Method and apparatus for evaluating power generation performance of a wind turbine generator system

Abstract

The present disclosure provides a method and device for evaluating power generation performance of a wind turbine generator set. The method for evaluating power generation performance comprises: obtaining historical operation data of the wind turbine generator set in n historical time periods, n being a positive integer; determining actual capacity coefficients and theoretical capacity coefficients of the wind turbine generator set in the n historical time periods according to the historical operation data; determining historical power generation performance coefficients of the wind turbine generator set in the n historical time periods based on the actual capacity coefficients and the theoretical capacity coefficients in the n historical time periods; obtaining a power generation performance change trend according to the historical power generation performance coefficients in the n historical time periods; and determining an estimated power generation performance coefficient of the wind turbine generator set in a target future time period according to the power generation performance change trend, the estimated power generation performance coefficient being used for estimating power generation performance in the target future time period. The present disclosure can effectively evaluate the power generation performance of the wind turbine generator set, and provide a basis for active update of subsequent control and operation and maintenance strategies of the wind turbine generator set.

Description

Technical Field

[0001] This disclosure relates to the field of wind power generation technology, and more specifically, to a method and apparatus for evaluating the power generation performance of a wind turbine generator set. Background Technology

[0002] As the installed capacity and operational years of wind turbine generators in the market increase, a large number of units are approaching their design lifespan or facing upgrades due to changes in electricity pricing policies. Throughout the entire lifecycle of a wind turbine generator, changes in its control and maintenance strategies are generally passively triggered by the failure of major components. To improve the overall profitability of wind turbine generators and proactively update control and maintenance strategies, it is necessary to assess the power generation performance of existing wind turbine generators as a basis for determining their future deployment.

[0003] Current wind turbine performance assessments primarily rely on power curves or power characteristics, failure rates, and other metrics to determine trends in turbine performance. However, these methods overlook external environmental factors that influence turbine performance beyond the turbine itself. While turbine-related performance changes are generally gradual, environmental factors are more pronounced. Therefore, changes in turbine-related performance over a given period can mask the turbine's inherent characteristics. Furthermore, these environmental influences are inherently volatile, making it difficult to assess long-term performance variations caused by the turbine itself. Summary of the Invention

[0004] Therefore, it is crucial to reliably assess the changes in power generation performance caused by the wind turbine itself.

[0005] In one general aspect, a method for evaluating the power generation performance of a wind turbine generator set is provided, comprising: acquiring historical operating data of the wind turbine generator set for each of n historical time periods, where n is a positive integer; determining the actual capacity coefficient and theoretical capacity coefficient of the wind turbine generator set for each of the n historical time periods based on the historical operating data; determining the historical power generation performance coefficient of the wind turbine generator set for each of the n historical time periods based on the actual capacity coefficient and theoretical capacity coefficient of the wind turbine generator set for each of the n historical time periods; obtaining the power generation performance change trend based on the historical power generation performance coefficient of the n historical time periods; and determining the estimated power generation performance coefficient of the wind turbine generator set for a target future time period based on the power generation performance change trend, wherein the estimated power generation performance coefficient is used to predict the power generation performance for the target future time period.

[0006] Optionally, determining the actual capacity coefficient and theoretical capacity coefficient of the wind turbine generator set in the n historical time periods based on the historical operating data includes: determining the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in the n historical time periods based on the historical operating data to obtain the actual capacity coefficient, wherein the power generation index includes at least one of power generation duration, grid-connected power, and power; and determining the ratio of the theoretical average power to the rated power of the wind turbine generator set in the n historical time periods based on the historical operating data to obtain the theoretical capacity coefficient.

[0007] Optionally, when the power generation index includes power, the historical operating data includes an actual power curve, multiple wind speed ranges, the frequency of each wind speed range, and the rated power. The step of determining the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in each of the n historical periods based on the historical operating data to obtain the actual capacity coefficient includes: for each historical period, determining the average actual power of each wind speed range based on the actual power curve; determining the expected value of the average actual power based on the average actual power and frequency of each wind speed range, as the actual average power of the wind turbine generator set in the historical period; and determining the ratio of the actual average power of the wind turbine generator set in the historical period to the rated power to obtain the actual capacity coefficient.

[0008] Optionally, the historical operating data further includes a theoretical power curve. The step of determining the ratio of the theoretical average power to the rated power of the wind turbine generator set within the n historical time periods based on the historical operating data to obtain the theoretical capacity coefficient includes: for each historical time period, determining the theoretical average power for each wind speed range based on the theoretical power curve; determining the expected value of the theoretical average power based on the theoretical average power and frequency of each wind speed range, as the theoretical average power of the wind turbine generator set in that historical time period; and determining the ratio of the theoretical average power to the rated power of the wind turbine generator set in that historical time period to obtain the theoretical capacity coefficient.

[0009] Optionally, the actual power average value is the average value of the actual power within the wind speed range; and / or the theoretical power average value is a value obtained by interpolation in the theoretical power curve based on the average wind speed of each wind speed range.

[0010] Optionally, the historical operating data includes wind speed distribution parameters and rated wind speed. The wind speed distribution parameters are used to describe the distribution probability of wind speed. The step of determining the ratio of the theoretical average power to the rated power of the wind turbine generator set in the n historical time periods based on the historical operating data to obtain the theoretical capacity coefficient includes: for each historical time period, determining the expected value of the cube of the wind speed based on the wind speed distribution parameters; and determining the ratio of the expected value of the cube of the wind speed to the cube of the rated wind speed to obtain the theoretical capacity coefficient.

[0011] Optionally, the wind speed distribution parameters include a wind speed probability density function or a wind speed probability distribution function; or the wind speed distribution parameters include multiple wind speed intervals and the frequency of each wind speed interval.

[0012] Optionally, the power generation performance evaluation method further includes: determining the estimated power generation of the wind turbine generator set in the target future period based on the estimated power generation performance coefficient; and determining the estimated revenue generated by the wind turbine generator set using the candidate generator set control strategy in the target future period based on the estimated power generation, the candidate generator set control strategy, and revenue-related variables, wherein the revenue-related variables are variables used to determine the power generation revenue of the wind turbine generator set.

[0013] Optionally, the revenue-related variables include the electricity price for the target future period, a pre-determined first relationship function, and a second relationship function. The first relationship function is a relationship function between the change in power generation and the adjustment of the unit control strategy, and the second relationship function is a relationship function between power generation and unit maintenance costs. The step of determining the estimated revenue generated by the wind turbine using the candidate unit control strategy in the target future period based on the estimated power generation, the candidate unit control strategy, and the revenue-related variables includes: determining the estimated change in power generation corresponding to the candidate unit control strategy based on the adjustment of the candidate unit control strategy relative to the current unit control strategy and the first relationship function; determining the sum of the estimated power generation and the estimated change in power generation as the estimated total power generation corresponding to the candidate unit control strategy; determining the estimated maintenance cost corresponding to the candidate unit control strategy based on the estimated total power generation and the second relationship function; and determining the estimated revenue generated by the wind turbine using the candidate unit control strategy in the target future period based on the estimated total power generation, the electricity price, and the estimated maintenance cost.

[0014] Optionally, the power generation performance evaluation method further includes: adjusting the control strategy of the candidate unit based on the estimated revenue to obtain an optimized unit control strategy.

[0015] In another general aspect, a wind turbine generator set power generation performance evaluation device is provided, comprising: an acquisition unit configured to acquire historical operating data of the wind turbine generator set in n historical time periods, where n is a positive integer; a determination unit configured to determine, based on the historical operating data, the actual capacity coefficient and theoretical capacity coefficient of the wind turbine generator set in the n historical time periods; the determination unit is further configured to determine, based on the actual capacity coefficient and theoretical capacity coefficient in the n historical time periods, the historical power generation performance coefficient of the wind turbine generator set in the n historical time periods; an organization unit configured to obtain a power generation performance change trend based on the historical power generation performance coefficient in the n historical time periods; and a prediction unit configured to determine, based on the power generation performance change trend, the estimated power generation performance coefficient of the wind turbine generator set in a target future period, wherein the estimated power generation performance coefficient is used to predict the power generation performance in the target future period.

