Energy storage type wind-driven seawater desalination water quality optimization method based on power fluctuation suppression

Abstract

The application provides a kind of energy storage type wind-driven seawater desalination water quality optimization method based on power fluctuation suppression, it is related to energy utilization and seawater desalination equipment control technical field, method includes: S1, the multi-objective optimization model of fusion volume configuration and bottom filtering performance evaluation parameter is established, the static physical volume of energy accumulator is output;S2, construct multi-objective optimization parameter performance evaluation function and carry out optimization iteration, carry out adaptive filtering smoothing, and output smooth target power;S3, establish the maximum water production target function, carry out multi-objective collaborative tracking control and adjust the opening degree of concentrated water valve.The application constructs the collaborative strategy that considers macro energy tracking and micro water quality guarantee, and dynamically matches the storage capacity and control algorithm, solves the water quality threat caused by wind power fluctuation to reverse osmosis membrane, makes the global maximum of fresh water production, the stable and standard product water quality and the optimal balance under the condition of variable wind, and meets engineering application.

Description

Technical Field

[0001] This invention relates to the field of energy utilization and seawater desalination equipment control technology, specifically to a method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression. Background Technology

[0002] The energy storage-type hydraulic wind-driven reverse osmosis seawater desalination system, hereinafter referred to as the system, is a novel energy and water system that deeply couples a hydraulic wind turbine (HWT), a hydraulic energy storage system (HES), and a reverse osmosis (RO) system. The system utilizes a wind turbine to drive a fixed-displacement hydraulic pump to output high-pressure oil flow, which is transmitted through high-pressure pipelines to a ground-side variable-displacement hydraulic motor. This motor then drives a high-pressure water pump to pressurize pretreated seawater and deliver it to the reverse osmosis membrane module for desalination. Compared to traditional battery-based energy storage microgrids, this hydraulic direct-drive architecture achieves a flexible connection between the wind energy capture end and the water production unit through an energy storage hub composed of a variable-displacement pump motor and a high-pressure accumulator, avoiding multi-stage energy conversion from wind to electricity to chemical energy to electricity to mechanical energy. Simultaneously, the accumulator can dynamically buffer wind power fluctuations, reducing the direct impact of random wind speed changes on the reverse osmosis membrane side. Because the reverse osmosis process is highly sensitive to the stability of operating pressure, medium- and high-frequency fluctuations in wind power can easily cause membrane inlet pressure fluctuations, exacerbating concentration polarization, leading to decreased desalination rate, water quality deterioration, and accelerated fatigue aging of the reverse osmosis membrane. Therefore, the hydraulic energy storage module plays a crucial role in energy regulation, ensuring the stability of product water quality and the normal operation of the reverse osmosis membrane. This system boasts advantages such as high power density, fast dynamic response, and flexible spatial layout, making it an ideal technical solution for the efficient and stable co-production of wind and freshwater in off-grid scenarios.

[0003] Preliminary progress has been made in research on this system, currently focusing primarily on two dimensions: system integration architecture and core drive control. Regarding system integration and energy storage buffering, existing solutions attempt to construct an independent drive system by adding a hydraulic energy storage module. This utilizes the coordination of the accumulator with variable pumps and variable motors to smooth wind power fluctuations, enabling flexible co-generation of wind power and freshwater without grid support. In terms of core drive and speed control, the industry has introduced control algorithms to improve the system's disturbance rejection performance. For example, a high-pressure water pump speed control model based on fuzzy adaptive PID has been constructed, dynamically adjusting the variable motor's swing angle to improve speed stability under complex wind conditions. However, existing technologies still mainly focus on speed tracking and energy capture, lacking active suppression of deteriorating power fluctuations. This means that wind power fluctuations can still easily be transmitted to the reverse osmosis membrane side, causing water quality fluctuations and membrane module fatigue damage. Simultaneously, relying solely on control algorithms to forcibly smooth power fluctuations without the dynamic energy buffering support of energy storage units can easily lead to problems such as accumulator pressure exceeding limits or emptying during drastic wind speed changes. In addition, existing energy storage devices are mostly selected based on static volume under extreme operating conditions, without fully considering the dynamic impact of control strategies on fluctuation smoothing performance. This results in a lack of coordination between control optimization and energy storage capacity configuration, making it difficult to simultaneously ensure water quality and normal system operation.

[0004] Currently, a single energy tracking control scheme is insufficient to meet the requirements of energy storage-type hydraulic wind-driven reverse osmosis seawater desalination systems for maximizing water production, ensuring high water quality, and maintaining normal system operation. Therefore, there is an urgent need to propose a collaborative control method that takes into account both macroscopic energy tracking and microscopic water quality assurance. This method should be able to effectively filter out deteriorating disturbances under varying operating conditions and achieve dynamic matching between the control algorithm and system capacity configuration while strictly protecting the physical boundaries of energy storage. This will promote the high-quality application of wind-storage seawater desalination technology. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention aims to provide a method for optimizing the water quality of wind-driven desalination plants based on power fluctuation suppression. This method involves constructing a joint control architecture combining offline capacity optimization, online adaptive filtering, and state tracking control; establishing a multi-objective configuration model based on Bayesian optimization to collaboratively determine the optimal static physical volume of the energy storage unit; utilizing an adaptive filter with pressure feedback to actively suppress broadband power fluctuations and output a stable target power within the energy storage boundary; generating a desired trajectory through dynamic water production target planning; and using a backstepping sliding mode control law to drive the actuators of the main drive system and the energy storage system, while coordinating the control law to adjust the concentrate valve to robustly track the target trajectory. This approach maximizes the global freshwater production under complex wind conditions, ensures stable water quality compliance, and achieves optimal system balance.

[0006] Specifically, the present invention provides a method for optimizing the water quality of energy storage-based wind-driven seawater desalination based on power fluctuation suppression, which includes the following steps: S1: Collect historical wind speed data and obtain the static physical volume of the accumulator based on the constraints of the hydraulic accumulator; establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters; perform collaborative optimization to determine the energy storage capacity of the energy storage-type wind-driven seawater desalination device, and output the globally optimal static physical volume of the accumulator. Used for physical equipment installation and piping configuration, defining physical hardware boundaries; S2: Acquire multi-source state data of the energy storage-type wind-driven seawater desalination device to determine the current driving power of the wind turbine's captured energy. Construct a multi-objective optimization parameter performance evaluation function to perform iterative optimization and obtain the current optimal filter parameter set. A time-varying filtering model with pressure feedback compensation is constructed, and adaptive filtering and smoothing processing is performed to output a smoothed target power. Power limits and power change rate limit Smoothing target power Saturation cutoff is performed, and the output is sent to S3 as the energy input boundary on the reverse osmosis permeate side. S3: Determine the real-time input energy boundary based on the energy transfer relationship of the main drive chain. Establish an objective function to maximize water production and output the optimal high-pressure water pump inlet flow rate. Converted to the corresponding desired common shaft speed Establish a wind turbine speed tracking backstepping sliding mode controller and a common shaft speed tracking backstepping sliding mode controller; perform multi-objective cooperative tracking control based on backstepping sliding mode and proportional-integral-derivative PID, and obtain and output the concentrate valve opening adjustment signal. The stepper motor or proportional electromagnet that drives the concentrate valve performs a throttling action to eliminate the static pressure difference in a closed loop.

[0007] Preferably, S1 specifically refers to: S11: Collect historical wind speed data for the target area where the energy storage-type wind-driven seawater desalination unit is installed; determine the static physical volume of the hydraulic accumulator. Precharge pressure Preset initial working pressure With rated volume Satisfying constraints Output accumulator static physical volume ; S12: Establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters, and construct a performance evaluation function for the multi-objective optimization model. Using the Bayesian optimization algorithm, a performance evaluation function for the above multi-objective optimization model is obtained through reverse optimization, and the globally optimal static physical volume of the energy storage device is output. .

