Super capacitor fatigue prediction method, device and wind turbine generator set variable pitch controller

Abstract

This invention provides a method, device, and pitch controller for predicting supercapacitor fatigue. By analyzing the operating data of the generator set, it determines whether the operating data meets the supercapacitor detection conditions. When the detection conditions are met, the voltage value of the supercapacitor is measured to obtain the voltage drop value. Then, based on the current value of the pitch motor at the moment of pitch adjustment, the discharge current of the supercapacitor is calculated. Next, the discharge current and the voltage drop value of the supercapacitor are substituted into Ohm's law to calculate the internal resistance value of the supercapacitor. Finally, the fatigue degree of the supercapacitor is predicted based on its internal resistance. This achieves real-time prediction of supercapacitor fatigue during the operation of the wind turbine generator set, ensuring the safe operation of the wind turbine generator set.

Description

Technical Field

[0001] This invention relates to the field of equipment monitoring technology, specifically to a method for predicting fatigue in supercapacitors and related equipment. Background Technology

[0002] In wind turbine generator sets, under normal operating conditions, if the wind speed exceeds the rated wind speed, the pitch system controls the blade pitch angle to maintain a constant rotor speed in order to control the power output of the wind turbine. When a wind turbine malfunctions, the pitch system executes an emergency pitch retraction function, achieving aerodynamic braking to ensure the safety of the unit. The pitch system operates relying on the power grid when there is normal grid power; however, when the grid fails (such as a power outage or low-voltage ride-through), the pitch system requires a backup power source to perform the pitch retraction operation. To prevent major accidents, strict monitoring of the backup power source's performance is crucial.

[0003] Supercapacitors are an important component of wind turbine generator sets. They have the following advantages: high power density (up to 300W / KG~5000W / KG, equivalent to 5 to 10 times that of ordinary batteries), fast charging speed (charging for 10 seconds to 10 minutes can reach more than 95% of its rated capacity), long cycle life (>500,000 cycles), and wide operating temperature range (-40℃ to +70℃). They are very suitable for the harsh working environment of wind turbine generator pitch systems.

[0004] Factors such as single-cell breakdown, open circuit, changes in electrical parameters (including capacitance deviation, increased loss tangent, decreased insulation performance, or fluctuating leakage current), environmental humidity, and length of use can all lead to a decrease in the lifespan of supercapacitors, and may even cause them to fail, which can seriously endanger the safety and reliability of wind turbine generators.

[0005] How to predict the fatigue of supercapacitors to ensure the normal operation of wind turbine generators is one of the technical problems that urgently need to be solved by those skilled in the art. Summary of the Invention

[0006] In view of this, embodiments of the present invention provide a method, apparatus and wind turbine pitch controller for predicting the fatigue of supercapacitors in wind turbines.

[0007] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0008] A method for predicting fatigue in supercapacitors, comprising:

[0009] Obtain operating data of wind turbine generators;

[0010] Based on the aforementioned operational data, determine whether the supercapacitor detection conditions have been met;

[0011] When the supercapacitor detection condition is met, the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met is obtained.

[0012] Obtain the minimum voltage value of the supercapacitor detected within the preset time period;

[0013] Based on the rated voltage of the supercapacitor and the voltage of the minimum supercapacitor, the voltage drop of the supercapacitor within the preset time period is calculated.

[0014] Obtain the current value of the pitch motor at the moment of pitch change;

[0015] The discharge current of the supercapacitor is calculated based on the current value of the pitch motor.

[0016] The internal resistance of the supercapacitor is calculated by substituting the discharge current and voltage drop of the supercapacitor into Ohm's law.

[0017] The fatigue degree of the supercapacitor is predicted based on its internal resistance value.

[0018] Optionally, the above-mentioned supercapacitor fatigue prediction method further includes:

[0019] Determine whether the fluctuation rate of the supercapacitor's voltage value within the preset time period is greater than a preset value. If it is greater, discard the detected supercapacitor voltage value.

[0020] Optionally, in the above-mentioned supercapacitor fatigue prediction method, obtaining the minimum voltage value of the supercapacitor detected within the preset time period includes:

[0021] The minimum voltage value of the supercapacitor detected within the preset time period is taken as the minimum voltage value of the supercapacitor.

[0022] Alternatively, once the supercapacitor voltage begins to decrease within a preset time period, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slopes are in the same direction, it indicates that the supercapacitor voltage continues to decrease. At the same time, the minimum detected supercapacitor voltage is continuously calculated and stored.

