Wind power plant output dynamic adjusting system

Abstract

The invention relates to the technical field of output regulation, in particular to a wind power plant output dynamic regulation system which comprises a wind speed trend recognition module, a rhythm fluctuation detection module, a synchronous reference screening module, a rhythm delay regulation module and an output rhythm distribution module. According to the method, trend direction inflection point statistics in a time period is introduced by constructing a wind speed change trend inversion sequence, accurate identification of a wind speed rhythm fluctuation state is realized in combination with a trend inversion frequency, an adjustment reference object is screened based on differentiation characteristics of a wind turbine generator in rhythm stability, and a unified adjustment rhythm reference is established; and forming synchronous and delay adjustment classification by analyzing the trend change difference of the non-reference unit, matching an adjustment time sequence according to a classification structure, and constructing an output change plan of each wind turbine unit, thereby realizing unified output rhythm regulation and control under the perception of a wind speed fluctuation structure. The response coordination among multiple units and the rhythm consistency of the output process are improved, and the overall output fluctuation influence of the wind power plant is effectively relieved.

Description

Technical Field

[0001] This invention relates to the field of power output regulation technology, and in particular to a dynamic power output regulation system for wind farms. Background Technology

[0002] The field of power output regulation technology involves the real-time monitoring and control of energy output in power systems. This includes regulating the output power of power generation units, matching grid load demand, optimizing energy allocation, and improving grid stability and reliability. It is widely used in various energy types such as thermal power, hydropower, wind power, and photovoltaic power. Especially with the increasing integration of renewable energy into the grid, power output regulation technology has become an important means to ensure the balanced operation of the power system. It achieves orderly control of various energy outputs through processes such as setting target output values, collecting current operating parameters, and executing regulation commands. Among these, traditional wind farm dynamic power output regulation systems refer to systems used to regulate the overall output power of wind farms based on wind speed changes, grid dispatch commands, or operating strategies. These systems address the power instability problem caused by the volatility and unpredictability of wind power output. They typically employ methods based on wind speed prediction models to determine short-term future output trends, and then combine these with real-time power detection results to achieve dynamic control of output power by adjusting pitch angles or wind turbine start-stop strategies. Historical operating data is used to set regulation thresholds to assist in optimizing the control strategy.

[0003] Current wind farm power output regulation mainly relies on a combination of wind speed prediction models and real-time power detection for control. This approach cannot fully identify the internal trend structure during wind speed changes, resulting in inaccurate judgment of wind speed fluctuation frequency and stability. Traditional methods focus on static threshold setting and single-point parameter monitoring, ignoring the structural rhythm characteristics of the wind speed sequence over time. The regulation process is prone to response lag or frequent adjustments due to prediction errors or local fluctuations, leading to low wind turbine regulation efficiency, inconsistent execution of control strategies, and drastic fluctuations in the overall power output plan. It is difficult to achieve uniformity of power output rhythm and rationality of regulation pace under multi-unit coordinated operation. Summary of the Invention

[0004] To address the technical problems existing in the prior art, embodiments of the present invention provide a dynamic power output adjustment system for wind farms. The technical solution is as follows: On the one hand, a dynamic power output adjustment system for wind farms is provided, the system comprising: The wind speed trend recognition module acquires wind speed sensor data from each wind turbine in the wind farm, constructs a wind speed change sequence at fixed time intervals, calculates the direction of the wind speed difference between two adjacent points and determines the sign change, counts the number of wind speed trend inflection points in each time period, and generates a trend reversal sequence. The rhythm fluctuation detection module compares the number of wind speed trend reversals in each time period with the benchmark value of wind speed trend change frequency based on the trend reversal sequence, marks stable and unstable periods, and generates a rhythm change label sequence. Based on the rhythm change label sequence, the synchronization benchmark screening module filters all wind turbine units marked as stable, selects the wind turbine unit with the smallest number as the unified adjustment reference unit, extracts the number of wind speed trend reversals of the corresponding wind turbine unit as the current rhythm benchmark, and generates a unified adjustment benchmark index. Based on the unified adjustment benchmark index, the rhythm delay control module extracts the number of wind speed trend reversals of non-reference wind turbines, performs sign difference judgment with the current rhythm benchmark, marks the adjustment rhythm of the wind turbines, and generates an adjustment rhythm mark sequence. The power output rhythm allocation module matches the number list of all wind turbine units in the current wind farm with the adjustment rhythm mark sequence, adjusts the rhythm in sequence, constructs the power output change plan for each wind turbine unit, and generates a wind farm power output rhythm adjustment record.

[0005] As a further aspect of the present invention, the stable period and the unstable period specifically refer to the following: if the number of wind speed trend reversals is less than the benchmark value of wind speed trend change frequency, it is marked as a stable period; if the number of wind speed trend reversals is greater than or equal to the benchmark value of wind speed trend change frequency, it is marked as an unstable period.

[0006] As a further aspect of the present invention, during the adjustment rhythm labeling process, if the trend change direction continues to be opposite, it is marked as a delayed adjustment category; if they are the same, it is marked as a synchronous adjustment category.