[0016] Optionally, the determining unit is further configured to: determine the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in the n historical time periods according to the historical operating data, and obtain the actual capacity coefficient, wherein the power generation index includes at least one of power generation duration, grid-connected power, and power; and determine the ratio of the theoretical average power to the rated power of the wind turbine generator set in the n historical time periods according to the historical operating data, and obtain the theoretical capacity coefficient.

[0017] Optionally, when the power generation index includes power, the historical operating data includes an actual power curve, multiple wind speed intervals, the frequency of each wind speed interval, and the rated power. The determining unit is further configured to: for each historical period, based on the actual power curve, determine the actual power average value for each wind speed interval; determine the expected value of the actual power average value as the actual average power of the wind turbine generator set in the historical period based on the actual power average value and frequency of each wind speed interval; and determine the ratio of the actual average power of the wind turbine generator set in the historical period to the rated power to obtain the actual capacity coefficient.

[0018] Optionally, the historical operating data further includes a theoretical power curve, and the determining unit is further configured to: for each historical period, based on the theoretical power curve, determine the theoretical power average value for each wind speed interval; determine the expected value of the theoretical power average value as the theoretical average power of the wind turbine generator set in the historical period according to the theoretical power average value and frequency of each wind speed interval; and determine the ratio of the theoretical average power of the wind turbine generator set in the historical period to the rated power to obtain the theoretical capacity coefficient.

[0019] Optionally, the actual power average value is the average value of the actual power within the wind speed range; and / or the theoretical power average value is a value obtained by interpolation in the theoretical power curve based on the average wind speed of each wind speed range.

[0020] Optionally, the historical operating data includes wind speed distribution parameters and rated wind speed. The wind speed distribution parameters are used to describe the distribution probability of wind speed. The determining unit is further configured to: for each historical period, determine the expected value of the cube of the wind speed based on the wind speed distribution parameters; determine the ratio of the expected value of the cube of the wind speed to the cube of the rated wind speed to obtain the theoretical capacity coefficient.

[0021] Optionally, the wind speed distribution parameters include a wind speed probability density function or a wind speed probability distribution function; or the wind speed distribution parameters include multiple wind speed intervals and the frequency of each wind speed interval.

[0022] Optionally, the power generation performance evaluation device further includes a prediction unit, configured to: determine the predicted power generation of the wind turbine generator set in the target future period based on the predicted power generation performance coefficient; and determine the predicted revenue generated by the wind turbine generator set using the candidate generator set control strategy in the target future period based on the predicted power generation, the candidate generator set control strategy, and revenue-related variables, wherein the revenue-related variables are variables required to determine the power generation revenue of the wind turbine generator set.

[0023] Optionally, the revenue-related variables include the electricity price for the target future period, a pre-determined first relationship function, and a second relationship function. The first relationship function is a relationship function between the change in power generation and the adjustment of the unit control strategy, and the second relationship function is a relationship function between power generation and unit maintenance costs. The estimation unit is further configured to: determine the estimated change in power generation corresponding to the candidate unit control strategy based on the adjustment of the candidate unit control strategy relative to the current unit control strategy and the first relationship function; determine the sum of the estimated power generation and the estimated change in power generation as the estimated total power generation corresponding to the candidate unit control strategy; determine the estimated maintenance cost corresponding to the candidate unit control strategy based on the estimated total power generation and the second relationship function; and determine the estimated revenue generated by the wind turbine generator unit adopting the candidate unit control strategy in the target future period based on the estimated total power generation, the electricity price, and the estimated maintenance cost.

[0024] Optionally, the power generation performance evaluation device further includes an optimization unit configured to adjust the candidate unit control strategy based on the estimated revenue to obtain an optimized unit control strategy.

[0025] In another general aspect, a computer-readable storage medium is provided, wherein when instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor causes the at least one processor to perform the power generation performance evaluation method as described above.

[0026] In another general aspect, a computer device is provided, comprising: at least one processor; at least one memory storing computer-executable instructions, wherein, when executed by the at least one processor, the computer-executable instructions cause the at least one processor to perform the power generation performance evaluation method as described above.

[0027] This disclosure utilizes an actual capacity factor that reflects actual power output, combined with a theoretical capacity factor, to derive historical power generation performance coefficients. This allows for effective evaluation of the generator set's own power generation performance. Furthermore, it leverages further determined trends in power generation performance to reflect changes in the generator set's performance. Moreover, by determining the projected power generation performance coefficients for the wind turbine generator set in a target future period based on these trends, a more intuitive future perspective on the changes in the wind turbine generator set's performance can be provided, thus offering a basis for proactive updates to subsequent control and maintenance strategies. Simultaneously, this disclosure uses historical operating data of the generator set, eliminating the need for additional measuring devices, resulting in low optimization costs and strong portability.

[0028] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0029] Figure 1 This is a flowchart illustrating a method for evaluating the power generation performance of a wind turbine generator set according to an embodiment of the present disclosure;

[0030] Figure 2 This is a schematic diagram illustrating the theoretical power curve according to an embodiment of the present disclosure;

[0031] Figure 3 This is a schematic diagram illustrating an actual power curve according to an embodiment of the present disclosure;

[0032] Figure 4 This is a schematic flowchart illustrating a control strategy optimization scheme according to a specific embodiment of the present disclosure;

[0033] Figure 5 This is a schematic flowchart illustrating a wind turbine generator performance evaluation system according to an embodiment of the present disclosure;

[0034] Figure 6 This is a block diagram illustrating a power generation performance evaluation apparatus for a wind turbine generator set according to an embodiment of the present disclosure;

[0035] Figure 7This is a block diagram illustrating a computer device according to an embodiment of the present disclosure. Detailed Implementation

[0036] The following detailed embodiments are provided to aid the reader in gaining a comprehensive understanding of the methods, apparatus, and / or systems described herein. However, various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but may be changed as will become clear upon understanding this disclosure, except for operations that must occur in a specific order. Furthermore, for clarity and conciseness, descriptions of features known in the art may be omitted.

[0037] The features described herein may be implemented in different forms and should not be construed as limited to the examples described herein. Rather, the examples described herein are provided only to illustrate some of the many feasible ways of implementing the methods, apparatus, and / or systems described herein, which will become clear upon understanding the disclosure of this application.

[0038] As used herein, the term “and / or” includes any one of the associated listed items and any combination of any two or more.

[0039] Although terms such as “first,” “second,” and “third” may be used herein to describe various components, assemblies, regions, layers, or parts, these components, assemblies, regions, layers, or parts should not be limited by these terms. Rather, these terms are used only to distinguish one component, assembly, region, layer, or part from another. Thus, without departing from the teaching of the examples described herein, the first component, first assembly, first region, first layer, or first part referred to as the first component, first assembly, first region, first layer, or first part may also be referred to as the second component, second assembly, second region, second layer, or second part.

[0040] In the specification, when an element (such as a layer, region, or substrate) is described as being "on" another element, "connected to," or "bonded to" another element, the element may be directly "on" another element, directly "connected to," or "bonded to" the other element, or one or more other elements may be present in between. Conversely, when an element is described as being "directly on" another element, "directly connected to," or "directly bonded to" another element, no other elements may be present in between.

[0041] The terminology used herein is for the purpose of describing various examples only and is not intended to limit disclosure. Unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. The terms “comprising,” “including,” and “having” indicate the presence of the described features, quantities, operations, components, elements, and / or combinations thereof, but do not preclude the presence or addition of one or more other features, quantities, operations, components, elements, and / or combinations thereof.

[0042] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains upon understanding this disclosure. Unless expressly defined herein, terms (such as those defined in a general dictionary) shall be interpreted as having a meaning consistent with their meaning in the context of the relevant field and in this disclosure, and shall not be interpreted in an idealized or overly formalistic manner.

