Maximum power point tracking sliding mode control method, device and equipment based on offshore wind power system

Abstract

The present application relates to the field of sliding mode control technology, and in particular to a sliding mode control method, device and equipment based on maximum power point tracking of offshore wind power systems. The solution provided by the present application determines a sliding mode speed controller based on a variable exponential convergence law through the dynamic equation of the transmission system of the offshore wind power system, the first state variable, the sliding surface equation and the variable exponential sliding mode controller; based on the nonlinear function to be improved, an improved nonlinear function is determined by interpolation fitting; based on the improved nonlinear function, an improved nonlinear function extended state observer is obtained; based on the sliding mode speed controller of the variable exponential convergence law and the improved nonlinear function extended state observer, the maximum power point tracking sliding mode control of the offshore wind power system is realized. This solution improves the anti-disturbance performance and control accuracy of the system, thereby improving the accuracy of maximum power point tracking of the offshore wind power generation system.

Description

Technical Field

[0001] The present application relates to the technical field of sliding mode control, and in particular to a maximum power point tracking sliding mode control method, device and equipment based on an offshore wind power system. Background Art

[0002] In recent years, China has introduced carbon peak and carbon neutrality policies to reduce carbon emissions, protect the ecological environment, and promote sustainable economic and social development. These policies aim to ensure timely reductions in carbon emissions. Offshore wind power systems, with their abundant resources, high spatial efficiency, and minimal environmental impact, are widely used as an important renewable energy source.

[0003] Tracking the maximum power point (MPP) of offshore wind turbines is crucial during their operation, as it directly impacts the system's energy conversion efficiency and economic benefits. Currently, the commonly used method, the perturbation-and-observe method, suffers from slow convergence, jitter, and poor interference immunity, making it difficult to track the optimal speed.

[0004] Therefore, improving the maximum power point tracking accuracy of offshore wind power generation systems has become an urgent problem that needs to be solved. Summary of the Invention

[0005] The present application provides a maximum power point tracking sliding mode control method, device and equipment based on an offshore wind power system, which can accurately track the maximum power point of the offshore wind power generation system.

[0006] In the first aspect, an embodiment of the present application provides a maximum power point tracking sliding mode control method based on an offshore wind power system, the method comprising: S1: determining a sliding mode speed controller based on a variable exponential reaching law based on a transmission system dynamic equation, a first state variable, a sliding surface equation and a variable exponential sliding mode controller of the offshore wind power system; S2: determining a nonlinear function to be improved based on a traditional nonlinear extended state observer and a second-order nonlinear system equation of an unknown disturbance; S3: improving the nonlinear function to be improved by using interpolation fitting to obtain an improved nonlinear function; S4: improving the traditional nonlinear extended state observer based on the improved nonlinear function to obtain an improved nonlinear function extended state observer; S5: realizing maximum power point tracking sliding mode control of the offshore wind power system based on the sliding mode speed controller based on the variable exponential reaching law and the improved nonlinear function extended state observer.

[0007] In a possible implementation, step S1 includes: S11: determining a transformation formula of the transmission system dynamic equation based on the transmission system dynamic equation of the offshore wind power system; the transmission system dynamic equation of the offshore wind power system is:

[0008]

[0009] Where J is the total moment of inertia of the transmission system, B is the damping coefficient of the transmission system, and T m is the mechanical torque of the fan, ω r is the mechanical angular velocity of the rotor, Te is the electromagnetic torque of the engine, ω m is the rotor mechanical angular velocity;

[0010] The transformation formula is:

[0011]

[0012] Where b is the coefficient term, u is the Q-axis component of the stator current in the synchronous rotating coordinate system DQ; the lumped disturbance f(t) = (Tm-Bω r ) / J; Yes m The first-order derivative of , the point on the top of the parameter represents the derivative, the same below;

[0013] S12: Determine the system state variable error equation based on the transformation formula and the first state variable;

[0014] The first state variable is:

[0015]

[0016] Where, e1 is the first tracking error, e2 is the second tracking error, ω ropt is the optimal speed corresponding to the wind speed; is the first derivative of e1;

[0017] The system state variable error equation is:

[0018]

[0019] S13: determining an intermediate equation based on the system state variable error equation and the sliding surface equation;

[0020] The sliding surface equation is:

[0021] s=e1+α|e1| γ +βe2 m / n ;

[0022] Wherein, α, β, and γ are all positive constants, and γ>1; m and n are positive odd numbers, 1<m / n<2, and γ>m / n;

[0023] The intermediate equation is:

[0024]

[0025] S14: Determine the sliding mode speed controller based on the variable exponential reaching law based on the intermediate equation and the variable exponential sliding mode controller;

[0026] The variable exponential sliding mode controller equation is:

[0027]

[0028] Where, k>0, q>0, δ>1, 0<ε<1, τ>0, exp represents the exponent, e represents the error of the system state variable, h(s)s δ represents the gain of the switching function, qs represents the exponential approach term, and s represents the sliding surface;

[0029] The sliding mode speed controller based on the variable exponential reaching law is:

[0030]

[0031] Wherein, α, β, and γ are all positive constants, m and n are positive odd numbers, 1<m / n<2, and γ>m / n.

[0032] In one possible implementation, determining the intermediate equation based on the system state variable error equation and the sliding surface equation includes: differentiating the sliding surface equation to determine the derivative equation of the sliding surface equation; substituting the system state variable error equation into the derivative equation of the sliding surface equation to determine the intermediate equation.

[0033] In a possible implementation, step S2 includes: S21: reconstructing the second-order nonlinear system equation of the unknown disturbance to determine the third-order system equation;

[0034] The second-order nonlinear system equation of the unknown disturbance is:

[0035]

[0036] Where x1(t), x2(t), and x3(t) represent system state variables, f(·) is a nonlinear function, d(t) is an unknown disturbance, u(t) is the control input, and b is the control gain.

[0037] The third-order system equation is:

[0038]

[0039] Where φ(t) is the total disturbance and y(t) is the system output;

[0040] S22: observing x3(t) in the third-order system based on the traditional nonlinear extended state observer to determine the nonlinear function to be improved;

[0041] The nonlinear function to be improved is:

[0042]

[0043] Where a and d are adjustable parameters, and x is the system state variable.

