearbox transmission ratio, the load on the energy storage motor during energy storage can be accurately described. Since the closing spring stiffness K and the closing spring preload F0 are unknown, such as... Figure 5 As shown, this is a curve illustrating the load change during the energy storage operation of the energy storage motor.

[0077] Analysis of the closing spring's motion mechanism revealed the functional relationship between the crank angular displacement and the load torque applied to the energy storage motor during the energy storage operation. Based on this, an accurate mathematical model describing the energy storage action of the spring-operated mechanism was established by analyzing the behavior of the energy storage motor. Figure 6 As shown, voltage V s The resistance applied to the winding terminals is that of the field winding and the armature winding connected in series. Typically, the resistance of these two parts (field resistance R) is used. e armature resistance R a ) and inductance (magnetizing inductance L) e armature inductance L a Describe its physical behavior. During motor operation, an induced electromotive force E is generated in the armature. a When the magnetic flux is stable, its relationship with the rotational speed ω m The voltages are directly proportional, therefore the voltage balance equation for the entire circuit is:

[0078]

[0079] Among them, E a =k e ω m ,(k e =K e Φ) (10)

[0080] R T =R e +R a (11)

[0081] L T =L e +L a (12)

[0082] When energized, the armature winding generates an electromagnetic torque T under the influence of the magnetic field. em When the motor speed is constant, the load torque T L No-load torque T0 and damping torque T f Same electromagnetic torque T em Therefore, the torque balance equation is:

[0083] T em =T L +T f +T0 (13)

[0084] in

[0085] T em =k t i,(k t =K t Φ) (14)

[0086]

[0087] T f =Bω m (16)

[0088]

[0089] g is the reduction ratio of the gearbox, and B and J are the equivalent damping coefficient and equivalent moment of inertia referred to the motor shaft, respectively.

[0090] According to equations (9)-(18), the energy storage motor can be regarded as a single-input single-output system, such as... Figure 7 As shown, voltage is the system input, and current is the system output. The transfer function is as follows:

[0091]

[0092] Where G1(s) and G2(s) are the transfer functions of the winding part and the mechanical part of the energy storage motor, respectively, as shown in equations (20) and (21). L{m[θ(t),K,F0]} is the Laplace transform of m[θ(t),K,F0], whose input is the crank angular displacement (), and whose output is multiplied by the reduction ratio g to obtain the load torque T with the closing spring stiffness K and the spring preload F0. L .

[0093]

[0094] like Figure 8 As shown, the operation of the energy storage motor can be divided into three stages:

[0095] 1) At time T1, the energy storage motor is energized, and the energy storage motor enters the power-on stage. The spike in the figure is the starting current, which is often relatively large.

[0096] 2) At time T2, due to the structure of the gearbox, although the motor rotates, the crank is stationary. At this time, the motor is in the no-load stage and the current tends to stabilize.

[0097] 3) At time T3, the motor begins to drive the crank extension spring to do work, and the energy storage motor enters the work-running stage. During this stage, the current changes in the same trend as the load.

[0098] Based on the above analysis, a model of the energy storage motor's energy storage operation was built in MATLAB / Simulink, as follows: Figure 9 As shown, the energy storage action model includes a voltage module, an energy storage motor module, a crank angular displacement module, and a load module; the voltage module is connected to the input terminal of the energy storage motor module, and the voltage is simulated using a step signal module.

[0099] The energy storage motor module includes a winding section transfer function submodule, a mechanical section transfer function submodule, a first gain submodule, and a second gain submodule. The winding section transfer function submodule describes the winding portion of the energy storage motor, and the mechanical section transfer function submodule describes the mechanical portion; these submodules are connected in parallel. The first and second gain submodules describe the electromagnetic torque coefficient and back electromotive force coefficient of the energy storage motor, and are connected in series. The first and second gain submodules are connected to the output and input terminals of the winding section transfer function submodule, respectively. The input to the winding portion is a voltage V. s With induced electromotive force E a The difference is output as winding current i. Electromagnetic torque T em With load torque T L The difference is input to the mechanical part, and its output is the motor speed ω.m .