[0008] Preferably, in S12, a multi-objective optimization model is established that integrates volume configuration and underlying filter performance evaluation parameters, and a performance evaluation function for the multi-objective optimization model is constructed. for: ; in, This is the performance evaluation function for a multi-objective optimization model. This is the optimal performance evaluation term for filter parameters; For the filter parameter set; For volumetric weight; This is the maximum allowable accumulator volume of the system; Input variable for the static physical volume of the accumulator; This represents the globally optimal static physical volume of the energy storage device.

[0009] Preferably, S2 specifically comprises: S21: Detect and acquire multi-source status data of the energy storage-type wind-driven seawater desalination device to determine the current drive power input after the wind turbine captures energy and converts it through the hydraulic main drive chain. ; S22: Perform online self-tuning of parameters for the pressure feedback online adaptive filter using rolling running data; construct a multi-objective optimization parameter performance evaluation function; perform optimization iteration based on Bayesian optimization; and utilize the multi-objective optimization parameter performance evaluation function. The proxy model is used to obtain the current optimal set of filter parameters. ; S23: Construct a time-varying filter model with pressure feedback compensation, and establish a system based on available drive power. To smooth target power The mapping relationship, the output variable is the power limit value. and power change rate limit Final smoothed target power after saturation truncation .

[0010] Preferably, the performance evaluation function for multi-objective optimization parameters in S22 is constructed as follows: ; in, To obtain the minimum value of the performance evaluation function for multi-objective optimization parameters; For multi-objective optimization of parameter performance evaluation function; The variance of output power; This represents the cumulative deviation of energy storage pressure from the target value. The integral of the square of the rate of change of power; The power variance weights; The cumulative weight of the energy storage pressure deviation from the target value; The weight of the square integral of the power change rate; Normalized pressure state variables; To smooth the target power; This is a time parameter.

[0011] Preferably, the multi-objective optimization parameter performance evaluation function in S22 The proxy model is as follows: ; in, This is the data acquisition function; This is the minimum performance evaluation parameter currently observed; The cumulative distribution function of the standard normal distribution; is the probability density function of the standard normal distribution; The mean value predicted by the Gaussian process surrogate model; The standard deviation predicted by the Gaussian process surrogate model; To maximize expectations; This is a function that maximizes the value of a function.

[0012] Preferably, in S23, a time-varying filter model with pressure feedback compensation is constructed, specifically as follows: ; in, To smooth the target power; For pressure proportional feedback; This is the integral reset term; These are the pressure-dependent filter coefficients; This refers to the actual pressure of the hydraulic accumulator. These are the effective filter coefficients; This represents the available drive power.

[0013] Preferably, S3 specifically comprises: S31: Determine the real-time input energy boundary in the optimization model based on the energy transfer relationship of the main drive chain. The high-pressure water pump inlet flow rate With reverse osmosis target pressure As joint decision variables, an objective function for maximizing water production is established; the optimal high-pressure water pump inlet flow rate is output. Based on the high-pressure water pump displacement relationship, the corresponding desired common shaft speed is converted. ; S32: Establish a wind turbine speed tracking backstepping sliding mode controller and obtain the output of the wind turbine speed tracking backstepping sliding mode controller. Establish a common shaft speed tracking backstepping sliding mode controller to obtain the energy storage pump motor displacement control signal. Multi-objective cooperative tracking control is performed based on backstepping sliding mode and proportional-integral-derivative PID to obtain and output the concentrate valve opening adjustment signal. .

[0014] Preferably, the objective function for maximizing water production in S31 is: ; in, The feed water flow rate provided by the high-pressure water pump to the reverse osmosis membrane module; The target operating pressure for the reverse osmosis membrane module; This refers to the permeate flow rate obtained by the reverse osmosis membrane module under the current feed water flow rate and target operating pressure. This is the physical mapping function for the reverse osmosis membrane process.

[0015] Preferably, in S32, a common shaft speed tracking backstepping sliding mode controller is established to obtain the energy storage pump motor displacement control signal as follows: ; in, This is the displacement control signal for the energy storage pump motor; The real-time displacement signal of the main drive motor; The derivative of the expected rotational speed of the common shaft; This refers to the coaxial speed tracking error. This refers to the displacement of the high-pressure water pump. The equivalent moment of inertia of the common axis; The viscous friction damping coefficient of the common shaft system; This refers to the actual rotational speed of the common shaft. This refers to the coaxial speed tracking error. For reverse osmosis pressure; For the mechanical efficiency of the variable pump motor; For motor mechanical efficiency; For the mechanical efficiency of high-pressure water pumps; The linear feedback gain for the common shaft speed error; The robustness coefficient; The boundary layer thickness is the saturation function. It is a saturation function.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention deeply integrates the physical constraints of the isothermal process of hydraulic accumulator with the performance evaluation parameters of online filtering control to establish an optimization model; through Bayesian algorithm, the optimal static capacity of accumulator is obtained offline in reverse optimization within the physical volume constraint space, which effectively solves the problem of equipment redundancy or insufficient anti-disturbance buffer caused by traditional configuration based on engineering experience, and realizes the global optimality of system configuration and underlying anti-disturbance performance; it breaks through the limitation of traditional static capacity selection and dynamic control being established independently, and realizes capacity collaborative configuration based on control boundary.

[0017] (2) This invention establishes a pressure feedback time-varying filter that integrates a V-shaped adaptive function and a piecewise exponential integral term, and introduces a Bayesian optimization algorithm in the background to perform online optimization tuning of the filter parameters. The pressure feedback time-varying filter mechanism with online parameter self-tuning gives the system a high degree of operational flexibility: under normal operating conditions, it actively smooths wide-frequency fluctuation power and achieves high-precision power smoothing output; when the pipeline pressure touches the physical boundary, it actively intervenes to unload dangerous pressure, avoids mechanical damage to core equipment and reverse osmosis membrane components from the control source, and improves the system's adaptive anti-interference performance in response to extreme wind conditions.

[0018] (3) This invention regards the reverse osmosis component as an actively adjustable dynamic matching load, constructs a nonlinear constraint optimization model with the goal of maximizing water production, and obtains a dynamic programming model constrained by multidimensional nonlinear boundaries; under the premise of strictly following the membrane permeation mechanism, system flow rate and upper limit of water production salinity, the smooth target power is dynamically decoupled to obtain the optimal expected reverse osmosis pressure and common shaft speed under the current wind energy support; effectively overcomes the problem that traditional fixed parameter control is difficult to adapt to wide frequency wind energy fluctuations, improves the wind energy utilization rate and water production stability of the system under extreme sea conditions, and realizes the global maximization of water production under fluctuating wind conditions.

[0019] (4) This invention addresses the frequency domain differences in the physical response of a multidimensional coupled system of machinery, liquid, and water. At the high-frequency power input side at the front end, the high robustness of the backstepping sliding mode is used to drive the main drive motor and the energy storage pump motor to quickly suppress wind speed disturbances. At the low-frequency water production side at the back end, PID closed-loop control is used to dynamically adjust the opening of the concentrate valve to eliminate steady-state error in operating pressure. An asynchronous composite control architecture of backstepping sliding mode and proportional-integral-derivative control is established to enhance the efficient decoupling and steady-state tracking of complex multivariable coupled systems. The composite mechanism realizes the complete decoupling and precise tracking of the three core actuators, effectively avoiding the system chattering risk that is easily caused by traditional global nonlinear control, and enabling the seawater desalination system to operate smoothly without steady-state error under wide-frequency environmental disturbances.

