High-voltage circuit breaker closing spring fatigue degree online monitoring method based on energy storage action model

Abstract

The invention discloses a high-voltage circuit breaker closing spring fatigue degree on-line monitoring method based on an energy storage action model, and belongs to the technical field of high-voltage circuit breaker monitoring, and the method specifically comprises the following steps: S1, constructing an energy storage action model; s2, acquiring a voltage value and a current value of an energy storage motor and an angle displacement signal of a crank of the operating mechanism; S3, determining a fitness function; s4, solving the energy storage action model by using a dogvessel colony algorithm and a fitness function to obtain the closing spring stiffness K; and S5, calculating the fatigue degree of the current closing spring according to the closing spring stiffness K. According to the online monitoring method for the fatigue degree of the closing spring of the high-voltage circuit breaker based on the energy storage action model, the energy storage action model including parameters such as crank angular displacement, closing spring rigidity, energy storage motor voltage and energy storage motor current is constructed, solving is carried out by adopting a dogvessel colony algorithm, and accurate online measurement of the closing spring rigidity is realized.

Description

Technical Field

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

[0002] As an important protection and control device in the power transmission and transformation system, the operating status of the high-voltage circuit breaker is related to the safe and stable operation of the power grid. The spring operating mechanism is widely used in the power grid because of its low pollution, low cost and easy use. However, with the increase in the number of opening and closing operations, the closing spring will be frequently stretched and compressed and will be in a stretched and compressed state for a long time. This will cause the spring to produce stress relaxation due to material fatigue and reduce stiffness, which will cause the closing speed to continue to decrease or even fail to operate, directly affecting the safe operation of the power grid. Therefore, real-time monitoring of the fatigue degree of the closing spring of the high-voltage circuit breaker is of great significance to ensure the safe operation of the power grid.

[0003] For the fatigue analysis and online monitoring of high-voltage circuit breaker springs, the existing methods mainly focus on the analysis based on the vibration and sound signal characteristics of the circuit breaker. Since the vibration and sound signals are indirect signals generated by the changes in the circuit breaker spring operating mechanism and the impact generated inside is transmitted to the circuit breaker housing through a series of complex transmissions, it is difficult to accurately diagnose spring fatigue failures based on the vibration and sound signal characteristics. At the same time, the existing literature mainly conducts qualitative fault diagnosis of spring fatigue based on vibration and sound feature vectors from the perspective of pattern recognition, and cannot quantitatively detect the degree of spring fatigue. The prior art also has a method for evaluating high-voltage circuit breaker springs based on energy storage current analysis. For example, the patent with application number 201911053115.6 discloses a method for evaluating the health status of a high-voltage circuit breaker energy storage device based on energy storage current analysis. An open-type current sensor is stuck on the power line of the energy storage motor to collect the current during the entire process of the motor operation. The current value collected by the open-type current sensor is transmitted to the mechanical property tester, and the mechanical property tester records the motor current waveform. In the online monitoring method, the measuring device is directly arranged on the circuit breaker energy storage device, and the spring can be realized without removing it; the measuring device is arranged during the operation of the circuit breaker, which does not affect the normal operation of the circuit breaker; the current of the circuit breaker energy storage motor is monitored in real time to ensure the integrity and continuity of the detection data, so as to analyze and judge the trend of the energy storage device defects. However, the use of a single parameter for analysis has low monitoring accuracy and cannot reflect the trend of the change of the closing spring stiffness. Summary of the invention

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

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

[0006] Step S1: obtaining fixed parameters of the high-voltage circuit breaker and constructing an energy storage action model;

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

[0008] Step S3: determining a fitness function according to the energy storage action model in step S1 and the signal collected in step S2;

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

[0010] Step S5: Calculate the current closing spring fatigue degree according to the closing spring stiffness K by using a fatigue degree 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 angle displacement module and a load module; the voltage module is connected to the input end of the energy storage motor module.

