Offshore wind energy coupling fused salt heat storage combined power supply method and system

Abstract

The invention relates to an offshore wind energy coupling fused salt heat storage combined power supply method and system. The method comprises the following steps: initializing and calibrating sensors, acquiring multi-source sensing data, performing real-time compensation and unit conversion, and performing multi-sensor data fusion and Kalman filtering to obtain a reliable system state; the wind energy capture power is calculated in real time, and the rotating speed of a mechanical energy stirrer is determined through a sliding mode variable structure control algorithm; stirring heat generation and fluid dynamic optimization are carried out based on the rotating speed of the mechanical energy stirrer, and the heat storage amount and the SOC state are calculated; calculating the output power of the thermopile, adjusting the rotating speed of a variable-frequency water pump in combination with PID and a fuzzy control strategy, performing active temperature control, and determining the output state of a thermoelectric system; multi-mode coordination control is carried out based on a hybrid intelligent algorithm, and smooth switching of power supply operation modes is carried out; the efficiency is calculated in real time, and a digital twinborn model is constructed for parameter self-purification. The energy conversion link is obviously reduced, and the system efficiency and reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of offshore renewable energy, in particular to a method and system for combined power supply of offshore wind energy coupled with molten salt heat storage. BACKGROUND

[0002] Offshore renewable energy development and utilization has become the core strategic direction of global energy transformation. Offshore wind power has become the focus of research and application in the field of renewable energy due to its advantages such as abundant resource reserves, high energy density, and less land space occupation. Its technical development potential far exceeds the current global electricity consumption scale, and the installed capacity is in a rapid growth stage worldwide, with a broad development prospect.

[0003] Although offshore wind power has significant development advantages, its large-scale development and utilization still faces severe technical bottlenecks. The core contradiction lies in the restriction of the inherent intermittency and strong volatility of wind energy on power grid accommodation and system stable operation. The operating state of offshore wind turbine is directly affected by wind speed: when the wind speed is higher than the cut-out wind speed (usually 25 meters / second) or lower than the cut-in wind speed (usually 3-4 meters / second), the unit needs to be shut down to ensure equipment safety, resulting in complete interruption of power output; within the rated wind speed range, the output power of the unit is positively related to the cube of the wind speed (i.e. P∝v³), which makes the output power fluctuate sharply. Such fluctuations can cause power quality problems such as power grid frequency deviation and voltage flicker, directly limiting the power grid's accommodation capacity for offshore wind power, and becoming a key factor restricting the large-scale application of offshore wind power.

[0004] To suppress the power fluctuation of offshore wind power, energy storage technology is considered as the core solution. However, the current mainstream energy storage technology has defects that are difficult to overcome in the special environment of offshore high humidity, high salt fog, and limited space. Although the electrochemical energy storage system represented by lithium-ion batteries has the advantage of fast response speed, its cycle life will be significantly reduced in the corrosive environment of the sea. In addition, the lithium-ion battery system has the risk of thermal runaway, and its fire safety design is difficult to achieve in the scene of "limited space and difficult rescue" on the offshore platform. At the same time, the unit energy storage cost of such energy storage system is high, and the economic efficiency is insufficient, which further restricts its large-scale application. As a kind of mechanical energy storage, pumped storage technology relies on specific topography and hydrological conditions and cannot be deployed in open waters. The compressed air energy storage (CAES) system has the problems of complex structure and low energy density, and its underwater gas storage device needs to solve the problems of high-pressure sealing and corrosion protection at the same time, which has low technical feasibility and is difficult to adapt to the offshore scene. The existing wind-heat coupling technology generally adopts an indirect energy conversion path of "mechanical energy → electrical energy → resistance heat → steam power cycle". This path needs to go through multiple conversion links, and each conversion is accompanied by irreversible energy loss, resulting in low overall efficiency of the system. In addition, the high-speed rotating equipment in the system has a complex structure, high maintenance requirements, and large start-stop inertia, which is seriously mismatched with the working condition requirements of "limited space and inconvenient maintenance" on the offshore platform.

[0005] The molten salt heat storage technology has been maturely applied in centralized solar thermal power stations such as trough type and tower type, and has high heat storage efficiency. The thermoelectric power generation technology based on the Seebeck effect has also been fully verified in the fields of aerospace and industrial waste heat recovery, and has high technical maturity. However, there is currently no mature technology that can systematically integrate molten salt heat storage technology and direct heat-to-electricity technology, and directly mechanically couple with offshore wind turbines to form a simplified "wind-heat-electricity" integrated system without intermediate power conversion links. The existing technical solutions have not fundamentally solved the core contradiction between "offshore wind power fluctuation" and "energy storage economy and reliability", and cannot provide effective technical support for the large-scale stable application of offshore wind power. SUMMARY

[0006] Therefore, it is necessary to provide a method and system for offshore wind energy coupling molten salt heat storage combined power supply to solve the above technical problems.

[0007] In a first aspect, the present application provides a method for offshore wind energy coupling molten salt heat storage combined power supply. The method comprises: initializing and calibrating the sensor, obtaining multi-source sensor data for real-time compensation and unit conversion, and performing multi-sensor data fusion and Kalman filtering to obtain reliable system state; Real-time calculation of wind energy capture power based on the reliable system state, determination of mechanical energy agitator speed using a sliding mode variable structure control algorithm; Based on the mechanical energy agitator speed, stirring heat generation and fluid dynamics optimization are performed, the heat storage capacity and SOC state are calculated, and the molten salt temperature field and SOC are obtained; Based on the molten salt temperature field and SOC, the thermoelectric output power is calculated, combined with PID and fuzzy control strategies, the variable frequency water pump speed is adjusted, active temperature control is performed, and the thermoelectric system output state is determined; Based on the hybrid intelligent algorithm, multi-modal coordinated control is performed to smoothly switch the power supply operation mode; Real-time calculation of efficiency, construction of a digital twin model for parameter self-purification.