[0043] Furthermore, in the description of the examples, detailed descriptions of well-known related structures or functions will be omitted when it is believed that such detailed descriptions would lead to a vague interpretation of this disclosure.

[0044] Figure 1 This is a flowchart illustrating a method for evaluating the power generation performance of a wind turbine generator set according to an embodiment of the present disclosure.

[0045] Reference Figure 1 In step S101, historical operating data of the wind turbine generator set is acquired for each of the n historical time periods. n is a positive integer. The length of each historical time period can be set as needed, such as one month, one quarter, or one year. Different historical time periods can have commonalities; for example, for the same year, historical operating data for each month, each quarter, or the entire year can be acquired simultaneously. However, in such cases, it is often unnecessary to repeatedly acquire the same data; it is sufficient to associate the data with its corresponding historical time period. For example, all historical operating data within the n historical time periods can be acquired uniformly, and each historical operating data point is accompanied by a timestamp. When historical operating data from a specific historical time period is needed, the historical operating data with the timestamp within that historical time period can be used.

[0046] The aforementioned operational data reflects the real-time operation of the unit at different times, including data such as wind speed, power, and electricity generation. As an example, historical operational data can be obtained from SCADA (Supervisory Control and Data Acquisition) systems; in another example, historical operational data can be collected from various sensors installed on the wind turbine generator set.

[0047] In step S102, based on historical operating data, the actual capacity factor and theoretical capacity factor of the wind turbine generator set are determined for each of the n historical time periods.

[0048] The capacity factor reflects the energy output of a wind turbine generator set. The actual capacity factor, as mentioned above, reflects the actual energy output of the wind turbine generator set; it can be the ratio of the actual power generation (actual power) to the rated power generation (rated power) of the generator set within a statistical period. The theoretical capacity factor, as mentioned above, reflects the theoretical energy output of the wind turbine generator set; it can be the ratio of the theoretical power generation (theoretical power) to the rated power generation (rated power) of the generator set within a statistical period.

[0049] The actual power of a wind turbine is affected by both the turbine's own power generation performance and environmental factors. The theoretical power, on the other hand, represents the turbine's power under ideal conditions (i.e., when its own power generation performance is optimal), reflecting the influence of environmental factors. Therefore, the difference between the two is caused by the turbine itself and reflects its own power generation performance. Furthermore, since both the actual and theoretical capacity factors incorporate a fixed value of rated power compared to actual and theoretical power, the difference between them can also reflect the difference between actual and theoretical power. This satisfies evaluation needs and provides more diverse calculation methods for the actual and theoretical capacity factors, ensuring the flexibility of the scheme. By acquiring historical operating data over n historical periods, the actual and theoretical capacity factors for each historical period can be calculated. Detailed calculation methods will be explained later and will not be repeated here.

[0050] In step S103, based on the actual capacity coefficient and theoretical capacity coefficient in n historical time periods, the historical power generation performance coefficient of the wind turbine generator set in each of the n historical time periods is determined.

[0051] The coefficient of performance (COP) is used to characterize the power generation performance of a wind turbine generator. For example, for a given historical period, the ratio of the actual capacity coefficient to the theoretical capacity coefficient of the wind turbine generator can be determined. This ratio equals the ratio of actual power to theoretical power. Using this as the historical COP for that period yields a dimensionless COP, which eliminates the influence of environmental factors and is not limited by the generator's rated power. It should be understood that the generator's performance often decreases with age, so actual power is often less than theoretical power, resulting in a ratio less than 1. The smaller the ratio, the greater the difference between actual and theoretical power, and the worse the generator's performance. In other words, the larger the ratio, the better the generator's performance.

[0052] Alternatively, this ratio can be converted to a power value. Specifically, first calculate the average theoretical power over n historical periods, then multiply this average by the aforementioned ratio for each historical period to obtain the actual power for each historical period. Using the average theoretical power over n historical periods as the conversion factor, rather than the theoretical power of a single year, allows for a unified evaluation standard.

[0053] In step S104, the power generation performance trend is obtained based on the historical power generation performance coefficients over n historical periods. As an example, the power generation performance trend can be obtained through fitting methods, such as, but not limited to, linear fitting. As mentioned earlier, different historical periods can have common components. For example, for the same year, historical operating data for each month, each quarter, and the entire year can be obtained simultaneously to obtain the historical power generation performance coefficients of the unit for each month, each quarter, and the entire year. Then, based on these historical power generation performance coefficients, the power generation performance trend is fitted.

[0054] In step S105, the estimated power generation performance coefficient of the wind turbine generator set for the target future period is determined based on the power generation performance change trend. The estimated power generation performance coefficient is used to predict the power generation performance of the wind turbine generator set for the target future period. By applying the power generation performance change trend to determine the estimated power generation performance coefficient, the generalized change trend can be transformed into a specific future point value, thus reflecting the power generation performance change results of the wind turbine generator set from a more intuitive future perspective, providing a basis for proactive updates to the unit's subsequent control and operation and maintenance strategies. It should be understood that, over time, steps S101 to S104 can be periodically executed to assess the power generation performance change trend of the wind turbine generator set; the entire power generation performance evaluation method of this disclosure can also be periodically executed.

[0055] The following section will provide a detailed explanation of how to determine the historical power generation performance coefficient, which is step S103.

[0056] Optionally, step S103 includes: determining the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in n historical time periods based on historical operating data, to obtain the actual capacity coefficient, wherein the power generation index includes at least one of power generation duration, grid-connected electricity, and power; and determining the ratio of the theoretical average power to the rated power of the wind turbine generator set in n historical time periods based on historical operating data, to obtain the theoretical capacity coefficient. As mentioned above, the actual capacity coefficient is the ratio of actual power to rated power. To calculate the actual capacity coefficient, the ratio can be calculated using the more readily available actual power generation duration and rated power generation duration, or the ratio can be calculated using the actual grid-connected electricity and rated grid-connected electricity. Alternatively, the actual power can be calculated using other methods, and then the ratio can be calculated using the actual power and rated power, ensuring the flexibility of the scheme. For the theoretical capacity coefficient, the theoretical power can be obtained through theoretical calculation. By using the theoretical average power in the targeted historical time period, the theoretical power in the corresponding historical time period can be characterized by the average level of theoretical power, ensuring the reliability of the calculation results.

[0057] Specifically, depending on the different data primarily used in historical operating data, this disclosure provides two types of methods for determining the actual capacity factor and the theoretical capacity factor.

[0058] One method primarily uses the unit's rated wind speed. The derivation of this method is as follows:

[0059] For the actual capacity factor (CF), the power generation indicators used can be power generation duration, on-grid power, or power. Taking a historical period of 1 year as an example, the actual capacity factor can be the ratio of the effective utilization hours of the unit in the year of operation to the total number of hours in the year, or as shown in formula (1), it can be the on-grid power E of the unit in the actual year of operation. real With rated annual on-grid electricity E rate The ratio, or as shown in formula (2), is the actual annual average power P of the unit. real With rated power P rate The ratio of .

[0060]

[0061]

[0062] For the theoretical capacity factor (ICF), only the environmental factor of wind speed needs to be considered. As shown in formulas (3) to (6), P ideal f w (v), P e (v) represent the theoretical average power, rated power, wind speed probability density function, and wind speed-related power calculation function, respectively; ρ, C pη, A, v r v cin v cout These are air density, power coefficient, efficiency coefficient, swept area, rated wind speed, cut-in wind speed, and cut-out wind speed, respectively. Where P... ideal It only relates to wind speed; the other parameters (ρ, C) are set to default. p η, A, v r ) and rated power P rate If they are consistent, then P ideal The calculation formula can also be written as shown in (5), P rate The unit design value can be used directly. Then the theoretical capacity factor ICF can be expressed by formula (6).