[0012] Preferably, the energy storage motor module includes a winding part transfer sub-function submodule, a mechanical part transfer sub-function submodule, a first gain submodule and a second gain submodule; the winding part transfer sub-function submodule is used to describe the winding part of the energy storage motor, the mechanical part transfer sub-function submodule is used to describe the mechanical part of the energy storage motor, and the winding part transfer sub-function submodule is connected in parallel with the mechanical part transfer sub-function submodule; the first gain submodule and the second gain submodule are used to describe the electromagnetic torque coefficient and back electromotive force coefficient of the energy storage motor, and the first gain submodule, the mechanical part transfer sub-function submodule and the second gain submodule are connected in series in sequence; the first gain submodule and the second gain submodule are respectively connected to the output end and the input end of the winding part transfer sub-function submodule.

[0013] Preferably, the crank angle displacement module comprises a delay submodule, a third gain submodule and an integral submodule which are sequentially connected in series, and an input end of the delay submodule is arranged between the second gain submodule and the mechanical part transfer subfunction submodule.

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

[0015] Preferably, in step S3,

[0016] Step S31: determining the objective function;

[0017] The integral square error between the current signal measured online 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 situation, and the objective function to be solved is as follows:

[0018]

[0019] Among them, ISE is the integrated square error, e(t) is the difference between the current value of the energy storage action model and the current value measured online, is the current value measured online at time t, i(t) is the current value of the energy storage action model at time t;

[0020]

[0021] Among them, L and L -1 are Laplace transform and inverse Laplace transform, G 1 (s) and G 2 (s) are the transfer functions of the energy storage motor winding part and the mechanical part,

[0022]

[0023] B and J are the equivalent damping coefficient and equivalent moment of inertia converted to the motor shaft, respectively. T is the equivalent winding inductance, R T is the equivalent winding resistance, k e is the back electromotive force coefficient, k t is the electromagnetic torque coefficient, g is the reduction ratio of the reduction box, s is the closing spring vector, is the load torque value function, and are the crank angle displacement signal and voltage signal measured online, K is the closing spring stiffness, F 0 is the spring preload;

[0024] Step S32: deriving the individual fitness function in the salp swarm algorithm according to the objective function, the individual fitness function f(X) is as follows:

[0025]

[0026] Where X is the position vector of an individual in the salp 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, K 0 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 adopts the above-mentioned method for online monitoring of fatigue degree of closing spring of high-voltage circuit breaker based on energy storage action model, which has the following beneficial effects:

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

[0032] (2) The effect of solving the parameters of the energy storage action model by using the salp scissor swarm algorithm is higher in optimization accuracy. It can effectively monitor the stiffness value of the high-voltage circuit breaker closing spring online and can significantly reflect the change trend of the closing spring stiffness. Through the mapping relationship between the closing spring stiffness and the fatigue degree of the high-voltage circuit breaker, combined with the online monitoring method of the closing spring stiffness, it is possible to realize the online effective monitoring of the fatigue degree of the closing spring.

[0033] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of an online monitoring method for fatigue degree of a closing spring of a high-voltage circuit breaker based on an energy storage action model of the present invention;

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

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

[0037] Figure 4 A simplified load model diagram of the energy storage action process of the energy storage motor of the present invention;

[0038] Figure 5 It is a load variation curve diagram of the energy storage motor during the energy storage action process;

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

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

[0041] Figure 8 It is the current curve diagram of the energy storage action process of the energy storage motor;

[0042] Fig. 9 This is the energy storage action model diagram of the present invention;

[0043] Fig.10 A current curve diagram obtained by the energy storage action model of the energy storage motor of the present invention;

[0044] Fig.11 This is a curve diagram of voltage, current and crank angle displacement signals of the energy storage motor in Experiment 1 of the present invention;

[0045] Fig.12 This is a curve diagram of voltage, current and crank angle displacement signals of the energy storage motor in Experiment 2 of the present invention;

[0046] Fig.13 This is a curve diagram of voltage, current and crank angle displacement signals of the energy storage motor in Experiment 3 of the present invention;

[0047] Fig.14 This is a comparison chart of the current signal generated by the algorithm detection result and the current signal curve measured experimentally;

[0048] Fig.15 It is the curve diagram of the change of the closing spring stiffness;

[0049] Fig.16 This is a curve diagram of the fatigue degree of the closing spring.

