Wind turbine generator super capacitor charging control method based on power grid voltage detection

Abstract

The invention relates to the field of wind turbine generator control, and discloses a wind turbine generator super capacitor charging control method based on power grid voltage detection, comprising the following steps: collecting power grid voltage data in real time, and constructing a power grid voltage state model; establishing a charging dynamic model and a health degree degradation model of the super capacitor; constructing a multi-target optimization problem with charging efficiency maximization, super capacitor health degree protection and power grid load stability as targets; calculating an optimal charging current path based on an optimization problem; the charging strategy is adjusted in sections according to the real-time power grid voltage state, so that the charging process dynamically adapts to the power grid voltage change. The method can effectively improve the charging efficiency of the super capacitor, prolongs the service life of the super capacitor, reduces the load impact of the charging process on a power grid, improves the operation safety and stability of a wind turbine generator, and is especially suitable for the intelligent management of a variable pitch system of a wind power plant.

Description

Technical Field

[0001] The present invention relates to the technical field of wind turbine control, and in particular to a supercapacitor charging control method for a wind turbine based on grid voltage detection. Background Art

[0002] As a key form of renewable energy generation, the safety and stability of wind turbines have a significant impact on the operation of the entire power grid. In wind turbine pitch systems, supercapacitors serve as emergency power supplies, ensuring safe retraction under extreme operating conditions, thereby preventing losses caused by overspeed or other wind turbine failures. Supercapacitor charging control is crucial to ensuring its effectiveness. Traditional charging control methods typically employ fixed constant current or constant voltage strategies. While these methods achieve basic charging functionality, they suffer from significant technical drawbacks in actual operation.

[0003] Current charging control methods generally fail to account for the dynamic fluctuations in grid voltage, which often fluctuates significantly in wind farms due to load changes and wind fluctuations. When the grid voltage is too low or too high, continuing to charge according to a constant charging strategy can cause the supercapacitor's health to rapidly deteriorate, or even damage it due to overcharging or over-discharging. Furthermore, traditional methods fail to fully optimize the relationship between charging efficiency and grid load stability. When the grid load is unbalanced, the charging process can have an additional impact on grid operation.

[0004] On the other hand, the lifespan of supercapacitors is significantly affected by the charging current and the number of charge-discharge cycles. Traditional charging strategies fail to dynamically adjust to the supercapacitor's health, further accelerating its performance degradation. This lack of adaptability makes it difficult to meet practical needs, especially in the complex operating environments of wind farms.

[0005] Therefore, a charging control method is needed that can dynamically adapt to changes in grid voltage and comprehensively consider charging efficiency, supercapacitor health protection and grid load stability to solve the problems existing in the existing technology and ensure the safe and efficient operation of wind turbines in complex environments. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a supercapacitor charging control method for wind turbines based on grid voltage detection, which solves the problems in the existing supercapacitor charging control method, such as the inability to dynamically adapt to grid voltage fluctuations, low charging efficiency, rapid health degradation, and impact on grid load.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: A wind turbine supercapacitor charging control method based on grid voltage detection, comprising the following steps: Collect grid voltage data in real time and discretize it to build a grid voltage state model; Establish a supercapacitor charging dynamics model and health degradation model; Construct a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid load; Based on the optimization problem, determine the optimal charging current path; Adjust the charging strategy in sections according to the real-time grid voltage status.

[0008] Preferably, the constructed grid voltage state model includes using a Markov chain to describe the dynamic changes of the voltage state, and the voltage state is discretized into three states: too low voltage, normal voltage and too high voltage.

[0009] Preferably, the formula of the supercapacitor charging kinetics model is as follows: in, is the voltage of the supercapacitor, is the charging current, is the capacitance value of the supercapacitor, Indicates the rate of change of supercapacitor voltage over time.

[0010] Preferably, the formula of the health degradation model is as follows: in, is the health of the supercapacitor, is the initial health of the supercapacitor, For charging time, is the health degradation coefficient, Indicates time The charging current, Represents time from 0 to time The cumulative degradation effect.