[0023] When the supercapacitor voltage starts to rise, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slope of change is in the same direction, it is determined that the supercapacitor voltage continues to rise.

[0024] When the supercapacitor voltage continuously rises to half of the amplitude change, it is determined that the supercapacitor voltage has recovered, data acquisition and recording are stopped, and the minimum recorded value is taken as the minimum value of the supercapacitor voltage detected within the preset time period.

[0025] Optionally, in the above-mentioned supercapacitor fatigue prediction method, determining whether the supercapacitor detection conditions have been met based on the operational data includes:

[0026] Based on the operational data, it is determined whether the wind turbine generator set has triggered pitch control or shutdown operation. When the pitch control or shutdown operation occurs, it indicates that the supercapacitor detection conditions have been met.

[0027] Optionally, in the above-mentioned supercapacitor fatigue prediction method, calculating the discharge current of the supercapacitor based on the current value of the pitch motor includes:

[0028] The current value of the pitch motor is integrated.

[0029] The current value of the pitch motor after integral calculation is corrected by a preset correction coefficient, and the corrected current value is used as the discharge current of the supercapacitor.

[0030] Optionally, in the above-mentioned supercapacitor fatigue prediction method, predicting the fatigue degree of the supercapacitor based on its internal resistance value includes:

[0031] The aging time of the supercapacitor is obtained based on a preset mapping relationship between the internal resistance value of the capacitor and the aging time.

[0032] Obtain the rated aging time of the supercapacitor;

[0033] The difference between the rated aging time and the aging time is taken as the remaining lifespan of the supercapacitor.

[0034] A supercapacitor fatigue prediction device, comprising:

[0035] The data acquisition unit is used to acquire the operating data of the wind turbine generator set;

[0036] The detection condition judgment unit is used to determine whether the supercapacitor detection condition has been met based on the operating data.

[0037] The internal resistance calculation unit is used to: acquire the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met; acquire the minimum voltage value of the supercapacitor detected within the preset time period; calculate the voltage drop value of the supercapacitor within the preset time period based on the rated voltage value of the supercapacitor and the minimum voltage value of the supercapacitor; acquire the current value of the pitch motor at the moment of pitch adjustment; calculate the discharge current of the supercapacitor based on the current value of the pitch motor; and calculate the internal resistance value of the supercapacitor by substituting the discharge current of the supercapacitor and the voltage drop value of the supercapacitor into Ohm's law.

[0038] The fatigue prediction unit is used to predict the fatigue of the supercapacitor based on its internal resistance value.

[0039] Optionally, the supercapacitor fatigue prediction device is installed in the wind turbine generator pitch controller.

[0040] A pitch controller for a wind turbine generator includes a memory and a processor;

[0041] The memory stores computer-executable instructions, wherein when the computer-executable instructions are executed by the processor, the processor implements the supercapacitor fatigue prediction method described in any of the preceding claims. A computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor performs the supercapacitor fatigue prediction method described in any of the preceding claims.

[0042] Based on the above technical solution, the solution provided by the embodiments of the present invention analyzes and judges the operating data of the generator set to determine whether the operating data meets the supercapacitor detection conditions. When the supercapacitor detection conditions are met, the voltage value of the supercapacitor is measured to obtain the voltage drop value of the supercapacitor. Then, based on the current value of the pitch motor at the moment of pitch change, the discharge current of the supercapacitor is calculated. Then, the discharge current of the supercapacitor and the voltage drop value of the supercapacitor are substituted into Ohm's law to calculate the internal resistance value of the supercapacitor. Finally, the fatigue degree of the supercapacitor is predicted based on the internal resistance of the supercapacitor. Thus, the fatigue degree of the supercapacitor can be predicted in real time during the operation of the wind turbine generator set, ensuring the safe operation of the wind turbine generator set. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0044] Figure 1 This is a schematic flowchart of the supercapacitor fatigue prediction method disclosed in the embodiments of this application;

[0045] Figure 2 The measurement curve of the minimum voltage value of the supercapacitor during the start-up feathering of the wind turbine generator;

[0046] Figure 3 for Figure 2 The corresponding motor current curve at the moment of startup of the pitch motor;

[0047] Figure 4 This is a schematic diagram of the structure of the supercapacitor fatigue prediction device disclosed in the embodiments of this application;

[0048] Figure 5 This is a schematic diagram of the structure of the wind turbine pitch controller disclosed in the embodiments of this application;

[0049] Figure 6 This is a schematic diagram of the pitch system structure of the wind turbine generator disclosed in the embodiments of this application. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] To predict the fatigue of supercapacitors in wind turbine generators and ensure their normal operation, this application discloses a supercapacitor fatigue prediction method. This method measures the supercapacitors in the wind turbine generator by real-time monitoring of their operating status. During the pitch control system startup and pitch adjustment operation of the wind turbine generator, or when the wind turbine is shut down and feathering is initiated, capacitor voltage and motor current values ​​are collected. The internal resistance of the supercapacitor is then calculated. This internal resistance maps to the supercapacitor's fatigue level, enabling timely detection of whether the supercapacitor can guarantee the normal operation of the wind turbine generator.