[0050] Reference numerals

[0051] 1. Closing spring; 2. Crank; 3. Reducer; 4. Cam; 5. Energy storage shaft; 6. Pull rod; 7. Connecting rod; 8. Switch main shaft; 9. Energy storage motor. DETAILED DESCRIPTION

[0052] Example

[0053] In the description of the present invention, it should be noted that the terms "upper", "lower", "inside", "outside", etc. indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, or the positions or positional relationships in which the invented product is usually placed when in use. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of the present invention, it should also be noted that, unless otherwise clearly specified and limited, the terms "setting", "installation", and "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be an indirect connection through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] The embodiments of the present invention are described in detail below in conjunction with the accompanying drawings.

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

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

[0057] The spring operating mechanism in this embodiment is as follows Figure 2 As shown, one end of the closing spring 1 is connected to a fixed pin shaft, and the other end is connected to the crank 2. The energy storage shaft 5 crosses the reduction box 3 and is connected to the worm gear in the reduction box 3 through a shaft sleeve. 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, the crank 2 is in the stretched position at the highest point, and the closing spring 1 is in a stretched state. After the closing signal is issued, the closing coil core hits the tripping device to unlock the closing latch, and then the closing spring 1 is released, and the crank 2 is pulled back to the lowest point, driving the energy storage shaft 5 to rotate, and the cam 4 drives the switch main shaft 8 to rotate through a set of connecting rods 7, and finally drives the pull rod 6 to move upward and contact the static contact. After the switch main shaft 8 rotates a certain angle, the opening latch limits it to complete the closing action. After the closing is completed, the mechanism performs an energy storage action, the energy storage motor 9 is powered on and rotates, and drives the closing spring 1 to stretch through the reduction box 3 and the crank 2. When the closing spring 1 is stretched to the highest point, the closing latch limits the crank 2, the power supply of the energy storage motor 9 is cut off, and the crank 2 returns to the stretched position, and the energy storage action is completed. When performing the energy storage action, the closing spring 1 is stretched as the crank 2 rotates, and the torque applied to the energy storage shaft 5 changes, and finally 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: the axis of the energy storage shaft 5 is taken as the origin, the line connecting the axis of the energy storage shaft 5 and the fixed end of the closing spring 1 is taken as the reference line, the direction pointing to the fixed end of the closing spring 1 is taken as the positive direction, and the angular displacement direction of the crank 2 is taken as the positive angle direction. Figure 4 As shown, the length of crank 2 is denoted as r 0 The initial angle between crank 2 and the reference line is θ. 0 , the length from the axis of crank 2 to the fixed end of closing spring 1 is recorded as b, and the original length of closing spring 1 is recorded as l 0 At any time, the angle that crank 2 rotates compared to its initial position is denoted as θ, and the vector of crank 2 at any time is denoted as c, with the direction pointing from the origin to the end of crank 2 and the length as r 0 At any time, the vector of closing spring 1 is recorded as s, and its direction is from the moving end to the fixed end of closing spring 1, and its length changes with the movement.

[0060] The simplified model can be used to obtain the expression of the initial length

[0061]

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

[0063] c=(r 0 cos(θ+θ 0 ),r 0 sin(θ+θ 0 )) (2)

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

[0065] s=(br 0 cos(θ+θ 0 ),-r 0 sin(θ+θ0 )) (3)

[0066] Without considering the complex nonlinear situation, the restoring force generated by the closing spring is determined by the elongation Δl of the closing spring and the stiffness K of the closing spring.

