Wind-wave combined power generation device and its wind-wave-storage island microgrid control method

Abstract

A wind-wave combined power generation device and its wind-wave-storage island microgrid control method are disclosed. The power generation device includes a wind power unit, a wave energy unit, and an energy coupling unit. The wind power unit and the wave energy unit, connected by a coupling, both use hydraulic transmission to drive hydraulic motors to rotate and perform work. They are fixedly connected by the coupling to couple and superimpose wind and wave power, supplying energy to the island microgrid through an energy conversion process of mechanical energy-hydraulic energy-mechanical energy-electrical energy. This invention retains the inertia support capability of rotating components through a converter-free wind-wave-storage multi-energy grid structure, effectively reducing the volatility of wind and wave energy. This allows them to participate in the island microgrid frequency regulation while simultaneously leveraging the power response and support advantages of coupled wind, wave, and storage, achieving multi-energy joint participation in microgrid frequency regulation and effectively improving the stability and survivability of the island microgrid system.

Description

Technical Field

[0001] This invention relates to a technology in the field of power systems, specifically a wind-wave combined power generation device and its wind-wave-storage island microgrid control method. Background Technology

[0002] Existing wind, wave, and energy storage independent units can all adjust power output within a certain range to stabilize the system frequency. Their frequency regulation capabilities differ in terms of response speed and continuous working time. Limited by factors such as response speed, support depth, and power stability, standalone energy storage networking or wind-wave networking solutions cannot cope with the variable working environment of island microgrids and cannot guarantee a stable and reliable energy supply. Summary of the Invention

[0003] This invention addresses the shortcomings of existing control methods, such as the lack of detailed modeling of island microgrid systems, unclear interaction mechanisms among units, and inability to consider the overall system performance. It also addresses the limitations of existing combined power generation devices, which use electrical-side power coupling, increasing power control difficulty and causing mechanical inertia loss due to converters, thus hindering system stability in island environments. Furthermore, it addresses the limitations of energy storage units and wind-wave combined power generation devices in response speed and support depth, making them unsuitable for independently undertaking island microgrid frequency regulation tasks. This invention proposes a wind-wave combined power generation device and its wind-wave-storage island microgrid control method. By using a converter-free wind-wave-storage multi-energy grid structure, the inertia support capability of rotating components is preserved, effectively reducing the volatility of wind and wave energy. This allows the device to participate in island microgrid frequency regulation while simultaneously leveraging the power response and support advantages of wind, wave, and storage, achieving multi-energy joint participation in microgrid frequency regulation and effectively improving the stability and survivability of the island microgrid system.

[0004] This invention is achieved through the following technical solution:

[0005] This invention relates to a wind and wave combined power generation device, comprising: a wind power unit, a wave energy unit, and an energy coupling unit, wherein: the wind power unit and the wave energy unit connected by a coupling both use hydraulic transmission to drive hydraulic motors to rotate and do work, and the two are fixedly connected by a coupling to couple and superimpose wind and wave power, and supply energy to the island microgrid through an energy conversion process of mechanical energy-hydraulic energy-mechanical energy-electrical energy.

[0006] The coupling is a rotating workpiece with stiffness and damping. Its equivalent model is inertia-spring-damping. It achieves speed balance of the hydraulic motor by absorbing the torque difference on both sides through torsional deformation.

[0007] The aforementioned wind and wave combined power generation device floats at sea to capture wind and wave energy and converts it into electricity on-site.

[0008] This invention relates to a control method for a wind-wave-storage island microgrid based on the aforementioned wind-wave combined power generation device. A microgrid system state prediction model is constructed based on the frequency response state-space model of the wind-wave-storage island microgrid system. When a limit is exceeded, the optimal adjustment values ​​of the motor opening and droop coefficient for the next n steps are predicted, and the system state variables are updated after executing the first step instruction. Once the frequency offset returns to within the limit, the motor opening is no longer adjusted; only the droop coefficient is adjusted.