[0011] Preferably, the step of constructing a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid load includes: The objective function of the multi-objective optimization problem is defined as: in, is the objective function value of the multi-objective optimization problem, is the total charging time, , , are the target weight coefficients, which respectively represent the importance of health protection, power consumption minimization and grid load stability goals, For supercapacitors in time health, is the equivalent internal resistance of the supercapacitor, is the charging current of the supercapacitor, For the power grid in time The actual load power, is the expected load power of the grid, Indicates the deviation between the actual load power of the power grid and the expected load power; The three items in the objective function correspond to three optimization objectives respectively: Item 1 Used to describe the supercapacitor health protection target; Item 2 Used to describe the goal of minimizing supercapacitor charging power consumption; Item 3 Used to describe grid load stability targets; The optimization process of the objective function is limited to the following constraints: in, For supercapacitors in time The voltage, is the capacitance value of the supercapacitor, is the maximum allowable voltage of the supercapacitor, For the power grid in time The voltage, and are the minimum and maximum values of the grid voltage, respectively. is the discrete state of the grid voltage at the current moment and the next moment, Indicates the grid voltage status from Transfer to probability.

[0012] Preferably, the step of determining the optimal charging current path based on the optimization problem includes: Construct the Hamiltonian function to describe the dynamic process of the optimization problem: in, is the Hamiltonian function value, Indicates the health protection target item, Indicates the charging power consumption target item, represents the grid load stability target item, represents the co-variable, represents the supercapacitor charging dynamics constraint; According to the optimal control theory, the following co-state equation is satisfied: in, represents the rate of change of the covariate variable over time, Represents the Hamiltonian function of the supercapacitor voltage The partial derivative of Determine the optimal control condition so that the Hamiltonian of the charging current The partial derivative of is zero, and substituting it into the Hamiltonian expression, we can get the analytical solution of the optimal charging current path: in, For the optimal charging current path, Indicates the dynamic impact of the current state on the optimal path; Combined with the real-time collected grid voltage , supercapacitor voltage and grid load power , dynamically adjust the optimal charging path to adapt to the actual working conditions.

[0013] Preferably, the step of adjusting the charging strategy in sections according to the real-time grid voltage state includes: Under normal grid voltage conditions, the optimal charging current path Charge; In the case of slight fluctuations, the dynamic weakening factor Adjust the charging current to meet ; When the power grid is in abnormal condition, charging is stopped.

[0014] The present invention also provides a wind turbine supercapacitor charging control device based on grid voltage detection, comprising: Voltage detection module, used to collect grid voltage data in real time and build a voltage status model; An optimization calculation module is used to calculate the optimal charging path based on the supercapacitor charging dynamics model and health degradation model; A control module is used to adjust the charging current according to the voltage status and implement normal charging, slow charging or stop charging strategies; The communication module is used to realize data interaction between the device and the wind turbine generator system main control system.

[0015] The present invention also provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method when executing the computer program.

[0016] The present invention also provides a storage medium storing a computer program, which implements the above method when executed by a processor.

[0017] The present invention provides a wind turbine supercapacitor charging control method based on grid voltage detection. It has the following beneficial effects: 1. By collecting grid voltage data in real time and constructing a grid voltage state model, this invention accurately reflects the dynamic characteristics of the grid and, in combination with state transition patterns, predicts future voltage fluctuation trends. Based on this, the charging control strategy of the present invention can adjust the charging current in real time, maintaining the safety and stability of system operation under different grid voltage conditions, effectively addressing the safety risks associated with abnormal grid voltages in traditional charging methods.

[0018] 2. This invention maximizes charging efficiency by constructing a multi-objective optimization problem while ensuring grid load stability and supercapacitor health. The design of the optimal charging path fully considers minimizing charging power consumption, avoiding unnecessary energy loss, and significantly improving the overall efficiency of the charging system compared to traditional constant current or constant voltage charging methods.

[0019] 3. This invention introduces a supercapacitor health degradation model to comprehensively quantify the impact of high current on supercapacitor lifespan. When optimizing the charging path, a health protection target is incorporated. A segmented adjustment strategy is implemented to prevent damage to the supercapacitor's health caused by excessive or insufficient current. This effectively slows the degradation of the supercapacitor, extending its service life and reducing wind turbine maintenance costs.

[0020] 4. By combining grid voltage conditions and load fluctuations, this invention dynamically adjusts charging current to mitigate the impact of grid load imbalance. Specifically, when grid voltage is abnormal, charging is actively reduced or stopped to avoid additional impact on the grid during charging. This design plays a significant role in improving grid operational stability.

[0021] 5. This invention can quickly respond and automatically stop charging when the grid voltage is severely abnormal, preventing supercapacitor failure due to overcharging, overheating, or excessive discharge. Furthermore, when the grid returns to normal, the system can gradually resume charging, ensuring the safe operation of the wind turbine. This is particularly suitable for variable pitch systems that require high reliability.