[0020] (5) The offline capacity optimization, online filtering and smoothing, and state tracking execution progressive joint control architecture proposed in this invention is logically rigorous and can be widely applied to wind-driven seawater desalination systems of various capacity levels. The modular execution structure facilitates deep integration with existing industrial programmable controllers or simulation platforms. It is especially suitable for remote islands or uninhabited offshore platforms that lack strong power grid support, providing them with highly reliable freshwater resources. It has good industrial applicability and off-grid sea condition adaptability. Attached Figure Description

[0021] Figure 1 This is a control block diagram of the energy storage-based wind-driven seawater desalination water quality optimization method based on power fluctuation suppression according to the present invention. Figure 2 This is a system architecture diagram of a hydraulic wind turbine generator set according to an embodiment of the present invention; Figure 3 This is a block diagram of the control architecture according to an embodiment of the present invention; Figure 4 This is a comparison chart of performance evaluation parameters for different volumes in the embodiments of the present invention; Figure 5 This is a graph showing the optimal water production curve under multiple physical constraints in an embodiment of the present invention. Figure 6 This is a physical diagram of the hardware-in-the-loop experimental platform in this embodiment of the invention; Figure 7 This is a typical turbulent wind speed time series curve diagram in an embodiment of the present invention; Figure 8 This is a wind turbine speed tracking error curve diagram in an embodiment of the present invention; Figure 9 This is a graph showing the motor speed tracking error in an embodiment of the present invention; Figure 10 This is a pressure error curve of the reverse osmosis system in an embodiment of the present invention; Figure 11 This is a diagram showing the RO drive power response curve in an embodiment of the present invention; Figure 12 This is a graph showing the water production flow rate response in an embodiment of the present invention; Figure 13 This is a TDS response curve of the produced water in an embodiment of the present invention; Figure 14 This is a diagram showing the SOC response curve of the energy storage system in an embodiment of the present invention. Detailed Implementation

[0022] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0023] This invention proposes a method for optimizing water quality in wind-driven seawater desalination based on power fluctuation suppression, such as... Figure 1As shown, a multi-objective optimization model integrating volume configuration and underlying filter performance evaluation parameters is established to output the static physical volume of the accumulator; a multi-objective optimization parameter performance evaluation function is constructed for optimization iteration, adaptive filtering and smoothing are performed, and the smoothed target power is output; an objective function for maximizing water production is established, and multi-objective collaborative tracking control is used to adjust the opening of the concentrate valve; a joint control architecture of offline capacity optimization, online filtering and smoothing, and state tracking execution is adopted to solve the problems of unstable seawater desalination water quality caused by wind energy fluctuations and the susceptibility of traditional control to physical constraints of equipment. Figure 3 The overall control architecture of the method of the present invention is shown, which corresponds to S1 to S3 in this embodiment, wherein, Figure 3 The accumulator volume optimization design section corresponds to S1, which is used to determine the static physical volume of the accumulator based on historical wind speed data, accumulator physical constraints, and filter performance evaluation parameters. Figure 3 The adaptive filtering and online parameter self-tuning part in S2 is used to generate a smooth target power based on the real-time collected system status and available drive power. Figure 3 The state tracking control section, corresponding to S3, is used to plan for maximizing water production based on the smoothed target power, and outputs control signals for the main drive motor displacement, the energy storage pump motor displacement, and the concentrate valve opening. The specific implementation steps are as follows: S1: Collect historical wind speed data and obtain the static physical volume of the accumulator based on the constraints of the hydraulic accumulator; establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters; perform collaborative optimization to determine the energy storage capacity of the energy storage-type wind-driven seawater desalination device, and output the globally optimal static physical volume of the accumulator. This is used for physical equipment installation and pipeline configuration, determining physical hardware boundaries. Before assembling the energy storage-type wind-driven seawater desalination device in actual engineering equipment, S1 is executed first to achieve coordinated matching between the smoothing potential of the control algorithm and the energy storage capacity of the energy storage-type wind-driven seawater desalination device. This embodiment of the invention takes a typical hydraulic blower as an example, summarizing the relevant parameters obtained from the actual unit parameter identification, and organizing them as shown in Table 1 below, listing the typical hydraulic parameters of the energy storage-type wind-driven seawater desalination device. The energy storage-type wind-driven seawater desalination device is as follows... Figure 2 As shown, it mainly consists of a hydraulic wind turbine unit, main drive pipeline, hydraulic energy storage center composed of variable pump motor and high-pressure accumulator, high-pressure water pump, reverse osmosis membrane module and concentrate end throttling valve.

[0024] Table 1 Typical Hydraulic Parameters

[0025] S11: Collect historical wind speed data for the target installation area of ​​the energy storage-type wind-driven seawater desalination unit. Determine the static physical volume of the hydraulic accumulator based on the isothermal process physical laws followed by the hydraulic accumulator. Precharge pressure Preset initial working pressure With rated volume Satisfying constraints And the static physical volume of the hydraulic accumulator. The core variable for offline collaborative optimization is determined, and the static physical volume of the energy storage device is output. Performance evaluation function for the multi-objective optimization model in S12 The independent variable.

[0026] S12: Establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters, and construct a performance evaluation function for the multi-objective optimization model. for: ; in, This is the performance evaluation function for a multi-objective optimization model. The optimal performance evaluation term for filter parameters is obtained through Bayesian optimization under a given volume. For the filter parameter set; For volumetric weight; This is the maximum allowable accumulator volume of the system; Input variable for the static physical volume of the accumulator; This represents the globally optimal static physical volume of the energy storage device.

[0027] For example, the filter performance evaluation parameters for each volume are compared. Figure 4 As shown, relying on the backend computing platform to run the Bayesian optimization algorithm, the globally optimal static physical volume of the energy storage device is obtained by reverse optimization, which minimizes the performance evaluation function of the above multi-objective optimization model. The actual hydraulic accumulator installation and pipeline configuration were completed strictly based on the volume result. Table 2 shows the optimal parameters for Bayesian optimization under typical turbulent conditions, which were used to run the Bayesian optimization process on the above computing platform.

[0028] Table 2. Optimal parameters for Bayesian optimization under typical turbulent conditions.

[0029] The output variable is the globally optimal static physical volume of the energy storage device that minimizes the joint evaluation index. It is used for physical equipment installation and piping configuration, and to define physical hardware boundaries.

[0030] To further illustrate the execution process of the method of this invention under actual engineering parameters, the following, combined with the parameters shown in Tables 1 and 2, presents a typical operating point calculation case from offline capacity optimization, online power smoothing to water production target planning. In S1, based on historical wind speed data of the target area, isothermal process constraints of the hydraulic accumulator, and filter performance evaluation function, Bayesian optimization is used to jointly optimize the static physical volume of the accumulator and the filter parameters. According to the optimization results shown in Table 2, the static physical volume of the accumulator obtained in this embodiment is... The corresponding online adaptive filter parameters for pressure feedback include: lower limit of smoothing coefficient. upper limit of smoothing coefficient Pressure proportional gain Basic integral gain Integral enhancement coefficient outside the target area , index steepness Thus, S1 completed the energy storage capacity boundary and filter initial parameter configuration of the energy storage wind-driven seawater desalination device.

[0031] S2: Acquire multi-source state data of the energy storage-type wind-driven seawater desalination device to determine the current driving power of the wind turbine's captured energy. Construct a multi-objective optimization parameter performance evaluation function to perform iterative optimization and obtain the current optimal filter parameter set. A time-varying filtering model with pressure feedback compensation is constructed, and adaptive filtering and smoothing processing is performed to output a smoothed target power. Power limits and power change rate limit Smoothing target power Saturation cutoff processing is performed, and the output is sent to S3 as the energy input boundary on the reverse osmosis permeate side; entering the real-time online operation stage, in order to avoid high-frequency wind speed pulsation penetrating the main drive chain and impacting the membrane module, sensing and filtering S2 is performed in real time.