[0009] The state-space model of the island microgrid system includes:

[0010] ① The state-space equations of the system frequency, motor opening degree, and energy storage droop coefficient as well as

[0011] ②System frequency Where: P m P b P L f represents the output power of the combined wind and wave power generation unit, the output power of the energy storage unit, the load demand power, and the system frequency, respectively; T m Let H be the equivalent time constant of the hydraulic motor, H be the system moment of inertia, f0 and Δf be the frequency reference value and the system frequency deviation, and SOC and W be the system frequency deviation. b For energy storage state of charge and capacity, ω out The rotational speed of the coupling; I is the identity matrix with the same dimension as matrix A, T s The system sampling period is k, where k is the sampling time. x, u, and y represent the state variable, control variable, and output variable, respectively; A, B u B d C represents the state matrix, control input matrix, non-control input matrix, and output matrix in the discrete time domain, respectively, and the subscript c represents the corresponding matrix in the continuous time domain.

[0012] The microgrid system state prediction model is specifically as follows:

[0013] Where: Y(k+n|k) and U(k) are the output prediction sequence and control quantity sequence within the n-step prediction interval, respectively, and S x S u S d These represent the state prediction matrix, control prediction matrix, and non-control prediction matrix, respectively, and Δ represents the difference in state quantities between two sampling times.

[0014] Technical effect

[0015] This invention leverages the complementary characteristics of the response speed and support depth of multiple energy sources—wind, wave, and energy storage—by coupling the rapid response of energy storage units with the high-power, long-term output of wind and wave power generation units. It performs refined modeling of the island microgrid's state space, clarifying the system variables and their interaction mechanisms that affect the overall system operation characteristics, and selecting key variables for precise constraints. It constructs an MPC cost function that penalizes system frequency deviation, motor opening increment, and droop coefficient deviation, using the minimum cost function as the system operation optimization objective, achieving multiple control objectives that balance power distribution and frequency optimization. Furthermore, it designs adaptive cost functions for different scenarios, aiming to reduce power margin differences and frequency overshoot, dynamically adjusting the weight of motor opening increment to achieve balanced power distribution and dynamic frequency optimization across the wind, wave, and energy storage power sources. Compared with existing technologies, this invention offers advantages in frequency support by coupling multiple power sources including wind, waves, and energy storage. It broadens the time scale for system response to microgrid frequency fluctuations, improving the stability and survivability of island microgrids. It effectively considers complex system state constraints and operational constraints, balancing multiple control objectives such as power allocation and frequency optimization. It adaptively adjusts the absolute weight of motor opening increments based on system frequency deviations. When the frequency deviation decreases, the weight coefficient of motor opening increments is increased, reducing the sensitivity of wind-wave motors to dynamic frequency deviations, thereby achieving rapid wind and wave power response and effectively avoiding frequency overshoot. Furthermore, it adaptively adjusts the relative weight of motor opening increments based on the current wind and wave power margin. Power sources with larger weights will increase or decrease power less during power changes, thereby reducing the power margin difference between wind and wave units and achieving balanced power distribution among multiple power sources. Attached Figure Description

[0016] Figure 1 This is a flowchart of the present invention;

[0017] Figure 2 This is a schematic diagram of a scenario for an example embodiment;

[0018] Figure 3 This is a scene model diagram for an example embodiment;

[0019] Figure 4 This is a schematic diagram of the system in the embodiment;

[0020] Figure 5 This is a flowchart of an implementation example;

[0021] Figure 6 A schematic diagram of the frequency control model for a combined wind and wave power generation device;

[0022] Figure 7 This is a schematic diagram illustrating the adaptive weight change characteristics.

[0023] Figure 8 A diagram showing the frequency modulation performance comparison of different networking schemes;

[0024] Figure 9 A schematic diagram illustrating the joint participation of wind, wave, and energy storage in frequency regulation of an island microgrid system;

[0025] In the figure: (a) represents the system frequency and the output power of each unit; (b) represents the motor opening degree and the energy storage droop coefficient.

[0026] Figure 10 A diagram showing the frequency modulation performance comparison of different networking schemes when wind and wave output is insufficient;

[0027] In the figure: (a) is the system frequency and (b) is the energy storage output power;

[0028] Figure 11 Schematic diagram of system frequency response characteristics under different control methods;

[0029] Figure 12 Schematic diagram of the power output characteristics of a combined wind and wave power generation device under different control methods;

[0030] Figure 13 A schematic diagram comparing the system state variables under fixed weights and adaptive weights;

[0031] In the figure: (a) compares the system frequencies; (b) compares the changes in motor opening.