[0022] 6. Through a flexible segmented adjustment strategy and optimization model, the charging control method of the present invention can adapt to a variety of complex operating environments, including grid fluctuations in large-scale wind farms, supercapacitor performance degradation, and ambient temperature changes. This method has strong versatility and scalability, providing important technical support for the intelligent operation of wind turbines. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 Schematic diagram of the method flow of the present invention; Figure 2 Schematic diagram of the device structure of the present invention; Figure 3 Schematic diagram of the computer device structure of the present invention.

[0024] Among them, 100 is a voltage detection module; 200 is an optimization calculation module; 300 is a control module; 400 is a communication module; 40 is a computer device; 41 is a processor; 42 is a memory; and 43 is a storage medium. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] Please see the attached Figure 1 This embodiment of the present invention provides a wind turbine supercapacitor charging control method based on grid voltage detection. This method aims to improve supercapacitor charging efficiency, protect the supercapacitor's state of health (SoH), and ensure the stability of the grid load during the charging process. The following describes the implementation details of the present method in conjunction with steps S1 to S5.

[0027] S1. Collect grid voltage data in real time and discretize it to build a grid voltage state model As the first step in the method, real-time grid voltage data collection and discretization are essential for subsequent optimization of charging paths and dynamic adjustment of charging strategies. In this step, high-precision voltage sensors are used to capture dynamic grid voltage data. This data is then combined with discretization algorithms and state modeling techniques to construct a grid voltage state model, enabling real-time monitoring and prediction of grid voltage.

[0028] It should be noted that the changing characteristics of grid voltage can be affected by a variety of factors, including wind turbine load fluctuations, voltage regulation of the regional power supply system, and other external environmental interference. Therefore, the data collection and modeling methods of the present invention are particularly suitable for the complex operating environment of wind farms, ensuring that the charging control strategy can adapt to various grid voltage fluctuation scenarios.

[0029] In this embodiment, real-time grid voltage data is first collected using a high-precision voltage sensor. The sampling frequency of the voltage sensor can be set to 1kHz or higher to ensure that the rapidly changing dynamic characteristics of the grid voltage can be captured. Alternatively, the voltage sensor can be directly connected to the wind turbine main control system, transmitting voltage signals via a data bus to reduce data transmission delays.

[0030] Specifically, the collected grid voltage data includes voltage amplitude and frequency In one possible implementation, the collected voltage data is processed by a filter to eliminate noise interference and ensure that the acquired signal has high accuracy.

[0031] In this embodiment, the voltage amplitude is discretized using the collected grid voltage data. Divided into multiple discrete state intervals. These intervals are set based on the voltage safe operating range and include the following three states: Low voltage state :when ,This state indicates that the grid voltage is lower than the set minimum safety threshold, which may result in insufficient charging power.

[0032] Normal voltage state :when , this state indicates that the grid voltage is in the normal range and is suitable for constant current mode charging.

[0033] Overvoltage status :when ,This state indicates that the grid voltage exceeds the maximum safety threshold and may cause ,impact on the supercapacitor charging system.

[0034] In this embodiment, the upper and lower limits of the voltage safety range and It can be set according to actual needs. For example, for a 220V voltage system, you can set , As an option, the voltage threshold can also be adjusted for different wind turbines according to their design parameters.

[0035] It should be noted that the discretization algorithm can choose a simple partitioning method based on a fixed threshold, or use a dynamic partitioning algorithm, such as adaptive partitioning technology, to adjust the division range of the state interval in real time according to the distribution characteristics of historical data.

[0036] In this embodiment, in order to further improve the accuracy of grid voltage state modeling, the collected discretized data is used to construct a dynamic model of grid voltage. Specifically, the change of grid voltage state is modeled by Markov chain, and the state transition probability matrix is defined. : in, Indicates that the grid voltage changes from state Transfer to state probability.

[0037] As an option, the state transition probability matrix can be obtained by statistically analyzing historical data of the power grid collected over a long period of time, or it can be dynamically updated in combination with real-time data. For example, if the voltage fluctuates frequently in the near future, the transition probability between the corresponding states may change significantly. For example, in a 220V system, when the power grid load increases, the state transition probability matrix changes from the normal state to the current state. Transfer to too low state Probability Will improve significantly.

[0038] It should be noted that the construction of the Markov chain model can not only reflect the current state of the grid voltage, but also predict the future voltage change trend through the state transition law, providing support for subsequent charging control strategies.

[0039] In some embodiments, the changing characteristics of the grid voltage state can be jointly modeled with other external environmental parameters (such as wind speed and load fluctuations). In this case, the state model can be expanded into a multivariable Markov chain, where each state includes not only voltage amplitude information but also parameters from other dimensions. For example, the operating characteristics of a wind turbine can be combined with wind speed and grid frequency to analyze their impact on voltage fluctuations.