[0044] In a possible implementation, step S3 includes: S31: determining a first-stage nonlinear function equation by interpolation fitting based on the nonlinear function to be improved;

[0045] The nonlinear function equation of the first stage is:

[0046]

[0047] Where k1, k2 and k3 are parameters;

[0048] S32: Based on the nonlinear function equation of the first stage and the nonlinear function to be improved, improving the nonlinear function to be improved, and determining the nonlinear function equation of the second stage;

[0049] The second stage nonlinear function equation is:

[0050]

[0051] S33: Based on the first-stage nonlinear function equation and the second-stage nonlinear function equation, determine the parameter values ​​of k1, k2, and k3:

[0052] The parameter values ​​of k1, k2 and k3 are:

[0053]

[0054] S34: Based on the k1, k2 and k3 parameter values ​​and the first stage nonlinear function equation, determine the improved nonlinear function;

[0055] The improved nonlinear function is:

[0056]

[0057] In a possible implementation, step S4 includes: S41: obtaining a lumped disturbance of the offshore wind power system, where the lumped disturbance includes internal parameter perturbations of mechanical torque, moment of inertia, and friction coefficient of the offshore wind power system and external time-varying disturbances;

[0058] S42: Determine a second system state variable equation based on the lumped disturbance; the second system state variable equation is:

[0059]

[0060] In the formula, the system state variable x1=ω m , expanded state variable x2=(T m -B ωm ) / J=f(t), where f(t) is the lumped disturbance; S43: determining a state-space equation of a second-order nonlinear system corresponding to the second-order system state variable equation based on the second-system state variable; the state-space equation of the second-order nonlinear system is:

[0061]

[0062] Where,

[0063] S44: improving the traditional nonlinear extended state observer based on the state space equation of the second-order nonlinear system and the improved nonlinear function to obtain an improved nonlinear function extended state observer;

[0064] The improved nonlinear function extended state observer is:

[0065]

[0066] Where, β 01 >0,β 02 >0 is the output error correction gain of the improved nonlinear function extended state observer, a1 and a2 are nonlinear factors, d1 and d2 are filter factors, and z1 is the value of ω m is an estimate of , and z2 is an estimate of the lumped disturbance.

[0067] In a possible implementation, step S5 includes: S51: determining an output equation of a sliding mode controller according to an output result of the improved nonlinear function extended state observer; S52: implementing maximum power point tracking sliding mode control of the offshore wind power system based on the output equation of the sliding mode controller;

[0068] The output equation of the sliding mode controller is:

[0069] in is the instantaneous value of current; where, is an estimate of the lumped disturbance.

[0070] In a second aspect, an embodiment of the present application provides a maximum power point tracking sliding mode control device based on an offshore wind power system, the device comprising: a determination module for determining a sliding mode speed controller based on a variable exponential reaching law based on the transmission system dynamic equation, the first state variable, the sliding surface equation and the variable exponential sliding mode controller of the offshore wind power system; the determination module is also used to determine the nonlinear function to be improved based on a traditional nonlinear extended state observer and a second-order nonlinear system equation of an unknown disturbance; a processing module is used to improve the nonlinear function to be improved by using interpolation fitting to obtain an improved nonlinear function; the processing module is also used to improve the traditional nonlinear extended state observer based on the improved nonlinear function to obtain an improved nonlinear function extended state observer; a control module is used to implement the maximum power point tracking sliding mode control of the offshore wind power system based on the sliding mode speed controller based on the variable exponential reaching law and the improved nonlinear function extended state observer.

[0071] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in the first aspect or any one of the implementation methods thereof is implemented.

[0072] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, it implements the method described in the first aspect or any one of the implementation methods thereof.

[0073] This application adopts a sliding mode control method based on disturbance compensation. This method enhances the performance of sliding mode control by incorporating an improved observer, thereby improving the power capture efficiency of offshore wind power systems under dynamically changing wind speed conditions and improving the accuracy of maximum power point tracking. First, based on the traditional exponential convergence law, an improvement is made by introducing a variable speed exponential convergence law that is dynamically related to the system state variables, and then a sliding mode controller based on the variable exponential convergence law is proposed, which effectively improves the system convergence speed and weakens the sliding mode jitter. Secondly, an improved nonlinear function is proposed, and a more accurate improved observer is designed. The observer is used to estimate the aggregate disturbance of the offshore wind power system and perform real-time dynamic compensation. This method effectively solves the problem of the upper limit of traditional sliding mode control in disturbance compensation, significantly improves the system's anti-disturbance performance and control accuracy, and ultimately improves the accuracy of maximum power point tracking of offshore wind power generation systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0075] Figure 1 The sliding mode control principle diagram provided for this application;

[0076] Figure 2 A flowchart of a maximum power point tracking sliding mode control method for an offshore wind power system provided in one embodiment of the present application;

[0077] Figure 3 A comparison diagram of the state phase trajectories of the traditional exponential reaching law and the variable exponential reaching law system provided in this application;

[0078] Figure 4 This is a simulation result diagram of the sliding mode control phase diagram curve based on the variable exponential reaching law provided by this application;

[0079] Figure 5 A comparison chart of the curves of the function fal(x, a, d) and the function ifal(x, a, d) provided in this application;

[0080] Figure 6 The structural diagram of the NLESO-based SMC method provided in this application;

[0081] Figure 7 The simulation results of the SMC method based on NLESO provided in this application are shown in the figure;

[0082] Figure 8 The overall control block diagram of the sliding mode control method based on disturbance compensation provided in this application;

[0083] Figure 9 Schematic diagram I of the simulation results provided for this application;

[0084] Figure 10 Schematic diagram II of the simulation results provided for this application;

[0085] Figure 11 Schematic diagram III of the simulation results provided for this application;

[0086] Figure 12 This is a structural block diagram of a maximum power point tracking sliding mode control device based on an offshore wind power system provided in one embodiment of the present application;

[0087] Figure 13 This is a schematic diagram of the structure of the electronic device provided in this application. DETAILED DESCRIPTION

[0088] The following will be combined with the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0089] In recent years, building on the success of its existing cranes in offshore wind turbine installation, Huisman has developed a series of cranes specifically designed for wind turbine installation. Huisman has officially announced the launch of a newly developed system that enables crane operators to predict approaching wind speed, gusts, and direction during wind turbine blade installation and take countermeasures. Huisman has also introduced a wind detection system to facilitate safe wind turbine installation. Dubbed "Gust Buster," this system provides 360-degree wind information around the boom tip, assisting crane operators and crane supervisors when installing large, heavy objects at height. A LiDAR system, installed at the crane boom tip, scans the horizontal area to measure incoming wind. The data is post-processed by the crane's automation system and displayed to the crane operator and other personnel, such as those in the crane vessel's bridge. The prediction window is typically five to eight minutes before a gust arrives, allowing ample time to decide whether to attach the blades to the nacelle. The system is applicable to both new and existing Huisman cranes.

[0090] Based on the above research, this application studies the permanent magnet direct-drive offshore wind power generation system, wind energy capture control below the rated wind speed, and realizes the tracking of the optimal speed of the offshore wind power system. The improved SMC method is used to design the offshore wind power system controller. The improved sliding mode control (SMC) has good dynamic response and anti-disturbance capabilities. A non-singular fast terminal sliding mode controller based on the variable exponential reaching law is proposed to solve the problems of slow convergence and jitter of the traditional sliding mode controller. A nonlinear extended state observer (NLESO) is proposed to observe the disturbance and perform real-time dynamic compensation, which improves the anti-disturbance capability of the offshore wind power system and solves the internal parameter perturbations and external time-varying disturbances of the offshore wind power system. Finally, the performance and advantages of the three control strategies of proportional integral control (PI), SMC, and disturbance compensation-based sliding mode control (DCSMC) are verified and compared through simulation.

