Optimal Power Capture Method for Wave Energy Devices in Floating Wind-Wave Power Platforms

Abstract

The present invention provides an optimal power capture method for wave energy devices in a floating wind-solar-wave platform, including: Step 1, establishing an actual system equation considering external disturbances; Step 2, designing a sliding mode surface and a reaching law using sliding mode control; Step 3, solving the control quantity of the nominal system and obtaining the control quantity of the actual system through correction by an auxiliary control input; Step 4, obtaining an optimal control quantity capable of effectively suppressing bus voltage fluctuations; Step 5, inputting the optimal control quantity and the actual value into a proportional-integral-derivative controller, decoupling through a decoupling controller, and obtaining switching signals using a space vector pulse width modulation algorithm for tracking control. The present invention can ensure that the wave energy device is always in the maximum power capture state in the unknown disturbances caused by wind turbines and complex marine environments.

Description

Technical Field

[0001] The present invention relates to the field of power generation of offshore multi - energy coupling platforms, and particularly to an optimal power capture method for wave energy devices in a floating wind - solar - wave platform. Background Art

[0002] At present, many places are vigorously constructing efficient and stable offshore floating power supply platforms, among which the floating wind - power - photovoltaic - wave energy power generation platform is regarded as the key technical path to solve the problem of stable power supply in the deep - sea area.

[0003] In actual operation, the coupling effect of the operation of the wind turbine on the power capture characteristics of the wave energy device cannot be ignored. Moreover, when multiple energy sources are jointly fed into the DC bus, the volatility of wind energy and photovoltaic power may cause multi - frequency disturbances to the DC bus voltage, especially high - frequency fluctuations, which pose challenges to the power generation performance of the platform. In addition, in traditional wave energy power capture algorithms, the float is prone to approach the structural edge under extreme sea conditions, resulting in the device being subjected to transient impact loads, further exacerbating the uncertainty of system operation. The existence of these problems poses higher requirements for the optimal power capture of wave energy devices in floating wind - solar - wave platforms. Summary of the Invention

[0004] Object of the Invention: The technical problem to be solved by the present invention is to provide an optimal power capture method for wave energy devices in a floating wind - solar - wave platform in view of the deficiencies of the prior art, including the following steps:

[0005] Step 1, establish an actual system equation considering external disturbances to describe the dynamic behavior of the wave energy power generation system in actual situations; at the same time, define the system without considering external disturbances as the nominal system;

[0006] Step 2, use sliding mode control (SMC) to design a sliding surface and a reaching law, and limit the error caused by external disturbances within a bounded pipeline by calculating the auxiliary control input;

[0007] Step 3, according to the input constraints of the actual system, combined with the auxiliary control input, obtain the input constraints of the nominal system; construct a cost function with the maximum power as the goal for the nominal system, solve the control quantity of the nominal system under the constraint conditions, and correct it through the auxiliary control input to obtain the control quantity of the actual system;

[0008] Step 4, design an extended state observer to observe the bus voltage fluctuation, combine the weighted fluctuation quantity with the control quantity of the actual system to obtain an optimal control quantity that can effectively suppress the bus voltage fluctuation;

[0009] Step 5: Input the optimal control quantity and the actual value into a Proportional Integral Derivative (PID) controller, decouple them through a decoupling controller, and use the Space Vector Pulse Width Modulation (SVPWM) algorithm to obtain switching signals for tracking control.

[0010] In Step 1, the discrete system affected by external disturbances is expressed as:

[0011] ,

[0012] where is the state of the disturbed system at time where denotes the n-dimensional real number space, is the actual system control input at time where denotes the m-dimensional real number space, is

[0013] the bounded disturbance at time

[0014] ,

[0015] where represents the actual system constraint set, and the superscript represents the actual system considering disturbances. The sets and are polyhedral, bounded, and full-dimensional, and contain the origin within the sets and ;

[0016] By eliminating the disturbance the state-space equation of the nominal system is obtained:

[0017] ,

[0018] where is the state of the nominal system at time and is

[0019] the nominal control input at time When the actual system is affected by disturbances, the actual state will be different from the nominal state trajectory, resulting in the generation of the deviation state at time ; To solve this problem, an auxiliary controller is added , is the gain coefficient, forcing the actual trajectory to be as close as possible to the nominal trajectory; thus, the actual system control input at time is expressed as:

[0020] ,

[0021] The constraint relationship between the actual system state and the control input is defined as:

[0022] ,

[0023] This represents that the nominal system constraint set will be shrunk from the actual system constraint set to provide a reliable state convergence process.