[0032] Figure 14 This is a schematic diagram of adaptive weight changes. Detailed Implementation

[0033] like Figure 2 The diagram shows the application scenario of this embodiment, namely the wind-wave-storage island microgrid system, which includes: a wind-wave combined power generation device, an energy storage unit, and island loads. The wind-wave combined power generation device collects wind and wave energy in the form of a floating platform at sea. The energy storage unit is connected to the AC bus after being controlled by the inverter droop, and together with the wind-wave combined power generation device, it supplies energy to the island loads.

[0034] like Figure 3 As shown, the wind and wave combined power generation device includes: a wind power unit and a wave energy unit connected to it via a coupling ⑦. The coupling ⑦ is connected to a synchronous generator ⑧ to drive power generation. Both the wind power unit and the wave energy unit use hydraulic transmission to collect wind and wave kinetic energy, and the energy is converted into mechanical energy-hydraulic energy-mechanical energy-electrical energy to supply energy to the island microgrid.

[0035] The wave energy unit includes: several parallel bidirectional hydraulic cylinders ① with one-way valves ② and a hydraulic motor ⑥ connected to a coupling ⑦, wherein: wave energy is collected to the hydraulic cylinders ① and driven by the variable displacement hydraulic motor ⑥ through the rectifier circuit of the one-way valves ②.

[0036] The bidirectional hydraulic cylinder ① and the hydraulic motor ⑥ are provided with an air-filled accumulator ③, a pressure relief valve ④ and a hydraulic tank ⑤. By compressing the internal air, the rapid changes in flow and pressure in the circuit are suppressed, and the output power fluctuation is reduced. When the pressure in the circuit exceeds the set threshold of the pressure relief valve, the excess energy is discharged from the system in the form of hydraulic energy.

[0037] The wind power unit includes: a wind turbine ⑩, a hydraulic pump ⑨, and a variable displacement hydraulic motor ⑥ connected in sequence, wherein: the wind turbine ⑩ drives the fixed displacement hydraulic pump ⑨ to rotate and pump out hydraulic oil, which then drives the variable displacement hydraulic motor through the hydraulic circuit.

[0038] The coupling ⑦ is equivalent to an inertia-spring-damping system, which realizes the synchronization of the speed of the two hydraulic motors and the superposition of mechanical power.

[0039] The output power of the aforementioned wind and wave combined power generation device Where: η is the energy transmission efficiency of the hydraulic motor, p is the hydraulic circuit pressure, and V max S represents the maximum displacement of the hydraulic motor, S represents the opening degree of the hydraulic motor, and the subscripts i = 1, 2 correspond to the wind power unit and the wave energy unit.

[0040] like Figure 6 As shown, the output power of the wind and wave combined power generation device and the frequency of the energy storage unit system are specifically as follows: Where: P m P b P L f and f are the reference values ​​ΔS of the wind and wave combined power generation unit based on the motor opening increment, respectively. * The output power, energy storage unit output power, load demand power, and system frequency; T m Here, H is the equivalent time constant of the hydraulic motor, H is the system moment of inertia, and s represents the differential element.

[0041] The aforementioned active adjustment refers to: when the system frequency f is disturbed, controlling the system mechanical power P by adjusting the motor opening S and the energy storage droop coefficient m. m and battery power P b This optimizes the dynamic response process of the frequency.

[0042] like Figure 4As shown, this embodiment illustrates a wind-wave-storage island microgrid control system for a wind-wave combined power generation device. The system includes a DSQ controller connected to both a state monitoring device and an execution device. The DSQ controller comprises a data storage module, a spatial matrix update module, a control decision module, a solution calculation module, and an instruction update module. The state monitoring device collects real-time data on system frequency, voltage, loop pressure, flow rate, motor opening, coupling speed, and the state of charge and droop coefficient of the energy storage unit. This data is integrated with the time series data to form a discrete system state data sequence. The data storage module stores the system state information over time and sends it to the controller according to accuracy requirements. The system transmits status information. The spatial matrix update module updates the weight matrix based on the transmitted status information and a pre-set weight tuning function. The control judgment module selects the frequency modulation command to be executed based on the current frequency deviation value of the system and sends it to the solution calculation module. The solution calculation module transforms the optimization constraint problem into a quadratic programming problem based on the updated weight matrix, a pre-set cost function, and system constraints, and selects the first increment of the control quantity as the optimal increment. The command update module updates the reference commands for the motor opening and droop coefficient based on the optimal increment of the solved control quantity. The execution device controls the servo mechanism and electronic module to adjust the hydraulic motor opening and energy storage droop coefficient based on the reference command information.