[0052] Figure 1 This is a flowchart illustrating the supercapacitor fatigue prediction method disclosed in the embodiments of this application. For details, please refer to... Figure 1 The supercapacitor fatigue prediction method disclosed in this application embodiment may include steps S101-S109.

[0053] Step S101: Obtain the operating data of the wind turbine generator set.

[0054] During wind turbine operation, real-time operational data is acquired. This data relates to pitch control and feathering operations, allowing for the determination of whether the wind turbine is performing these actions. Specifically, this data may include fault codes from the pitch controller, detection of shutdown commands from the main controller, and acquisition of pitch motor rotation direction (from 0) or pitch opening direction.

[0055] Step S102: Determine whether the supercapacitor detection conditions have been met based on the operating data.

[0056] In the technical solution disclosed in the embodiments of this application, some detection conditions for supercapacitors are preset, and the obtained operating data is analyzed and calculated to determine whether the operating status of the wind turbine generator set meets the supercapacitor detection conditions. When the preset conditions are met, the subsequent steps are continued; otherwise, the operating parameters of the wind turbine generator set are monitored.

[0057] In the technical solution disclosed in the embodiments of this application, the supercapacitor detection conditions may include, but are not limited to: the wind turbine generator triggering a pitch operation or a shutdown operation. At this time, determining whether the supercapacitor detection conditions are met based on the operating data includes: determining whether the wind turbine generator triggers a pitch operation or a shutdown operation based on the operating data. When the pitch operation or shutdown operation occurs, it indicates that the supercapacitor detection conditions have been met.

[0058] Step S103: When the supercapacitor detection condition is met, obtain the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met.

[0059] In this step, when the supercapacitor detection conditions are met, the voltage value of the supercapacitor to be detected is detected within a preset time range. In this step, the voltage value of the supercapacitor can be directly measured by a voltage sensor, or the voltage value of the supercapacitor can be retrieved by the battery management module of the wind turbine generator set.

[0060] Step S104: Obtain the minimum voltage value of the supercapacitor detected within the preset time period.

[0061] In this scheme, the minimum voltage value of the supercapacitor detected within a preset time period is determined by comparing the supercapacitors detected at two adjacent moments. The minimum voltage value of the supercapacitor refers to the minimum voltage value of the supercapacitor detected within the preset time period.

[0062] The reason for detecting the voltage value of the supercapacitor within a preset time period in this scheme is that the voltage of the supercapacitor changes rapidly at the moment the wind turbine pitch system starts feathering. After the preset time period, the voltage value of the supercapacitor will gradually recover under the action of the charger. Therefore, it is only necessary to measure the voltage value of the supercapacitor within the preset time period, which can be 200ms or other durations.

[0063] Assuming the initial voltage value of the supercapacitor is 150V, when the pitch or main control triggers a shutdown command, if the actual voltage value of the supercapacitor is detected to be less than 150V at a certain moment, the actual voltage value of the supercapacitor is written into the minimum voltage value. Then, the voltage of the supercapacitor is detected in the next cycle until the detection ends, and the minimum voltage value detected is recorded.

[0064] First, the minimum voltage of the supercapacitor is set to the rated voltage, denoted as 'a'. If the detected current voltage of the supercapacitor is less than 'a', the minimum voltage is updated to 'a'. For example, if the rated voltage is 100V, and the actual voltage of the supercapacitor is subsequently detected as 99.6V (99.6 < 100V), the minimum voltage is recorded as 99.6V. If the actual voltage is subsequently detected as 99.4V (99.4 < 99.6), the minimum voltage is recorded as 99.4V. If the actual voltage is subsequently detected as 99.8V (99.8 > 99.4), the minimum voltage remains 99.4V. If no voltage value lower than 99.4V is detected within the preset time period, the lowest voltage value of the supercapacitor is recorded as 99.4V.