[0008] Optionally, in an embodiment of the present application, the multi-sensor data fusion and Kalman filtering include: Defining a state vector based on multi-source sensor data; Establishing a state space model, including a state transition matrix and an observation matrix; Setting the process noise covariance matrix and the measurement noise covariance matrix; Performing Kalman filter iteration prediction and update to output the optimal system state.

[0009] Optionally, in an embodiment of the present application, the formula for real-time calculation of wind energy capture power based on the reliable system state is:

[0010] where, is the air density, is the wind wheel swept area, is the wind speed, is the wind energy utilization coefficient, is the tip speed ratio, is the pitch angle.

[0011] Optionally, in an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm includes: Based on the reliable system state, molten salt temperature field, and thermoelectric system output state, a state vector is determined, and a nonlinear state space model is established combined with a control vector; Establishing an optimization problem for rolling optimization.

[0012] Optionally, in an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm, which performs smooth switching of the power supply operation mode, further includes: Establishing a system global energy balance and multi-objective optimization problem for solution, and controlling the power supply operation mode; The decision mechanism based on the fuzzy membership function is used to perform smooth switching between power supply operation modes.

[0013] Optionally, in an embodiment of the present application, the power supply operation modes include an energy storage mode, a power generation mode, a hybrid mode and a safety protection mode.

[0014] Optionally, in an embodiment of the present application, the formula for calculating the system efficiency is:

[0015] wherein, is the system efficiency, is the net electric power output of the system, is the mechanical power captured by the wind turbine, is the absolute temperature of the environment, is the average thermodynamic temperature when the wind energy is converted into heat energy, is the power consumption of the auxiliary system.

[0016] In a second aspect, the present application further provides a combined power supply system of offshore wind energy coupled with molten salt heat storage. The system comprises: a wind energy capture module for calculating the wind energy capture power in real time based on the reliable system state, and determining the mechanical energy agitator speed by using a sliding mode variable structure control algorithm; a heat storage module for agitating heat generation and fluid dynamics optimization based on the mechanical energy agitator speed, calculating the heat storage amount and the SOC state, and obtaining the molten salt temperature field and the SOC; a thermoelectric conversion module for calculating the thermoelectric generator output power based on the molten salt temperature field and the SOC, adjusting the variable frequency water pump speed by combining the PID and fuzzy control strategies, performing active temperature control, and determining the thermoelectric system output state; an intelligent control module for performing multi-modal coordinated control based on a hybrid intelligent algorithm, and performing smooth switching between power supply operation modes.

[0017] In a third aspect, the present application further provides a computer device. The computer device comprises a memory and a processor, the memory stores a computer program, and the processor executes the steps of the method described in each of the above embodiments.

[0018] In a fourth aspect, the present application further provides a computer readable storage medium. The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method described in each of the above embodiments.

[0019] The aforementioned method and system for joint power supply of offshore wind energy coupled with molten salt thermal energy storage firstly involves initializing and calibrating sensors, acquiring multi-source sensor data for real-time compensation and unit conversion, and performing multi-sensor data fusion and Kalman filtering to obtain a reliable system state. Next, based on the reliable system state, the wind energy capture power is calculated in real time, and a sliding mode variable structure control algorithm is used to determine the rotational speed of the mechanical energy agitator. Then, based on the mechanical energy agitator rotational speed, stirring heat generation and fluid dynamics optimization are performed to calculate the heat storage capacity and state of charge (SOC), obtaining the molten salt temperature field and SOC. Following this, based on the molten salt temperature field and SOC, the thermoelectric stack output power is calculated, and combined with PID and fuzzy control strategies, the variable frequency pump speed is adjusted for active temperature control to determine the thermoelectric system output state. Subsequently, multi-modal coordinated control is performed based on a hybrid intelligent algorithm to smoothly switch power supply operation modes. Finally, efficiency is calculated in real time, and a digital twin model is constructed for parameter self-purification. A modular integrated architecture based on energy flow optimization is adopted to construct a complete energy chain of "wind energy capture → mechanical transmission → thermal energy storage → thermoelectric conversion → intelligent control". Through innovative modular integrated design, wind energy is directly converted into thermal energy storage and then directly generated through the thermoelectric effect, constructing a direct conversion path of "wind energy - thermal energy - electrical energy", which significantly reduces energy conversion links and improves system efficiency and reliability. Attached Figure Description

[0020] Figure 1 This is a schematic flowchart of a combined power supply method for offshore wind energy coupled with molten salt thermal energy storage in one embodiment. Figure 2 This is a schematic diagram of the core technology of the thermoelectric conversion process in one embodiment; Figure 3 This is a structural block diagram of a combined power supply system for offshore wind power coupled with molten salt thermal energy storage in one embodiment; Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] In one embodiment, such as Figure 1 As shown, a combined power supply method for offshore wind energy coupled with molten salt thermal energy storage is provided, including the following steps: S101: Initialize and calibrate the sensors, acquire multi-source sensor data for real-time compensation and unit conversion, and perform multi-sensor data fusion and Kalman filtering to obtain a reliable system state.

[0023] In the embodiments of the present application, first, the full-system sensing unit is initialized and calibrated to establish a reliable state perception basis. This process ensures that the accuracy and reliability of all measurement data meet the requirements of the control algorithm before entering the controller.

[0024] The ambient temperature measurement uses a Pt 1000 platinum resistance temperature sensor that meets the IEC 60751:2022 standard, with an accuracy of ±(0.15 + 0.002|t|)℃, and all probes are coated with EPOX-Z2 anti-salt mist corrosion coating. According to the resistance-temperature characteristic curve, the non-linear error of the sensor in the range of 0-100°C is less than 0.1%.