[0040] It's understood that real-time grid voltage data acquisition and state modeling are fundamental steps in this invention. Through high-precision data acquisition, discretization, and dynamic modeling, accurate input conditions are provided for subsequent steps. This design is particularly well-suited for the complex operating environments of wind farms, enhancing grid voltage monitoring capabilities while supporting intelligent charging control strategies.

[0041] S2. Establish a supercapacitor charging dynamics model and health degradation model To achieve precise and intelligent supercapacitor charging control, the present invention establishes a supercapacitor charging kinetics model and a health degradation model to describe the dynamic characteristics and health changes of the supercapacitor during the charging process. These models not only quantify the relationship between charging voltage and current, but also use the health degradation model to assess the impact of high current on the supercapacitor lifespan, providing basic data for subsequent optimization of charging paths and strategy adjustments.

[0042] It should be noted that the charging characteristics of a supercapacitor are a complex dynamic process, and its health degradation is not only related to the charging current, but also affected by external environmental factors such as the capacitor's material properties and operating temperature. Therefore, when establishing the relevant model, this paper pays special attention to the comprehensive description and mathematical modeling of these factors.

[0043] In this embodiment, the following kinetic model is established to describe the relationship between voltage and current for the charging process of the supercapacitor: in: :Supercapacitor in time The voltage in volts; : Charging current, in amperes; : The capacitance value of the supercapacitor, in farads; : The rate of change of supercapacitor voltage over time, in volts per second.

[0044] It should be noted that the voltage of a supercapacitor varies linearly with the charging current and capacitance, but in actual operation, the equivalent series resistance (ESR) of the capacitor introduces non-ideal characteristics. Therefore, in some embodiments, the formula can be modified to include the effect of internal resistance loss: in: : initial voltage; : Equivalent internal resistance of the supercapacitor, in ohms.

[0045] As an option, in a complex power grid environment, dynamic adjustment can be made based on historical operating data. to more accurately describe the non-ideal behavior of supercapacitors.

[0046] In this embodiment, in order to further optimize the charging control of the supercapacitor, a health degradation model is established to quantify the change of the supercapacitor's service life during the charging process. The mathematical expression is: in: :Supercapacitor in time The health level of the system is in the range of [0, 1], where 1 indicates complete health and 0 indicates complete failure. : Initial health, usually set to 1; : Health degradation coefficient, in ampere-squared seconds , used to characterize the effect of high current on supercapacitor life; : In time The charging current in amperes; : The cumulative effect of charging current on health, indicating that high current charging will accelerate the degradation of health.

[0047] It should be noted that the health degradation coefficient The value of is closely related to the material properties and operating temperature of the supercapacitor. For example, in a low temperature environment, The value of may increase due to changes in the conductivity of the material, thereby accelerating the decline in health.

[0048] In some embodiments, in order to improve the applicability of the health degradation model, the ambient temperature and charging cycles Correct the health. At this point, the degradation model can be expanded to: in: : Temperature dependence function of the degradation coefficient, indicating the effect of temperature change on health; : The amount of health loss caused by each charge and discharge cycle; : Cumulative number of charge and discharge cycles.

[0049] Alternatively, the temperature-dependent function It can be calculated according to the following formula: in: : benchmark degradation coefficient; : activation energy, in joules; : universal gas constant; : Ambient temperature in Kelvin.

[0050] By introducing corrections for temperature and cycle number, the health status of supercapacitors can be evaluated more comprehensively, providing a basis for charging control strategies under different environmental conditions.

[0051] In one possible implementation, to verify the accuracy of the health degradation model, the model can be compared with actual operating data. For example, under given charging current and environmental conditions, the capacity retention of the supercapacitor is measured and compared with the health value calculated by the model. Through error analysis, the degradation coefficient can be further optimized. The numerical setting of .

[0052] It's clear that the charging dynamics model and health degradation model are fundamental to implementing the technical solution of this invention. These models not only describe the electrical characteristics of supercapacitors but also quantify their lifespan degradation, providing accurate data support for subsequent charging path optimization and dynamic adjustment. This design is applicable to a wide range of supercapacitor types and has excellent applicability in wind turbine pitch systems.

[0053] S3. Construct a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid load. To achieve intelligent control of supercapacitor charging, this paper proposes a method for constructing a multi-objective optimization problem based on grid voltage fluctuations and charging system requirements during wind turbine operation. This optimization problem comprehensively considers the supercapacitor's charging efficiency, the degradation pattern of its health, and the dynamic stability of the grid load, laying the foundation for determining the optimal charging path.