[0024] In step 2, the sliding mode surface and the reaching law are set by the sliding mode controller to make the actual state as close as possible to the nominal state, and the state error equation is established:

[0025] ,

[0026] where, is the scale vector, is the auxiliary SMC control law to ensure the convergence of the error state at time. To reduce the jitter phenomenon in the tracking process, a chattering-free sliding mode reaching law method is adopted, which can improve the smoothness of the reaching process. Finally, the auxiliary SMC control law is:

[0027] ,

[0028] where, is the sliding mode surface coefficient, is the non-linear smoothing function, which increases the smoothness compared with , is the smoothing coefficient, ; , is the sliding mode surface slope adjustment parameter, is the time-related factor; , is the gain parameter of the sliding mode control law, is the control law smoothing parameter; is the discrete switching function, is the bounded disturbance at time.

[0029] In step 3, the constraint range of the model predictive current control (MPC) of the nominal system model is based on the boundary of the auxiliary sliding mode control (SMC) law and is expressed as:

[0030] ,

[0031] where, is the upper limit of the SMC control input, represents the addition of sets;

[0032] The cost function with the maximum power as the objective in the nominal system is:

[0033] ,

[0034] where, is the model prediction interval, represents the float motion speed at the -th prediction interval at time is the weight coefficient considering the copper loss of the motor, is the change in the control input of the nominal system, is the weight coefficient of the control input difference penalty term;

[0035] Therefore, the control input of the actual system combined with the auxiliary SMC is:

[0036] ,

[0037] In step 3, after obtaining the electromagnetic thrust of the motor, the corresponding q-axis current reference value is obtained through the electromagnetic relationship:

[0038] ,

[0039] where, is the electromagnetic thrust, is the pole pitch of the motor, is the magnetic flux linkage of the motor magnetic field, is the motor -axis current.

[0040] In step 4, the extended state observer is set as:

[0041] ,

[0042] where, is the observed value of the is the difference between the observed value of the current and the is Derivative of the shaft current observation value, is a newly defined quantity, and the bus voltage fluctuation can be deduced by combining it with other state quantities; 、 are observer parameters, is the motor inductance value, for the motor shaft voltage, is the derivative of the newly defined quantity, is the observation function of the extended state observer, is the amplification gain; is the linear segment range.

[0043] In step 4, the expression of the newly defined quantity is:

[0044] ,

[0045] where, is the motor resistance, is the bus voltage disturbance, is the motor rotor speed, is the motor flux, for the motor shaft current.

[0046] In step 4, the optimal control quantity obtained after weighting the bus voltage fluctuation is:

[0047] ,

[0048] where, is the optimal control quantity considering the bus voltage fluctuation, is the optimal control quantity corresponding to the control input of the actual system combined with the auxiliary SMC obtained in step 3, is the bus voltage disturbance weight value.

[0049] Step 5 includes: The decoupling controller output equation is:

[0050] ,

[0051] ,

[0052] where, is the shaft voltage reference value after decoupling the PID control output, is the shaft voltage reference value after decoupling the PID control output, is the shaft voltage reference value, for the motor shaft inductance, S represents the Laplace operator, 、 is the proportional parameter of PID control, and is the integral parameter of PID control. is the angular velocity of the motor, and are respectively the axis electromotive force and axis electromotive force of the motor.

[0053] The present invention also provides an electronic device, including a processor and a memory. The memory stores program codes. When the program codes are executed by the processor, the processor is caused to execute the steps of the method described above.