[0063]

[0064]

[0065]

[0066]

[0067] Based on the above derivation process, optionally, the historical operating data includes wind speed distribution parameters and rated wind speed. The wind speed distribution parameters are used to describe the distribution probability of wind speed. The step S103, determining the theoretical capacity coefficient over n historical time periods, includes: for each historical time period, determining the expected value of the cube of the wind speed based on the wind speed distribution parameters; determining the ratio of the expected value of the cube of the wind speed to the cube of the rated wind speed to obtain the theoretical capacity coefficient. This is achieved by utilizing the theoretical average power P. ideal With rated power P rate Consistent content can be achieved using wind speed distribution parameters and rated wind speed v. r This method determines the theoretical capacity factor (ICF), ensuring the simplicity and reliability of the calculation.

[0068] Alternatively, referring to formula (6), the wind speed distribution parameters can be described in two forms:

[0069] (1) Wind speed distribution parameters include the wind speed probability density function f w (v) or the wind speed probability distribution function, the theoretical wind speed probability density function f can be obtained by utilizing special distribution types. w (v) or the wind speed probability distribution function F(v) can be used to improve the feasibility of calculation. It should be understood that the wind speed probability density function f w (v) is the first derivative of the wind speed probability distribution function F(v).

[0070] As an example, suppose the wind speed data obtained is only the average wind speed v. aveThe wind speed distribution follows a Rayleigh distribution, yielding the wind speed probability distribution function:

[0071]

[0072] If the wind speed probability distribution function can be obtained, it is unnecessary to calculate the specific wind speed probability density function f. w (v), given the wind speed, the probability of the wind speed can be calculated using the wind speed probability distribution function, such as formula (7). Then, by taking enough wind speed points and calculating their probabilities, the integral problem can be transformed into a differential problem, which helps to reduce the computational difficulty and save computational resources.

[0073] (2) Wind speed distribution parameters include multiple wind speed intervals and the frequency f of each wind speed interval. i The frequency f of the wind speed range i This refers to the percentage of the total duration of wind speeds within a certain wind speed range within a statistical period, relative to the total duration of the statistical period. Referring to formula (6), this also does not involve integration and can be calculated directly using statistical data, reducing the computational difficulty. As an example, assuming that the obtained wind speed data is sufficient (meeting the definition of a large sample in statistics, i.e., sample size n≥30), we can start with the wind speed v cin Cut-out wind speed v cout Using a defined wind speed range as the interval, for example, 0.5 m / s, probability statistics are performed to obtain the frequency f. i .

[0074] Furthermore, PR (Performance Radio) can be defined as the historical power generation performance coefficient of the unit, as shown in Formula (8), which characterizes the power generation performance of the unit after removing the influence of wind speed.

[0075]

[0076] Another method primarily uses the unit's power curve.

[0077] As an example, such as Figure 2 The figure shows the theoretical power curve of a certain unit under turbulence intensity of 0.1 (turbulence intensity of 0.1 is selected here for reference only; the specific theoretical power curve selected can be determined according to the design parameters of the unit). Figure 3 The figure shown is the actual power curve of the unit (after filtering the data, starting from the cut-in wind speed v). cin Cut-out wind speed v cout Given a defined wind speed interval (e.g., 0.5 m / s as one interval), the average value of the actual power is used to obtain the frequency f of each wind speed interval. i Then the theoretical capacity factor ICF and the actual capacity factor CF can be calculated using formulas (9) and (10).

[0078]

[0079]

[0080] Among them, P ideal (i) P is obtained by interpolating the average wind speed in each wind speed range with the theoretical power curve. real (i) represents the average power across all wind speed ranges, f i The frequency for each wind speed range.

[0081] Optionally, when the power generation index includes power, the historical operating data includes the actual power curve, multiple wind speed ranges, the frequency of each wind speed range, and the rated power. Step S103, determining the actual capacity factor over n historical periods, includes: for each historical period, determining the average actual power for each wind speed range based on the actual power curve; determining the expected value of the average actual power based on the average actual power and frequency of each wind speed range, as the actual average power of the wind turbine generator set in the historical period; and determining the ratio of the actual average power of the wind turbine generator set to its rated power in the historical period to obtain the actual capacity factor. The actual power curve integrates discrete data of actual power detected during unit operation. By combining the actual power curve to determine the actual average power for historical periods, the detected data can be used directly, reducing computational difficulty. As an example, the average actual power is the average actual power within the wind speed range, which can be calculated based on the detected data, faithfully adhering to the detected data and ensuring the consistency of the scheme.

[0082] Optionally, the historical operating data also includes theoretical power curves. Step S103, determining the theoretical capacity coefficient over n historical periods, includes: for each historical period, determining the average theoretical power for each wind speed range based on the theoretical power curve; determining the expected value of the average theoretical power based on the average theoretical power and frequency for each wind speed range, as the theoretical average power of the wind turbine generator set during the historical period; and determining the ratio of the theoretical average power to the rated power of the wind turbine generator set during the historical period to obtain the theoretical capacity coefficient. The theoretical power curve is a power curve theoretically calculated under ideal design conditions. By combining the theoretical power curve to determine the theoretical average power for historical periods, the theoretical average power for each wind speed range can be directly used, reducing calculation difficulty. As an example, the average theoretical power is a value obtained by interpolation based on the average wind speed for each wind speed range. This allows for obtaining specific values ​​from a continuous theoretical curve, facilitating value selection and ensuring reliable value selection.

[0083] The following section will introduce the further utilization of the estimated power generation performance coefficient determined in step S105.

[0084] In some embodiments, after step S105, the power generation performance evaluation method according to the embodiments of this disclosure further includes: determining the estimated power generation of the wind turbine generator set in a target future period based on the estimated power generation performance coefficient; and determining the estimated revenue generated by the wind turbine generator set using the candidate generator set control strategy in the target future period based on the estimated power generation, the candidate generator set control strategy, and revenue-related variables, wherein the revenue-related variables are variables used to determine the power generation revenue of the wind turbine generator set. The estimated power generation performance coefficient can reflect the power generation performance of the wind turbine generator set in the target future period, and the estimated power generation in the target future period can be determined accordingly. The control strategy adopted by the generator set is reflected in the values ​​of multiple parameters such as cut-out wind speed, rated power, and rated speed. The difference between different control strategies lies in the specific values ​​of parameters such as cut-out wind speed, rated power, and rated speed. Accordingly, when adjusting the control strategy, the value of at least one of these parameters is adjusted, such as extending the cut-out wind speed, adjusting the rated power, and adjusting the rated speed. The power generation of the generator set will also be different when different control strategies are adopted. By combining estimated power generation with candidate unit control strategies, more accurate power generation can be predicted. Furthermore, by incorporating revenue-related variables, the estimated revenue from adopting a specific candidate unit control strategy can be determined, making the prediction results clearer and more intuitive. This provides a clear basis for proactively updating subsequent control and maintenance strategies for the units. It should be understood that there can be at least one target future period, allowing for the identification of the estimated revenue for at least one target future period as needed. When there are multiple target future periods, the sum of these estimated revenues can also be calculated to understand the overall estimated revenue.

[0085] Optionally, the revenue-related variables include the electricity price for the target future period, a predetermined first relationship function, and a second relationship function. The first relationship function is the relationship between the change in power generation and the adjustment amount of the unit control strategy. The adjustment amount of the unit control strategy is the adjustment amount of at least one of the aforementioned control parameters, such as cut-off wind speed, rated power, and rated speed. The first relationship function reflects how much the power generation will change relative to the current power generation after a certain amount of adjustment is made to the parameters based on the current unit control strategy. Since no adjustment is made to the unit control strategy when determining the estimated power generation, the prediction is the power output that the unit can produce if it continues to use the current unit control strategy. At this point, by comparing the adjustment amount between the candidate unit control strategy and the current unit control strategy, the change in power generation brought about by adopting the candidate unit control strategy can be determined in conjunction with the first relationship function. As an example, the candidate unit control strategy can be expressed in the form of the adjustment amount of the control parameters or in the form of the values ​​of the control parameters; this disclosure does not impose any restrictions on this. The second relationship function is the relationship between power generation and unit maintenance costs. The aforementioned process of determining the estimated revenue generated by the wind turbine generators using the candidate generator control strategy in the target future period, based on estimated power generation, candidate generator control strategies, and revenue-related variables, includes: determining the estimated power generation change corresponding to the candidate generator control strategy based on the adjustment amount of the candidate generator control strategy relative to the current generator control strategy and a first relationship function; determining the sum of the estimated power generation and the estimated power generation change as the estimated total power generation corresponding to the candidate generator control strategy; determining the estimated maintenance cost corresponding to the candidate generator control strategy based on the estimated total power generation and a second relationship function; and determining the estimated revenue generated by the wind turbine generators using the candidate generator control strategy in the target future period based on the estimated total power generation, electricity price, and estimated maintenance cost. By progressively determining the estimated power generation change, estimated total power generation, and estimated maintenance cost caused by the candidate generator control strategy, and combining the electricity price and estimated total power generation to determine the estimated power generation revenue, the difference between the estimated power generation revenue and the estimated maintenance cost can be obtained, thus yielding the estimated revenue. The calculation process is logically clear, ensuring the reliability of the estimation results.