[0032] S21: Detect and acquire multi-source status data of the energy storage-type wind-driven seawater desalination device to determine the current driving power of the wind turbine's captured energy. The real-time control device uses a 1ms sampling step size to collect real-time high-frequency ambient wind speed data. Actual rotational speed of the wind turbine High pressure pipeline pressure Common shaft speed Actual pressure of hydraulic accumulator and the actual working pressure on the reverse osmosis membrane side And based on the pressure of the high-pressure pipeline Real-time displacement of the main drive motor The current drive power input after the wind turbine captures energy and converts it through the hydraulic main drive chain is calculated. for: ; in, The current drive power for capturing energy from the wind turbine; The wind speed was measured in the actual environment. This refers to the actual rotational speed of the wind turbine. This refers to the pressure in the high-pressure pipeline. For the common shaft speed; This refers to the actual pressure of the hydraulic accumulator. This refers to the actual operating pressure on the reverse osmosis membrane side. The real-time displacement of the main drive motor; For time parameters; This is the parameter for pi.

[0033] The output variable for this step is the current drive power. , used in S22, as the time series input source for the online adaptive filter.

[0034] S22: Use rolling running data to perform online parameter self-tuning of the pressure feedback online adaptive filter.

[0035] S221: The input variable is the operating status feedback of the energy storage wind-driven seawater desalination device within the previous control cycle or a preset rolling time window, including the output power variance. Accumulated deviation of energy storage pressure from target value and the square integral of the rate of change of power Construct a parametric performance evaluation function. Determine the set of parameters to be tuned. The performance evaluation function for multi-objective optimization parameters is constructed as follows: ; in, To obtain the minimum value of the performance evaluation function for multi-objective optimization parameters; The variance of output power; This represents the cumulative deviation of energy storage pressure from the target value. The integral of the square of the rate of change of power; The power variance weights; The cumulative weight of the energy storage pressure deviation from the target value; The weight is the integral of the square of the power change rate.

[0036] The above parameter performance evaluation function is used to evaluate the impact of various filter parameter combinations on the operating status of the energy storage-type wind-driven seawater desalination unit; among which, the output power variance Used to assess the stability of power supply to the permeate side of reverse osmosis; pressure deviation term. Used to assess the degree to which the pressure of a hydraulic accumulator deviates from the central region; integral of the square of the power change rate. Used to assess the severity of changes in target power; through the above three indicators, this invention integrates power smoothing effect, hydraulic energy storage status and stable power supply requirements of reverse osmosis membrane modules into the filter parameter tuning process.

[0037] The output variable for this step is: the multi-objective optimization parameter performance evaluation function constructed by integrating various evaluation indicators. This is passed to S222 as the target basis for fitting and optimizing the Gaussian process surrogate model.

[0038] S222: Performance evaluation function for multi-objective optimization parameters constructed with input variables S221. The optimization process is iterative based on Bayesian optimization, utilizing a multi-objective optimization parameter performance evaluation function. The proxy model optimizes sampling by maximizing the expected value of the EI acquisition function, specifically as follows: ; in, This is the data acquisition function; This is the minimum performance evaluation parameter currently observed; The cumulative distribution function of the standard normal distribution; is the probability density function of the standard normal distribution; The mean value predicted by the Gaussian process surrogate model; The standard deviation predicted by the Gaussian process surrogate model; To maximize expectations.

[0039] The aforementioned expected improvement acquisition function is used to select the next set of parameters to be evaluated in the filter parameter space. This expression is not directly used to control the actuator, but rather to balance the utilization of existing better parameter regions and the exploration of potentially better parameter regions during the background online optimization process. The current optimal set of filter parameters is obtained through this acquisition function. The output is sent to S23 to update the core coefficients inside the adaptive filter online, enabling the filter to adaptively smooth according to the intensity of wind power fluctuations, the pressure state of the hydraulic accumulator, and the energy demand on the reverse osmosis permeate side.

[0040] The obtained optimal filter parameter set is sent to S23 for power smoothing. In the real-time computing platform deployment, the underlying data acquisition, available drive power extraction, and main drive control law calculation are all locked within a 1ms main loop step for high-frequency execution; while the optimization iteration based on S22 is separately allocated to a fixed background online computing cycle of 10ms for cyclical updates. Given that the dominant time constants of the fluid response of the hydraulic accumulator charging and discharging fluid and the reverse osmosis membrane module are usually in the hundreds of milliseconds to seconds, the 10ms optimization update can fully cover the frequency band evolution of variable wind conditions. Moreover, relying on the computing power support of the computing platform, it is sufficient to achieve real-time acquisition of Gaussian process regression and acquisition function maximization without overflow or timeout, ensuring the sensitivity of extreme sea state response and the absolute computational stability of global optimization from the system bottom layer.

[0041] The output variable for this step is the current optimal set of filter parameters obtained by maximizing the expected improvement of the EI acquisition function. The output is sent to S23 to update the core coefficients inside the adaptive filter online, thereby optimizing the parameters and achieving adaptive smoothing.

[0042] S23: Construct an online adaptive filter with pressure feedback based on the current driving power and online tuning parameters, and output a smoothed target power. Using the current driving power obtained in S21 as the filter input and the current optimal filter parameter set obtained in S22 as the filter parameters, construct a time-varying filter model with pressure feedback compensation, and establish a mapping relationship from the current driving power to the smoothed target power.

[0043] S231: Constructing effective filter coefficients The normalized pressure state variable is set as follows: ; in, Normalized pressure state variables; This refers to the actual pressure of the hydraulic accumulator. This is the lower limit of the actual pressure of the hydraulic accumulator; This represents the upper limit of the actual pressure of the hydraulic accumulator.

[0044] The above normalized pressure state variables, normalized pressure state variables Used to measure the actual pressure of the hydraulic accumulator Mapped to the lower pressure limit and pressure limit A defined dimensionless interval is established. Through this normalization process, hydraulic accumulators of all capacity levels and rated pressure ranges can be assessed using a unified pressure state method. This variable reflects the position of the hydraulic accumulator pressure within the normal operating range, providing the pressure state basis for subsequently constructing adaptive filter coefficients. Pressure-dependent filter coefficients are determined using a V-shaped adaptive function. for: ; in, These are the pressure-dependent filter coefficients; This represents the minimum filter coefficient when the pressure is moderate. This represents the maximum filter coefficient when the pressure approaches its limit.

[0045] The above V-shaped adaptive filter coefficients This is an improvement based on a fixed filter coefficient. The function is based on the normalized pressure state variable. Adjusting the filter strength: When the hydraulic accumulator pressure approaches the middle region, the filter coefficient tends to be close to... The filter enhances the suppression of high-frequency fluctuations in wind power; when the hydraulic accumulator pressure approaches the upper or lower pressure limit, the filter coefficient approaches... The filter improves the response speed to changes in current drive power to prevent the hydraulic accumulator from overpressure or venting; thus, the expression establishes a correspondence between the accumulator pressure position and the power smoothing intensity.

[0046] Introducing a piecewise exponential target region gain correction term for: ; in, This is the gain correction term for the target region; This is the pre-defined boundary of the first target area; This is the pre-defined boundary of the second target area; Steepness coefficient; It is a natural exponential function; This is a function that maximizes the value of a function.