[0054] It should be noted that the multi-objective optimization process requires building on the foundation of electrical and health models, combined with actual operating data and constraints, to ensure that the optimization results meet the practical application requirements of wind turbines. This paper comprehensively describes the multi-objective optimization problem using mathematical modeling, taking into account the dynamic changes in grid voltage and the operational characteristics of supercapacitors.

[0055] In this embodiment, the power consumption model of the supercapacitor is defined to maximize the charging efficiency. During the charging process, the power loss of the supercapacitor is determined by its equivalent internal resistance. and charging current Determine, the power consumption expression is: In order to reduce energy waste during the charging process, the present invention takes minimizing the power consumption of the supercapacitor as one of the optimization goals.

[0056] Specifically, the optimization goal is described in the overall objective function as: in: : total charging time; : Weight factor for power consumption optimization, used to adjust the importance of power consumption in the overall goal.

[0057] As an option, The value of can be dynamically adjusted based on the operating state of the supercapacitor. For example, in some embodiments, the equivalent internal resistance of the supercapacitor may change under different temperature conditions. In this case, the relationship between the actual operating current and voltage can be measured to calibrate the value. The numerical value of .

[0058] In this embodiment, in order to protect the health of the supercapacitor, the health degradation model is used as one of the optimization objectives. The degradation rate is determined by the charging current The specific model is determined by the square of . Please refer to the description in step S2.

[0059] In order to delay the degradation of health during charging, the present invention describes the health protection target as: in, is the weight factor for health optimization. It should be noted that by setting a larger If the value is too high, the life protection of the supercapacitor can be given priority; on the contrary, if the charging efficiency is the main concern, it can be appropriately reduced. .

[0060] In this embodiment, the goal of minimizing the power deviation of the power grid load is proposed to solve the problem of dynamic stability of the power grid load. Should be as close to its expected value as possible , the deviation expression is: In the overall optimization objective, load stability is described as: in: : actual load power of the grid, in watts; : expected grid load power, in watts; : Weight factor for load stability optimization.

[0061] As an option, in actual application, the real-time status of the power grid can be dynamically adjusted For example, when the wind speed is high, the power generation of the wind turbine increases. It can be increased appropriately to balance the overall power load of the power grid.

[0062] In this embodiment, the above three optimization objectives are combined to define the overall objective function of multi-objective optimization as: It should be noted that the weight factor 、 、 The value of can be adjusted according to actual application requirements to balance the priorities of different optimization objectives.

[0063] In the multi-objective optimization problem of this invention, in addition to clearly constructing the objective function, a series of constraints must be met to ensure that the optimization results are operational in both technical and practical applications. These constraints include not only the dynamic constraints and voltage range constraints of the supercapacitor, but also the constraints on the discrete states of the grid voltage and the restrictions on its dynamic transition patterns, further ensuring that the optimization results meet the complex environmental requirements of wind turbine operation.

[0064] In this embodiment, firstly, dynamic constraints are added to the charging process of the supercapacitor to describe the charging current and supercapacitor voltage Dynamic relationship: in: :Supercapacitor in time The voltage in volts; : Charging current of the supercapacitor, in amperes; : The capacitance value of the supercapacitor, in farads.

[0065] It can be understood that this dynamic constraint is used to calculate the change in supercapacitor voltage during the charging process in real time, providing data support for subsequent control of the charging path.

[0066] As an option, in actual operation, real-time monitoring can be used and Changes, corrections For example, when the capacitance of a supercapacitor decreases due to aging, the constraint model can automatically adapt to the changed parameters.

[0067] In this embodiment, in order to ensure the operational safety of the supercapacitor, a voltage range constraint is added: in: : The maximum rated voltage of the supercapacitor, in volts.

[0068] It should be noted that when near To protect the supercapacitor from overcharging, the charging current should be appropriately reduced. In some embodiments, the control system can detect and dynamically adjust the constraint boundaries of the optimization problem.

[0069] In this embodiment, based on the characteristics of the power grid where the wind turbine is located, a grid voltage range constraint is added: in: : The power grid at time The voltage in volts; and : The upper and lower limits of the safe range of the grid voltage, in volts.

[0070] As an option, for different grid design parameters, and The value can be adjusted flexibly.

[0071] It should be noted that when If the safe range is exceeded, the charging process may be interrupted and enter protection mode. In this case, the solution to the optimization problem automatically adjusts the charging current to zero to ensure system safety.