[0086] It should be understood that the first relationship function can be obtained through previous simulation calculations, reflecting the relationship between changes in power generation and adjustments in the unit control strategy. As mentioned earlier, the control strategy adopted by the unit is reflected in the values ​​of multiple parameters. Therefore, the candidate unit control strategy can be reflected in the adjustment amount of the unit control strategy in the form of specific parameter values. The adjustment amount can be an absolute value or a relative value, as long as it can reflect the adjustment situation. The second relationship function can be obtained by integrating historical power generation and maintenance costs. As time goes by, historical power generation and maintenance cost data will gradually increase, so the second relationship function can be continuously updated to improve its accuracy.

[0087] Optionally, the power generation performance evaluation method according to embodiments of this disclosure further includes: adjusting the candidate unit control strategy based on the estimated revenue to obtain an optimized unit control strategy. Each candidate unit control strategy can have its corresponding estimated revenue determined using the above method. Therefore, by selecting one from multiple candidate unit control strategies based on the estimated revenue, the optimized unit control strategy can be achieved, enabling proactive updates to the unit's subsequent control and maintenance strategies. It should be understood that selecting one from multiple candidate unit control strategies can be done by pre-listing multiple candidate unit control strategies, calculating the estimated revenue for each, and then selecting one; or by first giving an initial value for a candidate unit control strategy, and then iteratively calculating the candidate unit control strategy with the goal of satisfying a preset condition for the estimated revenue, for example, but not limited to, maximizing the estimated revenue, to obtain the optimized unit control strategy. Furthermore, the user can set a target revenue and require that the estimated revenue corresponding to the optimized unit control strategy must satisfy the target revenue. It should also be understood that when there are multiple target future time periods, the optimized unit control strategy can be determined separately for each target future time period. Furthermore, when using iterative calculation to determine the optimized unit control strategy, the sum of the estimated revenue of multiple target future time periods that meets the preset conditions can be used as the target and calculated together to take into account the overall revenue.

[0088] The following is a specific example of how to estimate the power generation performance coefficient and optimize the unit control strategy.

[0089] Figure 4 A schematic flowchart of this specific embodiment is shown. (Refer to...) Figure 4 In general, this specific embodiment takes the estimated power generation performance coefficient, the second relationship function between power generation and unit maintenance cost, the user-set target revenue, the preset operating years of the wind turbine, the annual grid electricity price, the initial value of the candidate unit control strategy, and the first relationship function between the power generation change and the unit control strategy adjustment as inputs. It optimizes the process by maximizing the estimated revenue as the objective function, continuously adjusting the candidate unit control strategy, and requiring that the estimated revenue corresponding to the optimized unit control strategy must meet the target revenue. Thus, while taking into account the target revenue, maintenance cost, the current power generation capacity of the unit, and the annual grid electricity price, the optimal control strategy for unit operation is obtained within the set operating years.

[0090] Specifically, regarding the second relationship function between power generation and unit maintenance cost, as the power generation of the unit increases, the corresponding unit operation and maintenance cost will also increase. Therefore, there is a positive correlation between the two, which can be obtained by fitting the annual power generation and annual maintenance cost in the SCADA data of the unit that has been in operation. As shown in the following formula (11), where E is the power generation, M is the unit maintenance cost, and f(E,M) is the second relationship function between power generation and unit maintenance cost. Through the following formula (11), the required unit maintenance cost can be estimated based on the set power generation.

[0091] E=f(E,M)×M(11)

[0092] Regarding the control strategy for candidate units, each adjustment to the control strategy, such as extending the cut-out wind speed, changing the rated power, or changing the rated speed, will cause a change in the unit's power generation. The first relationship function between the power generation caused by the adjustment of the control strategy can be obtained through previous simulation calculations. For example, the simulation calculation shows the change in the unit's power generation caused by each 0.5 m / s extension of the cut-out wind speed, and considering storm conditions, implementing control strategy adjustments such as a 5% adjustment of the rated power and a 0.5% adjustment of the rated speed. Table 1 below shows the relationship between the change in power generation and the adjustment amount of the unit's control strategy, which can be obtained through simulation library calculations. The adjustment amounts (0.5 m / s, 5%, 0.5%) are only example values; in actual simulations, they can be set according to the specific unit model.

[0093] Table 1

[0094]

[0095] The following formula (12) summarizes the relationship between the power generation change and the unit control strategy adjustment. ΔE represents the change in power generation, ΔC represents the adjustment of the unit control strategy, and f(ΔE,Δc) is the first relationship function between the power generation change and the unit control strategy adjustment. This relationship can be obtained by first summarizing the relationship between power generation E+ΔE and the unit control strategy C+ΔC. The change in power generation caused by the unit control strategy adjustment can be calculated using the following formula (12).

[0096] ΔE=f(ΔE,Δc)×ΔC(12)

[0097] In summary, in this specific embodiment, the calculation formulas for the estimated revenue over the m years of unit operation are as follows: formulas (13) and (14), where G is the total estimated revenue over m years, and Price... i For the annual electricity price, E i This is the estimated total annual power generation. The average theoretical power can be taken from the average theoretical power of previously obtained historical periods, or the power of the target future period can be further estimated to determine the average theoretical power of each historical period and the overall theoretical power of the target future period. 'hour' is the preset number of generating hours per year. The objective function 'maxG' is set, and the control strategy is continuously adjusted by adding to the unit's own generating capacity each year. Taking into account the unit's maintenance costs, the optimal unit control strategy is obtained, maximizing the objective function.

[0098]

[0099]

[0100] The wind turbine power generation performance evaluation method according to the embodiments of this disclosure can be executed by the wind farm's field-level controller, by the wind turbine's controller, or by a standalone power generation performance evaluation system. Specifically, a standalone power generation performance evaluation system may include a wind turbine power generation performance evaluator and a wind turbine parameter optimizer, as described above. Figure 5 The wind turbine power generation performance evaluator is used to evaluate historical power generation performance coefficients, power generation performance change trends, and estimated power generation performance coefficients. The wind turbine parameter optimizer is used to determine the estimated power generation and optimize the unit control strategy based on the estimated power generation performance coefficients.

[0101] Figure 6 This is a block diagram illustrating a power generation performance evaluation apparatus for a wind turbine generator set according to an embodiment of the present disclosure.

[0102] Reference Figure 6 The wind turbine generator performance evaluation device 600 includes an acquisition unit 601, a determination unit 602, a processing unit 603, and a prediction unit 604.

[0103] The acquisition unit 601 can acquire historical operating data of the wind turbine generator set for n historical time periods, where n is a positive integer. This operating data reflects the real-time operating status of the generator set at different times, including data such as wind speed, power, and power generation. As an example, the historical operating data can be obtained from SCADA; in another example, the historical operating data can be collected from various sensors installed on the wind turbine generator set.

[0104] The determining unit 602 can determine the actual capacity factor and theoretical capacity factor of the wind turbine generator set in n historical time periods based on historical operating data.