[0054] The present invention can be used to improve the power generation efficiency of wave energy devices and enhance the energy capture stability of other power generation devices (wind power and photovoltaic). In offshore power generation applications, waves usually appear as irregular waves, and the wave force magnitude changes irregularly with the wave height. In addition, the operation disturbance of the fan affects the wave energy device. The superposition of these two factors makes the energy collection part show irregular fluctuations, resulting in the system being difficult to operate stably at the maximum power point in real time, thereby reducing the overall power generation efficiency. In view of the limitations of the existing wave energy power capture algorithm, the present invention proposes a wave energy maximum power capture method based on an improved robust tube model predictive control strategy. By introducing an error tube structure and combining with sliding mode control (SMC), it is ensured that the system always maintains a state close to maximum power capture in the presence of unknown disturbances. At the same time, the rolling optimization mechanism of MPC can enable the float to adjust the motion state of the float in advance when facing large wave heights and optimize the force state. This advance adjustment can not only reduce mechanical shock but also ensure the energy capture efficiency, thereby improving the stability and reliability of the system under extreme sea conditions. Further, through an extended state observer (ESO), the bus voltage fluctuation is observed in real time, and through wave energy compensation, the voltage fluctuation caused by sea condition changes and multi-energy merging into the bus is effectively suppressed, ensuring that the floating wind-solar-wave platform can still operate stably and efficiently in a complex marine environment.

[0055] Beneficial effects: Compared with traditional control methods, the present invention can ensure that the wave energy device is always in the maximum power capture state in the unknown disturbances caused by the fan and the complex marine environment. By accurately predicting the motion state of the float, the energy capture ability of the device is further improved, and its durability and reliability are significantly enhanced, thus effectively extending the service life of the equipment. In addition, an extended state observer is used to monitor the bus voltage fluctuation in real time, and a smooth adjustment is carried out through the dynamic compensation mechanism of the wave energy device, ensuring the overall power supply efficiency of the floating wind-solar-wave platform. The innovation of the present invention combines advanced dynamic modeling, robust control theory and real-time power regulation technology, fully considering the influence of external disturbances and environmental factors on the system, and realizing the efficient, stable and sustainable operation of the wave energy device of the floating wind-solar-wave platform. Description of the Drawings

[0056] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0057] Figure 2 This is a schematic diagram of the floating wind-solar-wave platform of the present invention.

[0058] Figure 3 This is a schematic diagram of the improved robust pipeline model predictive control principle of the present invention.

[0059] Figure 4 This is an effect diagram of the improved robust pipeline model predictive control of the present invention.

[0060] Description of the reference numerals: 1 is a wind turbine; 2 is the main floating body platform, which is the main load-bearing structure of the entire platform; 3 is a connecting bracket (right arm); 31 is a connecting bracket (left arm); 34 is a wave energy power generation device; 35 is a photovoltaic panel. Detailed Embodiments

[0061] The following further detailed description of the present invention will be made in conjunction with the drawings and specific embodiments, and the above and / or other advantages of the present invention will become clearer.

[0062] As Figure 1 shown, the embodiment of the present invention provides an optimal power capture method for the wave energy device in the floating wind-solar-wave platform, including the following steps:

[0063] Establish an actual system equation considering external disturbances to describe the dynamic behavior of the wave energy power generation system in actual situations; at the same time, define the system without considering external disturbances as the nominal system.

[0064] Use sliding mode control (SMC) to design a sliding surface and a reaching law, and limit the error caused by external disturbances within a bounded pipeline range by calculating the auxiliary control input;

[0065] According to the input constraints of the actual system and combined with the auxiliary control input, the input constraints of the nominal system are obtained; a cost function with the maximum power as the target for the nominal system is constructed, the control quantity of the nominal system is solved under the constraint conditions, and the control quantity of the actual system is obtained by correcting through the auxiliary control input;

[0066] An extended state observer is designed to observe the bus voltage fluctuation. The fluctuation quantity is weighted and combined with the previously calculated control quantity of the actual system to obtain an optimal control quantity that can effectively suppress the bus voltage fluctuation;

[0067] The optimal control quantity and the actual value are input into a proportional integral derivative (PID) controller and decoupled, and the space vector pulse width modulation (SVPWM) algorithm is used to obtain the switching signal for tracking control.