[0043] like Figure 1 As shown, this embodiment illustrates the control method based on the aforementioned system. It controls the opening degree of the hydraulic motor of the wind-wave-storage island microgrid system's combined wind and wave power generation device and the droop coefficient of the energy storage unit. When the system frequency is disturbed, the optimal motor opening degree and droop coefficient increment are calculated, and the hydraulic motor opening degree is actively adjusted to control the output power. This controls the coordinated output of multiple power sources, achieving balanced power distribution across the wind, wave, and energy storage systems, as well as dynamic optimization of the system frequency. Specific steps include:

[0044] Step ① Model Establishment: Establish a frequency response state-space model based on the system parameters;

[0045] Step 2 System Settings: Input the initial system state, adaptive weight tuning function, system cost function, and state and operational constraints in the controller;

[0046] Step ③ Data Acquisition: The real-time operating status parameters of the system are collected by the status monitoring device;

[0047] Step 4: Parameter Update: Update the state space matrix and weight matrix based on the current state parameters;

[0048] Step 5: Solving the algorithm: After transforming the constrained optimization problem, the optimal control increments for motor opening and droop coefficient are solved using the model predictive control algorithm.

[0049] Step 6: Adjust the hydraulic motor opening and energy storage sag coefficient according to the solution results;

[0050] Step ⑦ is repeated cyclically: Repeat steps ③-⑥ to achieve real-time control of the system.

[0051] The system cost function mentioned in step ② is specifically as follows:

[0052] Where: α, β, and γ are penalty coefficients and weighting coefficients. Weighting coefficient Wherein, Δf1 and Δf2 are frequency deviation reference points. When the system frequency is lower than the rated value, the wind and wave units need to increase their output. At this time, the power supply with a higher power margin increases its power output more, so its motor adjustment has a smaller weight. Conversely, when the system frequency is higher than the rated value, the power supply with a higher power margin needs to have a larger weight. This reduces the margin difference between the wind energy unit and the wave energy unit.

[0053] As the frequency deviation decreases, the weights of ΔS1 and ΔS2 gradually increase, thus reducing the sensitivity of the combined wind and wave power generation system to the frequency deviation. Simultaneously, the relative magnitudes of their weights are adaptively adjusted based on margin and frequency deviation to achieve a balanced distribution of wind and wave output power. Figure 7 As shown.

[0054] A larger penalty coefficient indicates a greater penalty for the corresponding term; reducing this value will bring greater benefits in reducing system "costs." The three terms in the cost function represent the impact of dynamic frequency deviation, motor opening adjustment, and droop coefficient deviation on the overall system performance. Since the power response of the energy storage unit is positively correlated with the droop coefficient, to avoid overresponse when the system frequency fluctuates slightly, the droop coefficient needs to be maintained near the reference value during stable system operation. Therefore, when the droop coefficient deviation increases, the third penalty term can accelerate the adjustment of the droop coefficient to the reference value.

[0055] The constraint optimization problem transformation mentioned in step ⑤ refers to the following: considering that rapid and significant changes in motor opening and droop coefficient will cause over-response in output power, thus affecting the system frequency stability, and that both the controlled variable and the rate of change are subject to certain limitations, the system frequency transient process optimization problem can be transformed into a constraint optimization problem with the controlled variable increment ΔU(x) as the optimization variable, specifically: Where: x′(k) is a system state variable that is not defined as a control variable, such as p i λ, μ, etc., P b0 and R b0 k represents the rated output power and rated ramp rate of the energy storage unit. p and k r This is a correction factor for output power and gradeability.

[0056] The output power is constrained by the state of charge (SOC) of the energy storage unit and is a piecewise linear function, specifically: Where: a and b are both energy storage output constraint parameters.