[0065] Considering that the voltage of the supercapacitor is inherently decreasing at the moment the wind turbine starts feathering, methods such as variance analysis, standard deviation analysis, and amplitude analysis (the fluctuation range between the maximum and minimum values) cannot accurately identify whether the supercapacitor voltage is decreasing or fluctuating. This application also discloses another method for determining the minimum voltage value of the supercapacitor. Specifically, the method is as follows:

[0066] Based on the voltage values ​​of the supercapacitor detected within the preset time period, after the voltage value of the supercapacitor begins to decrease, the slope of the voltage change of the supercapacitor at two adjacent time points is continuously recorded. If the direction of the slope is consistent and the magnitude is close (the difference is less than a preset value), it is determined that the voltage value of the supercapacitor continues to decrease; at the same time, the minimum voltage value is continuously calculated and stored. Simultaneously, when the voltage value of the supercapacitor begins to increase, the slope of the voltage change of the supercapacitor at two adjacent time points is continuously recorded. If the direction of the slope is consistent and the magnitude is close (the difference is less than a preset value), it is determined that the voltage value of the supercapacitor continues to decrease. If the voltage value of the supercapacitor continuously increases to half of the amplitude change, it is determined that the capacitor voltage has recovered, and data acquisition and recording are stopped. The minimum voltage value of the supercapacitor recorded is taken as the minimum voltage value of the supercapacitor detected within the preset time period. For example, if the voltage value of the supercapacitor is 99.968V before the drop and 99.406V after the drop, and the voltage continuously increases to 99.406+(99.968-99.406) / 2=99.687V, it is determined that the capacitor voltage has recovered, and data acquisition and recording are stopped.

[0067] In another embodiment of this application, to ensure the reliability of the calculation results, it can be determined whether the fluctuation rate of the supercapacitor's voltage value within the preset time period is greater than a preset value. If it is greater, the detected supercapacitor voltage value is discarded. For example, if the supercapacitor's voltage value is detected to have fluctuated more than twice consecutively within the preset time period, it can be considered that the detection of the supercapacitor's minimum voltage value has been interfered with, and the current detection is abandoned. Specifically: it is detected whether the supercapacitor's voltage value has fluctuated within the preset time period; if no fluctuation has occurred, the next step is executed; otherwise, the detection is not performed. All methods here use a slope bidirectional judgment method to identify the supercapacitor's minimum voltage value within the preset time period, that is, when the supercapacitor's voltage value reaches its minimum value, the voltage value only increases and decreases once. Otherwise, it is considered that a fluctuation has occurred, and after a fluctuation occurs, the process ends, and the current detection is not performed.

[0068] In this scheme, a voltage fluctuation is defined as the supercapacitor's voltage value continuously increasing from a low point to half of its amplitude change. Specifically, the slope of the supercapacitor's voltage value change is continuously recorded for two adjacent time points. If the slopes are in the same direction and their magnitudes are similar (the difference is less than a preset value), the supercapacitor's voltage value is determined to be continuously decreasing. Simultaneously, the minimum voltage value is continuously calculated and stored. Conversely, when the supercapacitor's voltage value is detected to begin rising, the slope of the supercapacitor's voltage value change is continuously recorded for two adjacent time points. If the slopes are in the same direction and their magnitudes are similar (the difference is less than a preset value), the capacitor voltage is determined to be continuously rising. A voltage fluctuation is defined as the supercapacitor's voltage value continuously increasing to half of its amplitude change.

[0069] For example, see Figure 2 , Figure 2 This is a measurement curve of the minimum voltage value of the supercapacitor during the start-up feathering of a wind turbine generator. The horizontal axis represents time, and the vertical axis represents the voltage value. Figure 2 It can be seen that in -3.7 seconds, the voltage of the supercapacitor is the actual rated voltage of 99.968V. Then, at the instant the motor starts, the voltage of the supercapacitor drops to 99.406V. After the motor starts, the voltage of the supercapacitor gradually recovers to close to the actual rated voltage under the influence of the charger. Meanwhile, in... Figure 2 In this case, the voltage value of the supercapacitor changes from its maximum to its minimum value only once, without repeated changes. Therefore, the measurement result of the supercapacitor's minimum voltage value is reliable.

[0070] For example, see Figure 3 ,yes Figure 2 The corresponding motor current curve at the moment of startup of the pitch motor; the horizontal axis represents time, and the global axis represents the motor current value; from Figure 3 As can be seen, the motor current is very large at the moment the pitch motor starts.