[0091] Specifically, the traditional nonlinear system equation is a finite-dimensional first-order ordinary differential equation:

[0092]

[0093] Here x represents the state, u represents the input, and t represents the time. The dot on top of the parameter represents the derivative, and the same applies below.

[0094] The sliding surface in the state space is: s(x) = c n x n +c n-1 x n-1 +…+c1x1, (2); the state space can be divided into two parts by the sliding surface: s(x)>0 and s(x)<0, c1, c2, …, c n are design items, x1, x2, ..., x n is the system state vector; Figure 1 The sliding mode control principle diagram is shown in Figure 2. The sliding mode motion process is as follows: Figure 1 As shown in (a), the usual point A, starting point B, and ending point C, the distribution diagram of the sliding surface is as follows Figure 1 As shown in (b), Figure 1 The normal point A in (b) indicates that the system enters another control state from one control state; the starting point B indicates that the system is in an unstable state, leaving the sliding surface and entering another control state; only the ending point C meets the stability requirements.

[0095] If the switching surface represented by the state variable function is s(x), s∈R n , then the control function u(x) can be expressed as equation:

[0096]

[0097] Where u(x) and s(x) are both continuous, and the control function needs to meet the following three conditions:

[0098] (i) Existence:

[0099]

[0100] (ii) Accessibility:

[0101]

[0102] (iii) Stability, we choose Lyapunov equation (6):

[0103]

[0104] V(x) is a scalar function defined by the system state vector x; taking the derivative of equation (6), we can get When the reachability condition is met When , the system is stable.

[0105] The linear sliding surface s(x) is linearly related to the system state, and its expression is equation (7):

[0106]

[0107] Where x(t) is the system state, and parameter C = [c1, c2, ... c(m-1), 1] T , and it is necessary to ensure that the polynomial p m -1 +c n-1 p m-2 +......+c2p+p is the Hurwitz operator, where p represents the Laplace operator. When m = 2, the sliding surface is s(x) = c1x1 + x2, where c1 > 0. Table 1 shows the expressions of several commonly used terminal sliding surfaces and their convergence times.

[0108] Table 1. Common terminal sliding surfaces

[0109]

[0110] The sliding mode reaching law is expressed as equation (8):

[0111]

[0112] Where, sign(s)=[sign(s1),...,sign(sm)] T , f(s)=[f(s1),...,f(sm)] T , K=diag[l1,...,lm],l i >0, i=1, 2,…,m; Q=diag[q1,…,qm], qi>0, sifi(si)>0, f i (0)=0.

[0113] Common reaching laws are shown in Table 2:

[0114] Table 2. Common convergence laws

[0115]

[0116] Figure 2 A flowchart of a maximum power point tracking sliding mode control method for an offshore wind power system provided in one embodiment of the present application is provided. The method includes:

[0117] S210 , determining a sliding mode speed controller based on a variable exponential reaching law based on a transmission system dynamic equation, a first state variable, a sliding surface equation, and a variable exponential sliding mode controller of the offshore wind power system.

[0118] Specifically, the variable exponential sliding mode controller equation proposed in this application is:

[0119]

[0120] Where, k>0, q>0, δ>1, 0<ε<1, τ>0; exp represents the exponent; e represents the error of the system state variable; the gain of the switching function h(S)|s| δ A larger value can increase the approach speed; qs represents the exponential approach term, which tends to zero in this equation, and S represents the sliding surface; the motion state of the system is determined by the variable speed approach term -kh(s)|s| δ sgn(s) can improve the system convergence speed and weaken chattering.

[0121] The variable exponential reaching law can be simplified to Its discretized form is equation (10):

[0122] s(n+1)-s(n)=-kh(s)|s| δ sgn(s)T; (10)

[0123] Where s→0, T is the sampling period, when the system approaches the sliding surface from the S<0 side, S(n)=0 - , then the next sampling period is equation (11):

[0124] s(n+1)=kh(s(n))|s(n)| δ T=0; (11)

[0125] When the system approaches the sliding surface from the S>0 side, S(n)=0 + , then the next sampling period is equation (12):

[0126] s(n+1)=-kh(s(n))|s(n)| δ T=0;(12)

[0127] From equations (11) and (12), the bandwidth of the switching band of the variable exponential reaching law can be calculated as equation (13):

[0128] Δ=kh(s)|s| δ T; (13)

[0129] The bandwidth of the switching band of the exponential reaching law is Equation (14):

[0130] Δ1=kT; (14)

[0131] Figure 3 A comparison diagram of the state phase trajectory of the traditional exponential reaching law and the variable exponential reaching law system provided in this application, Figure 3 (a) is the traditional exponential convergence law, Figure 3 (b) is the reaching law of variable exponential. Figure 3 As shown in the figure, the motion chattering problem of the system based on the variable exponential reaching law can be significantly improved after reaching the sliding mode surface.

[0132] Assume that the state of this nonlinear system is equation (15):

[0133]

[0134] Where, is the system state variable, Assume that the initial value of the system is x(0) = [0.5 0.5] T , the sliding surface is designed to be s = 15x1 + x2. In the variable exponential reaching law, ε = 0.01, τ = 3, δ = 1.1, and the other parameters are selected with the same values ​​k = 5, q = 10. The simulation results of the sliding mode control phase diagram curve based on the variable exponential reaching law are shown as follows Figure 4 As shown, 4(a) represents the convergence process of the state variable x1, Figure 4 (b) represents the convergence process of the state variable x2, Figure 4 (c) represents the output of the controller, Figure 4 (d) represents the phase trajectory of the module motion.

[0135] from Figure 4 It can be observed that the convergence time of the system state variables x1 and x2 under the variable exponential reaching law is 0.25 seconds, significantly less than the 0.4 seconds under the traditional exponential reaching law. The variable exponential reaching law converges faster and can improve the dynamic quality of the system. From the zoomed-in view of the sliding motion of the two state variables after reaching the sliding mode surface, the controller output, and the phase trajectory of the sliding mode motion, the proposed variable exponential reaching law exhibits variable speed characteristics, resolving the conflict between sliding mode reaching speed and chattering, primarily due to the adaptive and variable switching gain.

[0136] In one possible implementation, a sliding mode speed controller based on a variable exponential reaching law is determined based on a transmission system dynamic equation, a first state variable, a sliding surface equation, and a variable exponential sliding mode controller of an offshore wind power system, including: determining a system state variable error equation based on the transmission system dynamic equation and the first state variable of the offshore wind power system; determining an intermediate equation based on the system state variable error equation and the sliding surface equation; and determining a sliding mode speed controller based on a variable exponential reaching law based on the intermediate equation and the variable exponential sliding mode controller.