[0057] The solution mentioned in step ⑤ refers to transforming the constrained optimization problem into a quadratic programming problem. The specific transformation relationship is as follows: Where: U is the independent variable, H is the coefficient of the quadratic term, and G is the coefficient of the linear term, satisfying... Where: Q, R1, and R2 are the weight matrices for frequency deviation, motor opening increment, and sag coefficient deviation, respectively; Ref is the reference value matrix; m ref y is the reference value for the droop coefficient. c This is the system output vector.

[0058] Based on specific practical experiments, a typical island microgrid power system hardware-in-the-loop (RTLAB) semi-physical simulation model was established, including a wind and wave capture unit, a hydraulic transmission device, a synchronous generator, an energy storage unit, and other equivalent loads. The RT-LAB OP7000 hardware simulation real-time simulator was used, and the AN706 controller sampled signals such as system frequency, power output, motor opening, and internal state variables of the hydraulic system. A predictive control algorithm was implemented using an XLINX FPGA ZYNQ7020 FPGA development board, and simulation results were obtained through measurement using a Tektronix MSO44 oscilloscope. A wind-wave-load-storage island microgrid power system was constructed based on an island in the South China Sea, and the parameters are shown in Table 1.

[0059] Table 1 Parameters of the Island Microgrid Power System

[0060]

[0061]

[0062] The frequency regulation of the island microgrid system adopts an online model predictive control method. To ensure the speed and accuracy of the solution, the sampling step size T is... s Set to 0.02s, and control the time domain to 5 steps.

[0063] The constraints selected are the actual state constraints and operational constraints of the island microgrid system. Sensitivity analysis was performed on the cost function weights to select appropriate weight coefficients. The system constraints and weight settings are shown in Table 2.

[0064] Table 2 System State Constraints and Weighting Coefficients

[0065]

[0066] Compared with wind-wave frequency regulation and energy storage-only frequency regulation, the transient and steady-state characteristics of frequency regulation by combining coupled wind-wave-storage units in island microgrids are improved. To compare the system operation characteristics of different networking schemes, the frequency regulation effects of the three networking schemes are compared and analyzed under the same operating parameters and system constraints.

[0067] The wind and wave inputs to the combined wind and wave power generation unit are: mean 8 m / s and variance 0.25 m. 2 / s 2 Random wind; wave force with a period of 2πs and an amplitude of 400kN. When the system load demand increases by 20kW (25% of the installed capacity) within 1 second, the system frequency response characteristics of the three networking schemes—wind-wave-storage joint frequency regulation, wind-wave joint frequency regulation, and standalone energy storage droop frequency regulation—are as follows: Figure 8 As shown.

[0068] Independent wind-wave frequency control can restore the system frequency to the rated value when wind and wave energy is sufficient, but the long mechanical adjustment response time worsens the transient process of frequency recovery; independent energy storage droop control can respond quickly to frequency changes, but there is a difference between the steady-state operating point of the adjusted system frequency and the rated value, and the energy storage unit needs to output continuously, which has high requirements for capacity configuration and state of charge.

[0069] Compared to wind-wave frequency regulation and energy storage frequency regulation, this invention exhibits better performance in terms of frequency recovery transient processes and reducing energy storage output, reducing the maximum frequency deviation by 89.3% and 51.9%, respectively. During the rapid frequency drop phase, the energy storage unit rapidly outputs power to ensure the lowest frequency point does not exceed the limit, while the wind-wave unit responds to frequency changes by adjusting the motor opening to increase output power. As the system frequency recovers, the energy storage gradually withdraws from operation, and the droop coefficient gradually decreases to the rated value to improve the system's anti-interference capability. Figure 9 As shown.

[0070] Furthermore, when wind and wave power is insufficient, the wind and wave units output full power, and the power gap is supported by the energy storage unit, maintaining system frequency stability to the greatest extent possible. The test simulated a scenario where wind and wave inputs both decreased to 50% of their current levels. At this point, even if the hydraulic motors operated at full power, the output of wind and waves was still insufficient to meet the load requirements.