[0105] The capacity factor reflects the energy output of a wind turbine generator set. The actual capacity factor, as mentioned above, reflects the actual energy output of the wind turbine generator set; it can be the ratio of the actual power generation (actual power) to the rated power generation (rated power) of the generator set within a statistical period. The theoretical capacity factor, as mentioned above, reflects the theoretical energy output of the wind turbine generator set; it can be the ratio of the theoretical power generation (theoretical power) to the rated power generation (rated power) of the generator set within a statistical period.

[0106] The actual power of a wind turbine is affected by both the turbine's own power generation performance and environmental factors. The theoretical power, on the other hand, represents the turbine's power under ideal conditions (i.e., when its own power generation performance is optimal), reflecting the influence of environmental factors. Therefore, the difference between the two is caused by the turbine itself and reflects its own power generation performance. Furthermore, since both the actual and theoretical capacity factors incorporate a fixed value of rated power compared to actual and theoretical power, the difference between them can also reflect the difference between actual and theoretical power. This satisfies evaluation needs and provides more diverse calculation methods for the actual and theoretical capacity factors, ensuring the flexibility of the scheme. By acquiring historical operating data over n historical periods, the actual and theoretical capacity factors for each historical period can be calculated. Detailed calculation methods will be explained later and will not be repeated here.

[0107] The determining unit 602 can also determine the historical power generation performance coefficients of the wind turbine generator set in the n historical periods based on the actual capacity coefficients and theoretical capacity coefficients in the n historical periods.

[0108] The coefficient of performance (COP) is used to characterize the power generation performance of a wind turbine generator. For example, for a given historical period, the determining unit 602 can determine the ratio of the actual capacity coefficient to the theoretical capacity coefficient of the wind turbine generator during that period. This ratio is equal to the ratio of actual power to theoretical power. Using this as the historical COP for that historical period yields a dimensionless COP, which eliminates the influence of environmental factors and is not limited by the generator's rated power. It should be understood that the generator's power generation performance often decreases with age, so the actual power is often less than the theoretical power, resulting in a ratio less than 1. The smaller the ratio, the greater the difference between the actual and theoretical power, and the worse the generator's power generation performance. In other words, the larger the ratio, the better the generator's power generation performance.

[0109] Furthermore, the determining unit 602 can also convert this ratio to a power value. Specifically, it first calculates the average theoretical power over n historical periods, and then multiplies this average value by the aforementioned ratio for each historical period to obtain the actual power for each historical period. Using the average theoretical power over n historical periods as the conversion factor, rather than the theoretical power of a single year, allows for a unified evaluation standard.

[0110] The processing unit 603 can obtain the trend of power generation performance changes based on the historical power generation performance coefficients over n historical periods. As an example, the trend of power generation performance changes can be obtained through fitting methods, such as, but not limited to, linear fitting.

[0111] The prediction unit 604 can determine the estimated power generation performance coefficient of the wind turbine generator set in the target future period based on the power generation performance change trend. The estimated power generation performance coefficient is used to predict the power generation performance of the wind turbine generator set in the target future period. By applying the power generation performance change trend to determine the estimated power generation performance coefficient, the general change trend can be converted into a specific future point value, thereby reflecting the power generation performance change result of the wind turbine generator set from a more intuitive future perspective, providing a basis for the proactive updating of the unit's subsequent control strategy and operation and maintenance strategy. It should be understood that, as time goes by, the above steps S101 to S104 can also be executed periodically to evaluate the power generation performance change trend of the wind turbine generator set; the entire power generation performance evaluation device 600 of this disclosure can also be run periodically.

[0112] Optionally, the determining unit 602 can also determine the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in n historical time periods based on historical operating data, to obtain the actual capacity coefficient. The power generation index includes at least one of power generation duration, grid-connected electricity, and power. Based on historical operating data, it can also determine the ratio of the theoretical average power to the rated power of the wind turbine generator set in n historical time periods, to obtain the theoretical capacity coefficient. As mentioned earlier, the actual capacity coefficient is the ratio of actual power to rated power. To calculate the actual capacity coefficient, the ratio can be calculated using the more readily available actual power generation duration and rated power generation duration, or the ratio can be calculated using the actual grid-connected electricity and rated grid-connected electricity. Alternatively, the actual power can be calculated using other methods, and then the ratio can be calculated using the actual power and rated power, ensuring the flexibility of the scheme. For the theoretical capacity coefficient, the theoretical power can be obtained through theoretical calculation. By using the theoretical average power within the targeted historical time period, the theoretical power of the corresponding historical time period can be characterized by the average level of theoretical power, ensuring the reliability of the calculation results.

[0113] Optionally, when power generation indicators include power, historical operating data includes actual power curves, multiple wind speed ranges, the frequency of each wind speed range, and rated power. The determining unit 602 can also, for each historical period, determine the average actual power for each wind speed range based on the actual power curve; determine the expected value of the average actual power based on the average actual power and frequency of each wind speed range, as the actual average power of the wind turbine generator set during the historical period; and determine the ratio of the actual average power of the wind turbine generator set to its rated power during the historical period to obtain the actual capacity factor. The actual power curve integrates discrete data of actual power detected during unit operation. By combining the actual power curve to determine the actual average power for historical periods, the detected data can be used directly, reducing computational difficulty. As an example, the average actual power is the average actual power within a wind speed range, which can be calculated based on the detected data, faithfully adhering to the detected data and ensuring the consistency of the scheme.

[0114] Optionally, historical operating data also includes theoretical power curves. The determining unit 602 can further determine the theoretical average power for each wind speed range based on the theoretical power curve for each historical period; determine the expected theoretical average power based on the theoretical average power and frequency for each wind speed range, using this as the theoretical average power of the wind turbine generator set during the historical period; and determine the ratio of the theoretical average power to the rated power of the wind turbine generator set during the historical period to obtain the theoretical capacity factor. The theoretical power curve is a power curve obtained through theoretical calculations under ideal design conditions. By combining the theoretical power curve to determine the theoretical average power for historical periods, the theoretical average power for each wind speed range can be directly used, helping to reduce calculation difficulty and save computational resources. As an example, the theoretical average power is a value obtained by interpolation based on the average wind speed for each wind speed range. This allows specific values ​​to be obtained from a continuous theoretical curve, facilitating value selection and ensuring reliable value selection.

[0115] Optionally, historical operating data includes wind speed distribution parameters and rated wind speed. The wind speed distribution parameters describe the probability distribution of wind speed. The determining unit 602 can also, for each historical period, determine the expected value of the cube of the wind speed based on the wind speed distribution parameters; and determine the ratio of the expected value of the cube of the wind speed to the cube of the rated wind speed to obtain the theoretical capacity coefficient. By utilizing the fact that the theoretical average power is consistent with the rated power, the theoretical capacity coefficient can be determined using the wind speed distribution parameters and rated wind speed, ensuring the simplicity and reliability of the calculation.

[0116] Optionally, the wind speed distribution parameters include the wind speed probability density function or the wind speed probability distribution function. The theoretical wind speed probability density function or the wind speed probability distribution function can be obtained by using special distribution types, thereby improving the feasibility of the calculation.

[0117] Optionally, the wind speed distribution parameters include multiple wind speed intervals and the frequency of each wind speed interval. Since they do not involve integration problems, they can be calculated directly using statistical data, which helps to reduce the computational difficulty.