[0071] Step S105: Based on the rated voltage of the supercapacitor and the voltage of the smallest supercapacitor, calculate the voltage drop of the supercapacitor within the preset time period.

[0072] In this step, after obtaining the minimum value of the supercapacitor within a preset time period, the difference between the minimum value and the rated voltage value of the supercapacitor is calculated, and the difference is used as the voltage drop value of the supercapacitor.

[0073] This step calculates the voltage change at the instant the supercapacitor discharges. The voltage value after discharge is the minimum voltage value of the supercapacitor detected in the previous step. Assuming the rated voltage of the supercapacitor is 100V and the recorded minimum voltage value is 99.4V, the voltage drop at the instant the supercapacitor discharges is calculated to be 100-99.4=0.6V.

[0074] Step S106: Obtain the current value of the pitch motor at the moment of pitch change.

[0075] In this step, after calculating the voltage drop value of the supercapacitor, the current value of the pitch motor of the wind turbine generator is collected during pitch control and shutdown. In order to ensure the reliability of the collected results, the collected current value can also be integrated to obtain the integrated current value.

[0076] Step S107: Calculate the discharge current of the supercapacitor based on the current value of the pitch motor.

[0077] In this step, based on the operating characteristics of the driver, a preset correction coefficient is used to correct the current value after integration, and the corrected current value is used as the discharge current of the supercapacitor.

[0078] Since the pitch motor is the main power-consuming component of the supercapacitor, and the pitch motor is generally a three-phase motor, according to the law of conservation of energy, 1.732 times the single-phase current of the motor is approximately equal to the discharge current of the supercapacitor; therefore, the discharge current of the supercapacitor is 1.732 times the current value of the pitch motor at the moment of pitch change, that is, the correction factor is 1.732.

[0079] Step S108: Substitute the discharge current of the supercapacitor and the voltage drop of the supercapacitor into Ohm's law to calculate the internal resistance of the supercapacitor.

[0080] Voltage drop when a supercapacitor is used as a backup power source: Due to the large internal resistance of a supercapacitor, there is a voltage drop at the moment of discharge, which is: ΔV=IR; where ΔV is the voltage drop, I is the discharge current, and R is the internal resistance of the supercapacitor.

[0081] Therefore, in this step, after obtaining the voltage drop value Δv of the supercapacitor and the discharge current I of the supercapacitor, the internal resistance R of the supercapacitor can be calculated by substituting the two into the formula Δv=IR.

[0082] Step S109: Based on the internal resistance value of the supercapacitor, predict the fatigue degree of the supercapacitor.

[0083] In this step, the remaining service life of the supercapacitor, i.e., the fatigue level of the supercapacitor, can be assessed based on the degree of decrease in the internal resistance and the theoretical lifespan. The fatigue level corresponding to each capacitor's internal resistance value can be obtained by looking up a preset mapping table, which stores the mapping relationship between the capacitor's internal resistance value and its fatigue level.

[0084] In this solution, the aging time of the supercapacitor can be calculated based on the predicted fatigue level. The remaining service life of the supercapacitor can be obtained based on the difference between the aging time and the rated aging time. Specifically, this step may include: obtaining the aging time of the supercapacitor based on a preset mapping relationship between the internal resistance value of the capacitor and the aging time; obtaining the rated aging time of the supercapacitor; and using the difference between the rated aging time and the aging time as the remaining service life of the supercapacitor. The rated aging time is the maximum aging time of the supercapacitor; when this rated aging time is reached, it indicates that the supercapacitor urgently needs to be replaced.

[0085] For supercapacitors (and other batteries similarly), the more dielectric material, the larger the capacity; the higher the density, the lower the resistance. Conversely, the less dielectric material, the smaller the capacity; the lower the density, the higher the resistance. In other words, as the capacitance of a supercapacitor decreases, its internal resistance increases. When charging and discharging a supercapacitor, after determining the required capacitance and internal resistance, it is necessary to consider and utilize the influence of resistance and capacitance on the discharge characteristics. Therefore, in this scheme, in addition to predicting the fatigue degree of the supercapacitor, the capacitance value and the rate of capacitance decrease can be calculated based on the internal resistance of the supercapacitor.

[0086] For example, based on the relationship between the internal resistance and capacitance of supercapacitors in laboratory aging tests, when the internal resistance of a single supercapacitor module is greater than 0.5mΩ, it indicates that the supercapacitor is aging quite severely, and at this time, its capacitance decrease rate is about 20%.