[0025] The wind speed and direction measurement uses a Gill WindSonic4 two-dimensional ultrasonic anemometer, with a range of 0-60 m / s, an accuracy of ±0.1 m / s, and an output frequency of 1 Hz. The installation position strictly follows the requirements of the IEC 61400-12-1:2022 standard, located 2.5 times the rotor diameter upstream of the hub height to eliminate the tower shadow effect.

[0026] The atmospheric pressure is measured by a Druck RPT410 absolute pressure sensor with an accuracy of ±0.1% FS, which is used for accurate calculation of air density. According to the ideal gas state equation, air density ρ = P / (R·T), where R is 287.05 J / (kg·K).

[0027] The molten electrolyte temperature monitoring uses an N-type thermocouple that meets the ASTM E230 / E230M-2023 standard, with a measurement range of 0-800℃ and an accuracy of ±1.5℃ below 400℃. Inside the storage tank, it is arranged in an axial 4-layer, radial 8-point matrix, fully monitoring the temperature distribution. Based on the principles of heat transfer, this arrangement can accurately capture temperature stratification phenomena.

[0028] The electrical power measurement uses a Cirrus Logic CS5484 energy metering IC that meets the IEC 62053-22:2023 standard, with a dynamic range of 1000:1 and a measurement error of <0.1% in the range of 10%-100% of the rated power. This chip uses Σ-Δ ADC technology and has excellent anti-interference performance.

[0029] The mechanical rotational speed and vibration monitoring uses a Kistler 8704B magneto-electric rotational speed sensor and an ADI ADXL1001 MEMS accelerometer to monitor the rotational speed of the transmission shaft and the mechanical vibration state, respectively. The vibration monitoring frequency range is 0.5-10 kHz, meeting the ISO 10816-1 mechanical vibration standard requirements.

[0030] During system initialization, the microprocessor calls the calibration curve (such as resistance-temperature characteristic table, voltage-pressure relationship, etc.) for each individual sensor from the Flash memory, and performs real-time nonlinear compensation and unit conversion on the original sampling value, converting it into an engineering value with physical meaning.

[0031] To eliminate measurement noise and random interference and obtain the optimal estimation of the system state, the compensated multi-source sensor data is subjected to "multi-sensor data fusion based on Kalman filter" processing.

[0032] Specifically, in an embodiment of the present application, the multi-sensor data fusion and Kalman filtering include: S201: Defining a state vector based on multi-source sensor data.

[0033] S202: Establishing a state space model, including a state transition matrix and an observation matrix.

[0034] S203: Setting a process noise covariance matrix and a measurement noise covariance matrix.

[0035] S204: Performing Kalman filter iteration prediction and update, and outputting the optimal system state.

[0036] In an embodiment of the present application, according to the system control requirements, the state vector of the discrete-time Kalman filter is defined as In the initialization phase, this vector mainly contains physical quantities that can be directly or indirectly measured, such as , which contains the ambient temperature , wind speed , molten salt temperature at multiple measuring points , and transmission shaft speed , etc.

[0037] The state space model is established, including the state transition matrix and the observation matrix. The state transition matrix (Fk): is constructed based on the physical evolution law of each state quantity. For slowly changing parameters (such as ambient temperature), the state transition equation can be modeled as , where is the process noise. For dynamic quantities such as transmission shaft speed, the state transition relationship is established according to the simplified mechanical motion equation.

[0038] The observation matrix (H k ): This matrix establishes the linear relationship between the state vector and the observation vector (i.e., the sensor measurement value vector z k ). Its elements are directly determined according to the corresponding relationship between the physical quantities measured by the sensor and the state vector. For example, the observation equation of a certain temperature sensor is , where To measure the noise.

[0039] Set process noise covariance matrix and measurement noise covariance matrix: process noise covariance matrix (Q k ): preliminary setting by statistical analysis of the rate of change of state variables in historical data, the value reflects the uncertainty of the model. Measurement noise covariance matrix (R k ): diagonal matrix initialization according to the accuracy index of each sensor factory calibration (such as accuracy ±0.1 m / s), the elements on the diagonal line represent the uncertainty of each sensor measurement value.

[0040] In each sampling period k, the filter performs the following two core steps: (1) Prediction (Predict): using the posteriori estimation of the last time, predict the state and error covariance at the current time:

[0041]

[0042] (2) Update (Update): combine the actual measurement value z k at the current time to correct the predicted value and get the optimal state estimation at the current time .

[0043] First, calculate the Kalman gain :

[0044] Then update the state estimation and error covariance:

[0045]

[0046] Finally, the optimal estimation value of the state vector output by the filter is sent to the subsequent controller. After the first operation or maintenance of the system, the noise covariance matrix Q k and R k can be fine-tuned by comparing the filter output with the readings of high-precision reference instruments under known working conditions to further optimize the filtering effect.

[0047] In the embodiments of the application, through the systematic processing flow, the sensor noise and measurement outliers are effectively suppressed, providing stable, reliable and consistent system state input for subsequent control algorithms, which is the basis for precise control.

[0048] S102: Real-time calculation of wind energy capture power based on the reliable system state, and determination of mechanical energy mixer speed by using a sliding mode variable structure control algorithm.

[0049] In this embodiment, the wind energy capture power is calculated in real time based on the reliable system state, and the rotational speed of the mechanical energy mixer is determined by the sliding mode variable structure control algorithm, so as to efficiently and stably convert random wind energy into mechanical energy.