[0137] For example, the dynamic equation of the transmission system of the offshore wind power generation system is:

[0138]

[0139] Where J is the total moment of inertia of the transmission system, B is the damping coefficient of the transmission system, and T m is the mechanical torque of the fan, ω r is the mechanical angular velocity of the rotor, Te is the electromagnetic torque of the engine, ω m is the rotor mechanical angular velocity. Equation (16-b) can be derived from the dynamic equation (16-a) of the transmission system of the offshore wind power generation system:

[0140]

[0141] In one possible implementation, f(t)=(T m -Bω r ) / J is regarded as a lumped disturbance. Based on the dynamic equation of the transmission system of the offshore wind power system, the transformation formula of the dynamic equation of the transmission system can be determined based on equation (16-b):

[0142]

[0143] In the formula, the coefficient b = -1.5n p ψ f / J, where n p is the number of motor pole pairs, Ψ f is the magnetic flux of the rotor permanent magnet; u=i q is the Q-axis component of the stator current in the synchronous rotating coordinate system DQ; the lumped disturbance f(t)=(Tm-Bω r ) / J; Yes m The first derivative of .

[0144] Furthermore, the first state variable of the system is defined as equation (18):

[0145]

[0146] Where, e1 is the first tracking error, e2 is the second tracking error, ω ropt is the optimal speed corresponding to the wind speed, which is determined by the actual wind speed in nature. Substituting Equation (17) into the derivative of Equation (18), we can obtain the system state variable error equation (19). Based on the transformation formula and the first state variable, the system state variable error equation is determined as:

[0147]

[0148] In one possible implementation, determining an intermediate equation based on a system state variable error equation and a sliding surface equation includes: differentiating the sliding surface equation to determine a derivative equation of the sliding surface equation; substituting the system state variable error equation into the derivative equation of the sliding surface equation to determine an intermediate equation;

[0149] Specifically, the sliding surface equation is:

[0150] s=e1+α|e1| γ +βe2 m / n ; (20)

[0151] Wherein, α, β, and γ are all positive constants, and γ>1; m and n are positive odd numbers, 1<m / n<2, and γ>m / n; e1 and e2 are the first and second tracking errors, respectively.

[0152] When the system moves to the sliding surface s = 0, the derivative of the second tracking error e2 is:

[0153]

[0154] According to formula (21), the exponential term αe1 γ / β determines the convergence speed; taking the derivative of equation (20), we get equation (22):

[0155]

[0156] Substituting equation (19) into equation (22), we can get the intermediate equation (23):

[0157]

[0158] By combining the sliding mode speed controller equation (9) and the intermediate equation (23), the sliding mode speed controller based on the variable exponential reaching law is obtained as equation (24):

[0159]

[0160] Furthermore, the q-axis reference current is obtained as equation (25):

[0161]

[0162] Since 1 < m / n < 2, and γ > m / n, the exponents of the calculated state variables in the q-axis reference current are all greater than zero. When the system is in a sliding motion toward the origin, the exponential factor is close to 1, which can weaken the sliding mode chattering to a certain extent, allowing the system to smoothly approach the origin. Furthermore, the control law contains an integral term, which can further reduce the system's sliding mode chattering. Therefore, the sliding mode controller proposed in this application ensures that the offshore wind power system has a certain degree of anti-disturbance capability and robustness.

[0163] After designing the sliding mode speed controller based on the variable exponential reaching law, it is necessary to prove and analyze the stability of the controller.

[0164] This application uses Lyapunov stability theory for analysis, and selects the Lyapunov function to construct equation (26) with reference to equation (6):

[0165]

[0166] Taking the derivative of equation (26), we can get equation (27):

[0167]

[0168] According to the sliding surface equation (20), equation (28) can be obtained:

[0169]

[0170] Substituting the obtained control law (25) into equation (28), we can obtain equation (29)

[0171]

[0172] but The expression of is as follows:

[0173]

[0174] Since 0<ε<1, τ>0, then h(s)>0 holds; and since δ>1, β>0, k>0, q>0, m and n are positive odd numbers, and 1<m / n<2, therefore, Lyapunov function V1 is positive definite, is negatively definite, satisfying the Lyapunov stability theory. The proposed controller satisfies the stability conditions of sliding mode motion. The system is convergent and asymptotically stable, and the system can reach the sliding surface in a finite time.

[0175] When the system state moves to the sliding surface, s = 0. Substituting into equation (20) yields equation (31):

[0176]

[0177] Equation (31) can be transformed into Equation (32):

[0178]

[0179] Assuming that the initial value of the system state e1(0)≠0, and the time when it moves to the sliding surface is tr, then by integrating the equations on both sides of equation (32), equation (33) can be derived:

[0180]

[0181] Equation (34) can be derived:

[0182]

[0183] Therefore, the system state variable error can converge to zero in a finite time.

[0184] S220, determining a nonlinear function to be improved based on a traditional nonlinear extended state observer and a second-order nonlinear system equation of an unknown disturbance.

[0185] In one possible implementation, based on a traditional nonlinear extended state observer and a second-order nonlinear system equation with unknown disturbance, a nonlinear function to be improved is determined, including: reconstructing the second-order nonlinear system equation with unknown disturbance to determine a third-order system equation; based on a traditional nonlinear extended state observer, observing x3(t) in the third-order system to determine the nonlinear function to be improved.

[0186] The extended state of the nonlinear system is estimated using the Extended State Observer (ESO). The second-order nonlinear system with unknown disturbance is expressed as Equation (35):

[0187]

[0188] Where x1(t) and x2(t) are system state variables, f(·) is a nonlinear function, d(t) is an unknown disturbance, u(t) is the control input, b is the control gain, and y(t) is the system output. ESO considers φ(t) = f(·) + d(t) as the total disturbance and expands φ(t) into the new state variable of system (35), thus obtaining x3(t) = φ(t). The second-order nonlinear system (35) is expanded into a third-order system, and reconstructed to obtain the third-order system equation (36):

[0189]

[0190] Where x1(t), x2(t), and x3(t) represent system state variables.