[0047] The above target region gain correction term This is used to enhance the filter's response performance when the hydraulic accumulator pressure is near its limit. It also applies when the hydraulic accumulator pressure is above the upper boundary of a preset target area. Or below the lower boundary of the preset target area At that time, the exponential correction term makes Increasing the filter coefficient rapidly increases the effective filtering coefficient. This process allows the filter to promptly adjust the smoothing target power when the accumulator pressure approaches a dangerous level, preventing the energy storage system pressure from exceeding its limits.

[0048] Obtain the actual pressure of the hydraulic accumulator And transform it into a normalized pressure state variable. and the preset target area boundary , Determine the effective filter coefficients. for: ; in, These are the effective filter coefficients.

[0049] The above effective filter coefficients Pressure-dependent filter coefficients With target region gain correction term The formula is jointly determined. It incorporates both the location of the hydraulic accumulator pressure and the degree to which the pressure approaches the boundary into the filter coefficient calculation. This allows the filter to prioritize power smoothing under normal operating conditions and energy storage protection near the boundary. Therefore, the smoothed target power can simultaneously meet the water quality stability requirements of the reverse osmosis membrane module and the normal operation requirements of the hydraulic energy storage system.

[0050] The output variable in this step is the effective filter coefficient that combines the V-shaped adaptive function and the piecewise exponential target region gain. Substitute this into the filter backbone structure constructed by S233, and use it as the current driving power. The time-varying weighting coefficients.

[0051] S232: Input variable is the actual pressure of the hydraulic accumulator. Determine the piecewise exponential integral term; calculate the integral gain in the integral reset term using the following piecewise exponential form. To eliminate static pressure difference, specifically: ; in, This refers to the integral gain in the integral reset term; Basic integral gain; The enhancement coefficient outside the target area; This is the median value of the working pressure range of the hydraulic accumulator.

[0052] The above piecewise exponential integral gain Used to determine the pressure deviation of the hydraulic accumulator. The integral reset strength is dynamically adjusted to the degree of pressure deviation. When the pressure deviation is small, the integral gain remains small to avoid excessive correction to the smoothing target power; when the pressure deviation increases, the exponential term increases the integral gain, enabling the integral reset term to pull the accumulator pressure back to the middle region more quickly. This treatment enhances the pressure recovery performance of the energy storage system under conditions of sudden wind speed changes and power fluctuations.

[0053] The output variable for this step is the integral gain in the integral reset term, calculated using a piecewise exponential form. The integral reset term passed to S233 It is used to eliminate static pressure difference.

[0054] S233: Based on effective filter coefficients Integral gain in the integral reset term Construct a time-varying first-order inertial filter structure; obtain the current driving power extracted by S21. Construct a time-varying filter model with pressure feedback compensation, and establish a system based on available driving power. To smooth target power The mapping relationship is as follows: ; in, To smooth the target power; For pressure proportional feedback; This is the integral reset term; These are the pressure-dependent filter coefficients; This refers to the actual pressure of the hydraulic accumulator. These are the effective filter coefficients; This represents the available drive power.

[0055] The above formula is an improvement on the conventional first-order inertial filtering formula. Conventional first-order inertial filtering typically only uses fixed filter coefficients to smooth the input power, while this invention introduces the pressure state of the hydraulic accumulator in the energy storage-type wind-driven seawater desalination device into the filtering process, making the filtered output consistent with the current driving power. Smooth target power from the previous moment Related, and also subject to pressure ratio feedback items and integral reset term Correction. Through this process, the target power is smoothed. It can dynamically adjust according to the charging and discharging state of the hydraulic accumulator, avoiding the direct transmission of high-frequency fluctuations in wind power to the reverse osmosis membrane module. Specifically: ; in, This is the median value of the operating pressure range of the hydraulic accumulator; This is the pressure proportionality coefficient.

[0056] The aforementioned pressure feedback correction term is an improved structure formed by introducing the pressure state of the hydraulic accumulator on the basis of conventional first-order inertial filtering. Specifically, the pressure proportional feedback term is used to respond quickly to the instantaneous pressure deviation of the hydraulic accumulator: when the actual pressure of the hydraulic accumulator is higher than the median pressure, it indicates that the energy storage system has relatively sufficient energy. The pressure proportional feedback term increases the smoothing target power, causing the reverse osmosis permeate side to consume more hydraulic energy and promote the release of stored energy. When the actual pressure of the hydraulic accumulator is lower than the median pressure, it indicates that the energy storage system has insufficient energy. The pressure proportional feedback term reduces the smoothing target power to reduce the instantaneous extraction of hydraulic energy from the reverse osmosis permeate side.

[0057] The integral reset term is used to accumulate the long-term deviation of the hydraulic accumulator pressure relative to the median pressure and continuously correct the target power for smoothing. Compared with the regulation method that only uses proportional feedback, the integral reset term can reduce the steady-state deviation of the hydraulic accumulator pressure during long-term operation, allowing the energy storage system pressure to gradually return to the middle range. Therefore, the pressure feedback correction term can establish a correspondence between the hydraulic accumulator pressure state and the power demand of the reverse osmosis membrane module, enabling the energy storage-type wind-driven seawater desalination unit to balance power smoothing, energy storage, and membrane-side pressure stability under fluctuating wind power conditions.

[0058] This output establishes the available drive power. To smooth target power The mapping structure includes a pressure proportional feedback term. and integral reset term These are combined to form the complete adaptive filter backbone formula, which is used to calculate the final output power.

[0059] The smoothed target power is initially calculated using the above formula. Introducing power limits and power change rate limit Smoothing target power Saturation truncation is applied, and the actuator's physical boundary constraints are applied; the output variable is the power limit after passing through. and power change rate limit Final smoothed target power after saturation truncation The final filtered result is output to S312 and used as the energy input boundary on the reverse osmosis permeate side.

[0060] In S2, real-time data such as high-pressure pipeline pressure, variable motor swing angle, and common shaft speed are collected to calculate the current drive power. Taking the stable operating point shown in Table 1 as an example, the variable motor displacement gradient coefficient is... In steady state, the swing angle of the variable motor is Then the real-time displacement of the main drive motor at that operating point for: Substituting the parameters yields Take the high-pressure pipeline pressure under stable conditions. Variable motor speed in steady state The current drive power input after the wind turbine captures energy and converts it through the hydraulic main drive chain is: Substituting the parameters, we get: ; At this typical operating point, the hydraulic main drive chain can supply approximately [amount missing] to the reverse osmosis permeate side. The current drive power. This result serves as the input power for the pressure feedback online adaptive filter. At the stable operating point where the hydraulic accumulator pressure is in the middle of the operating range and the pressure feedback correction is small, the smoothing target power is expressed as: When wind speed fluctuations cause When rapid changes occur, S2 further calculates the effective filter coefficient, pressure proportional feedback term, and integral reset term based on the filter parameters in Table 2 and the actual pressure state of the hydraulic accumulator. Smoothing is performed to obtain a smoothed target power suitable for stable operation of the reverse osmosis membrane module. .

[0061] S3: Determine the real-time input energy boundary based on the energy transfer relationship of the main drive chain. Establish an objective function to maximize water production and output the optimal high-pressure water pump inlet flow rate. Converted to the corresponding desired common shaft speed Establish a wind turbine speed tracking backstepping sliding mode controller and a common shaft speed tracking backstepping sliding mode controller; perform multi-objective cooperative tracking control based on backstepping sliding mode and proportional-integral-derivative PID, and obtain and output the concentrate valve opening adjustment signal. The stepper motor or proportional electromagnet that drives the concentrate valve performs a throttling action to eliminate the static pressure difference in a closed loop.