[0100] The crank angular displacement module comprises a delay submodule, a third gain submodule, and an integral submodule connected in series. The input of the delay submodule is located between the second gain submodule and the mechanical transfer function submodule. The delay module is used for simulation in the no-load phase model. The third gain submodule represents the reduction ratio g of the gearbox. The motor speed is reduced by the gearbox to obtain the crank speed, and then the angular displacement of the crank is obtained through the integral submodule.

[0101] The load module comprises a user-defined function submodule and a fourth gain submodule connected in series. The output of the fourth gain submodule is located between the first gain submodule and the mechanical transfer function submodule. The input of the user-defined function submodule is connected to the output of the integral submodule. The fourth gain submodule represents the reduction ratio of the gearbox. The user-defined function submodule is connected to the load function; the angular displacement of the crankshaft is calculated by the load function and then converted by the gearbox to obtain the load applied to the motor. Figure 10 As shown, its trend matches well with the actual measured current curve. This indicates that the established energy storage action model can well describe the behavior of the energy storage motor during energy storage. By replacing the voltage part in the model with the online measured voltage signal of the energy storage motor, and simultaneously replacing the crank angular displacement part in the model with the online measured crank angular displacement signal, we can obtain the operating parameters of the operating mechanism (closing spring stiffness K, closing spring preload F0) and system parameters (equivalent winding resistance R) in the output of the energy storage action model. T Equivalent winding inductance L T Motor torque coefficient k t ; back electromotive force coefficient k e The current signal (equivalent damping coefficient B; equivalent moment of inertia J) is used to determine the energy storage action model. When the current signal generated by the model matches the online measured current signal, the current energy storage action model parameters are considered to accurately reflect the state of the operating mechanism. At this point, the model for the energy storage motor's energy storage action is complete, and the problem is transformed into finding the optimal solution for the model parameters to achieve the optimal match between the model current signal and the actual current signal.

[0102] Step S2: Deploy the data acquisition components, which include a voltage sensor, a current sensor, and an angle displacement sensor. The voltage sensor and the current sensor acquire the voltage and current signals of the energy storage motor, respectively, and the angle displacement sensor is used to acquire the angle displacement signal of the crank of the operating mechanism in the high-voltage circuit breaker.

[0103] Step S3: Determine the fitness function based on the energy storage action model in Step S1 and the signals collected in Step S2. To avoid large deviations, the integral of squared error (ISE) between the online measured current signal and the current signal output by the model is used to describe the deviation between the energy storage action model and the actual situation.

[0104] Step S31: Determine the objective function;

[0105] The integral squared error between the online measured current signal and the current signal output by the energy storage action model is used to describe the deviation between the energy storage action model and the actual current. The objective function to be solved is as follows:

[0106]

[0107] Among them, o ISE Let be the integral squared error, and e(t) be the difference between the current value of the energy storage action model and the current value measured online.

[0108]

[0109] Let i(t) be the current value measured online at time t, and i(t) be the current value of the energy storage action model at time t.

[0110] From equation (19), we can see that:

[0111]

[0112] Among them, L and L -1 Let G1(s) and G2(s) be the Laplace transform and inverse Laplace transform, respectively; G1(s) and G2(s) be the transfer functions of the winding and mechanical parts of the energy storage motor, respectively; B and J be the equivalent damping coefficient and equivalent moment of inertia referred to the motor shaft, respectively; and L be the equivalent moment of inertia referred to the motor shaft, respectively. T For the equivalent winding inductance, R T k is the equivalent winding resistance. e k is the back electromotive force coefficient. t ρ is the electromagnetic torque coefficient, g is the reduction ratio of the gearbox, and s is the closing spring vector. This is a function of the load torque value. and These are the crank angular displacement signal and voltage signal measured online, respectively. K is the closing spring stiffness, and F0 is the spring preload.