[0118] Optionally, the power generation performance evaluation device also includes a prediction unit (not shown in the figure), which can determine the estimated power generation of the wind turbine generator set in the target future period based on the predicted power generation performance coefficient; and determine the estimated revenue generated by the wind turbine generator set using the candidate generator set control strategy in the target future period based on the estimated power generation, candidate generator set control strategy, and revenue-related variables. The revenue-related variables are the variables used to determine the power generation revenue of the wind turbine generator set. The predicted power generation performance coefficient reflects the power generation performance of the wind turbine generator set in the target future period, and can be used to determine the estimated power generation in the target future period. The control strategy adopted by the generator set is reflected in the values ​​of multiple parameters such as cut-out wind speed, rated power, and rated speed. The difference between different control strategies lies in the specific values ​​of these parameters. Accordingly, when adjusting the control strategy, the value of at least one of these parameters is adjusted; for example, the cut-out wind speed can be extended, the rated power adjusted, or the rated speed adjusted. The power generation of the generator set will also differ when different control strategies are adopted. By combining estimated power generation with candidate unit control strategies, more accurate power generation can be predicted. Furthermore, by incorporating revenue-related variables, the estimated revenue from adopting a specific candidate unit control strategy can be determined, making the prediction results clearer and more intuitive. This provides a clear basis for proactively updating subsequent control and maintenance strategies for the units. It should be understood that there can be at least one target future period, allowing for the identification of the estimated revenue for at least one target future period as needed. When there are multiple target future periods, the sum of these estimated revenues can also be calculated to understand the overall estimated revenue.

[0119] Optionally, the revenue-related variables include the electricity price for the target future period, a predetermined first relationship function, and a second relationship function. The first relationship function is the relationship between the change in power generation and the adjustment amount of the unit control strategy. The adjustment amount of the unit control strategy is the adjustment amount of at least one of the aforementioned control parameters, such as cut-off wind speed, rated power, and rated speed. The first relationship function reflects how much the power generation will change relative to the current power generation after a certain amount of adjustment is made to the parameters based on the current unit control strategy. Since no adjustment is made to the unit control strategy when determining the estimated power generation, the prediction is the power output that the unit can produce if it continues to use the current unit control strategy. At this point, by comparing the adjustment amount between the candidate unit control strategy and the current unit control strategy, the change in power generation brought about by adopting the candidate unit control strategy can be determined in conjunction with the first relationship function. As an example, the candidate unit control strategy can be expressed in the form of the adjustment amount of the control parameters or in the form of the values ​​of the control parameters; this disclosure does not impose any restrictions on this. The second relationship function is the relationship between power generation and unit maintenance costs. The estimation unit can also determine the estimated power generation change corresponding to the candidate unit control strategy based on the adjustment amount of the candidate unit control strategy relative to the current unit control strategy and the first relationship function; determine the sum of the estimated power generation and the estimated power generation change as the estimated total power generation corresponding to the candidate unit control strategy; determine the estimated maintenance cost corresponding to the candidate unit control strategy based on the estimated total power generation and the second relationship function; and determine the estimated revenue generated by the wind turbine generator unit using the candidate unit control strategy in the target future period based on the estimated total power generation, electricity price, and estimated maintenance cost. By progressively determining the estimated power generation change, estimated total power generation, and estimated maintenance cost caused by the candidate unit control strategy, and combining the electricity price and estimated total power generation to determine the estimated power generation revenue, the difference between the estimated power generation revenue and the estimated maintenance cost can be obtained, thus yielding the estimated revenue. The calculation process is logically clear, ensuring the reliability of the estimation results. It should be understood that the first relationship function can be obtained through previous simulation calculations, reflecting the relationship between changes in power generation and adjustments in the unit control strategy. As mentioned earlier, the control strategy adopted by the unit is reflected in the values ​​of multiple parameters. Therefore, the candidate unit control strategy can be reflected in the adjustment amount of the unit control strategy in the form of specific parameter values. The adjustment amount can be an absolute value or a relative value, as long as it can reflect the adjustment situation. The second relationship function can be obtained by integrating historical power generation and maintenance costs. As time goes by, historical power generation and maintenance cost data will gradually increase, so the second relationship function can be continuously updated to improve its accuracy.

[0120] Optionally, the power generation performance evaluation device also includes an optimization unit (not shown in the figure), which can adjust the control strategies of candidate units based on estimated benefits to obtain an optimized unit control strategy. Each candidate unit control strategy can have its corresponding estimated benefits determined by the optimization unit. Therefore, by selecting one from multiple candidate unit control strategies based on estimated benefits, the optimized unit control strategy can be achieved, enabling proactive updates to the unit's subsequent control and operation and maintenance strategies. It should be understood that selecting one from multiple candidate unit control strategies can be done by pre-listing multiple candidate unit control strategies, calculating the estimated benefits for each, and then selecting one; or by first giving an initial value for a candidate unit control strategy, and then iteratively calculating the candidate unit control strategy with the goal of satisfying preset conditions for estimated benefits, for example, but not limited to, maximizing the estimated benefits, to obtain the optimized unit control strategy. It should also be understood that when there are multiple target future time periods, the optimized unit control strategy can be determined separately for each target future time period. Furthermore, when using iterative calculation to determine the optimized unit control strategy, the sum of the estimated revenue of multiple target future time periods that meets the preset conditions can be used as the target and calculated together to take into account the overall revenue.

[0121] It should be noted that, corresponding to the aforementioned power generation performance evaluation system, the acquisition unit 601, determination unit 602, sorting unit 603, and prediction unit 604 of this disclosure can be classified as a wind turbine power generation performance evaluator, and the estimation unit and optimization unit can be classified as a wind turbine parameter optimizer.

[0122] The wind turbine generator performance evaluation method according to embodiments of this disclosure can be programmed into a computer program and stored on a computer-readable storage medium. When the instructions corresponding to the computer program are executed by a processor, the wind turbine generator performance evaluation method described above can be implemented. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-RLTH, B D-RE, Blu-ray or optical disc storage, hard disk drive (HDD), solid-state drive (SSD), card storage (such as multimedia cards, secure digital (SD) cards, or ultra-fast digital (XD) cards), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid-state drive, and any other device configured to store a computer program and any associated data, data files, and data structures in a non-transitory manner and to provide the computer program and any associated data, data files, and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files, and data structures are distributed across a networked computer system, such that the computer program and any associated data, data files, and data structures are stored, accessed, and executed in a distributed manner through one or more processors or computers.

[0123] Figure 7 This is a block diagram illustrating a computer device according to an embodiment of the present disclosure.

[0124] Reference Figure 7 The computer device 700 includes at least one memory 701 and at least one processor 702. The at least one memory 701 stores a set of computer-executable instructions. When the set of computer-executable instructions is executed by the at least one processor 702, a method for evaluating the power generation performance of a wind turbine generator set according to an exemplary embodiment of the present disclosure is executed.

[0125] As an example, computer device 700 may be a PC, tablet device, personal digital assistant, smartphone, or other device capable of executing the aforementioned set of instructions. Here, computer device 700 is not necessarily a single electronic device, but may be a collection of any devices or circuits capable of executing the aforementioned instructions (or instruction sets) individually or in combination. Computer device 700 may also be part of an integrated control system or system manager, or may be configured to interconnect with a portable electronic device locally or remotely (e.g., via wireless transmission) through an interface.

[0126] In computer device 700, processor 702 may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller, or a microprocessor. By way of example and not limitation, processor may also include analog processors, digital processors, microprocessors, multi-core processors, processor arrays, network processors, etc.

[0127] The processor 702 can execute instructions or code stored in the memory 701, which can also store data. Instructions and data can also be sent and received via a network through a network interface device, which can employ any known transmission protocol.

[0128] The memory 701 can be integrated with the processor 702, for example, by placing RAM or flash memory within an integrated circuit microprocessor. Alternatively, the memory 701 can include a separate device, such as an external disk drive, a storage array, or other storage device usable by any database system. The memory 701 and the processor 702 can be operatively coupled, or can communicate with each other, for example, via I / O ports, network connections, etc., enabling the processor 702 to read files stored in the memory.

[0129] In addition, computer device 700 may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, mouse, touch input device, etc.). All components of computer device 700 can be interconnected via a bus and / or network.