[0050] To suppress torque oscillations in the drive train caused by wind speed fluctuations, while maintaining the agitator speed. Within the optimal operating range of 30–100 rpm, the system employs a sliding mode variable structure control (SMC) algorithm. Based on nonlinear control theory, the sliding surface is designed as follows:

[0051] in, Tracking error ; This is the target speed (ReferenceSpeed) of the stirrer. This is the actual speed of the mixer.

[0052] so, It directly measures the difference between the current speed and the target speed.

[0053] The integral term represents the cumulative amount of all speed errors from a certain point in the past to the present. The purpose of introducing the integral term is to eliminate steady-state errors. Even if the system still has a small error after stabilizing at a certain point, the integral term will increase over time, thus "remembering" this error and continuously applying control force until the error is completely eliminated.

[0054] A positive weighting coefficient matrix (which may be a scalar in practical applications). It determines the integral term on the sliding surface. The relative importance of [the subject / method]. A higher value indicates a greater "concern" from error accumulation by the controller, resulting in faster elimination of steady-state errors. However, it may also affect the system's dynamic response speed. The parameter matrix is ​​to be determined using the pole placement method.

[0055] The control law is designed as follows:

[0056] in, For equivalent control, K is the switching gain, Φ is the boundary layer thickness, and sat(·) is the saturation function. It is proven using Lyapunov's second method that this control law ensures the system state trajectory approaches and remains on the sliding surface within a finite time, thus achieving strong robustness against parameter variations and external disturbances.

[0057] s: sliding surface variable. It is a quantity that combines the current error and the cumulative past errors. The ultimate goal of the controller is to drive this s to zero.

[0058] When s = 0, the system state is "locked" on the ideal sliding surface. At this time, the dynamic response of the system will be determined solely by , exhibiting a first-order exponential decay characteristic, and the error will converge to zero stably and rapidly, and is insensitive to the parameter variations of the system model itself. This is the source of strong robustness of the sliding mode control.

[0059] In an embodiment of the present application, the formula for calculating the wind energy capture power in real time based on the reliable system state is:

[0060] wherein, is the air density, is the wind wheel swept area, is the wind speed, is the wind energy utilization coefficient, is the tip speed ratio, is the pitch angle.

[0061] In an embodiment of the present application, the wind turbine operation control is based on real-time wind speed optimization of operation parameters, and the following control strategy is adopted, and the controller calculates in real time based on the following aerodynamic power formula:

[0062] wherein: : air density (kg / m³), taking 1.225; : wind wheel swept area (m²); v: wind speed (m / s); : wind energy utilization coefficient, which is a function of the tip speed ratio λ and the pitch angle β.

[0063] The drive system control adopts an adaptive PID algorithm to ensure that the speed of the stirrer is stabilized in the range of 30-100 rpm.

[0064] The system Flash pre-stores a three-dimensional lookup table based on the aerodynamic data of the NREL Phase VI wind turbine and the blade element momentum theory (BEM) optimization. According to the aerodynamic analysis, the maximum value is 0.486, and the corresponding optimal tip speed ratio λopt = 8.1 and the optimal pitch angle βopt = 0°.

[0065] ​S103: Based on the mechanical energy stirrer speed, the stirring heat generation and fluid dynamics optimization are performed, the heat storage and SOC state are calculated, and the molten salt temperature field and SOC are obtained.

[0066] In the embodiments of the present application, by performing stirring heat generation and fluid dynamics optimization based on the mechanical energy stirrer speed, calculating the heat storage and SOC state, and obtaining the molten salt temperature field and SOC, the mechanical energy is converted into heat energy through viscous dissipation, and efficient storage is achieved. From the perspective of thermodynamics and fluid mechanics, this process involves complex heat-flow coupling phenomena.

[0067] First, the heat storage is accurately measured, and thermodynamic analysis is performed.

[0068] The specific heat capacity empirical formula suitable for Solar Salt is used:

[0069] The stored heat is obtained by integrating the temperature difference:

[0070] The integral result can be analytically solved as:

[0071] Where: m: molten electrolyte mass (kg), : temperature-dependent specific heat capacity function (J / kg·K), : initial temperature (K), : final temperature (K).

[0072] SOC is defined as , where is the current volume-weighted average temperature of the molten electrolyte, with the unit of K, is the maximum temperature, is the minimum temperature.

[0073] Then, the fluid dynamics optimization of stirring power is performed. For the Rushton turbine stirrer in the storage tank, in the turbulent state (Re = (p·N·D²) / μ>10^4), its power number Np is approximately constant 6.0. The stirring power is given by:

[0074] Where: : power number, taking a value of 0.8-1.2; : molten electrolyte density (kg / m³); N: stirring speed (rps); D: stirrer diameter (m).

[0075] The controller optimizes the rotation speed N in real time to minimize the Based on computational fluid dynamics simulations, the optimal rotation speed range is 40-70 rpm, corresponding to a stirring power of 5-25 kW.

[0076] S104: Calculate the thermoelectric output power based on the molten salt temperature field and SOC, adjust the variable frequency water pump speed combining PID and fuzzy control strategies, perform active temperature control, and determine the output state of the thermoelectric system.

[0077] In the embodiments of the present application, heat energy is directly converted into electricity through the Seebeck effect. From the perspective of thermoelectric physics, this process involves the transport phenomena of charge carriers and phonons.