[0062] S31: Decoupling of multi-objective control laws and desired objective planning. Obtaining the smoothed target power output from S22. The desired target of maximum water production is optimized and decoupled. Before constructing the optimization model, the smoothed target power output of the previous adaptive filter is used. This serves as the energy input boundary on the reverse osmosis permeate side. Based on the energy transfer relationship of the main drive train, the real-time input energy boundary in the optimization model is determined. satisfy: ; in, The real-time available energy boundary, determined by the target power of the front-end smoothing, is used to limit the maximum hydraulic drive power that the reverse osmosis permeate side is allowed to consume at the current moment. To improve the mechanical efficiency of the high-pressure water pump, the system uses the actively smoothed and stable driving energy as the supply boundary through this mapping relationship, thus isolating the penetrating impact of external high-frequency fluctuation power.

[0063] Based on this, a nonlinear constrained optimization model is constructed with the objective of maximizing water production, taking the high-pressure water pump inlet flow rate as an example. With reverse osmosis target pressure As joint decision variables, the objective function for maximizing water production is established as follows: ; in, The objective function is to maximize water production. Constraints for the physical mapping model of the reverse osmosis membrane process; The actual hydraulic power consumed is limited by the supply constraint of the available smooth energy boundary; The upper limit of the influent flow rate is constrained; As a constraint on the upper limit of water production flux; Constraints for the joint normal operation of the system; Minimum discharge constraint for concentrated wastewater; The salinity of the produced water is constrained. The feed water flow rate provided by the high-pressure water pump to the reverse osmosis membrane module; The target operating pressure for the reverse osmosis membrane module; This refers to the permeate flow rate obtained by the reverse osmosis membrane module under the current feed water flow rate and target operating pressure. This is a physical mapping function for the reverse osmosis membrane process, used to characterize the feed water flow rate. Target pressure for reverse osmosis With water production flow rate The correspondence between them; This refers to the actual hydraulic power consumed. The maximum allowable inlet water flow rate limit for the equipment corresponds to boundary AB in the diagram, which is the upper limit constraint for the inlet water flow rate. The maximum permissible permeate flux limit to prevent membrane element fouling corresponds to boundary BC in the figure, which is the upper limit constraint on permeate flux. The system's joint operational limit state implicit function corresponds to boundary CD in the diagram, and the system's joint normal operation constraint; The minimum concentrate discharge flow rate required to remove concentrated salts from the membrane surface corresponds to boundary DE in the figure, which is the minimum concentrate discharge constraint. To meet the maximum salinity red line of the drinking water quality standard, corresponding to boundary EA in the figure, the upper limit constraint of salinity of the produced water is the minimum driving boundary to overcome osmotic pressure.

[0064] The following five inequalities in the above formula correspond to the system operation windows, such as... Figure 5 The five boundary segments shown correspond to [the desired parameters]. The output variable for this step is the optimal reverse osmosis target pressure obtained from the optimization model. With the optimal high-pressure water pump inlet flow rate , the optimal reverse osmosis target pressure The expected pressure value for the concentrated water valve pressure closed-loop control is used for subsequent concentrated water valve opening adjustment; the optimal high-pressure water pump inlet flow rate. Convert the high-pressure water pump displacement relationship into the corresponding desired common shaft speed. This serves as the reference trajectory for common shaft speed tracking control; specifically, when the high-pressure water pump displacement is... Volumetric efficiency is At that time, the expected common shaft speed satisfy: ; in, The desired common shaft speed; This refers to the displacement of the high-pressure water pump. This refers to the volumetric efficiency of the high-pressure water pump.

[0065] Therefore, this step transforms the optimal operating point on the reverse osmosis permeate side into expected pressure and rotational speed that can be tracked by the actuators. The output is sent to the concentrated water valve pressure closed-loop controller in S323. The output is sent to the common axis speed tracking backstepping sliding mode controller in S322.

[0066] S32: Multi-objective cooperative tracking control based on backstepping sliding mode and proportional-integral-derivative PID.

[0067] S321: Obtain the measured environmental wind speed collected by S21 The optimal rotor speed is calculated based on the optimal tip speed ratio method and used as the desired rotor speed trajectory. ; the desired trajectory of the wind turbine rotation speed This serves as the reference target trajectory for the wind turbine speed tracking backstepping sliding mode controller.

[0068] Set tracking error: in The wind turbine rotation speed, The desired trajectory of the wind turbine rotation speed; the real-time system state variables collected from the underlying layer are: the actual rotation speed of the wind turbine. Actual pressure of high-pressure pipeline and the actual speed of the common shaft Establish a wind turbine speed tracking backstepping sliding mode controller to obtain the main drive motor displacement control signal, specifically: ; in, The output of the wind turbine speed tracking backstepping sliding mode controller; This refers to the actual rotational speed of the common shaft. Let be the derivative of the desired wind turbine rotational speed; This refers to the tracking error of the wind turbine speed. The actual pressure of the high-pressure pipeline With the first-level virtual control law of the backstep method Direct tracking error; Let be the derivative of the expectation of the virtual control law; The displacement of the hydraulic metering pump that is coaxially connected to the wind turbine; The leakage coefficient of the high-pressure pipeline and motor system; This refers to the total volume of the high-pressure pipeline and the motor oil inlet chamber. The effective bulk modulus of hydraulic oil; This is the equivalent moment of inertia of the wind turbine and transmission system. This is the positive definite linear feedback gain of the second step of the backstepping controller; The robustness coefficient; The boundary layer thickness is the saturation function. This refers to the actual rotational speed of the wind turbine. This represents the actual pressure of the high-pressure pipeline. It is a saturation function used to replace the sign function and perform boundary layer smoothing on the robust terms in sliding mode control to reduce chattering of the main drive motor displacement control signal.

[0069] The output variable in this step is the main drive system motor displacement control signal. Drive the variable motor; simultaneously calculate the results. As a known state variable, it is input to S322 for decoupling.

[0070] S322: Obtain the desired common shaft speed provided by S31 The main drive motor displacement control signal calculated by S321 The system's real-time state variables are: actual pressure of the accumulator. and reverse osmosis pressure .

[0071] Set tracking error: ,middle This refers to the actual rotational speed of the common shaft. Let the expected actual rotational speed of the common shaft be denoted as . A common shaft rotational speed tracking backstepping sliding mode controller is established to obtain the energy storage pump motor displacement control signal as follows: ; in, This is the displacement control signal for the energy storage pump motor; The real-time displacement signal of the main drive motor; The derivative of the expected rotational speed of the common shaft; This refers to the coaxial speed tracking error. This refers to the displacement of the high-pressure water pump. The equivalent moment of inertia of the common axis; The viscous friction damping coefficient of the common shaft system; For the mechanical efficiency of the variable pump motor; For motor mechanical efficiency; For the mechanical efficiency of high-pressure water pumps; The linear feedback gain for the common shaft speed error; The robustness coefficient; The boundary layer thickness is the saturation function. This is the reverse osmosis pressure.

[0072] The output variable is the energy storage pump motor displacement control signal. It drives the variable pump motor of the energy storage system to achieve target speed tracking of the system's common shaft.

[0073] S323: Obtain the expected reverse osmosis pressure as planned in S312 and the actual reverse osmosis pressure collected by S21 Set tracking error: ,in For reverse osmosis pressure, The desired reverse osmosis pressure is calculated. The tracking error is passed to a discrete PID controller to obtain and output the concentrate valve opening adjustment signal. for: ; in, This is the signal for adjusting the opening degree of the concentrate valve; for Tracking error at any given moment; For tracking error; For integration time; This is the proportional gain coefficient of the concentrated water valve pressure closed-loop controller. This is the integral gain coefficient of the concentrated water valve pressure closed-loop controller. This is the differential gain coefficient of the concentrated water valve pressure closed-loop controller.