[0113] Step S32: Based on the objective function, derive the individual fitness function in the *Sulphurella salina* swarm algorithm. The objective function contains eight undetermined parameters: 1) Equivalent winding resistance R T ;2) Equivalent winding inductance L T 3) Motor torque coefficient kt ;4) Back electromotive force coefficient k e ;5) Equivalent damping coefficient B converted to the motor shaft;6) Equivalent moment of inertia J converted to the motor shaft;7) Closing spring stiffness K;8) Spring preload F0. Among them, the closing spring stiffness K and the closing spring preload F0 are the operating parameters of the operating mechanism of interest in this embodiment, and the rest are the system parameters of the operating mechanism. The objective function is very complex, and it is difficult to solve it analytically. Therefore, this embodiment uses the tunic swarm algorithm to solve the optimal solution of the objective function. According to the objective function (22), the individual fitness function f(X) in the tunic swarm algorithm is obtained as shown in equation (25), where X is the position vector of the individual in the tunic swarm.

[0114] The individual fitness function f(X) is as follows:

[0115]

[0116] Where X is the position vector of an individual in the tunicate group.

[0117] Step S4: Solve the energy storage action model using the tunic group algorithm and the fitness function obtained in step S3 to obtain the closing spring stiffness K.

[0118] The algorithm flow for the tunic group is as follows:

[0119] 1) Initialize the population to obtain:

[0120]

[0121] Where N is the population size, and D is the number of variables to be solved in the objective function. ub represents the position of the i-th individual in the j-th dimension. j and lb j These are the upper and lower bounds in the j-th dimension, respectively.

[0122] 2) Calculate the fitness value of each individual in the population in the fitness function.

[0123] 3) Select the current food source location and sort the individuals according to their fitness values. Since the location of the food source is considered to be the optimal solution of the objective function, the location of the individual with the highest fitness is considered to be the current food source location and is placed at the top of the group.

[0124] 4) Selecting Leaders and Followers. After the first individual is identified, the remaining N-1 individuals in the group are classified. The individuals in the first half are considered leaders, and the rest are followers.

[0125] 5) Updating individual positions. Leaders and followers follow different behavioral patterns. Leaders' positions are primarily influenced by the location of food sources, while followers' positions are only updated based on the individuals ahead of them.

[0126] Leader's position updated:

[0127]

[0128] Wherein, the position of the i-th leader in the j-th dimension during the t-th iteration is denoted as The current position of the food source in the j-th dimension is denoted as F. j c2 and c3 are both random numbers uniformly distributed in the interval [0,1]. A convergence factor c1 is introduced to balance global exploration and local exploitation, and its expression is:

[0129]

[0130] Where L is the maximum number of iterations.

[0131] Follower location update:

[0132]

[0133] The position of the i-th follower in the j-th dimension during the t-th iteration is denoted as... Its position depends on its position in the (t-1)th iteration along the same dimension as the previous individual.

[0134] 6) Determine if the result meets the convergence condition. If it does, output the current food position; otherwise, repeat steps 2) to 5) until the convergence condition is met or the maximum number of iterations is reached. The output food position is the optimal solution for the model parameters.

[0135] It should be noted that for the same high-voltage circuit breaker, all eight parameters need to be solved simultaneously only during the first monitoring. In subsequent monitoring, only the closing spring stiffness K and the closing spring preload F0 need to be solved, while other parameters remain constant. This not only better reflects the actual situation and can more accurately detect changes in the closing spring stiffness, but also improves efficiency and saves computing power.

[0136] Step S5: Calculate the current fatigue level of the closing spring based on the closing spring stiffness K using the fatigue level quantification formula based on the closing spring stiffness value.

[0137] The stiffness K of the closing spring is determined by the spring mean diameter Z, the spring wire diameter d, the effective number of spring coils n, and the shear modulus G of the spring material, as shown in equation (30):

[0138]

[0139] By introducing fatigue damage degree D to correct the material shear modulus G, the calculation formula for spring stiffness under different fatigue degrees is obtained, as shown in equation (31):

[0140]

[0141] K0 is defined as the initial stiffness value of the closing spring when there is no fatigue damage. From equation (31), the fatigue degree quantification formula based on the stiffness value of the closing spring can be derived, as shown in equation (32):

[0142]

[0143] By using this fatigue quantification formula in conjunction with the aforementioned online measurement method for closing spring stiffness based on an energy storage action model, online monitoring of the fatigue level of the closing spring in a high-voltage circuit breaker can be achieved.