[0067] F=KΔl=K(|s|-l 0 ) (4)

[0068] Considering that the initial length of the closing spring before being stretched is not necessarily its original length, that is, when the energy storage motor has not stretched the spring to do work, there is a spring preload, and F is introduced. 0 Describes the spring preload. So the restoring force is:

[0069]

[0070] The restoring force generated by the closing spring is represented by a vector F, which has a magnitude of F and a direction that is the same as s.

[0071]

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

[0073]

[0074] Since the crank length r 0 , the length b from the crank axis to the fixed end of the closing spring can be directly measured and remains unchanged for the same assembled high-voltage circuit breaker. Crank initial angle θ 0 and angular displacement (in actual test, they are measured by the same angular displacement sensor, so we may as well describe them uniformly with function θ(t). Therefore, the load torque (M( can be simply expressed as the angular displacement function θ(t), the closing spring stiffness K and the spring preload F 0 Function:

[0075] |M|=m[θ(t),K,F 0 ] (8)

[0076] By converting the gear ratio of the reduction box, the load of the energy storage motor during energy storage can be accurately described. 0 Unknown, such as Figure 5 As shown, the load change curve of the energy storage motor during the energy storage action process.

[0077] The analysis of the closing spring movement mechanism has resulted in a functional relationship between the crank angle displacement and the load torque applied to the energy storage motor during the energy storage action. On this basis, the behavior of the energy storage motor is analyzed to establish a mathematical model that accurately describes the energy storage action of the spring operating mechanism. Figure 6 As shown, the voltage Vs Applied to the winding terminals, the winding consists of the excitation winding and the armature winding in series, and the resistance of these two parts (excitation resistance R e , armature resistance R a ) and inductance (magnetizing inductance L e , armature inductance L a ) describes its physical behavior. During the operation of the motor, an induced electromotive force E is generated in the armature a , when the magnetic flux is stable, it is related to the speed ω m So the voltage balance equation of the whole 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] After power is turned on, the armature winding generates electromagnetic torque T under the action of the magnetic field. em When the motor speed is constant, the load torque T L , no-load torque T 0 and the damping torque T f Same electromagnetic torque T em Balance, so the torque balance equation is:

[0083] T em =T L +T f +T 0 (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 reduction box, B and J are the equivalent damping coefficient and equivalent moment of inertia converted 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 input of the system and current is the output of the system. The transfer function is as follows:

[0091]

[0092] Among them G 1 (s) and G 2 (s) are the transfer functions of the energy storage motor winding part and the mechanical part, as shown in equations (20) and (21), L{m[θ(t),K,F 0 ]} is m[θ(t),K,F 0 ], whose input is the crank angle displacement (, and the output is multiplied by the reduction ratio g to obtain the closing spring stiffness K and spring preload F 0 Load torque T L .

[0093]

[0094] like Figure 8 As shown, the work of the energy storage motor is divided into three stages:

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

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

[0097] 3) At T3, the motor starts to drive the crank to stretch the spring to do work, and the energy storage motor enters the work stage. In this stage, the current changes with the load.

[0098] Based on the above analysis, a model of energy storage motor energy storage action was built in MATLB / Simulink, such as Fig. 9 As shown, the energy storage action model includes a voltage module, an energy storage motor module, a crank angle displacement module and a load module; the voltage module is connected to the input end of the energy storage motor module, and the voltage is simulated by a step signal module.

[0099] The energy storage motor module includes a winding part transfer sub-function sub-module, a mechanical part transfer sub-function sub-module, a first gain sub-module and a second gain sub-module; the winding part transfer sub-function sub-module is used to describe the winding part of the energy storage motor, the mechanical part transfer sub-function sub-module is used to describe the mechanical part of the energy storage motor, and the winding part transfer sub-function sub-module is connected in parallel with the mechanical part transfer sub-function sub-module; the first gain sub-module and the second gain sub-module are used to describe the electromagnetic torque coefficient and back electromotive force coefficient of the energy storage motor, and the first gain sub-module, the mechanical part transfer sub-function sub-module and the second gain sub-module are connected in series in sequence; the first gain sub-module and the second gain sub-module are respectively connected to the output end and the input end of the winding part transfer sub-function sub-module. The input of the winding part is a voltage V s and the induced electromotive force E a The output is the 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 angle displacement module includes a delay submodule, a third gain submodule and an integral submodule connected in series. The input end of the delay submodule is set between the second gain submodule and the mechanical part transfer subfunction submodule. The delay module is used for simulation in the no-load stage model. The third gain submodule represents the reduction ratio g of the reduction box. The motor speed is reduced by the reduction box to obtain the crank speed, and then the crank angular displacement is obtained through the integral submodule.