[0078] Consider the thermoelectric stack as a network composed of n cells, and the total output power as the sum of individual cell outputs. The output of a single thermoelectric cell is based on an exact model that takes into account contact resistance and the Thomson effect:

[0079] where, P i: Output electrical power of the i-th thermoelectric cell. Unit is watt. S: Seebeck coefficient of the thermoelectric material. It measures the material's ability to convert temperature differences directly into voltage. Unit is volt per kelvin. ΔT i: Temperature difference applied across the i-th thermoelectric cell, i.e., the difference between hot-side temperature and cold-side temperature. Unit is kelvin. A: Cross-sectional area of the thermoelectric element. Unit is square meter. R: Resistivity of the thermoelectric material. It measures the material's ability to impede electric current. Unit is ohm-meter. L: Length of the thermoelectric element (length along the direction of heat flow). Unit is meter. R c: Contact resistance. Additional resistance due to non-ideal contact between electrodes and thermoelectric material. Unit is ohm-square meter. ZT: Dimensionless figure of merit. It is a comprehensive indicator of the performance of thermoelectric materials, calculated as where σ: Electrical conductivity, κ: Thermal conductivity, and T is the average absolute temperature. A higher ZT value indicates a higher thermoelectric conversion efficiency of the material.

[0080] For Bi2Te3 / Sb2Te3 superlattice materials, the output power of a single thermoelectric pair is approximately 1.2 W when ΔT = 250 K, and the total theoretical power of 250 thermoelectric cells is 300 W. Considering a system efficiency coefficient of 0.75, the actual output power is approximately 225 W.

[0081] The cold-end heat dissipation system design follows the guidelines of ASHRAE Fundamental Handbook 2021. The selection and operation of the plate heat exchanger are calculated based on the ε-NTU (efficiency-to-number of heat transfer units) method.

[0082] in, Heat exchanger efficiency. It is a dimensionless number defined as the ratio of actual heat transfer to maximum possible heat transfer. Its value is between 0 and 1, with values ​​closer to 1 indicating more perfect heat exchange performance.

[0083] NTU: Number of heat transfer units. It is a dimensionless number that reflects the size and performance of the heat exchanger. The calculation formula is NTU=(U·A) / C_min. Where: U: Overall heat transfer coefficient; A: Heat transfer area; C_min: The smaller of the two fluids' heat capacity flow rate.

[0084] Heat capacity flow rate ratio. It is a dimensionless number, defined as follows: =C_min / C_max. Where C_min and C_max are the smaller and larger thermal capacity flow rates of the two fluids, respectively. Their values ​​are between 0 and 1.

[0085] By combining PID control with fuzzy control, the speed of the variable frequency water pump is adjusted to ensure that the cold junction temperature of the thermoelectric element remains stable below 50℃. During the system commissioning phase, the PID parameters of the temperature control loop are initially tuned using the Ziegler-Nichols second method (attenuation curve method). Fine-tuning is then performed under typical operating conditions. Finally, a set of parameters that minimizes system overshoot (less than 5%) and settling time is fixed in the controller. The fuzzy logic controller uses the Mamdani inference system, which includes five trigonometric membership functions.

[0086] The system components, key materials, and operating parameters involved in the above thermoelectric conversion process, such as... Figure 2 As shown. Figure 2 The diagram illustrates the core technical details of the thermoelectric conversion process: its hot end is tightly coupled to the outer wall of the storage tank, while its cold end is connected to an active cooling system, thus establishing an operating temperature difference (ΔT) exceeding 250K. The diagram clearly identifies the high ZT value (≥1.4) Bi₂Te₃ / Sb₂Te₃ superlattice thermoelectric material used and marks key parameters such as the theoretical output power of a single thermoelectric pair and the entire thermoelectric stack under typical operating conditions (e.g., approximately 1.2W per pair when ΔT = 250K). This diagram visually demonstrates the specific technical implementation scheme of the "thermoelectric conversion system based on irreversible thermodynamics."

[0087] S105: Multi-modal coordinated control based on hybrid intelligent algorithm is performed to smoothly switch the power supply operation mode.

[0088] Specifically, in an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm comprises: S301: Determine a state vector based on a reliable system state, a molten salt temperature field, and a thermoelectric system output state, and establish a nonlinear state space model in combination with a control vector.

[0089] S302: Establish an optimization problem for rolling optimization.

[0090] In an embodiment of the present application, the controller performs rolling optimization based on a simplified nonlinear state space model:

[0091]

[0092] wherein the state vector contains: ω: rotational speed of the transmission shaft (rad / s), T: temperature of the molten electrolyte (K), V: output voltage (V), I: output current (A).

[0093] The control vector wherein β: pitch angle of the wind turbine. The wind energy capture efficiency is changed by adjusting the blade angle. : rotational speed of the stirrer in the heat storage tank. Controlling the rotational speed can optimize the heat mixing efficiency and manage its own power consumption. : duty cycle of the power converter. Control the electric power finally output to the grid or load. : represents transposition.

[0094] The state vector , and the control vector The prediction horizon is set to 5 minutes, the control horizon is 1 minute, and the sampling period is 1 second. The optimization problem is expressed as:

[0095]

[0096]

[0097]

[0098] wherein, : minimize the objective function J. k: discrete time step. k=0 represents the current time, k=1 represents the next time, and so on. : prediction horizon. represents the number of future steps that the controller predicts forward. For example, if the sampling time is 1 second, The controller predicts the future 5 minutes. : The predicted system output at time k. For example, y(k) = [P_supply(k), T(k),... ]^T, which means the predicted supply power, molten salt temperature, etc. : The reference trajectory or setpoint at time k. That is, the target value that we want the system output y(k) to reach. : The tracking error, i.e., the deviation between the predicted output and the target value. Q: A weight matrix (usually diagonal) that is used to penalize the output tracking error. The larger the value of an element in the Q matrix, the higher the tracking accuracy requirement of the controller on that output. : This term is used to penalize the control action itself. R is also a weight matrix (usually diagonal). Increasing the weight of R will make the controller tend to use gentler and smaller control actions, improving the system stability.

[0099] In an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm for smooth switching of the power supply operation mode further comprises: S401: Establish a system global energy balance and solve a multi-objective optimization problem to control the power supply operation mode.