[0072] In this embodiment, in order to further improve the adaptability of the optimization problem to complex environments, the following constraints can be added based on the grid voltage fluctuation characteristics and supercapacitor charging characteristics: This constraint is used to describe the grid voltage state at the current moment. and the next moment As an option, the constraint can be dynamically adjusted by calculating the transition probability in real time to ensure that the solution of the optimization problem can adapt to the changing trend of the grid voltage.

[0073] It is understood that by introducing the aforementioned constraints on dynamics, voltage range, discrete states, and state transitions, the multi-objective optimization problem of the present invention can fully adapt to the wind turbine operating environment and the actual usage characteristics of supercapacitors. This design not only enhances the physical significance of the optimization problem but also provides reliable boundary conditions for subsequent optimal charging path calculations.

[0074] S4. Determine the optimal charging current path based on the optimization problem An embodiment of the present invention proposes a method for determining an optimal charging current path based on an optimization problem. By constructing and solving a multi-objective optimization problem, combined with a model of the dynamic change of grid voltage and the health characteristics of supercapacitors, an optimal charging current path that conforms to the grid state and supercapacitor operating characteristics is determined. It should be noted that this optimal path not only improves charging efficiency but also strikes an optimal balance between health protection and grid stability.

[0075] It is understood that calculating the optimal charging path is the core of the entire charging control method, and its results directly affect charging efficiency, supercapacitor lifespan, and grid stability. Therefore, this invention utilizes advanced control theory and optimization algorithms to ensure that the calculation of the optimal path can meet the actual needs of complex dynamic environments.

[0076] In this embodiment, in order to solve the optimal charging current path, a Hamiltonian function corresponding to the optimization problem is first constructed: in: , , : are the weight coefficients for health protection, power consumption minimization and grid stability respectively; :Supercapacitor in time health; : The equivalent internal resistance of the supercapacitor, in ohms; : Charging current of the supercapacitor, in amperes; : actual load power of the grid, in watts; : expected grid load power, in watts; : Co-state variables, used to describe the impact of state variables on the objective function; : The capacitance of the supercapacitor, in farads.

[0077] It should be noted that the Hamiltonian function is the core of the entire optimization process and comprehensively describes the relationship between the objective function and the state variables.

[0078] In this embodiment, according to optimal control theory, the optimal path of the charging current satisfies the following conditions: By calculating the partial derivative of the Hamiltonian function, the optimal charging current is obtained The analytical solution is: in, The value of is determined by the co-state equation, which is expressed as: It should be noted that the covariate variable The dynamic change of is affected by the health of the supercapacitor, the charging current and its rate of change. In one possible implementation, the co-state equation can be discretized by numerical methods (such as finite difference method) to calculate The real-time value of .

[0079] In this embodiment, in order to ensure the feasibility of the optimal charging path, the constraints in the optimization problem are also combined. The value of must meet the following restrictions: in: :Supercapacitor in time voltage; : The maximum safe voltage of the supercapacitor.

[0080] It should be noted that when the supercapacitor voltage When approaching its maximum safe value, the solution to the optimization problem will automatically reduce the charging current to avoid overcharging.

[0081] In one possible implementation, the optimal path can be adjusted in real time based on the dynamic changes in grid voltage by combining the results of discrete state modeling. For example, when the grid voltage is in a normal state When the solution of the optimization problem is Execute; when the grid voltage is in a state of slight fluctuation ( or ), a weakening factor can be introduced , adjust the charging current: in, The value of can be dynamically calculated based on the degree of deviation of the grid voltage.

[0082] For example, when the grid voltage is lower than hour, Take a smaller value to reduce the impact of charging current on the power grid; when the voltage gradually returns to normal, It gradually approaches 1.

[0083] In some embodiments, in order to further improve the adaptability of the optimal charging path, the state transition probability matrix of the grid voltage can be combined , predictive adjustment is made to the optimal path. Specifically, when the power grid changes from state Transfer to When the probability of a high , thereby smoothly adapting to future voltage fluctuations.

[0084] It should be noted that the above-mentioned path adjustment mechanism can effectively avoid the adverse effects of the charging process on the power grid and extend the service life of the supercapacitor.

[0085] As can be understood, this invention constructs and solves a multi-objective optimization problem, combining Hamiltonian functions and state transition laws to calculate the optimal charging path that meets actual operational requirements. This design is not only applicable to the complex operating environments of wind turbines, but also adapts to the operating characteristics of various supercapacitors and power grids, providing important support for intelligent charging control strategies.

[0086] S5. Adjust the charging strategy in sections according to the real-time grid voltage status.