[0101] The load module includes a custom function submodule and a fourth gain submodule connected in series. The output end of the fourth gain submodule is set between the first gain submodule and the mechanical part transfer submodule, and the input end of the custom function submodule is connected to the output end of the integral submodule. The fourth gain submodule represents the reduction ratio of the reduction box. The custom function submodule is connected to the load function. The angular displacement of the crank is calculated by the load function and then converted by the reduction box to obtain the load applied to the motor. Fig.10 As shown, the trend of the actual measured current curve is in good agreement. This shows that the established energy storage action model can well describe the behavior of the energy storage motor when performing energy storage action. The voltage part of the model is replaced by the online measured energy storage motor voltage signal, and at the same time, the crank angle displacement part of the model is replaced by the online measured crank angle displacement signal. The operating parameters of the operating mechanism (closing spring stiffness K, closing spring preload F) contained in the output of the energy storage action model can be obtained. 0 ) and system parameters (equivalent winding resistance R T ; Equivalent winding inductance L T ; Motor torque coefficient k t ; Back electromotive force coefficient k e; equivalent damping coefficient B; equivalent moment of inertia J;), when the current signal generated by the energy storage action model matches the current signal measured online, it is considered that the current energy storage action model parameters accurately reflect the state of the operating mechanism. At this point, the model of the energy storage action of the energy storage motor is established, and the problem is transformed into finding the optimal solution of the model parameters to achieve the optimal match between the model current signal and the actual current signal.

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

[0103] Step S3: Determine the fitness function according to the energy storage action model in step S1 and the signal collected in step S2. To avoid large deviations, the integral square error (ISE) between the current signal measured online 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: determining the objective function;

[0105] The integral square error between the current signal measured online 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 situation, and the objective function to be solved is as follows:

[0106]

[0107] Among them, ISE is the integrated square error, e(t) is the difference between the current value of the energy storage action model and the current value measured online,

[0108]

[0109] is the current value measured online at time t, i(t) is the current value of the energy storage action model at time t;

[0110] From formula (19), we can know that:

[0111]

[0112] Among them, L and L -1 are Laplace transform and inverse Laplace transform, G 1 (s) and G 2 (s) are the transfer functions of the energy storage motor winding part and the mechanical part, B and J are the equivalent damping coefficient and equivalent moment of inertia converted to the motor shaft, respectively. Tis the equivalent winding inductance, R T is the equivalent winding resistance, k e is the back electromotive force coefficient, k t is the electromagnetic torque coefficient, g is the reduction ratio of the reduction box, s is the closing spring vector, is the load torque value function, and are the crank angle displacement signal and voltage signal measured online, K is the closing spring stiffness, F 0 For spring preload.

[0113] Step S32: deriving the individual fitness function in the salp swarm algorithm according to the objective function, wherein the objective function contains eight parameters to be determined: 1) the equivalent winding resistance R T ; 2) Equivalent winding inductance L T ; 3) The torque coefficient k of the motor t ; 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 F 0 The closing spring stiffness K and closing spring preload F 0 are the operating parameters of the operating mechanism concerned in this embodiment, and the rest are system parameters of the operating mechanism. The objective function is very complex and it is difficult to solve it using an analytical method. Therefore, this embodiment uses the salp 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 salp swarm algorithm is shown in formula (25), where X is the position vector of the individual in the salp swarm.