[0100] S402: Use a decision mechanism based on a fuzzy membership function to perform smooth switching between power supply operation modes.

[0101] In an embodiment of the present application, the system uses a hierarchical intelligent control architecture, with the bottom layer performing regular PID control and the upper layer deploying a model predictive control (MPC) framework based on physical information to achieve multi-objective dynamic optimization and multi-modal coordinated operation of the system.

[0102] The system global energy balance is described by the following differential equation:

[0103] wherein, : Total system energy storage (J), including mechanical kinetic energy and thermal internal energy; : Wind power capture (W), : Mechanical transmission loss (W), : Thermal loss of the heat storage system (W), : Electrical transmission loss (W), : Net output power (W).

[0104] To achieve the best overall performance, the controller solves the following multi-objective optimization problem in the top-level decision:

[0105] wherein, : Objective function value, : Weighting coefficient, dynamically adjusted by LSTM. : Load demand power (W). : System supply power (W). : Heat loss rate (W). : Stirring power (W). : Heat storage system state change rate.

[0106] Weighting coefficient to is not a fixed value, but is dynamically adjusted in real time by an embedded long short-term memory network (LSTM). The LSTM network analyzes historical operation data to predict the wind speed change trend and load fluctuation pattern in the next 300 seconds. For example, when it is predicted that the wind speed will soon increase, the system may reduce (stirring power consumption weight) in advance, allowing the stirrer to speed up appropriately to store wind energy faster; when the load demand is urgent, it increases (supply reliability weight) to ensure supply priority.

[0107] To achieve smooth and disturbance-free switching between operating modes and avoid frequent oscillation at the mode boundary, the system uses a decision-making mechanism based on fuzzy membership functions. This mechanism converts rule-based mode switching conditions into continuous and weighted transition processes through quantization.

[0108] For each operating mode, a fuzzy membership function is defined, with a value between 0 and 1, indicating the degree to which the system "belongs" to that mode. Taking the "energy storage mode" and "power generation mode" as examples, the membership calculation is as follows: Energy storage mode membership calculation:

[0109] where: is the "high" membership of wind speed power, and when ≥ its value tends to 1. is the "low" membership of , and when <100%, its value is greater than 0, and the lower the SOC, the higher the membership.

[0110] Power generation mode membership calculation:

[0111] where: is the "positive" membership of load power, and when > 0, its value tends to 1. is the "high" membership of SOC, when SOC > 20%, its value is greater than 0, the higher the SOC, the higher the membership.

[0112] In each control cycle, the final output of the control vector is no longer the decision of a single mode, but the weighted sum of the control outputs of all applicable modes:

[0113] Wherein: is the ideal control quantity calculated by NMPC in the i-th operating mode. is the membership of the i-th operating mode under the current system state.

[0114] Through the above formula: when the system state belongs to a certain mode (such as ≈ 1, and others ≈ 0), then ≈ , the system behaves as a pure energy storage mode.

[0115] When the system state is in the transition zone of mode switching (such as ≈ 0.5, ≈ 0.5), then = 0.5 + 0.5 , the control instruction is a smooth mixture of two modes, so as to realize the smooth switching without disturbance.

[0116] This fuzzy membership-based weighted decision-making method mathematically guarantees the continuity and smoothness of the operating mode switching, effectively avoiding power fluctuations caused by mode mutation or boundary oscillation, and is the key algorithm to achieve the stable output target of the system.

[0117] In the embodiments of the present application, MPC provides strict constraint processing capability and model-based feedforward optimization, ensuring that the system can operate safely and stably under any working condition. The prediction capability of LSTM gives the controller foresight, enabling it to "predict" changes in wind resources and loads and adjust the operating strategy in advance, significantly smoothing power fluctuations. The dynamic weight adjustment mechanism enables the system to achieve the best balance among meeting power supply demand ( ), reducing heat loss ( ), reducing auxiliary power consumption ( ), and maintaining smoothness of the heat storage state ( ). Through the above hybrid intelligent algorithm based on the NMPC framework and combined with the prediction capability of LSTM, adaptive and forward-looking optimization control is realized under multiple operating modes, which can theoretically suppress the output power fluctuation within 12% of the rated value, and significantly improve the overall energy efficiency and operating economy of the system.

[0118] In an embodiment of the present application, the power supply operation mode includes energy storage mode, power generation mode, hybrid mode and safety protection mode.

[0119] In an embodiment of the present application, based on the finite state machine theory, the system operation mode includes: Energy storage mode: activated when ≥ and SOC < 100%.

[0120] Power generation mode: activated when > 0 and SOC > 20%. The system realizes the maximum power point tracking (MPPT) of the thermoelectric system through the improved perturb and observe method. The perturbation step is adaptively adjusted according to the sign and size of dP / dV.

[0121] Hybrid mode: activated when can partially meet and SOC is in an intermediate state, the controller solves the optimization problem in real time and dynamically allocates the proportion of wind power direct power supply and heat storage system power supply.

[0122] Safety protection mode: when any key parameter (such as > 620°C, vibration acceleration exceeds 4 g RMS, key sensor communication interruption) is out of limit, immediately cut into this mode, execute the preset safety sequence, including sequential shutdown, enable standby cooling, etc.

[0123] S106: Real-time calculation of exergy efficiency, construction of digital twin model for parameter self-purification.

[0124] In an embodiment of the present application, the global performance index based on exergy efficiency is calculated periodically (such as every 15 minutes). According to the Carnot theorem, exergy efficiency reflects the ability of the system to convert input wind energy into useful electric power, which is an important indicator to evaluate the thermodynamic perfection of the system. In addition, a high-fidelity digital twin model is maintained in the cloud or edge server. The model is constructed by finite element method (FEM) and includes computational fluid dynamics (CFD) and thermoelectric coupling field analysis. All running data are synchronized to the digital twin model through OPCUA protocol for real-time calibration.