[0191] Design NLESO and observe the expanded state variable x3(t), and obtain equation (37):

[0192]

[0193] Then the nonlinear function to be improved is obtained as:

[0194]

[0195] Where a and d are adjustable parameters. It is recommended that the value of a be the same as that of h. 01 , β 02 and β 03 When properly selected, the NLESO can observe the system state variables x1(t), x2(t), and x3(t), i.e., z1(t)→x1(t), z2(t)→x2(t), and z3(t)→x3(t). When fal(·) is replaced by the error e1, the NLESO becomes a linear ESO, and Equation (37) becomes Equation (39):

[0196]

[0197] By performing Laplace transformation on equation (39), we can obtain its characteristic equation (40):

[0198] p(s)=s 3 +β 01 s 2 +β 02 s+β 03 ; (40)

[0199] Configure Equation (40) to (s+ω c ) 3 In the form of , we can get: β 01 =3ω c 、

[0200] Applying the nonlinear function of NLESO, the expression of fal'(x,a,d) can be derived as Equation (41):

[0201]

[0202] S230, interpolation fitting is used to improve the nonlinear function to be improved, and the improved nonlinear function is obtained. From Equation (41), it can be seen that the function fal(x,a,d) is a linear interval in the interval x∈[-d,d]. A nonlinear function ifal(x,a,d) that is continuous and smooth at the segmented points is proposed to improve the accuracy of NLESO. Using interpolation fitting, the nonlinear function fal(x,a,d) is improved, and the first-stage nonlinear function equation ifal(x,a,d) is obtained (42):

[0203]

[0204] From formula (42), we can see that the function fal(x,a,d) in the range of |x|≤d is improved to be the polynomial and the function (exp x +1) / (exp x -1) combination, select (expx +1) / (exp x -1), its convergence performance at the origin is better than x 3 , since the function fal(x,a,d) is continuous and differentiable at the origin and the dividing point ±d, based on the first-stage nonlinear function equation and the nonlinear function to be improved, the nonlinear function to be improved is improved, the second-stage nonlinear function equation is determined, and equation (43) is derived:

[0205]

[0206] Combining the first-stage nonlinear function equation (42) and the second-stage nonlinear function equation (43), solving for parameters k1, k2, and k3, we can obtain equation (44):

[0207]

[0208] Substituting the parameters k1, k2, and k3 into the first-stage nonlinear function equation (42) yields the improved nonlinear function ifal(x, a, d), which is expressed as equation (45):

[0209]

[0210] The improved function ifal(x,a,d) has advantages over the traditional function fal(x,a,d). Figure 5 This is a curve comparison diagram of the function fal(x,a,d) and the function ifal(x,a,d). Let the adjustable parameter a = 0.25 and change d. The response curves of the two functions are as follows Figure 5 (a); let the adjustable parameter d = 0.1, change a, the response curves of the two functions are as follows Figure 5 (b) shown.

[0211] from Figure 5 It can be seen that a and d have the same influence on the function ifal(x,a,d) and the function fal(x,a,d). a determines the nonlinearity of the two functions, and d determines the width of the linear interval of the two functions. Figure 5 (b) It can be clearly observed that at the cutoff point ±0.1, the smooth transition of the function ifal(x,a,d) is better than that of the function ifal(x,a,d). This property holds true for any parameters a and d. By adjusting parameters a and d to meet the engineering requirements of "small error, large gain; large error, small gain," the function ifal(x,a,d) can be used to improve the NLESO when designing an NLESO, overcoming the chattering problem caused by the non-smoothness of the function fal(x,a,d) at the cutoff point.

[0212] The NLESO is improved using the improved nonlinear function ifal(·) to estimate the total disturbance of the nonlinear system and feed it back to the sliding mode controller for compensation. This control method that combines the NLESO and the sliding mode controller is called the disturbance compensation-based sliding mode control (DCSMC) method. By adjusting the parameters of the improved NLESO, the control system can achieve accurate and stable control effects. The structural block diagram of the SMC method based on NLESO is shown in the figure. Figure 6 As shown. To study the performance of NLESO, a first-order nonlinear system is used as the controlled object. The nonlinear system is equation (46):

[0213]

[0214] Assume the total disturbance is And expand it into new variables, then the expanded second-order system is equation (47):

[0215]

[0216] The simulation example uses the integral sliding surface s = c0∫edt+e and the reaching law To design the sliding mode control law, the tracking target is set to a sine function with an amplitude of 1 and a period of 2π, that is, r = sin(t). The sampling frequency in the simulation is set to 1 kHz. The parameters of the NLESO and sliding mode controller are: β 01 =20,β 02 =400, a1=0.9, a2=0.01, d1=0.02, d2=0.001, c0=1, k=10, q=100, the simulation results of the SMC method based on NLESO are shown in the figure Figure 7 As shown, Figure 7 (a) is the target expected value and actual value, Figure 7 (b) is the control signal, Figure 7 (c) is the actual state value x1(t) and the observed value z1(t), Figure 7 (d) is the actual state value x2(t) and the observed value z2(t).

[0217] from Figure 7 (a) It can be seen that the sliding mode controller can make the system state stably track the desired output. Figure 7 (b) shows that there is no obvious chattering in the control signal. Figure 7(c) and (d) show the observed output curves of the improved NLESO, demonstrating that it accurately estimates the system state x1 and the expanded state x2. The enlarged plot shows that the improved NLESO achieves higher prediction accuracy than the NLESO based on the fal(·) function. Therefore, the proposed improved NLESO accurately and accurately observes the total disturbance of the nonlinear system and feeds it back into the sliding mode controller for compensation, effectively improving the nonlinear system's disturbance tolerance.

[0218] S240, improving the traditional nonlinear extended state observer based on the improved nonlinear function to obtain an improved nonlinear function extended state observer.

[0219] In one possible implementation, a traditional nonlinear extended state observer is improved based on an improved nonlinear function to obtain an improved nonlinear function extended state observer, including: obtaining a lumped disturbance of an offshore wind power system, the lumped disturbance including internal parameter perturbations and external time-varying disturbances of the mechanical torque, moment of inertia and friction coefficient of the offshore wind power system; determining a second system state variable equation based on the lumped disturbance; determining a state space equation of a second-order nonlinear system corresponding to the second system state variable equation based on the second system state variable; improving a traditional nonlinear extended state observer based on the state space equation of the second-order nonlinear system and the improved nonlinear function to obtain an improved nonlinear function extended state observer.

[0220] For offshore wind power generation systems, the internal parameter perturbations such as mechanical torque, moment of inertia, and friction coefficient transmitted to the PMSG by the offshore wind turbine and the external time-varying disturbances are regarded as lumped disturbances f(t) and expanded into a new state variable. The NLESO is designed to observe the lumped disturbances, and the second system state variable equation (48) is obtained:

[0221]

[0222] In the above formula, the system state variable x1=ω m , expanded state variable x2=(T m -B ωm ) / J=f(t), then the second system state variable equation (48) is expanded into the state space expression of the second-order nonlinear system as equation (49):

[0223]

[0224] Where, C=[0 1].

[0225] The second-order NLESO equation of system (48) constructed using the improved nonlinear function ifal(·) is (50):

[0226]

[0227] Where, β 01 >0,β 02 >0 is the output error correction gain of the improved nonlinear function extended state observer, a1 and a2 are nonlinear factors, d1 and d2 are filter factors, and z1 is the value of ω m Estimates, is an estimate of f(t). By adjusting these parameters, NLESO can observe all state variables of the second-order nonlinear system (49) and achieve the tracking effect of z1→x1, z2→x2.