[0087] For example, the relationship between the internal resistance, aging time, capacitance, and capacitance change rate of Maxwell 1 (Type B), Maxwell 2 (Type B), and Maxwell 3 (Type B) supercapacitors can be seen in Table 1. Table 1 presents data from accelerated aging tests conducted in the laboratory, with an aging time of 800 hours. As can be seen from Table 1, the capacitance of the supercapacitor gradually decreases with increasing aging time, while the internal resistance gradually increases. When the single-cell internal resistance (ESR) of the supercapacitor exceeds 0.5 mΩ, the capacitance decrease rate approaches 20%.

[0088]

[0089] In another embodiment of the present application, after determining the internal resistance of the supercapacitor, the capacitance value of the supercapacitor can be calculated by using a proportional curve fitting relationship.

[0090] Table 2 shows an example of the capacitance detection method involved in this scheme. It is assumed that the theoretical capacitance of the supercapacitor is 518F / 7 = 74F; where 518F is the capacitance of a single supercapacitor module, and 7 is the number of supercapacitor modules connected in series. The capacitance values ​​in Table 1 are the capacitance values ​​of each individual supercapacitor cell. Each module consists of 6 cells connected in series, therefore the capacitance value of a single module is 3110F / 6 - 518F. The 3110F is the average capacitance value of supercapacitor modules of types Maxwell 1 (Type B), Maxwell 2 (Type B), and Maxwell 3 (Type B). Based on the data in Table 2, the internal resistance of a single cell can be calculated to be 0.2041 mΩ. Then, using curve fitting, the capacitance value and internal resistance value of the supercapacitor in Table 1 are fitted together, yielding a capacitance value of approximately 72F.

[0091] The algorithm for evaluating the lifespan of supercapacitors is as follows: Time is converted from internal resistance value to aging time, and this is equivalent to the theoretical remaining lifespan. For example, if the internal resistance of a single cell is 0.2041 mΩ, the corresponding aging time is approximately 122 hours. Since an aging time of 800 hours corresponds to a supercapacitor lifespan of 10 years, 122 hours corresponds to 1.525 years. Therefore, the theoretical remaining lifespan of this group of capacitors is 10 - 1.525 = 8.475 years. This application allows these mapping relationships to be pre-added to a mapping table. After obtaining the internal resistance of the supercapacitor, the corresponding data can be retrieved based on this mapping table.

[0092]

[0093] For the specific working content of each unit in this embodiment, please refer to the content of the above method embodiment. The supercapacitor fatigue prediction device provided in the embodiment of the present invention is described below. The supercapacitor fatigue prediction device described below and the supercapacitor fatigue prediction method described above can be referred to each other.

[0094] For details, see Figure 4 The supercapacitor fatigue prediction device disclosed in this application embodiment may include: a data acquisition unit A, a detection condition judgment unit B, an internal resistance calculation unit C, and a fatigue prediction unit D.

[0095] Data acquisition unit A, which corresponds to step S101 in the above method, is used to acquire the operating data of the wind turbine generator set;

[0096] The detection condition judgment unit B, which corresponds to step S102 in the above method, is used to determine whether the supercapacitor detection condition has been met based on the running data.

[0097] The internal resistance calculation unit C, corresponding to steps S103-S108 in the above method, is used to: acquire the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met; acquire the minimum voltage value of the supercapacitor detected within the preset time period; calculate the voltage drop value of the supercapacitor within the preset time period based on the rated voltage value of the supercapacitor and the minimum voltage value of the supercapacitor; acquire the current value of the pitch motor at the moment of pitch change; calculate the discharge current of the supercapacitor based on the current value of the pitch motor; and calculate the internal resistance value of the supercapacitor by substituting the discharge current of the supercapacitor and the voltage drop value of the supercapacitor into Ohm's law.

[0098] The fatigue prediction unit D, which corresponds to step S109 in the above method, is used to predict the fatigue of the supercapacitor based on its internal resistance value.

[0099] The supercapacitor fatigue prediction device described in the above embodiments of this application can be installed in the pitch controller of a wind turbine generator set.

[0100] The supercapacitor fatigue prediction device can also realize other functions disclosed in the above-described method embodiments of this application, which will not be elaborated here.

[0101] Corresponding to the above method, this application also discloses a wind turbine pitch controller, see [link to relevant documentation]. Figure 5 As shown, the wind turbine pitch controller may include: at least one processor 100, at least one communication interface 200, at least one memory 300 and at least one communication bus 400;

[0102] In this embodiment of the invention, the number of processor 100, communication interface 200, memory 300, and communication bus 400 is at least one, and the processor 100, communication interface 200, and memory 300 communicate with each other through communication bus 400; obviously, Figure 5 The communication connections shown for the processor 100, communication interface 200, memory 300, and communication bus 400 are optional.