[0087] An embodiment of the present invention proposes a segmented charging strategy adjustment method based on real-time grid voltage status. By dynamically monitoring the real-time grid voltage and combining a discretized voltage state model and the optimal charging path, the supercapacitor charging current is dynamically adjusted to adapt to the operating requirements under different grid voltage conditions. It should be noted that this segmented adjustment strategy not only ensures the safe operation of the supercapacitor, but also effectively reduces the impact of the charging process on the grid load, extending the service life of the supercapacitor.

[0088] It is understandable that the fluctuation characteristics of the grid voltage have a significant impact on the design of the charging strategy. The present invention associates the optimal path with the dynamic changes of the grid voltage through a segmented adjustment strategy, thereby achieving more flexible and efficient charging control.

[0089] In this embodiment, when the grid voltage is in a normal state (Right now ), the constant current mode is used for charging. Specifically, the charging current is The calculation of is based on the optimal path determined in step S4: in: : The optimal charging current path calculated by the Hamiltonian function and the optimal control condition.

[0090] It should be noted that under the condition of stable grid voltage, the constant current charging mode can fully utilize the power supply capacity of the grid while reducing damage to the health of the supercapacitor.

[0091] In this embodiment, when the grid voltage fluctuates slightly (ie enters a too low state), or too high state ), the charging current is weakened to adjust. The expression is: in: : Dynamic weakening factor, the value range is .

[0092] As an option, The specific value of can be calculated based on the deviation of the grid voltage. For example, when the voltage is lower than hour, Can be set to: When the voltage is higher than hour, Can be set to: It should be noted that by introducing the weakening factor, the charging current of the supercapacitor can be reduced when the grid voltage is abnormal, reducing the impact of the charging process on the grid and avoiding grid load imbalance or capacitor overheating due to excessive current.

[0093] In this embodiment, when the grid voltage seriously deviates from the normal range (i.e. or ), charging stops immediately. The setting value of charging current at this time is: in, It is an additional safety voltage margin and is set according to the grid operating conditions.

[0094] It should be noted that the purpose of stopping charging is to protect the safety of supercapacitors and grid equipment. When the grid voltage returns to the normal range, the system will automatically restart the charging process and gradually restore to the optimal path.

[0095] In some embodiments, in order to improve the dynamic adaptability of the charging strategy, the charging behavior can be adjusted predictively based on the state transition probability of the grid voltage. Transfer to too low state When the probability of a high , thus ensuring a smooth transition when voltage fluctuations occur.

[0096] For example, when the state transition probability matrix middle When the voltage is larger, the charging current can be adjusted as follows: in, It is the forecast adjustment coefficient, which is used to control the adjustment range.

[0097] It is understood that the segmented adjustment strategy of the present invention flexibly adjusts the charging current by real-time monitoring of the grid voltage state and integrating it with the grid dynamic model. This segmented control mechanism not only adapts to the complex operating environment of wind turbines, but also achieves a good balance between supercapacitor protection and grid stability, providing a key guarantee for the reliability and efficiency of the charging control system. This design is particularly suitable for variable pitch control systems in large-scale wind farms.

[0098] In summary, the present invention establishes a grid voltage state model by collecting grid voltage data in real time. Combined with the supercapacitor charging dynamics model and health degradation model, this model constructs a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid loads. Hamiltonian functions and optimal control theory are used to calculate the optimal charging path, and the charging strategy is adjusted in stages based on the real-time state of the grid voltage, achieving efficient charging control that dynamically adapts to complex grid environments.

[0099] The wind turbine supercapacitor charging control device based on grid voltage detection described below and the wind turbine supercapacitor charging control method based on grid voltage detection described above can refer to each other.

[0100] Please see the attached Figure 2 The present invention also provides a wind turbine supercapacitor charging control device based on grid voltage detection, comprising: The voltage detection module 100 is used to collect grid voltage data in real time and build a voltage state model; An optimization calculation module 200 is used to calculate an optimal charging path based on a supercapacitor charging kinetics model and a health degradation model; The control module 300 is used to adjust the charging current according to the voltage state and execute normal charging, slow charging or stop charging strategies; The communication module 400 is used to realize data interaction between the device and the wind turbine generator system.

[0101] The device of this embodiment can be used to execute the above method embodiment, and its principles and technical effects are similar, so they will not be repeated here.

[0102] Please see the attached Figure 3 The present invention further provides a computer device 40, comprising: a processor 41 and a memory 42, wherein the memory 42 stores a computer program executable by the processor, and when the computer program is executed by the processor, the above method is performed.

[0103] The present invention further provides a storage medium 43 on which a computer program is stored. When the computer program is run by the processor 41 , the above method is executed.