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

[0115]

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

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

[0118] The salp swarm algorithm process is as follows:

[0119] 1) Initialize the population and get:

[0120]

[0121] Where N is the population size, D is the number of variables to be solved in the objective function, represents the position of the i-th individual in the j-th dimension, ub j and lbj are the upper and lower limits on 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 to the objective function, the location of the individual with the highest fitness is considered to be the location of the current food source and is placed at the top of the group.

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

[0125] 5) Update individual positions. Leaders and followers follow different behaviors. The leader's position update is mainly affected by the location of the food source, while the follower's position update only depends on the individual in front of it.

[0126] Leader's position update:

[0127]

[0128] The position of the i-th leader individual on the j-th dimension in the t-th iteration is recorded as The position of the current food source in the jth dimension is denoted as F j .c 2 and c 3 are all random numbers that are uniformly distributed in the interval [0,1]. In order to balance global exploration and local development, a convergence factor c is introduced. 1 , whose expression is:

[0129]

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

[0131] Followers' location updates:

[0132]

[0133] The position of the i-th follower individual in the j-th dimension in the t-th iteration is recorded as It is determined by its position in the t-1th iteration on the same dimension as the previous individual.

[0134] 6) Determine whether the result meets the convergence condition. If so, output the current food position. Otherwise, repeat 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, only the eight parameters need to be solved at the same time during the first monitoring. During subsequent monitoring, only the closing spring stiffness K and the closing spring preload F need to be solved. 0 , other parameters remain constant, which is not only more in line with the actual situation and can more accurately detect the changes in the stiffness of the closing spring, but also improves efficiency and saves computing power.

[0136] Step S5: Calculate the current closing spring fatigue degree according to the closing spring stiffness K by using a fatigue degree quantification formula based on the closing spring stiffness value.

[0137] The stiffness K of the closing spring is determined by the spring center diameter Z, the spring wire diameter d, the number of effective spring turns n, and the shear modulus G of the spring material. The relationship is shown in formula (30):

[0138]

[0139] The fatigue damage degree D is introduced to correct the material shear modulus G, and the calculation formula of the spring stiffness under different fatigue degrees is obtained, as shown in formula (31):

[0140]

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

[0142]

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

[0144] In order to verify the reliability of the method of this embodiment, a test experiment was carried out. A LW42A-40.5 outdoor high-voltage vacuum circuit breaker was used as the experimental object. The reduction ratio of its reducer was 1500:1. The experimental process required the measurement of the crank angle displacement signal, the current and voltage signals of the energy storage motor. Since the crank position space is too small to arrange the sensor, the sensor is arranged on one side of the cam. The two are driven by the same energy storage shaft, so they are equivalent in angular displacement. The selected angular displacement sensor has a range of 0 to 360° and a refresh rate of 0.6ms. The selected current sensor has a range of 0 to 30A, and a measurement frequency range of 20 to 20kHz. The selected voltage sensor has a range of 0 to 600V, and a measurement frequency range of 0 to 6kHz. The sampling frequency is set to 100Hz, and the signal acquisition time is 10s. In this embodiment, springs of different stiffness are replaced to simulate different degrees of fatigue of the closing spring, as shown in Table 1.

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

[0146]

[0147] The maximum stiffness value is defined as the initial stiffness value K of the closing spring without fatigue damage. 0 , then its fatigue degree D = 0, and based on this, the fatigue degree of other situations is calculated. The voltage, current and crank angle displacement signals of the energy storage motor under different closing spring stiffness are measured. Some normalized sample data are as follows Figure 11-14 As shown, it can be seen that as the degree of fatigue increases and the stiffness of the closing spring decreases, the maximum current of the energy storage motor during the working stage will decrease accordingly.