[0071] The frequency modulation performance of the three networking schemes is as follows: Figure 10As shown, the transient frequency fluctuation phase of a standalone wind-wave frequency regulation system is a process in which the gas-filled energy storage unit slowly releases energy to maintain stable power output. After a period of time, the system enters a steady state, but periodic frequency fluctuations still exist. This is caused by the periodic changes in wave force input. At this time, the hydraulic motor of the combined power generation unit operates at full power, and the wind and wave units no longer have frequency regulation capabilities. After the wind and wave output decreases, the energy storage unit further increases its output power in the standalone energy storage droop frequency regulation system, and the island microgrid frequency reaches a new steady-state operating point, but this leads to a further increase in steady-state frequency deviation and a rapid decrease in energy storage SOC. In contrast, this invention has a higher power output capability and can adjust the system power output over a wider time scale. The maximum steady-state frequency deviation is reduced by 98.2% and 71.4%, respectively, while the time to recover to steady state is reduced by 95.7% and 80%, respectively. The fluctuation of wind and wave input power also impacts the frequency of the combined power generation system, but the fast power response characteristics of the energy storage unit effectively smooth out frequency fluctuations. As wind, wave, and energy storage output capabilities further decline, microgrid frequency deviation will no longer be able to be maintained within limits. At this point, unnecessary equipment can be shut down to reduce system power imbalance.

[0072] Further comparisons were made under the same system parameters and constraints, comparing the system frequency and power output variation characteristics of the wind-wave combined power generation unit under different PI parameters, as shown in Table 3.

[0073] Table 3 PI Parameter Settings

[0074]

[0075] The system frequency response curves under different control methods and control parameters are as follows: Figure 11 As shown, the frequency response speed increases with increasing proportional gain, but excessive proportional gain can lead to frequency overshoot. Comparing the frequency modulation results of this invention and PI1, this invention reduces the maximum frequency deviation by 53.6% and the frequency recovery time by 66% compared to the hybrid frequency modulation method.

[0076] The performance difference between the two control methods is mainly due to the different control speed and accuracy of the combined power generation unit's output power, such as... Figure 12 As shown, the response speed of the output power corresponds to the frequency dynamic characteristics. The faster the power response, the faster the corresponding frequency control transient process and the higher the minimum frequency point.

[0077] Further, by comparing the system operating characteristics under adaptive weight and fixed weight cost functions, focusing on indicators such as frequency recovery time, frequency overshoot, and margin difference change, the results are as follows: Figure 13 As shown in Table 4:

[0078] Compared to model predictive control using a fixed-weight cost function, the adaptive-weight control method reduces the maximum frequency deviation by 7% and the frequency recovery time by 60%, while avoiding frequency overshoot. Simultaneously, during the frequency regulation transient process, the motor opening difference, which measures the wind and wave power margin, is reduced by 63%, verifying the distribution of wind and wave unit output power according to margin during the transient process. The weight changes of ΔS1 and ΔS2 during this process are as follows: Figure 14 As shown.

[0079] When the system frequency decreases, the weights of ΔS1 and ΔS2 decrease, increasing the sensitivity of motor opening changes to frequency deviations, allowing wind and wave energy to respond quickly to frequency changes. At the same time, the weights of wave energy units with larger margins decrease more significantly, ensuring that they are allocated more power output indicators during transient processes, thereby narrowing the power margin difference between wind and wave energy units. As the system frequency returns to its rated value, the weights of ΔS1 and ΔS2 increase, reducing the sensitivity of the combined power generation units to frequency deviations and preventing excessive power regulation that could lead to system frequency overshoot.

[0080] Experiment 1: Comparison of different networking schemes

[0081] Table 4 Performance Comparison of Different Networking Schemes (Sufficient Wind and Wave Energy)

[0082]

[0083] Table 5 Performance Comparison of Different Networking Schemes (Insufficient Wind and Wave Energy)

[0084]

[0085] Experiment 2: Comparison of Different Control Methods

[0086] Table 6 Performance Comparison of Different Control Methods

[0087]

[0088] Experiment 3: Comparison of Adaptive Weighting and Fixed Weighting Control

[0089] Table 7 Comparison of Adaptive Weighted and Fixed Weighted Control Performance

[0090]

[0091] In summary, this invention enables multiple energy sources to participate in microgrid frequency regulation, effectively improving the stability and survivability of island microgrid systems.