[0104] Among them, the storage medium 43 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0105] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A wind turbine supercapacitor charging control method based on grid voltage detection, characterized in that: The following steps are involved: Collect grid voltage data in real time and discretize it to build a grid voltage state model; Establish a supercapacitor charging dynamics model and health degradation model; Construct a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid load; Based on the optimization problem, determine the optimal charging current path; Adjust the charging strategy in sections according to the real-time grid voltage status.

2. A wind turbine supercapacitor charging control method based on grid voltage detection according to claim 1, characterized in that: The constructed grid voltage state model includes using a Markov chain to describe the dynamic changes of the voltage state, and the voltage state is discretized into three states: too low voltage, normal voltage and too high voltage.

3. The method for controlling supercapacitor charging of a wind turbine generator system based on grid voltage detection according to claim 1, wherein: The formula of the supercapacitor charging kinetics model is as follows: in, is the voltage of the supercapacitor, is the charging current, is the capacitance value of the supercapacitor, Indicates the rate of change of supercapacitor voltage over time.

4. The method for controlling supercapacitor charging of a wind turbine generator system based on grid voltage detection according to claim 1, characterized in that: The formula of the health degradation model is as follows: in, is the health of the supercapacitor, is the initial health of the supercapacitor, For charging time, is the health degradation coefficient, Indicates time The charging current, Represents time from 0 to time The cumulative degradation effect.

5. The method for controlling supercapacitor charging of a wind turbine generator system based on grid voltage detection according to claim 1, characterized in that: The steps of constructing a multi-objective optimization problem with the goals of maximizing charging efficiency, protecting supercapacitor health, and stabilizing grid load include: The objective function of the multi-objective optimization problem is defined as: in, is the objective function value of the multi-objective optimization problem, is the total charging time, , , are the target weight coefficients, which respectively represent the importance of health protection, power consumption minimization and grid load stability goals, For supercapacitors in time health, is the equivalent internal resistance of the supercapacitor, is the charging current of the supercapacitor, For the power grid in time The actual load power, is the expected load power of the grid, Indicates the deviation between the actual load power of the power grid and the expected load power; The three items in the objective function correspond to three optimization objectives respectively: Item 1 Used to describe the supercapacitor health protection target; Item 2 Used to describe the goal of minimizing supercapacitor charging power consumption; Item 3 Used to describe grid load stability targets; The optimization process of the objective function is limited to the following constraints: in, For supercapacitors in time The voltage, is the capacitance value of the supercapacitor, is the maximum allowable voltage of the supercapacitor, For the power grid in time The voltage, and are the minimum and maximum values of the grid voltage, respectively. is the discrete state of the grid voltage at the current moment and the next moment, Indicates the grid voltage status from Transfer to probability.

6. The method for controlling supercapacitor charging of a wind turbine generator system based on grid voltage detection according to claim 1, characterized in that: The step of determining the optimal charging current path based on the optimization problem includes: Construct the Hamiltonian function to describe the dynamic process of the optimization problem: in, is the Hamiltonian function value, Indicates the health protection target item, Indicates the charging power consumption target item, represents the grid load stability target item, represents a co-variable, represents the supercapacitor charging dynamics constraint; According to the optimal control theory, the following co-state equation is satisfied: in, represents the rate of change of the covariate variable over time, Represents the Hamiltonian function of the supercapacitor voltage The partial derivative of Determine the optimal control condition so that the Hamiltonian of the charging current The partial derivative of is zero, and substituting it into the Hamiltonian expression, we can get the analytical solution of the optimal charging current path: in, For the optimal charging current path, Indicates the dynamic impact of the current state on the optimal path; Combined with the real-time collected grid voltage , supercapacitor voltage and grid load power , dynamically adjust the optimal charging path to adapt to the actual working conditions.

7. A wind turbine supercapacitor charging control method based on grid voltage detection according to claim 6, characterized in that: The step of adjusting the charging strategy in sections according to the real-time grid voltage state includes: Under normal grid voltage conditions, the optimal charging current path Charge; In the case of slight fluctuations, the dynamic weakening factor Adjust the charging current to meet ; When the power grid is in abnormal condition, charging is stopped.

8. A wind turbine supercapacitor charging control device based on grid voltage detection, applied to the method according to any one of claims 1 to 7, characterized in that: include: Voltage detection module, used to collect grid voltage data in real time and build a voltage status model; An optimization calculation module is used to calculate the optimal charging path based on the supercapacitor charging dynamics model and health degradation model; A control module is used to adjust the charging current according to the voltage status and implement normal charging, slow charging or stop charging strategies; The communication module is used to realize data interaction between the device and the wind turbine generator system main control system.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

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

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