[0228] To prove the stable convergence of NLESO, the observation errors are defined as e1 = z1 - x1 and e2 = z2 - x2. Then, from the nonlinear system equation (49) and the NLESO equation (50), the observation system error can be derived as equation (51):

[0229]

[0230] Let e 21 =e1, Then equation (51) can be transformed into equation (52):

[0231]

[0232] The Lyapunov function of equation (52) is selected as equation (53):

[0233]

[0234] Then there is always a point x∈[0,e 21 ], so that equation (54) holds true:

[0235]

[0236] And since ifal(x,a1,d1) and e 21 Same sign, and β 02 >0, we can get:

[0237]

[0238] The derivative of the Lyapunov function (53) is calculated as equation (56):

[0239]

[0240] Since ifal(·) is a monotonically increasing function, fal'(e1,a1,d1)≥0 holds, and assuming that it is a bounded real number within a finite error range, let fal'(e1,a1,d1)=M new , then equation (56) can be simplified to equation (57):

[0241]

[0242] when When satisfied or hour,

[0243] when When satisfied or hour,

[0244] When e 22 =0,

[0245] According to Lyapunov stability theory, the improved NLESO is gradually stable.

[0246] S250, a sliding mode speed controller based on a variable exponential reaching law and an improved nonlinear function extended state observer, realizes maximum power point tracking sliding mode control of offshore wind power systems.

[0247] In one possible implementation, a sliding mode speed controller based on a variable exponential reaching law and an improved nonlinear function extended state observer is used to implement maximum power point tracking sliding mode control for an offshore wind power system, including: determining an output equation of the sliding mode controller according to an output result of the improved nonlinear function extended state observer; and implementing maximum power point tracking sliding mode control for the offshore wind power system based on the output equation of the sliding mode controller.

[0248] Specifically, the sliding mode speed controller based on the variable exponential reaching law and the improved nonlinear function extended state observer realize the maximum power point tracking sliding mode control of the offshore wind power system. The overall control block diagram of the sliding mode control (DCSMC) method based on disturbance compensation is as follows: Figure 8 As shown, the output of the sliding mode controller is equation (58):

[0249]

[0250] Figure 8This is the overall block diagram of the permanent magnet synchronous generator (PMSG) generator-side control system. Using the NLESO to observe the wind power system's lumped disturbances in real time and feed them back to the sliding mode speed controller for dynamic compensation, this disturbance compensation-based sliding mode control (DCSMC) approach significantly improves the system's dynamic performance and disturbance tolerance.

[0251] Simulation results and analysis: In order to verify the control performance of the DCSMC method proposed in this paper, a permanent magnet direct drive offshore wind power system and a machine-side converter control system model were built in the MATLAB / Simulink simulation environment. The simulation parameters of the permanent magnet direct drive wind turbine system are shown in Table 3. The proposed DCSMC method was simulated and compared with the traditional PI control method and the sliding mode control (SMC) method based on the traditional reaching law and linear sliding surface. The three methods were completed by constructing MATLAB-function functions through MATLAB programming. ode23tb (stiff / TR-BDF2) was selected as the solver algorithm to calculate the control input. The machine-side rectifier switching frequency was 5 kHz and the simulation time was 8 s. The specific simulation results are shown in Figure 3. Figure 9 shown.

[0252] Table 3 Simulation parameters of permanent magnet direct drive fan system

[0253]

[0254] The parameter adjustment results of the sliding mode controller based on NLESO are shown in Table 4.

[0255] Table 4 Main parameters of DCSMC method

[0256]

[0257] (1) Performance verification under step wind speed

[0258] Starting from 8m / s, it steps up to 10m / s and 12m / s at t=2s and t=4s respectively, and returns to 10m / s at 6s. The waveform of the change is as follows: Figure 9 As shown in (a).

[0259] Figure 9(b) A comparative analysis of PI control, SMC, and the proposed DCSMC under step wind speed conditions was performed. The three strategies were compared to evaluate the generator speed response. The results show that all methods have good speed tracking. Calculated using the optimal tip speed ratio method, the optimal speeds are 82.5, 103.1, 123.7, and 103.1 r / min, respectively. DCSMC demonstrates good accuracy in tracking the optimal speed command, effectively improving wind energy capture efficiency. DCSMC performs particularly well under sudden wind speed changes, as torque varies rapidly with wind speed, creating external disturbances. The NLESO employed in this paper effectively estimates these disturbances and provides real-time feedback to the sliding mode controller for compensation, thereby reducing overshoot. The sliding mode controller also improves system convergence speed. Because PI control parameters are based on local linearization, performance degrades when the offshore wind power system is disturbed due to changes in the operating point, manifesting as large overshoot. While SMC can compensate for nonlinear disturbances, this compensation has an upper limit, and overshoot still exists. At the second wind speed step (t = 4s), the convergence times of PI, SMC, and DCSMC were 0.12s, 0.12s, and 0.02s, respectively. DCSMC achieved almost no overshoot, while PI and SMC achieved overshoots of 12.21% and 11.1%, respectively. This demonstrates DCSMC's superior anti-disturbance capability in the face of sudden wind speed changes.

[0260] Figure 9 (c) and Figure 9 (d) The wind energy utilization coefficient and tip speed ratio response curves for the three methods described above under step wind speeds are shown. All three methods stabilize these curves at their maximum values ​​of 0.48 and optimal values ​​of 8.1, respectively. This demonstrates the effectiveness of this study's use of the optimal tip speed ratio method and dual closed-loop vector control structure to design the generator-side converter control system. Using the DCSMC method, both the wind energy utilization coefficient and tip speed ratio curves converge to their optimal values ​​within 0.03 seconds, demonstrating extremely fast response speed and zero overshoot, as well as strong anti-disturbance capability. This demonstrates the proposed DCSMC method's superior tracking accuracy.

[0261] Figure 10 (e) shows the wind turbine output power curve under step wind speed conditions. It can be observed that the DCSMC method can enable the offshore wind turbine to output more energy and the power change is smoother. Figure 10 (f) shows the three-phase current curve of the PMSG using the DCSMC method, whose amplitude changes with wind speed. The enlarged view shows that the three-phase current is not distorted and changes sinusoidally, indicating good quality.

[0262] (2) Performance verification under random wind speed

[0263] In order to further verify the performance of the DCSMC control strategy proposed in this paper, this paper uses the Kaimal random wind speed model to generate a random wind speed signal, whose waveform is as follows: Figure 10 (g) shown. Figure 10 (h) Comparison of the generator speed tracking performance under random wind speed conditions using PI control, SMC, and DCSMC methods is shown. The tracking performance under PI control fluctuates most severely near the optimal speed. Although the SMC method can bring the generator speed closer to the reference value to a certain extent, some fluctuations still exist. NLESO effectively mitigates speed fluctuations by observing the system's aggregated disturbances and performing real-time compensation. Using the DCSMC method, the generator speed tracks the optimal speed more smoothly, quickly, and accurately, demonstrating the proposed method's strong anti-disturbance performance.