[0092] The above-described specific implementations can be partially adjusted by those skilled in the art in different ways without departing from the principles and purpose of the present invention. The scope of protection of the present invention is defined by the claims and is not limited to the above-described specific implementations. All implementation schemes within the scope of the claims are bound by the present invention.

Claims

1. A wind and wave combined power generation device, characterized in that, include: The system includes wind power units, wave energy units, and energy coupling units. The wind power units and wave energy units connected by couplings both use hydraulic transmission to drive hydraulic motors to rotate and do work. The two units are fixedly connected by a coupling that is equivalent to an inertia-spring-damping system to couple and superimpose wind and wave power. The energy is then converted from mechanical energy to hydraulic energy to mechanical energy to electrical energy to supply energy to the island microgrid. The output power of the aforementioned wind and wave combined power generation device ,in: The energy transfer efficiency of the hydraulic motor. For hydraulic circuit pressure, This is the maximum displacement of the hydraulic motor. The subscript represents the opening degree of the hydraulic motor. Corresponding to wind power units and wave energy units; The output power of the wind and wave combined power generation device and the frequency of the energy storage unit system are as follows: ,in: , , , These are reference values ​​for the combined wind and wave power generation unit based on the motor opening increment. The output power, energy storage unit output power, load demand power, and system frequency; The equivalent time constant of the hydraulic motor is... Let s represent the system's rotational inertia, and s represent the differential element. When the system frequency During disturbances, adjust the motor opening. and energy storage droop coefficient mechanical power of the control system and battery power This optimizes the dynamic response process of the frequency. The aforementioned wind-wave combined power generation device constructs a microgrid system state prediction model based on the frequency response state-space model of the wind-wave-storage island microgrid system. When a limit is exceeded, it predicts the future... The system updates the system status after executing the first step instruction and setting the optimal adjustment values ​​for the motor opening and droop coefficient. Once the frequency offset returns to within the limit, the motor opening is no longer adjusted; only the droop coefficient is adjusted.

2. The wind and wave combined power generation device according to claim 1, characterized in that, The wave energy unit includes: several parallel bidirectional hydraulic cylinders with one-way valves and a hydraulic motor connected to a coupling, wherein: wave energy is replenished to the hydraulic cylinders and driven by the variable displacement hydraulic motor through the one-way valve rectifier circuit. The wind power unit includes a wind turbine, a hydraulic pump, and a variable displacement hydraulic motor connected in sequence. The wind turbine drives the fixed displacement hydraulic pump to rotate and pump out hydraulic oil, which then drives the variable displacement hydraulic motor through the hydraulic circuit.

3. The wind and wave combined power generation device according to claim 2, characterized in that, The bidirectional hydraulic cylinder and the hydraulic motor are equipped with an air-filled accumulator, a pressure relief valve and a hydraulic tank. By compressing the internal air, the rapid changes in flow and pressure in the circuit are suppressed, and the output power fluctuation is reduced. When the pressure in the circuit exceeds the set threshold of the pressure relief valve, the excess energy is discharged from the system in the form of hydraulic energy.

4. The wind and wave combined power generation device according to claim 1, characterized in that, The state-space model of the island microgrid system includes: ① The state-space equations of the system frequency, motor opening degree, and energy storage droop coefficient ,as well as ②System frequency ,in: , , , These are the output power of the combined wind and wave power generation unit, the output power of the energy storage unit, the load demand power, and the system frequency, respectively. The equivalent time constant of the hydraulic motor is... Let the system's rotational inertia be... , For frequency reference value and system frequency deviation, , For energy storage state of charge and capacity, The rotational speed of the coupling; , For the matrix Identity matrices of the same dimension The system sampling period is At the sampling time, x, u, and y represent the state variable, control variable, and output variable, respectively; A, B u B d C represents the state matrix, control input matrix, non-control input matrix, and output matrix in the discrete time domain, respectively, and the subscript c represents the corresponding matrix in the continuous time domain.

5. The wind and wave combined power generation device according to claim 1, characterized in that, The microgrid system state prediction model is specifically as follows: ,in: and They are respectively The output prediction sequence and control sequence within the step prediction interval, S x S u S d These represent the state prediction matrix, control prediction matrix, and non-control prediction matrix, respectively, with Δ representing the difference in state quantities between two sampling times. , , .