[0130] The methods and apparatus described in this disclosure are designed for wind turbines already in operation in wind farms. They require no additional measuring devices and are highly adaptable. Primarily based on SCADA data generated in real-time by the operating wind turbines, including wind speed distribution and real-time turbine power, the methods assess the power generation performance of the wind turbines during their operational period by eliminating the influence of the external environment and considering only the turbines themselves. Based on the historical power generation performance coefficients obtained from the assessment, the changes in the turbines' power generation performance during their operational period can be clearly identified, and the trend of these changes can be obtained, thus allowing for predictions of future power generation performance. Furthermore, this disclosure can combine the prediction results with comprehensive consideration of maintenance costs and feed-in tariffs, and provide corresponding optimized turbine control strategies based on the user's target revenue. These strategies can address different scenarios, such as maintaining the current power generation status, accelerating production to maximize revenue, and decommissioning / replacing turbines, ensuring the turbines meet user expectations.

[0131] The specific embodiments of this disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that modifications and variations can be made to these embodiments without departing from the principles and spirit of this disclosure, which are defined by the claims and their equivalents. Such modifications and variations should also be within the protection scope of the claims of this disclosure.

Claims

1. A method of evaluating power generation performance of a wind turbine generator system, characterized by, include: Obtain historical operating data of the wind turbine generator set in n historical time periods, where n is a positive integer; Based on the historical operating data, the actual capacity factor and theoretical capacity factor of the wind turbine generator set are determined for each of the n historical time periods. Based on the actual capacity coefficient and theoretical capacity coefficient in the n historical periods, the historical power generation performance coefficient of the wind turbine generator set in the n historical periods is determined respectively. The historical power generation performance coefficient is used to characterize the power generation performance of the wind turbine generator set itself after removing the influence of environmental factors. Based on the historical power generation performance coefficients over the n historical periods, the trend of power generation performance changes is obtained; Based on the trend of power generation performance change, the estimated power generation performance coefficient of the wind turbine generator set in the target future period is determined, and the estimated power generation performance coefficient is used to predict the power generation performance in the target future period. The step of determining the actual capacity factor and theoretical capacity factor of the wind turbine generator set in the n historical time periods based on the historical operating data includes: Based on the historical operating data, the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in the n historical periods is determined respectively to obtain the actual capacity coefficient, wherein the power generation index includes at least one of power generation duration, grid-connected power and power. Based on the historical operating data, the ratio of the theoretical average power to the rated power of the wind turbine generator set is determined for each of the n historical time periods, and the theoretical capacity coefficient is obtained.

2. The power generation performance evaluation method according to claim 1, characterized by, When the power generation index includes power, the historical operating data includes an actual power curve, multiple wind speed ranges, the frequency of each wind speed range, and the rated power. Based on the historical operating data, the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in each of the n historical time periods is determined to obtain the actual capacity coefficient, including: For each historical period, the average actual power for each wind speed range is determined based on the actual power curve; Based on the actual power average value and frequency of each wind speed range, the expected value of the actual power average value is determined as the actual average power of the wind turbine generator set in the historical period. The actual capacity factor is obtained by determining the ratio of the actual average power of the wind turbine generator set to the rated power during the historical period.

3. The power generation performance evaluation method as described in claim 2, characterized in that, The historical operating data also includes theoretical power curves. Based on the historical operating data, the ratio of the theoretical average power to the rated power of the wind turbine generator set within the n historical time periods is determined to obtain the theoretical capacity coefficient, including: For each historical period, the theoretical power average value for each wind speed range is determined based on the theoretical power curve; Based on the theoretical power average value and frequency of each wind speed range, the expected value of the theoretical power average value is determined as the theoretical average power of the wind turbine generator set in the historical period. The theoretical capacity factor is obtained by determining the ratio of the theoretical average power to the rated power of the wind turbine generator set during the historical period.

4. The power generation performance evaluation method as described in claim 3, characterized in that, The actual average power is the average of the actual power within the wind speed range; and / or The theoretical power average value is obtained by interpolation of the theoretical power curve based on the average wind speed of each wind speed range.

5. The power generation performance evaluation method as described in claim 1, characterized in that, The historical operating data includes wind speed distribution parameters and rated wind speed. The wind speed distribution parameters describe the probability distribution of wind speed. The step of determining the ratio of the theoretical average power to the rated power of the wind turbine generator set within the n historical time periods based on the historical operating data to obtain the theoretical capacity coefficient includes: For each historical period, the expected value of the cube of the wind speed is determined based on the wind speed distribution parameters; The theoretical capacity coefficient is obtained by determining the ratio of the expected value of the cube of the wind speed to the cube of the rated wind speed.

6. The power generation performance evaluation method as described in claim 5, characterized in that, The wind speed distribution parameters include the wind speed probability density function; or The wind speed distribution parameters include multiple wind speed ranges and the frequency of each wind speed range.

7. The power generation performance evaluation method according to any one of claims 1 to 6, characterized in that, The power generation performance evaluation method also includes: Based on the estimated power generation performance coefficient, the estimated power generation of the wind turbine generator set in the target future period is determined; Based on the estimated power generation, candidate turbine control strategies, and revenue-related variables, the estimated revenue generated by the wind turbine using the candidate turbine control strategies in the target future period is determined, wherein the revenue-related variables are the variables used to determine the power generation revenue of the wind turbine.

8. The power generation performance evaluation method as described in claim 7, characterized in that, The revenue-related variables include the electricity price for the target future period, a pre-determined first relationship function, and a second relationship function. The first relationship function is the relationship between changes in power generation and adjustments to the unit control strategy, and the second relationship function is the relationship between power generation and unit maintenance costs. The step of determining the estimated revenue generated by the wind turbine generator using the candidate generator control strategy in the target future period, based on the estimated power generation, candidate generator control strategy, and revenue-related variables, includes: Based on the adjustment amount of the candidate unit control strategy relative to the current unit control strategy and the first relationship function, determine the estimated power generation change corresponding to the candidate unit control strategy; The sum of the estimated power generation and the estimated change in power generation is determined as the total estimated power generation corresponding to the candidate unit control strategy. Based on the estimated total power generation and the second relationship function, the estimated maintenance cost corresponding to the candidate unit control strategy is determined; Based on the estimated total power generation, the electricity price, and the estimated maintenance cost, the estimated revenue generated by the wind turbine generator set using the candidate generator set control strategy in the target future period is determined.

9. The power generation performance evaluation method as described in claim 7, characterized in that, The power generation performance evaluation method also includes: Based on the estimated revenue, the control strategy of the candidate units is adjusted to obtain the optimized unit control strategy.

10. A device for evaluating the power generation performance of a wind turbine generator set, characterized in that, include: The acquisition unit is configured to acquire historical operating data of the wind turbine generator set in n historical time periods, where n is a positive integer; The determining unit is configured to determine the actual capacity factor and theoretical capacity factor of the wind turbine generator set in the n historical time periods based on the historical operating data. The determining unit is further configured to determine the historical power generation performance coefficient of the wind turbine generator set in the n historical periods based on the actual capacity coefficient and theoretical capacity coefficient in the n historical periods, wherein the historical power generation performance coefficient is used to characterize the power generation performance of the wind turbine generator set itself after removing the influence of environmental factors. The data processing unit is configured to obtain the trend of power generation performance changes based on the historical power generation performance coefficients within the n historical time periods; The prediction unit is configured to determine the estimated power generation performance coefficient of the wind turbine generator set in a target future period based on the power generation performance change trend, and the estimated power generation performance coefficient is used to predict the power generation performance in the target future period. The determining unit is further configured to: Based on the historical operating data, the ratio of the actual value to the rated value of the power generation index of the wind turbine generator set in the n historical periods is determined respectively to obtain the actual capacity coefficient, wherein the power generation index includes at least one of power generation duration, grid-connected power and power. Based on the historical operating data, the ratio of the theoretical average power to the rated power of the wind turbine generator set is determined for each of the n historical time periods, and the theoretical capacity coefficient is obtained.

11. 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 causes the at least one processor to perform the power generation performance evaluation method as described in any one of claims 1 to 9.

12. A computer device, characterized in that, include: At least one processor; At least one memory that stores computer-executable instructions. Wherein, when the computer-executable instructions are executed by the at least one processor, they cause the at least one processor to execute the power generation performance evaluation method as described in any one of claims 1 to 9.

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