[0125] The calibrated model is used for offline simulation, and the Bayesian optimization algorithm is used to explore a more optimal set of control parameters. Bayesian optimization constructs a proxy model of the objective function through Gaussian process regression, and then uses acquisition functions (such as ExpectedImprovement) to guide parameter search, which is more suitable for high-dimensional, time-consuming black-box optimization problems than traditional optimization methods.

[0126] In an embodiment of the present application, the calculation formula of exergy efficiency is:

[0127] wherein, η is the exergy efficiency of the system, Wnet is the net electrical power output of the system, Wmech is the mechanical power captured by the wind turbine, T0 is the ambient absolute temperature, Tavg is the average thermodynamic temperature when the wind energy is converted into heat energy, Waux is the electrical power consumption of the auxiliary system. ηC is the Carnot efficiency factor, which converts the input mechanical energy into the thermodynamic work potential at a temperature of T0 according to the second law of thermodynamics. This factor converts the "quantity" of energy into the "quality" (exergy) of energy. Wtot represents the total exergy input by the wind energy.

[0128] In the above-mentioned method for combined power supply of offshore wind energy coupled with molten salt heat storage, first, the sensors are initialized and calibrated, multi-source sensor data are obtained for real-time compensation and unit conversion, and multi-sensor data fusion and Kalman filtering are performed to obtain reliable system state; then, the wind energy capture power is calculated in real time based on the reliable system state, and the mechanical energy stirrer speed is determined by using a sliding mode variable structure control algorithm; then, based on the mechanical energy stirrer speed, stirring heat generation and fluid dynamics optimization are performed, the heat storage capacity and SOC state are calculated, and the molten salt temperature field and SOC are obtained; then, the thermoelectric generator output power is calculated based on the molten salt temperature field and SOC, and the variable frequency water pump speed is adjusted by combining PID and fuzzy control strategies to determine the thermoelectric system output state; then, multi-modal coordinated control is performed based on a hybrid intelligent algorithm to smoothly switch the power supply operation mode; finally, the exergy efficiency is calculated in real time, and a digital twin model is constructed for parameter self-purification. A complete energy chain of "wind energy capture → mechanical transmission → heat energy storage → thermoelectric conversion → intelligent control" is constructed by using a modular integrated architecture based on energy flow optimization. Through innovative modular integrated design, wind energy is directly converted into heat energy storage, and electricity is generated directly through thermoelectric effect, thereby constructing a direct conversion path of "wind energy → heat energy → electricity", significantly reducing the energy conversion link, and improving the system efficiency and reliability.

[0129] ​It should be understood that although the steps in the flowcharts involved in the embodiments described above are shown in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the embodiments described above can include multiple steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily sequential, but can be alternately executed with at least part of other steps or stages.

[0130] Based on the same inventive concept, the embodiments of the present application also provide an offshore wind energy coupled molten salt heat storage combined power supply system for implementing the offshore wind energy coupled molten salt heat storage combined power supply method described above. The problem-solving implementation scheme provided by the system is similar to the implementation scheme described in the above method, so the specific limitations in one or more offshore wind energy coupled molten salt heat storage combined power supply system embodiments provided below can be referred to the limitations of the offshore wind energy coupled molten salt heat storage combined power supply method described above, which will not be repeated here.

[0131] In one embodiment, as shown in Figure 3 An offshore wind energy coupled molten salt heat storage combined power supply system is provided, comprising: a wind energy capture module, a heat storage module, a thermoelectric conversion module, and an intelligent control module, wherein: The wind energy capture module is configured to calculate wind energy capture power in real time based on reliable system state, and determine mechanical energy agitator speed by using a sliding mode variable structure control algorithm.

[0132] The heat storage module is configured to perform stirring heat generation and fluid dynamics optimization based on the mechanical energy agitator speed, calculate heat storage and SOC state, and obtain molten salt temperature field and SOC.

[0133] The thermoelectric conversion module is configured to calculate thermoelectric generator output power based on the molten salt temperature field and SOC, adjust variable frequency pump speed by combining PID and fuzzy control strategies, perform active temperature control, and determine thermoelectric system output state.

[0134] The intelligent control module is configured to perform multi-modal coordinated control based on a hybrid intelligent algorithm, and perform smooth switching of power supply operation modes.

[0135] In one embodiment of the present application, the multi-sensor data fusion and Kalman filtering include: Defining a state vector based on multi-source sensing data; Establishing a state space model, including a state transition matrix and an observation matrix; setting a process noise covariance matrix and a measurement noise covariance matrix; performing Kalman filter iteration prediction and update, and outputting an optimal system state.

[0136] In an embodiment of the present application, the formula for calculating the wind energy capture power in real time based on the reliable system state is:

[0137] wherein, is the air density, is the wind wheel swept area, is the wind speed, is the wind energy utilization coefficient, is the tip speed ratio, is the pitch angle.

[0138] In an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm comprises: determining a state vector based on the reliable system state, the molten salt temperature field, and the thermoelectric system output state, and establishing a nonlinear state space model in combination with a control vector; establishing an optimization problem for rolling optimization.

[0139] In an embodiment of the present application, the multi-modal coordinated control based on the hybrid intelligent algorithm, the smooth switching of the power supply operation mode further comprises: establishing a system global energy balance and a multi-objective optimization problem for solving, and controlling the power supply operation mode; adopting a decision mechanism based on a fuzzy membership function to perform smooth switching between the power supply operation modes.

[0140] In an embodiment of the present application, the power supply operation mode comprises an energy storage mode, a power generation mode, a hybrid mode, and a safety protection mode.