[0103] Optionally, the communication interface 200 can be an interface of a communication module, such as the interface of a GSM module;

[0104] Processor 100 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention.

[0105] The memory 300 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0106] Specifically, processor 100 is used for:

[0107] Obtain operating data of wind turbine generators;

[0108] Based on the aforementioned operational data, determine whether the supercapacitor detection conditions have been met;

[0109] When the supercapacitor detection condition is met, the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met is obtained.

[0110] Obtain the minimum voltage value of the supercapacitor detected within the preset time period;

[0111] Based on the rated voltage of the supercapacitor and the voltage of the minimum supercapacitor, the voltage drop of the supercapacitor within the preset time period is calculated.

[0112] Obtain the current value of the pitch motor at the moment of pitch change;

[0113] The discharge current of the supercapacitor is calculated based on the current value of the pitch motor.

[0114] The internal resistance of the supercapacitor is calculated by substituting the discharge current and voltage drop of the supercapacitor into Ohm's law.

[0115] The fatigue degree of the supercapacitor is predicted based on its internal resistance value.

[0116] The processor 100 is also used to perform other steps of the supercapacitor fatigue prediction method disclosed in the above embodiments of this application, which will not be described in detail here.

[0117] See Figure 6 This application also discloses a pitch control system for a wind turbine generator set, see [link to relevant documentation]. Figure 6 The pitch system may include: a supercapacitor 101, a pitch motor 102, a frequency converter 103, a charger 104, a grid input 105, and a controller 106, wherein the controller is the aforementioned wind turbine pitch controller.

[0118] The supercapacitor 101 is used to continue supplying power to the inverter 103 when an abnormality occurs on the grid input side; the inverter 103 is used to control the operation of the pitch motor 102; the charger 104 is used to charge the supercapacitor when the grid input 105 is normal; the controller 106 is used to control the operation of the pitch system and the inverter 103, and the controller 106 and the charger 104 communicate with each other for data exchange.

[0119] like Figure 6 As shown, the "+" terminal of the charger 104 output is electrically connected to the "+" terminal of the supercapacitor 101 and the "+" terminal of the inverter 103; the "-" terminal of the charger 104 output is electrically connected to the "-" terminal of the supercapacitor 101 and the "-" terminal of the inverter 103.

[0120] The working principle of the charger 104 is as follows: The charger 104 monitors the voltage value of the supercapacitor 101 in real time and compares it with the preset voltage value. When the voltage value of the supercapacitor 101 drops due to the energy consumption of the pitch motor 102, the charger 104 starts to charge the supercapacitor 101. The charging process is PID control, that is, the input is the preset voltage value of the supercapacitor, the feedback is the actual voltage value of the supercapacitor, and the output is the magnitude of the charging current.

[0121] The system operates as follows: When the pitch motor 102 is running, the supercapacitor 101 begins to provide power to the inverter 103 to drive the pitch motor 102. Simultaneously, the charger 104 charges the supercapacitor 101; and the smaller the voltage drop of the supercapacitor, the smaller the charging current (when the difference is close to 0, the charging current is close to 0). On the other hand, the charging process of the charger 104 is as follows: charging only begins after a voltage drop in the supercapacitor is detected, i.e., after a deviation between the actual voltage value and the target voltage value is detected within the charger. Therefore, charging has a certain degree of lag.

[0122] The controller uses the supercapacitor fatigue prediction method disclosed in the above embodiments of this application to predict the fatigue value of the supercapacitor, so as to ensure the normal operation of the system.

[0123] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0124] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting fatigue in supercapacitors, characterized in that, include: Obtain operating data of wind turbine generators; Based on the operating data, it is determined whether the wind turbine generator set has triggered pitch operation or shutdown operation. When the pitch operation or shutdown operation occurs, it indicates that the supercapacitor detection conditions have been met. When the supercapacitor detection condition is met, the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met is obtained. The minimum voltage value of the supercapacitor detected within the preset time period is taken as the minimum voltage value of the supercapacitor. Based on the rated voltage of the supercapacitor and the voltage of the minimum supercapacitor, the voltage drop of the supercapacitor within the preset time period is calculated. Obtain the current value of the pitch motor at the moment of pitch change; The current value of the pitch motor is integrated. The current value of the pitch motor after integral calculation is corrected by a preset correction coefficient, and the corrected current value is used as the discharge current of the supercapacitor. The internal resistance of the supercapacitor is calculated by substituting the discharge current and voltage drop of the supercapacitor into Ohm's law. The fatigue degree of the supercapacitor is predicted based on its internal resistance value.