[0144] To verify the reliability of the method in this embodiment, a test experiment was conducted. An LW42A–40.5 outdoor high-voltage vacuum circuit breaker was used as the test object, with a gearbox reduction ratio of 1500:1. The experiment required measuring the crank angular displacement signal, and the current and voltage signals of the energy storage motor. Due to the limited space at the crank position, insufficient space to accommodate the sensor, the sensor was placed on one side of the cam. Both are driven by the same energy storage shaft, thus they are equivalent in angular displacement. The selected angular displacement sensor has a range of 0–360° and a refresh rate of 0.6 ms. The selected current sensor has a range of 0–30A and a measurement frequency range of 20–20 kHz. The selected voltage sensor has a range of 0–600V and a measurement frequency range of 0–6 kHz. The sampling frequency was set to 100 Hz, and the signal acquisition time was 10 s. In this embodiment, springs of different stiffness were used to simulate different degrees of fatigue in the closing spring, as shown in Table 1.

[0145] Table 1. Fatigue simulation of closing springs at different levels.

[0146]

[0147] The initial stiffness value K0 of the closing spring without fatigue damage is defined as the value with the largest stiffness. Its fatigue degree D = 0. The fatigue degree in other cases is calculated based on this value. The voltage, current, and crank angular displacement signals of the energy storage motor under different closing spring stiffnesses were measured. Partially normalized sample data are shown below. Figure 11-14 As shown, with the increase in fatigue and the decrease in the stiffness of the closing spring, the maximum current during the power operation of the energy storage motor will decrease accordingly.

[0148] The signal from the closing spring when it has no fatigue damage was used not only to determine the initial stiffness value K0 but also for the system parameters of the energy storage action model, playing a crucial role in the subsequent detection of the closing spring's fatigue level. Therefore, a relatively large population size (150) and a maximum number of iterations (5000) were each run independently 20 times. The quality of the detection results was judged based on the objective function value under the current parameters, with the group with the smallest value considered the optimal detection result. To verify the superiority of the SalpSwarm Algorithm (SSA), the Ant Lion Optimiser (ALO) and Grey Wolf Optimiser (GWO) algorithms were run 20 times each with the same population size and maximum number of iterations for comparison. The detection results are shown in Table 2.

[0149] Table 2 Detection Results

[0150]

[0151] Table 2 shows that the optimal SSA detection result has a smaller fitness function value, and the closing spring stiffness is closer to the theoretical value. Meanwhile, the differences between the optimal and worst results of SSA detection are small except for the spring preload F0, with better detection results tending towards a spring preload F0 of 0. The results using ALO detection also show small differences and stability. The results using the GWO algorithm show larger differences. In summary, compared with ALO and GWO algorithms, SSA demonstrates better solution stability and higher accuracy in the fitness function search optimization proposed in this paper.

[0152] Using the optimal detection results of SSA in Table 2 as the system parameters of the energy storage action model, the algorithm was used. Only the closing spring stiffness K and the closing spring preload F0 were used as search variables. The population size and the maximum number of iterations were 30 and 1000, respectively. Under this setting, two sets of data with different spring states were detected, and the detection results are shown in Table 3.

[0153] Table 3. Detection results of system parameters based on the SSA solution model.

[0154]

[0155]

[0156] like Figure 15-16 As shown, under the same model system parameters, the method proposed in this paper can effectively detect the stiffness change and fatigue degree of the closing spring and significantly demonstrate its trend. This embodiment is an online monitoring method for the fatigue of the closing spring of a high-voltage circuit breaker, and can be extended to the fatigue detection of springs in high-voltage circuit breakers, GIS and other switches equipped with spring operating mechanisms.