[0074] The signal is adjusted by controlling the opening degree of the concentrate valve. Real-time drive of the concentrate valve stepper motor or proportional electromagnet to change the throttling area at the concentrate end and eliminate the static pressure difference on the membrane side; output concentrate valve opening adjustment signal. A stepper motor or proportional electromagnet drives the concentrate valve to perform a throttling action to eliminate static pressure difference in a closed loop.

[0075] Hardware-in-the-loop (HIL) experiments were conducted to verify and analyze the control method proposed in the embodiments of this invention. The HIL experiment configuration is as follows: Figure 6As shown, the hardware-in-the-loop model consists of six key components: a wind turbine system, a hydraulic main drive system, a hydraulic energy storage system, a seawater desalination system, an electrical control system, and a host computer system. The wind turbine simulation system generates the corresponding torque and speed to drive the metering pump of the main drive system. The hydraulic main drive system transmits energy to the seawater desalination system to produce fresh water. The hydraulic energy storage system is responsible for absorbing and smoothing wide-frequency power fluctuations in the main drive chain, filling and storing energy when there is excess input energy, and draining to compensate when there is insufficient energy, thus reducing the direct impact of transient pressure pulsations on the reverse osmosis membrane modules and achieving flexible connection and stable energy supply between the wind energy capture end and the water production unit. The electrical control system is responsible for acquiring sensor signals and receiving and sending control signals. The host computer displays the various states of the system in real time and sends control signals to each system.

[0076] In S3, the smoothed target power obtained in S2 is converted into the real-time energy input boundary on the reverse osmosis permeate side. If the mechanical efficiency of the high-pressure water pump in this embodiment is taken as... Then the real-time energy input boundary at this typical operating point for: Substituting the parameters, we get: Therefore, the water production maximization optimization model in S3 is not obtained under infinite energy conditions, but rather under the energy boundary that the current hydraulic main drive system and hydraulic energy storage system can stably provide. Internally, the inlet flow rate of the high-pressure water pump and the target pressure of reverse osmosis are jointly optimized.

[0077] In this embodiment, the high-pressure water pump displacement and high-pressure water pump volumetric efficiency For the given parameters, in, These are the fixed structural parameters after the high-pressure water pump selection has been determined. These are known parameters determined based on the high-pressure water pump sample parameters. The optimal reverse osmosis target pressure and optimal high-pressure water pump inlet flow rate obtained by S3 are not fixed constants, but rather time-varying sequences that are dynamically updated with the smoothed target power and system state, denoted as follows: and The desired trajectory of the wind turbine rotational speed is calculated based on the optimal tip speed ratio method. Its expression is: ; in, The optimal tip speed ratio; The wind speed was measured in the actual environment. The radius of the wind turbine; The time-varying reference trajectory is used for the wind turbine speed tracking backstepping sliding mode controller.

[0078] At the same time, the optimized high-pressure water pump inlet flow rate will be obtained. Convert to common shaft desired speed Its expression is: ; in, To track the time-varying reference trajectory of the backstepping sliding mode controller for the common axis speed, the optimization result of S3 forms a control objective that can directly affect the actuator: The output is sent to the concentrate valve pressure closed-loop controller as the expected value of the reverse osmosis membrane side pressure. Output to the wind turbine speed tracking backstepping sliding mode controller; Output to the common axis speed tracking backstepping sliding mode controller.

[0079] The backstep control process involves This is a virtual control quantity for high-pressure pipeline pressure, used to construct an intermediate virtual target in the main drive motor displacement control law; it is not used as an independent external actuator command output. Finally, combined with hardware-in-the-loop experimental results under typical turbulent wind conditions, it can be seen that the method of this invention reduces the standard deviation of the reverse osmosis system drive power change rate from... Reduce to The smoothing effect reached approximately 92.2%; the average permeable flow rate reached... The average TDS of the produced water was The average SOC of the energy storage system was 71.51%. These results indicate that, following the complete evolutionary process of energy storage capacity optimization, current drive power calculation, smooth target power generation, energy boundary constraint optimization, actuator target trajectory conversion, and multi-actuator collaborative tracking, this invention can ensure stable energy supply to the reverse osmosis permeable side, water quality compliance, and normal operation of the energy storage system under actual wind power fluctuation conditions.

[0080] The experimental results of a power fluctuation suppression-based energy storage-driven wind-driven seawater desalination water quality optimization method are as follows: like Figure 7 The figure shows the wind speed time series curve under typical turbulent wind conditions; it also shows the system state variable tracking response curve under typical turbulent wind conditions for an energy storage-based wind-driven seawater desalination water quality optimization method based on power fluctuation suppression according to the present invention. Figure 8 The figure shows the wind turbine speed tracking error curve; as can be seen from the graph, the wind turbine speed tracking error controlled by this invention is small, with a root mean square error of only 2.21 rpm, indicating good tracking performance. Figure 9 The figure shows the motor speed tracking error curve. The motor speed tracking error is extremely small, with a root mean square error of only 0.47 rpm, indicating excellent tracking performance. The motor speed response closely follows the target value. Figure 10The figure shows the motor speed tracking error curve. The reverse osmosis system pressure has almost no tracking error, with a root mean square error of 0.0285 bar. The reverse osmosis system pressure can smoothly follow the target trajectory with almost no fluctuations. This verifies the effectiveness of the pressure independent control and adaptive filtering decoupling proposed in this invention.

[0081] like Figure 11 The figure shows the RO drive power response curve. Under conditions where the wind turbine capture power variation rate is large, with a standard deviation of 6061.52 W / s, the method proposed in this invention can reduce the drive power variation rate for reverse osmosis systems to 471.64 W / s, achieving a smoothing effect of 92.2%. Figure 12 The figure shows the product water flow rate response curve, with a standard deviation of 0.553 m³ / h. 3 / h, with an average production flow rate of 4.199 m³ / h. 3 / h. For example... Figure 13 The figure shows the TDS response curve of the permeate; the standard deviation of the permeate TDS is 16.66 mg / L, and the mean is only 81.65 mg / L, indicating that both the permeate flow rate and permeate quality are relatively stable. Figure 14 The figure shows the SOC response curve of the energy storage system. The average SOC of the energy storage system is 71.51%, and the standard deviation is 10.33%, indicating that the energy storage system charges and discharges more frequently to actively smooth out fluctuations, fully utilizing its storage and discharge performance, and the pressure remains within the specified range. This verifies that the present invention can enable the reverse osmosis membrane element to operate under stable conditions, achieving a desalination effect with stable flow and better water quality. Figures 11 to 14 The system output response curve of the energy storage-based wind-driven seawater desalination water quality optimization method based on power fluctuation suppression invented by the joint group under typical turbulent wind conditions.

[0082] The beneficial effects of this invention are as follows: This invention proposes a power fluctuation suppression-based energy storage-type wind-driven seawater desalination water quality optimization method, which effectively solves the problems of equipment redundancy or insufficient disturbance buffering caused by traditional configuration based on engineering experience. It combines traditional static capacity selection with dynamic control for coordinated capacity configuration; actively suppresses wideband power fluctuations under normal operating conditions, and achieves high-precision power smoothing output; when pipeline pressure reaches physical boundaries, it actively intervenes to unload dangerous pressure, avoiding mechanical damage to core equipment and reverse osmosis membrane components from the control source, and improving the system's adaptive disturbance rejection performance in response to extreme wind conditions; it overcomes the difficulty of adapting to traditional fixed parameter control. To address the issue of wideband wind energy fluctuations, this invention improves the system's wind energy utilization and water production stability under extreme sea states, maximizing global water production under fluctuating wind conditions. It effectively avoids the system chattering risk easily caused by traditional global nonlinear control, enabling the seawater desalination system to operate smoothly without static error under wideband environmental disturbances. This invention is applicable to wind-driven seawater desalination systems of various capacity levels. Its modular execution structure facilitates deep integration with existing industrial programmable controllers or simulation platforms, making it particularly suitable for remote islands or uninhabited offshore platforms lacking robust power grid support, providing them with highly reliable freshwater resource security. It possesses excellent industrial applicability and off-grid sea state adaptability.