[0141] In an embodiment of the present application, the formula for calculating the system efficiency is:

[0142] wherein, is the system efficiency, is the net output electric power of the system, is the mechanical power captured by the wind turbine, is the environmental absolute temperature, is the average thermodynamic temperature when the wind energy is converted into heat energy, is the auxiliary system power consumption.

[0143] The modules in the above-mentioned offshore wind energy coupled molten salt heat storage combined power supply system can be implemented wholly or partially by software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in the computer device in software form, so as to be called and executed by the processor to perform the operations corresponding to the modules.

[0144] In one embodiment, a computer device, which can be a terminal, is provided, and an internal structure diagram of the computer device can be as shown in FIG. 1. Figure 4 The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved by WIFI, mobile cellular network, NFC (near field communication), or other technologies. The computer program is executed by the processor to implement an offshore wind energy coupled molten salt heat storage combined power supply method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball, or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0145] Those skilled in the art can understand that Figure 4 The structure shown in FIG. 1 is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied. Specifically, the computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-mentioned method embodiments.

[0147] In one embodiment, a computer readable storage medium is provided, which stores a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned method embodiments.

[0148] In one embodiment, a computer program product is provided, including a computer program. The computer program is executed by a processor to implement the steps in the above-mentioned method embodiments.

[0149] It should be noted that the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in the present application are all information and data authorized by the user or authorized by all parties.

[0150] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments of each method. Any reference to memory, database or other medium used in the embodiments provided by the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in the embodiments provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., without being limited thereto. The processor involved in the embodiments provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., without being limited thereto.

[0151] The technical features of the above embodiments can be combined in any way. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0152] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method for joint power supply using offshore wind energy coupled with molten salt thermal energy storage, characterized in that, The method includes: The sensors are initialized and calibrated, multi-source sensor data is acquired for real-time compensation and unit conversion, and multi-sensor data fusion and Kalman filtering are performed to obtain the reliable system status. Based on the real-time calculation of the wind energy capture power according to the reliable system state, the mechanical energy mixer speed is determined by the sliding mode variable structure control algorithm. Based on the rotational speed of the mechanical energy stirrer, the stirring heat generation and fluid dynamics are optimized, the heat storage and SOC state are calculated, and the molten salt temperature field and SOC are obtained. The output power of the thermoelectric pile is calculated based on the molten salt temperature field and SOC. By combining PID and fuzzy control strategies, the speed of the variable frequency water pump is adjusted to perform active temperature control and determine the output state of the thermoelectric system. Multimodal coordinated control based on hybrid intelligent algorithms enables smooth switching of power supply operation modes; Real-time calculation of efficiency, and construction of a digital twin model for parameter self-purification.

2. The method for combined power supply of offshore wind energy coupled with molten salt thermal energy storage according to claim 1, characterized in that, The multi-sensor data fusion and Kalman filtering process includes: Define state vectors based on multi-source sensor data; Establish a state-space model, including the state transition matrix and the observation matrix; Define the process noise covariance matrix and the measurement noise covariance matrix; Perform Kalman filtering iterative prediction and update to output the optimal system state.

3. The method for combined power supply of offshore wind energy coupled with molten salt thermal energy storage according to claim 1, characterized in that, The formula for calculating wind energy capture power in real time based on the reliable system state is as follows: in, air density, For the area swept by the wind turbine, For wind speed, The wind energy utilization coefficient, For the tip speed ratio, It is the propeller pitch angle.

4. The method for combined power supply of offshore wind energy coupled with molten salt thermal energy storage according to claim 1, characterized in that, The multimodal coordinated control based on hybrid intelligent algorithms includes: The state vector is determined based on the reliable system state, molten salt temperature field, and thermoelectric system output state, and a nonlinear state-space model is established in combination with the control vector. Establish an optimization problem and perform rolling optimization.

5. A combined power supply method for offshore wind energy coupled with molten salt thermal energy storage according to claim 4, characterized in that, The multimodal coordinated control based on hybrid intelligent algorithms for smooth switching of power supply operation modes also includes: The system establishes a global energy balance and multi-objective optimization problem for solution, and controls the power supply operation mode. A decision-making mechanism based on fuzzy membership functions is adopted to smoothly switch between power supply operation modes.

6. A combined power supply method for offshore wind energy coupled with molten salt thermal energy storage according to claim 5, characterized in that, The power supply operation modes include energy storage mode, power generation mode, hybrid mode, and safety protection mode.

7. A combined power supply method for offshore wind energy coupled with molten salt thermal energy storage according to claim 1, characterized in that, The formula for calculating the efficiency is: in, For the efficiency of the system, This represents the net electrical power output of the system. Mechanical power captured by wind turbines The absolute temperature of the environment. The average thermodynamic temperature at which wind energy is converted into heat energy. This refers to the power consumption of the auxiliary system.

8. A combined power supply system for offshore wind energy coupled with molten salt thermal energy storage, characterized in that, The system includes: The wind energy capture module is used to calculate the wind energy capture power in real time based on the reliable system status, and uses a sliding mode variable structure control algorithm to determine the rotation speed of the mechanical energy mixer; The thermal storage module is used to optimize stirring heat generation and fluid dynamics based on the mechanical energy stirrer speed, calculate the heat storage and SOC state, and obtain the molten salt temperature field and SOC. The thermoelectric conversion module is used to calculate the output power of the thermopile based on the molten salt temperature field and SOC, and combined with PID and fuzzy control strategies, adjust the speed of the variable frequency water pump, perform active temperature control, and determine the output status of the thermoelectric system. The intelligent control module is used for multimodal coordinated control based on hybrid intelligent algorithms to smoothly switch power supply operation modes.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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