[0068] It should be further noted that in the specific implementation process, referring to Figure 1 the left half, the discrete system affected by external disturbances is expressed as:

[0069] ,

[0070] where is the state of the perturbed system at time, where represents the n-dimensional real number space, is the control input at time, where represents the m-dimensional real number space, is the bounded disturbance at time, where represents the disturbance set; the source of the disturbance is as Figure 2 shown. The wind turbine will have an unknown disturbance effect on the wave energy device at the bottom of the platform during operation. A is the state transition matrix of the actual system, and B is the control input matrix.

[0071] The constraint relationship between the actual system state and the control input is defined by the following formula:

[0072] ,

[0073] where represents the actual system constraint set, and the sets and are polyhedral, bounded and full-dimensional, and contain the origin inside the sets and ;

[0074] By eliminating the interference Obtain the state - space equation of the nominal system:

[0075] ,

[0076] where, is the nominal system state at time and is the nominal control input at time;

[0077] When the actual system is subject to disturbances, the actual state will be different from the nominal state trajectory, resulting in the generation of the deviation state at time, denoted as ; To solve this problem, an auxiliary controller , where is the gain coefficient, is added to force the actual trajectory to be as close as possible to the nominal trajectory. Therefore, the actual system control input at time should consist of two parts:

[0078] ,

[0079] The constraint relationship between the actual system state and control input is defined as:

[0080] ,

[0081] This means that the nominal system constraint set will be shrunk from the actual system constraint set to provide a reliable state convergence process.

[0082] By setting the sliding mode surface and reaching law through a sliding - mode controller, the actual state is made as close as possible to the nominal state, and the state error equation is established:

[0083] ,

[0084] where, is the scale vector, is the auxiliary SMC control law that ensures the convergence of the error state at time. To reduce the chattering phenomenon during the tracking process, a chattering - free sliding - mode reaching law method is adopted, which can improve the smoothness of the reaching process. Finally, the auxiliary SMC control law is:

[0085] ,

[0086] where, is the sliding - mode surface coefficient, is the non - linear smoothing function, which increases the smoothness compared to , is the smoothing coefficient, ; , is the sliding mode surface slope adjustment parameter, is the time-related factor; , is the gain parameter of the sliding mode control law, is the control law smoothing parameter; is the discrete switching function, is the bounded disturbance at time

[0087] It should be further noted that in the specific implementation process, as Figure 3 shown, the flow schematic diagram of the robust pipeline MPC improved based on the auxiliary SMC control law is described. The constraint range of the nominal system model predictive (Model predictive current control, MPC) is based on the boundary of the auxiliary SMC control law and is expressed as:

[0088] ,

[0089] where, is the upper limit of the SMC control input, represents the addition of sets.

[0090] The cost function with the maximum power as the goal in the nominal system is:

[0091] ,

[0092] where, is the model prediction interval, represents the float movement speed at time in the is the weight coefficient considering the copper loss of the motor, is the change in the control input of the nominal system, is the weight coefficient of the control input difference penalty term;

[0093] Therefore, the control input of the actual system combined with the auxiliary SMC is:

[0094] ,

[0095] After obtaining the electromagnetic thrust of the motor, the corresponding q-axis current reference value is obtained through the electromagnetic relationship:

[0096] ,

[0097] where, is the electromagnetic thrust, is the pole pitch of the motor, is the magnetic flux linkage of the motor magnetic field, is the motor axial current.

[0098] It should be further noted that in the specific implementation process, as Figure 1 shown on the right, the extended state observer is set as:

[0099] ,

[0100] where, is the observed value of the axial current, is the difference between the observed value of the current and the axial current, is the derivative of the observed value of the axial current, is a newly defined quantity, and the bus voltage fluctuation can be deduced by combining it with other state variables. , are the observer parameters, is the motor axial voltage, is the derivative of the newly defined quantity, is the observation function of the extended state observer, is the amplification gain. is the linear segment range, all of which are common parameters of the extended state observer.

[0101] The expression of the newly defined quantity is:

[0102] ,

[0103] where, is the motor resistance, is the bus voltage disturbance, is the rotor speed of the motor, is the motor magnetic flux, is the motor axial current.