[0083] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression, characterized in that, It includes: S1: Collect historical wind speed data and obtain the static physical volume of the accumulator based on the constraints of the hydraulic accumulator. Establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters; perform collaborative optimization to determine the energy storage capacity of the energy storage-type wind-driven seawater desalination device, and output the globally optimal static physical volume of the accumulator. Used for physical equipment installation and piping configuration, defining physical hardware boundaries; S2: Acquire multi-source state data of the energy storage-type wind-driven seawater desalination device to determine the current driving power of the wind turbine's captured energy. ; Construct a multi-objective optimization parameter performance evaluation function to perform iterative optimization and obtain the current optimal filter parameter set. A time-varying filtering model with pressure feedback compensation is constructed, and adaptive filtering and smoothing processing is performed to output a smoothed target power. Power limits and power change rate limit Smoothing target power Saturation cutoff is performed, and the output is sent to S3 as the energy input boundary on the reverse osmosis permeate side. S3: Determine the real-time input energy boundary based on the energy transfer relationship of the main drive chain. Establish an objective function to maximize water production and output the optimal high-pressure water pump inlet flow rate. Converted to the corresponding desired common shaft speed Establish a wind turbine speed tracking backstepping sliding mode controller and a common shaft speed tracking backstepping sliding mode controller; Multi-objective cooperative tracking control is performed based on backstepping sliding mode and proportional-integral-derivative PID, and the concentrated water valve opening adjustment signal is obtained and output. The stepper motor or proportional electromagnet that drives the concentrate valve performs a throttling action to eliminate the static pressure difference in a closed loop.

2. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression as described in claim 1, characterized in that: S1 specifically refers to: S11: Collect historical wind speed data for the target area where the energy storage-type wind-driven seawater desalination unit is installed; determine the static physical volume of the hydraulic accumulator. Precharge pressure Preset initial working pressure With rated volume Satisfying constraints ; Output accumulator static physical volume ; S12: Establish a multi-objective optimization model that integrates volume configuration and underlying filter performance evaluation parameters, and construct a performance evaluation function for the multi-objective optimization model. Using the Bayesian optimization algorithm, a performance evaluation function for the above multi-objective optimization model is obtained through reverse optimization, and the globally optimal static physical volume of the energy storage device is output. .

3. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 2, characterized in that: In S12, a multi-objective optimization model is established that integrates volume configuration and underlying filter performance evaluation parameters, and a performance evaluation function for the multi-objective optimization model is constructed. for: ； in, This is the performance evaluation function for a multi-objective optimization model. This is the optimal performance evaluation term for filter parameters; For the filter parameter set; For volumetric weight; This is the maximum allowable accumulator volume of the system; Input variables for the static physical volume of the accumulator; This represents the globally optimal static physical volume of the energy storage device.

4. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 1, characterized in that: S2 specifically refers to: S21: Detect and acquire multi-source status data of the energy storage-type wind-driven seawater desalination device to determine the current drive power input after the wind turbine captures energy and converts it through the hydraulic main drive chain. ; S22: Perform online self-tuning of parameters for the pressure feedback online adaptive filter using rolling running data; construct a multi-objective optimization parameter performance evaluation function; perform optimization iteration based on Bayesian optimization; and utilize the multi-objective optimization parameter performance evaluation function. The proxy model is used to obtain the current optimal set of filter parameters. ; S23: Construct a time-varying filter model with pressure feedback compensation, and establish a system based on available drive power. To smooth target power The mapping relationship, the output variable is the power limit value. and power change rate limit Final smoothed target power after saturation truncation .

5. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 4, characterized in that: The performance evaluation function for multi-objective optimization parameters in S22 is constructed as follows: ； in, To obtain the minimum value of the performance evaluation function for multi-objective optimization parameters; For multi-objective optimization of parameter performance evaluation functions; The variance of output power; This represents the cumulative deviation of energy storage pressure from the target value. The integral of the square of the rate of change of power; The power variance weights; The cumulative weight of the energy storage pressure deviation from the target value; The weight of the square integral of the power change rate; Normalized pressure state variables; To smooth the target power; This is a time parameter.

6. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 4, characterized in that: Performance evaluation function for multi-objective optimization parameters in S22 The proxy model is as follows: ； in, This is the data acquisition function; This is the minimum performance evaluation parameter currently observed; The cumulative distribution function of the standard normal distribution; is the probability density function of the standard normal distribution; The mean value predicted by the Gaussian process surrogate model; The standard deviation predicted by the Gaussian process surrogate model; To maximize expectations; This is a function that maximizes the value of a function.

7. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 4, characterized in that: In S23, a time-varying filter model with pressure feedback compensation is constructed, specifically as follows: ； in, To smooth the target power; For pressure proportional feedback; This is the integral reset term; These are the pressure-dependent filter coefficients; This refers to the actual pressure of the hydraulic accumulator. These are the effective filter coefficients; This represents the available drive power.

8. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 1, characterized in that: S3 specifically refers to: S31: Determine the real-time input energy boundary in the optimization model based on the energy transfer relationship of the main drive chain. The high-pressure water pump inlet flow rate With reverse osmosis target pressure As joint decision variables, an objective function for maximizing water production is established; the optimal high-pressure water pump inlet flow rate is output. Based on the high-pressure water pump displacement relationship, the corresponding desired common shaft speed is converted. ; S32: Establish a wind turbine speed tracking backstepping sliding mode controller and obtain the output of the wind turbine speed tracking backstepping sliding mode controller. Establish a common shaft speed tracking backstepping sliding mode controller to obtain the energy storage pump motor displacement control signal. Multi-objective cooperative tracking control is performed based on backstepping sliding mode and proportional-integral-derivative PID to obtain and output the concentrate valve opening adjustment signal. .

9. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression as described in claim 8, characterized in that: The objective function for maximizing water production in S31 is: ； in, The feed water flow rate provided by the high-pressure water pump to the reverse osmosis membrane module; The target operating pressure for the reverse osmosis membrane module; This refers to the permeate flow rate obtained by the reverse osmosis membrane module under the current feed water flow rate and target operating pressure. This is the physical mapping function for the reverse osmosis membrane process.

10. The method for optimizing water quality in energy storage-based wind-driven seawater desalination based on power fluctuation suppression according to claim 8, characterized in that: In S32, a common shaft speed tracking backstepping sliding mode controller is established to obtain the energy storage pump motor displacement control signal as follows: ； in, This is the displacement control signal for the energy storage pump motor; The real-time displacement signal of the main drive motor; The derivative of the expected rotational speed of the common shaft; This refers to the coaxial speed tracking error. This refers to the displacement of the high-pressure water pump. The equivalent moment of inertia of the common axis; The viscous friction damping coefficient of the common shaft system; This refers to the actual rotational speed of the common shaft. This refers to the coaxial speed tracking error. For reverse osmosis pressure; For the mechanical efficiency of the variable pump motor; For motor mechanical efficiency; For the mechanical efficiency of high-pressure water pumps; The linear feedback gain for the common shaft speed error; The robustness coefficient; The boundary layer thickness is the saturation function. It is a saturation function.

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