2. The supercapacitor fatigue prediction method according to claim 1, characterized in that, The process of taking the minimum voltage value of the supercapacitor detected within the preset time period as the minimum voltage value of the supercapacitor includes: Once the supercapacitor voltage starts to drop within a preset time period, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slope direction is consistent and the difference is less than the preset value, it indicates that the supercapacitor voltage continues to drop. At the same time, the minimum detected supercapacitor voltage is continuously calculated and stored. When the supercapacitor voltage starts to rise, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slope of change is in the same direction and the difference is less than the preset value, it is determined that the supercapacitor voltage continues to rise. Determine whether the voltage value of the supercapacitor experiences only one continuous decrease and one continuous increase within the preset time period. If so, when the supercapacitor voltage continuously increases to half of the amplitude change, determine that the supercapacitor voltage has recovered, stop data acquisition and recording, and take the recorded minimum value as the minimum value among the supercapacitor voltage values ​​detected within the preset time period. Otherwise, discard the voltage value detected this time.

3. The supercapacitor fatigue prediction method according to claim 1, characterized in that, Based on the internal resistance value of the supercapacitor, fatigue prediction of the supercapacitor is performed, including: The aging time of the supercapacitor is obtained based on a preset mapping relationship between the internal resistance value of the capacitor and the aging time. Obtain the rated aging time of the supercapacitor; The difference between the rated aging time and the aging time is taken as the remaining lifespan of the supercapacitor.

4. A supercapacitor fatigue prediction device, characterized in that, include: The data acquisition unit is used to acquire the operating data of the wind turbine generator set; The detection condition judgment unit is used to determine whether the wind turbine generator set has triggered pitch operation or shutdown operation based on the operating data. When the pitch operation or shutdown operation occurs, it indicates that the supercapacitor detection condition has been met. An internal resistance calculation unit is used to obtain the voltage value of the supercapacitor within a preset time period after the supercapacitor detection condition is met. The minimum voltage value of the supercapacitor detected within the preset time period is taken as the minimum voltage value of the supercapacitor. Based on the rated voltage of the supercapacitor and the voltage of the minimum supercapacitor, the voltage drop of the supercapacitor within the preset time period is calculated; the current value of the pitch motor at the moment of pitch change is obtained. The current value of the pitch motor is integrated. The current value of the pitch motor after integral calculation is corrected by a preset correction coefficient, and the corrected current value is used as the discharge current of the supercapacitor. The internal resistance of the supercapacitor is calculated by substituting the discharge current and voltage drop of the supercapacitor into Ohm's law. The fatigue prediction unit is used to predict the fatigue of the supercapacitor based on its internal resistance value.

5. The supercapacitor fatigue prediction device according to claim 4, characterized in that, The process by which the internal resistance calculation unit takes the minimum voltage value of the supercapacitor detected within the preset time period as the minimum voltage value of the supercapacitor includes: Once the supercapacitor voltage starts to drop within a preset time period, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slope direction is consistent and the difference is less than the preset value, it indicates that the supercapacitor voltage continues to drop. At the same time, the minimum detected supercapacitor voltage is continuously calculated and stored. When the supercapacitor voltage starts to rise, the slope of change between two adjacent voltage acquisition points is continuously recorded. If the slope of change is in the same direction and the difference is less than the preset value, it is determined that the supercapacitor voltage continues to rise. Determine whether the voltage value of the supercapacitor experiences only one continuous decrease and one continuous increase within the preset time period. If so, when the supercapacitor voltage continuously increases to half of the amplitude change, determine that the supercapacitor voltage has recovered, stop data acquisition and recording, and take the recorded minimum value as the minimum value among the supercapacitor voltage values ​​detected within the preset time period. Otherwise, discard the voltage value detected this time.

6. The supercapacitor fatigue prediction device according to claim 5, characterized in that, The supercapacitor fatigue prediction device is installed in the pitch controller of the wind turbine generator set.

7. A pitch controller for a wind turbine generator set, characterized in that, Including memory and processor; The memory stores computer-executable instructions, wherein when the processor executes the computer-executable instructions, the processor implements the supercapacitor fatigue prediction method as described in any one of claims 1 to 3.

8. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor performs the supercapacitor fatigue prediction method as described in any one of claims 1 to 3.

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