[0104] The optimal control quantity obtained by weighting the bus voltage fluctuation is:

[0105] ,

[0106] where, is the optimal control quantity considering the bus voltage fluctuation, is the optimal control quantity corresponding to the control input of the actual system combined with the auxiliary SMC before, is the weight value of the bus voltage disturbance.

[0107] The output equation of the decoupling controller is:

[0108] ,

[0109] ,

[0110] Among them, is the decoupled shaft voltage reference value of the PID control output, is the decoupled shaft voltage reference value of the PID control output, , is the proportional parameter of the PID control, , is the integral parameter of the PID control. is the angular velocity of the motor, , is the , axis electromotive force of the motor. After decoupling, the switching signal is obtained by using the SVPWM algorithm, and the frequency is set according to the actual IGBT specifications.

[0111] It should be further noted that in the specific implementation process, in order to further verify the practicability of the present invention, as Figure 4 shown, the robust tube MPC is used to constrain the disturbed actual system values within the tube to achieve the goal of the cost function.

[0112] Establish the system state space model

[0113] ,

[0114] Among them, has a dimension of 2×1, has a dimension of 1×1, has a dimension of 2×1.

[0115] The state matrix and input matrix of the system are respectively:

[0116] ,

[0117] The value of the disturbance is restricted within a convex polyhedron defined as:

[0118] ,

[0119] This convex hull restricts the upper and lower bounds of the disturbance, ensuring that the system design is robust within a reasonable disturbance range.

[0120] The constraint set of the state is:

[0121] ,

[0122] Control input constraint set is

[0123] ,

[0124] These constraints ensure that the system state and input are physically feasible.

[0125] Prediction horizon length = 10;

[0126] Cost function weight matrix :

[0127] ,

[0128] Use a robust tube MPC controller to optimize the control of the state such that:

[0129] 1. At each time , the controller can output a robust control input ;

[0130] 2. The control path gradually converges to the robust disturbance set ;

[0131] 3. The system state always satisfies the constraint set .

[0132] After simulation calculations, as Figure 4 shown, the following results can be obtained:

[0133] 1. The system state trajectory gradually tends to the robust maximum invariant set and successfully converges within 15 steps;

[0134] 2. The system path always satisfies the state and input constraints;

[0135] 3. Compared with the traditional MPC method, the robustness of the present invention to disturbances is significantly improved, and it can converge more quickly in the face of disturbances.

[0136] The present invention provides an optimal power capture method for wave energy devices in a floating wind-solar-wave platform. There are many methods and ways to specifically implement this technical solution. The above description is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention. Each component not clearly defined in this embodiment can be implemented by existing technologies.

Claims

1. An optimal power capture method for wave energy devices in a floating wind-solar-wave platform, characterized in that, Including the following steps: Step 1: Establish an actual system equation considering external disturbances to describe the dynamic behavior of the wave energy power generation system under actual conditions; Meanwhile, the system without considering external disturbances is defined as the nominal system; Step 2: Use sliding mode control to design the sliding surface and reaching law, and limit the error caused by external disturbances within a bounded pipe range by calculating the auxiliary control input; Step 3: According to the input constraints of the actual system, combined with the auxiliary control input, obtain the input constraints of the nominal system; construct a cost function with the maximum power as the target for the nominal system, solve the control quantity of the nominal system under the constraint conditions, and correct it through the auxiliary control input to obtain the control quantity of the actual system; Step 4: Design an extended state observer to observe the bus voltage fluctuation, combine the weighted fluctuation amount with the control amount of the actual system, and obtain an optimal control amount that can effectively suppress the bus voltage fluctuation ; Step 5, apply the optimal control quantity to the proportional-integral-derivative (PID) controller of the q-axis current input of the motor, decouple it through the decoupling controller, and use the space vector pulse width modulation algorithm to obtain the switching signal for tracking control.