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for online monitoring of the fatigue degree of the closing spring of a high-voltage circuit breaker based on an energy storage action model, characterized in that, The specific steps are as follows: Step S1: Obtain the fixed parameters of the high-voltage circuit breaker and construct the energy storage operation model; Step S2: Deploy the data acquisition components, which include a voltage sensor, a current sensor, and an angle displacement sensor. The voltage sensor and the current sensor acquire the voltage and current signals of the energy storage motor, respectively, and the angle displacement sensor is used to acquire the angle displacement signal of the crank of the operating mechanism in the high-voltage circuit breaker. Step S3: Determine the fitness function based on the energy storage action model in Step S1 and the signals collected in Step S2; In step S3, Step S31: Determine the objective function; The integral squared error between the online measured current signal and the current signal output by the energy storage action model is used to describe the deviation between the energy storage action model and the actual current. The objective function to be solved is as follows: in, The integral squared error, This is the difference between the current value of the energy storage operation model and the current value measured online. , Let t be the current value measured online. The value of the energy storage action model at time t; in, and These are the Laplace transform and the inverse Laplace transform, respectively. and These are the transfer functions for the winding section and the mechanical section of the energy storage motor, respectively. B and J are the equivalent damping coefficient and equivalent moment of inertia referred to the motor shaft, respectively. For the equivalent winding inductance, This is the equivalent winding resistance. The back electromotive force coefficient, The electromagnetic torque coefficient, The reduction ratio of the gearbox. The closing spring vector, This is a function of the load torque value. and These are the crank angular displacement signal and voltage signal measured online, respectively. For the closing spring stiffness, This refers to the spring preload. Step S32: Derive the individual fitness function in the tunic swarm algorithm based on the objective function. as follows: in, Let be the position vector of an individual within the tunicate group; Step S4: Solve the energy storage action model using the tunic group algorithm and the fitness function obtained in step S3 to obtain the closing spring stiffness K; Step S5: Calculate the current fatigue level of the closing spring based on the closing spring stiffness K using the fatigue level quantification formula based on the closing spring stiffness value.

2. The online monitoring method for fatigue degree of high-voltage circuit breaker closing spring based on energy storage action model according to claim 1, characterized in that: In step S1, the energy storage action model includes a voltage module, an energy storage motor module, a crank angular displacement module, and a load module; the voltage module is connected to the input terminal of the energy storage motor module.

3. The online monitoring method for fatigue degree of high-voltage circuit breaker closing spring based on energy storage action model according to claim 2, characterized in that: The energy storage motor module includes a winding section transfer function submodule, a mechanical section transfer function submodule, a first gain submodule, and a second gain submodule. The winding section transfer function submodule describes the winding section of the energy storage motor, and the mechanical section transfer function submodule describes the mechanical section of the energy storage motor. The winding section transfer function submodule and the mechanical section transfer function submodule are connected in parallel. The first gain submodule and the second gain submodule describe the electromagnetic torque coefficient and back electromotive force coefficient of the energy storage motor. The first gain submodule, the mechanical section transfer function submodule, and the second gain submodule are connected in series. The first gain submodule and the second gain submodule are respectively connected to the output and input terminals of the winding section transfer function submodule.

4. The online monitoring method for fatigue degree of high-voltage circuit breaker closing spring based on energy storage action model according to claim 3, characterized in that: The crank angular displacement module includes a delay submodule, a third gain submodule, and an integral submodule connected in series. The input terminal of the delay submodule is located between the second gain submodule and the mechanical part transfer function submodule.

5. The online monitoring method for fatigue degree of high-voltage circuit breaker closing spring based on energy storage action model according to claim 4, characterized in that: The load module includes a custom function submodule and a fourth gain submodule connected in series. The output of the fourth gain submodule is located between the first gain submodule and the mechanical part transfer subfunction submodule. The input of the custom function submodule is connected to the output of the integral submodule.

6. The online monitoring method for fatigue degree of high-voltage circuit breaker closing spring based on energy storage action model according to claim 5, characterized in that: In step S5, the fatigue degree quantification formula based on the closing spring stiffness value is as follows: in, For fatigue damage degree, This represents the initial stiffness value of the closing spring when it is free from fatigue damage. This refers to the stiffness of the closing spring.

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