[0264] Figure 10 (i) is the wind energy utilization coefficient curve of the wind turbine when the three control methods are used respectively. When the DCSMC method is used, its C P The C value is always higher than that of the other two control methods. P The DCSMC method captures wind energy more effectively. Due to the strong random waveform of natural wind, the PI control method performs the worst, with the wind utilization coefficient curve fluctuating the most. The SMC method offers some improvement in wind energy capture performance. Figure 10 (j) Further demonstrating the differences in tip speed ratio response among the three control methods. The DCSMC method exhibits superior regulation, while the tip speed ratio response curves of the PI control and SMC methods exhibit some fluctuation. Furthermore, in the initial stage, the DCSMC method can adjust the tip speed ratio to the optimal value within 0.05 s, while the PI control and SMC methods require 0.2 s to reach the optimal value. This demonstrates that the DCSMC method has significant advantages in terms of dynamic response speed and control accuracy for offshore wind power systems.

[0265] Figure 11 (k) Shows the offshore wind turbine output power of three control strategies under random wind speed. The DCSMC method can capture more wind energy than the PI control and SMC methods. Figure 11 (l) is the three-phase current change curve of PMSG under random wind speed when using the DCSMC method. The amplitude changes with the wind speed and the quality is good, indicating that the PMSG works well.

[0266] Based on the theoretical and simulation foundations of wind power systems studied, this application builds a hardware-in-the-loop simulation platform for permanent magnet direct-drive wind power generation systems using StartSim software independently developed by Yuankuan Energy Technology and PXI hardware from National Instruments (NI). This platform conducts hardware-in-the-loop experiments and analysis on the DCSMC method based on a full-order sliding mode observer. The hardware-in-the-loop real-time simulation system for offshore wind power systems consists of two major components: the hardware architecture and the supporting experimental software. The experimental platform built in this paper specifically includes key components such as a real-time simulator, a rapid prototyping controller, an adapter board, a host computer, and an oscilloscope.

[0267] During the hardware-in-the-loop simulation experiment test, this application used the Simulink tool to construct a detailed model of the permanent magnet direct-drive offshore wind power generation system. In order to further verify the correctness of the closed-loop control based on the DCSMC method of the full-order sliding mode observer, this application used a hardware-in-the-loop platform based on StarSim and PXI to simulate the working status of the offshore wind turbine generator set in real time. The power coefficient, output power, generator rotor position, speed, etc. of the offshore wind turbine are sent to the oscilloscope in the form of analog signals through the IO adapter board for waveform display. In view of the fact that the voltage range of the analog output channel of the real-time simulator is ±10V, the over-limit signal is proportionally reduced accordingly, and the specific reduction ratio parameters are set as follows: wind speed is 1 / 2, power is 1 / 10000, three-phase current is 1 / 26, and speed is 1 / 15.

[0268] To improve the maximum power point tracking performance of permanent magnet direct-drive offshore wind power generation systems, this application addresses the shortcomings of traditional PI control in tracking the optimal speed and proposes a disturbance compensation-based sliding mode control (DCSMC) method. This DCSMC method enhances the performance of SMC by incorporating an improved NLESO, thereby improving the power capture efficiency of offshore wind power systems under dynamically changing wind speed conditions. First, based on the traditional exponential reaching law, an improvement is made by introducing a variable speed exponential reaching law that is dynamically related to the system state variables. Then, a non-singular fast terminal sliding mode controller based on the variable exponential reaching law is proposed, which effectively improves the system convergence speed and weakens the sliding mode chattering. Secondly, an improved nonlinear function is proposed, and a more accurate improved NLESO is designed. The NLESO is used to estimate the lumped disturbance of the offshore wind power system and perform real-time dynamic compensation. This method effectively solves the problem of the upper limit of traditional SMC in disturbance compensation, significantly improving the system's anti-disturbance performance and control accuracy. The stability of the non-singular fast terminal sliding mode speed controller and the improved NLESO is proved using Lyapunov stability theory. Simulation analysis was conducted under step wind speed conditions. The results demonstrated that the proposed DCSMC method outperformed both PI control and SMC methods in achieving MPPT. A hardware-in-the-loop platform for a permanent magnet direct-drive wind turbine system, based on StarSim and PXI, was constructed. Experiments were conducted using this platform to test a disturbance-compensated sliding mode control strategy using a full-order sliding mode observer. The results demonstrated the feasibility of the proposed strategy.

[0269] Figure 12 This is a structural block diagram of a maximum power point tracking sliding mode control device based on an offshore wind power system provided in one embodiment of the present application. For the sake of convenience, only the parts related to the embodiment of the present application are shown. Figure 12 The device 1200 includes a determination module 1201 , a processing module 1202 , and a control module 1203 .

[0270] In one implementation, the apparatus 1200 may be used to implement the above Figure 2 For example, the determination module 1201 is used to implement steps S210 and S220, the processing module 1202 is used to implement steps S230 and S240, and the control module 1203 is used to implement step S250.

[0271] The device described in this application first improves on the traditional exponential reaching law by introducing a variable-speed exponential reaching law that is dynamically related to the system state variables. This leads to a non-singular fast terminal sliding mode controller based on the variable exponential reaching law, which effectively improves the system convergence speed and weakens sliding mode chattering. Secondly, an improved nonlinear function is proposed, and a more accurate improved NLESO is designed. This NLESO is used to estimate the aggregate disturbance of the offshore wind power system and perform real-time dynamic compensation. This method effectively solves the problem of the upper limit of disturbance compensation in traditional SMC and significantly improves the system's anti-disturbance performance and control accuracy.

[0272] Figure 13 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 13 As shown, the electronic device 13 of this embodiment includes: at least one processor 130 ( Figure 13 Only one is shown in the figure) a processor, a memory 131, and a computer program 132 stored in the memory 131 and executable on the at least one processor 130, wherein the processor 130 implements the steps of any of the above method embodiments when executing the computer program 132.

[0273] The electronic device 13 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device may include, but is not limited to, a processor 130 and a memory 131. Those skilled in the art will understand that Figure 13 This is merely an example of the electronic device 13 and does not constitute a limitation on the electronic device 13 . The electronic device 13 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, etc.

[0274] The processor 130 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0275] In some embodiments, the memory 131 may be an internal storage unit of the electronic device 13, such as a hard disk or memory of the electronic device 13. In other embodiments, the memory 131 may also be an external storage device of the electronic device 13, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 13. Furthermore, the memory 131 may also include both an internal storage unit of the electronic device 13 and an external storage device. The memory 131 is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as the program code of the computer program. The memory 131 may also be used to temporarily store data that has been output or is to be output.