6. A wind-wave-storage island microgrid control method based on the device described in any one of claims 1-5, characterized in that, specifically... include: Step ① Model Establishment: Establish a frequency response state-space model based on the system parameters; Step 2 System Settings: Input the initial system state, adaptive weight tuning function, system cost function, and state and operational constraints in the controller; Step ③ Data Acquisition: The real-time operating status parameters of the system are collected by the status monitoring device; Step 4: Parameter Update: Update the state space matrix and weight matrix based on the current state parameters; Step 5: Solving the algorithm: After transforming the constrained optimization problem, the optimal control increments for motor opening and droop coefficient are solved using the model predictive control algorithm. Step 6: Adjust the hydraulic motor opening and energy storage sag coefficient according to the solution results; Step ⑦ is repeated cyclically: Repeat steps ③-⑥ to achieve real-time control of the system.

7. The wind-wave-storage island microgrid control method according to claim 6, characterized in that, The system cost function mentioned in step ② is specifically as follows: ,in: , , Penalty coefficient, weighting coefficient Weighting coefficient Where Δf1 and Δf2 are frequency deviation reference points. When the system frequency is lower than the rated value, the wind and wave units need to increase their output. At this time, the power supply with a high power margin increases its power output more, so its motor adjustment has a smaller weight. Conversely, when the system frequency is higher than the rated value, the power supply with a high power margin needs to have a larger weight, thereby reducing the margin gap between the wind energy unit and the wave energy unit.

8. The wind-wave-storage island microgrid control method according to claim 6, characterized in that, The constraint optimization problem transformation mentioned in step ⑤ refers to the following: Considering that rapid and significant changes in motor opening and droop coefficient will cause over-response in output power, thus affecting the system frequency stability, and that both the controlled variable and the rate of change are subject to certain limitations, the system frequency transient process optimization problem is transformed into one that uses the increment of the controlled variable as the controllable variable as the controllable variable. To optimize the constrained optimization problem of the variables, specifically: ,in: For system state variables that are not defined as control variables, such as , , wait, and The rated output power and rated ramp rate of the energy storage unit. and This is a correction factor for output power and gradeability; The solution mentioned in step ⑤ refers to transforming the constrained optimization problem into a quadratic programming problem. The specific transformation relationship is as follows: Where: U is the independent variable, H is the coefficient of the quadratic term, and G is the coefficient of the linear term, satisfying... Where: Q, R1, and R2 are the weight matrices for frequency deviation, motor opening increment, and sag coefficient deviation, respectively; Ref is the reference value matrix; m ref y is the reference value for the droop coefficient. c This is the system output vector.

9. The wind-wave-storage island microgrid control method according to claim 6, characterized in that, The output power is constrained by the state of charge (SOC) of the energy storage unit and is a piecewise linear function, specifically: Where: a and b are energy storage output constraint parameters.

10. A wind-wave-storage island microgrid control system for a wind-wave combined power generation device implementing the method of any one of claims 6-9, characterized in that, include: The DSQ controller, connected to both the condition monitoring device and the execution device, includes a data storage module, a spatial matrix update module, a control decision module, a solution calculation module, and an instruction update module. The condition monitoring device collects real-time data on system frequency, voltage, loop pressure, flow rate, motor opening, coupling speed, and the state of charge and droop coefficient of the energy storage unit within the combined wind and wave power generation unit. This data is integrated with the time series data to form a discrete system state data sequence. The data storage module stores the system state information over time and transmits it to the controller according to accuracy requirements. The spatial matrix update module updates the system state information based on the transmitted data. The system updates the weight matrix based on the input status information and the pre-set weight tuning function. The control judgment module selects the frequency modulation command to be executed based on the current frequency deviation value of the system and sends it to the solution calculation module. The solution calculation module transforms the optimization constraint problem into a quadratic programming problem based on the updated weight matrix, the pre-set cost function, and the system constraints, and selects the first step increment of the control quantity as the optimal increment. The command update module updates the reference commands for the motor opening and droop coefficient based on the optimal increment of the solved control quantity. The execution device controls the servo mechanism and electronic module to adjust the hydraulic motor opening and energy storage droop coefficient based on the reference command information.

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