[0148] The signal of the closing spring without fatigue damage is not only used to determine the initial stiffness value K 0 It is also used as a system parameter of the energy storage action model, which plays a vital role in the subsequent detection of the fatigue degree of the closing spring. Therefore, a larger population size (150) and a maximum number of iterations (5000) were selected and run independently for 20 times. The quality of the test results was determined according to the numerical value of the objective function under the current parameters, and the group with the smallest value was considered to be the optimal test result. In order to verify the superiority of the SalpSwarm Algorithm (SSA), the Ant Lion Optimiser (ALO) and the Grey Wolf Optimiser (GWO) were run 20 times under the same population size and maximum number of iterations for comparison. The test results are shown in Table 2.

[0149] Table 2 Test results

[0150]

[0151] From Table 2, it can be seen that the fitness function value of the SSA optimal detection result is smaller, and the closing spring stiffness is closer to the theoretical value. At the same time, the optimal and worst results of the SSA detection have no significant difference except the spring preload F. 0 The differences are small, and the better test results tend to be spring preload F 0 is 0. The results of ALO detection are also small and stable. The results of GWO algorithm detection are quite different. In summary, compared with ALO and GWO algorithms, SSA has good solution stability and higher accuracy in the fitness function search and optimization proposed in this paper.

[0152] The optimal detection results of SSA in Table 2 are used as the system parameters of the energy storage action model through the Salp Sea Squid algorithm. Only the closing spring stiffness K and the closing spring preload F are used. 0To search for variables, the population size and the maximum number of iterations are 30 and 1000 respectively. Under this setting, two sets of data with different spring states are tested, and the test results are shown in Table 3.

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

[0154]

[0155]

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

[0157] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. An online monitoring method for fatigue degree of closing spring of high-voltage circuit breaker based on energy storage action model, characterized in that: The specific steps are as follows: Step S1: obtaining fixed parameters of the high-voltage circuit breaker and constructing an energy storage action model; Step S2: Arrange a data acquisition component, which includes a voltage sensor, a current sensor, and an angle displacement sensor. The voltage sensor and the current sensor respectively collect voltage and current signals of the energy storage motor, and the angle displacement sensor is used to collect an angle displacement signal of a crank of an operating mechanism in a high-voltage circuit breaker; Step S3: determining a fitness function according to the energy storage action model in step S1 and the signal collected in step S2; Step S4: using the salp swarm algorithm and the fitness function obtained in step S3 to solve the energy storage action model and obtain the closing spring stiffness K; Step S5: Calculate the current closing spring fatigue degree according to the closing spring stiffness K by using a fatigue degree quantification formula based on the closing spring stiffness value.

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

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

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

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

6. The method for online monitoring fatigue degree of closing spring of high-voltage circuit breaker based on energy storage action model according to claim 5 is characterized in that: In step S3, Step S31: determining the objective function; The integral square error between the current signal measured online 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 situation, and the objective function to be solved is as follows: Among them, ISE is the integrated square error, e(t) is the difference between the current value of the energy storage action model and the current value measured online, is the current value measured online at time t, i(t) is the current value of the energy storage action model at time t; Among them, L and L -1 are Laplace transform and inverse Laplace transform respectively, G1(s) and G2(s) are the transfer functions of the winding part and the mechanical part of the energy storage motor respectively, B and J are the equivalent damping coefficient and equivalent moment of inertia converted to the motor shaft, respectively. T is the equivalent winding inductance, R T is the equivalent winding resistance, k e is the back electromotive force coefficient, k t is the electromagnetic torque coefficient, g is the reduction ratio of the reduction box, s is the closing spring vector, is the load torque value function, and are the crank angle displacement signal and voltage signal measured online, K is the closing spring stiffness, and F0 is the spring preload; Step S32: deriving the individual fitness function in the salp swarm algorithm according to the objective function, the individual fitness function f(X) is as follows: Where X is the position vector of an individual in the salp group.

7. The method for online monitoring fatigue degree of closing spring of high-voltage circuit breaker based on energy storage action model according to claim 6 is characterized in that: In step S5, the fatigue degree quantification formula based on the closing spring stiffness value is as follows: 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.

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