[0276] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A maximum power point tracking sliding mode control method based on an offshore wind power system, characterized in that: The method comprises: S1: Determine a sliding mode speed controller based on a variable exponential reaching law based on a dynamic equation of a transmission system of the offshore wind power system, a first state variable, a sliding surface equation, and a variable exponential sliding mode controller; Step S1 includes: S11: determining a transformation formula of the transmission system dynamic equation based on the transmission system dynamic equation of the offshore wind power system; The dynamic equation of the transmission system of the offshore wind power system is: Where J is the total moment of inertia of the transmission system, B is the damping coefficient of the transmission system, and T m is the mechanical torque of the fan, ω r is the mechanical angular velocity of the rotor, T e is the engine electromagnetic torque, ω m is the mechanical angular velocity of the rotor; dt is the increment per unit time; The transformation formula is: Where b is the coefficient term; u is the Q-axis component of the stator current in the synchronous rotating coordinate system DQ; the lumped disturbance f(t) = (T m -Bω r ) / J; Yes m The first-order derivative of , the point on the top of the parameter represents the derivative, the same below; S12: Determine a system state variable error equation based on the transformation formula and the first state variable; The first state variable is: Where, e1 is the first tracking error, e2 is the second tracking error, ω ropt is the optimal speed corresponding to the wind speed; is the first derivative of e1; The system state variable error equation is: S13: Determining an intermediate equation based on the system state variable error equation and the sliding surface equation; including: Derivative the sliding surface equation to determine a derivative equation of the sliding surface equation; Substituting the system state variable error equation into the derivative equation of the sliding surface equation to determine the intermediate equation; The equation of the sliding surface s is: s=e1+α|e1| γ +βe2 m / n ; Wherein, α, β, and γ are all positive constants, and γ>1; m and n are positive odd numbers, 1<m / n<2, and γ>m / n; The intermediate equation is: S14: Determine the sliding mode speed controller based on the variable exponential reaching law based on the intermediate equation and the variable exponential sliding mode controller; The variable exponential sliding mode controller equation is: Where, k>0, q>0, δ>1, 0<ε<1, τ>0, exp represents the exponent, e represents the error of the system state variable, h(s)|s| δ represents the gain of the switching function, qs represents the exponential approach term, and s represents the sliding surface; The sliding mode speed controller based on the variable exponential reaching law is: Where α, β, and γ are all positive constants, m and n are positive odd numbers, 1<m / n<2, and γ>m / n; sgn(s) is the sign function; S2: Determine the nonlinear function to be improved based on the second-order nonlinear system equations of the traditional nonlinear extended state observer and unknown disturbances; Step S2 includes: S21: Reconstruct the second-order nonlinear system equation of the unknown disturbance to determine the third-order system equation; The second-order nonlinear system equation of the unknown disturbance is: Where x1(t), x2(t), and x3(t) represent system state variables, f(·) is f(t), d(t) is the unknown disturbance, u(t) is the control input, and b is a parameter term. The third-order system equation is: Where φ(t) is the total disturbance and y(t) is the system output; S22: observing x3(t) in the third-order system based on the traditional nonlinear extended state observer to determine the nonlinear function to be improved; The nonlinear function to be improved is: Where a and d are adjustable parameters, x is the system state variable; sign(x) is the sign function; S3: improving the nonlinear function to be improved by using interpolation fitting to obtain an improved nonlinear function; Step S3 includes: S31: Based on the nonlinear function to be improved, determining a first-stage nonlinear function equation through interpolation fitting; The nonlinear function equation of the first stage is: Where k1, k2 and k3 are parameters; S32: Based on the nonlinear function equation of the first stage and the nonlinear function to be improved, improving the nonlinear function to be improved, and determining the nonlinear function equation of the second stage; The second stage nonlinear function equation is: S33: Based on the first-stage nonlinear function equation and the second-stage nonlinear function equation, determine parameter values ​​of k1, k2, and k3: The parameter values ​​of k1, k2 and k3 are: S34: Determine the improved nonlinear function based on the parameter values ​​of k1, k2 and k3 and the first-stage nonlinear function equation; The improved nonlinear function is: S4: improving the traditional nonlinear extended state observer based on the improved nonlinear function to obtain an improved nonlinear function extended state observer; Step S4 includes: S41: Obtaining a lumped disturbance of the offshore wind power system, where the lumped disturbance includes internal parameter perturbations of mechanical torque, moment of inertia, and friction coefficient of the offshore wind power system and external time-varying disturbances; S42: Determine a second system state variable equation based on the lumped disturbance; The state variable equation of the second system is: In the formula, the system state variable x1=ω m , expanded state variable x2=(T m -Bω m ) / J=f(t); S43: Determine, based on the second system state variable, a state space equation of a second-order nonlinear system corresponding to the second system state variable equation; The state space equation of the second-order nonlinear system is: Where, C = [0 1]; S44: improving the traditional nonlinear extended state observer based on the state space equation of the second-order nonlinear system and the improved nonlinear function to obtain an improved nonlinear function extended state observer; The improved nonlinear function extended state observer is: Where, β 01 >0,β 02 >0 is the output error correction gain of the improved nonlinear function extended state observer, a1 and a2 are nonlinear factors, d1 and d2 are filter factors, and z1 is the value of ω m An estimate of , z2 is an estimate of the lumped disturbance; S5: Based on the sliding mode speed controller based on the variable exponential reaching law and the improved nonlinear function extended state observer, the maximum power point tracking sliding mode control of the offshore wind power system is realized.

2. The maximum power point tracking sliding mode control method based on an offshore wind power system according to claim 1, characterized in that: Step S5 includes: S51: Determine an output equation of a sliding mode controller according to an output result of the improved nonlinear function extended state observer; S52: Implementing maximum power point tracking sliding mode control of the offshore wind power system based on an output equation of the sliding mode controller; The output equation of the sliding mode controller is: in is the instantaneous value of current; where, is an estimate of the lumped disturbance.

3. A maximum power point tracking sliding mode control device based on an offshore wind power system, used to implement the method according to any one of claims 1-2, characterized in that: The device comprises: a determination module for determining a sliding mode speed controller based on a variable exponential reaching law based on a transmission system dynamic equation, a first state variable, a sliding surface equation, and a variable exponential sliding mode controller of the offshore wind power system; The determination module is further configured to determine the nonlinear function to be improved based on a traditional nonlinear extended state observer and a second-order nonlinear system equation of an unknown disturbance; A processing module, configured to improve the nonlinear function to be improved by using interpolation fitting to obtain an improved nonlinear function; The processing module is further configured to improve the traditional nonlinear extended state observer based on the improved nonlinear function to obtain an improved nonlinear function extended state observer; A control module is used to implement maximum power point tracking sliding mode control of the offshore wind power system based on the sliding mode speed controller based on the variable exponential reaching law and the improved nonlinear function extended state observer.

4. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 2 is implemented.

5. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 2 is implemented.

Images