2. The method according to claim 1, wherein In Step 1, the actual system equation is expressed as: ， wherein, is the state of the perturbed system at time, where represents the n-dimensional real space, is the actual system control input at time, where represents the m-dimensional real space, is the bounded disturbance at time, where represents the disturbance set; A is the state transition matrix of the actual system, and B is the control input matrix; The constraint relationship between the actual system state and the control input is defined by the following formula: ， Among them, represents the set of actual system constraints, and the superscript represents the actual system considering disturbances, and the sets and are polyhedral, bounded, and full-dimensional, and contain the origin within the sets and ; By eliminating the interference The state-space equation of the nominal system is obtained as follows: ， wherein, is the nominal system state at time is the nominal control input at time When the actual system is disturbed, the actual state will be different from the nominal state trajectory, resulting in the generation of the deviation state at the moment, denoted as ; adding an auxiliary controller , where is the gain coefficient, the control input of the actual system at the moment is expressed as: ， The constraint relationship between the actual system state and the control input is defined as: 。 3. The method according to claim 2, wherein In Step 2, establish the state error equation: ， Among them, is the scale vector, is the auxiliary sliding mode control law that ensures the convergence of the error state at all times; The final auxiliary sliding mode control law is: ， Among them, is the sliding mode surface coefficient, is a non-linear smoothing function, which increases the smoothness compared with sgn(s(k)), is the smoothing coefficient, ; , is the sliding mode surface slope adjustment parameter, is the time-related factor; , is the gain parameter of the sliding mode control law, is the control law smoothing parameter; is the discrete switching function, is the bounded disturbance at the moment.

4. The method according to claim 3, characterized in that In Step 3, the constraint range of the nominal system model control prediction is based on the boundary of the auxiliary sliding mode control law and is expressed as: ， Among them, is the upper limit of the sliding mode control input, represents the addition of sets; Cost function targeting maximum power in the nominal system is as follows: ， Among them, is the model prediction interval, represents the float motion speed of the th prediction interval at time is the weight coefficient considering the copper loss of the motor, is the change in the nominal system control input, is the weight coefficient of the control input difference penalty term; The control input of the actual system combined with the auxiliary sliding mode is: 。 5. The method according to claim 4, characterized in that, In step 3, through After obtaining the electromagnetic thrust of the motor, the corresponding q-axis current reference value is obtained through electromagnetic relations: ， Among them, is the electromagnetic thrust, is the pole pitch of the motor, is the magnetic flux linkage of the motor magnetic field, is the optimal control quantity corresponding to the control input of the actual system combined with the auxiliary sliding mode obtained in step 3.

6. The method according to claim 5, characterized in that In Step 4, the extended state observer is set as: ， Among them, is the measured value of shaft current, is the q-axis current of the motor, is the difference between the measured current value and the shaft current, is the derivative of the measured value of shaft current, is a newly defined quantity; and are the observer parameters, is the inductance value of the motor, is the axis voltage of the motor, is the derivative of the newly defined quantity, is the observation function of the extended state observer, is the amplification gain; is the linear segment range.

7. The method according to claim 6, characterized in that, In Step 4, the new defined quantity expression is: ， Among them, is the motor resistance, is the bus voltage disturbance, is the motor rotor speed, is the motor magnetic flux, is the motor shaft current.

8. The method according to claim 7, wherein In Step 4, the optimal control quantity obtained after weighting the bus voltage fluctuation quantity is: ， Among them, is the optimal control quantity considering the bus voltage fluctuation amount, is the bus voltage disturbance weight value.

9. The method according to claim 8, characterized in that, Step 5 includes: The decoupling controller output equation is: ， ， Among them, is the axis voltage reference value after decoupling of the PID control output, is the axis voltage reference value after decoupling of the PID control output, is axis voltage reference value, is the axis inductance of the motor, L q represents the q-axis inductance of the motor; S represents the Laplace operator, , are the proportional parameters of the PID control, , are the integral parameters of the PID control; is the angular velocity of the motor, , are respectively the axis electromotive force and axis electromotive force of the motor.

10. An electronic device, characterized in that, Including a processor and a memory, the memory stores program code, and when the program code is executed by the processor, the processor is caused to execute the steps of the method according to any one of claims 1 to 9.

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