[0007] As a further aspect of the present invention, during the sequential rhythm adjustment process, a backward time node in the current output target rhythm is assigned to all wind turbines of the delayed adjustment category, and the current rhythm center node is matched to all wind turbines of the synchronous adjustment category.

[0008] As a further embodiment of the present invention, the trend reversal sequence includes inflection point quantity labels, direction change symbols, and reversal statistics within a time period; the rhythm change label sequence includes stable period identifiers, unstable period identifiers, and wind speed trend change frequency judgment results; the unified adjustment benchmark index includes reference wind turbine unit number, rhythm benchmark value, and stable unit statistical results; the adjustment rhythm mark sequence includes delayed adjustment identifiers, synchronous adjustment identifiers, and trend difference judgment labels; and the wind farm output rhythm adjustment record includes delayed time node matching results, rhythm center node mapping information, and output change plan arrangements for each wind turbine unit.

[0009] As a further aspect of the present invention, the wind speed trend recognition module includes: The data stream receiving submodule acquires the voltage signal data collected by the wind speed sensors of each wind turbine in the wind farm, converts the analog voltage signal into the corresponding digital wind speed value through the analog-to-digital converter, and divides the wind speed data obtained by continuous sampling into wind speed change segment sequences according to a fixed time interval to establish wind speed change segment sequence groups. The trend inflection point identification submodule calculates the direction of the wind speed difference between two consecutive data points based on adjacent data point pairs in each sequence of the wind speed change segment sequence group. It determines whether a change in sign occurs based on the product of the directions of the consecutive differences. Points with a change in direction sign are marked as trend inflection points. The number of trend inflection points in each time period is summarized to generate a list of trend inflection point counts. The reverse sequence generation submodule calculates the number of trend inflection points for each time period based on the list of trend inflection points, arranges them in the order of the time period sequence, constructs the trend change state vector of the corresponding time series, and generates a trend reversal sequence.

[0010] As a further aspect of the present invention, the rhythm fluctuation detection module includes: The trend quantity extraction submodule extracts the trend state quantity of the fluctuation segment in each time period based on the wind speed trend change state vector recorded in the trend reversal sequence, counts the quantity and binds the corresponding time index information to establish a trend reversal quantity table. The fluctuation type determination submodule compares the number of trend reversals in each time period in the trend reversal quantity table with the wind speed trend change frequency benchmark value. If the number of trend reversals is less than the wind speed trend change frequency benchmark value, the time period is marked as a stable time period; otherwise, it is marked as an unstable time period, and a stability determination state sequence is generated. The rhythm tag generation submodule generates mapping tag values ​​based on the stability determination state sequence and the determination results of stability and instability. Stable periods are marked as 0 and unstable periods are marked as 1. The time-series mapping matrix is ​​constructed by arranging the values ​​in chronological order to generate a rhythm change tag sequence.

[0011] As a further aspect of the present invention, the synchronous benchmark screening module includes: The stable unit screening submodule extracts records with values ​​of 0 based on the tag values ​​of each wind turbine in the rhythm change tag sequence corresponding to the time period and establishes a stable unit set. It divides the set according to the time period dimension, counts the stable unit number information in each time period, and obtains a stable unit number list. The reference unit identification submodule performs a character sorting operation on the unit numbers according to the set of stable units corresponding to each time period in the list of stable unit numbers, selects the first number in the sorting result, extracts the wind turbine identification value corresponding to the number, and establishes a unified regulation reference identifier. The benchmark index extraction submodule generates unified regulation benchmarks by binding the reference unit number and time index to the number of wind speed trend reversals of the unit corresponding to the unified regulation reference identifier during the time period.

[0012] As a further aspect of the present invention, the rhythm delay control module includes: The non-reference unit extraction submodule, based on the reference wind turbine number information of each time period in the unified adjustment benchmark index, filters out all wind turbines whose numbers are inconsistent with the reference wind turbine numbers in the corresponding time period, extracts the number of wind speed trend reversals of each non-reference unit in the corresponding time period, and generates a non-reference unit trend dataset. The trend sign difference determination submodule performs difference calculations item by item based on the number of wind speed trend reversals of each unit in the non-reference unit trend dataset, combined with the benchmark values ​​of the same time period in the unified adjustment benchmark index, and judges the directional relationship based on the positive or negative difference. If the trend directions of the time period are opposite, it is marked as a delay type; if they are the same, it is marked as a synchronization type, and a trend direction difference label group is generated. The adjustment tag generation submodule encodes tags based on the direction difference type of each wind turbine in the trend direction difference tag group, constructs the adjustment control matrix in the order of time periods, and generates the adjustment rhythm tag sequence.

[0013] As a further aspect of the present invention, the output rhythm distribution module includes: The rhythm category matching submodule, based on the adjustment category labels of each wind turbine recorded in the adjustment rhythm mark sequence for each time period, and combined with the complete list of wind turbine numbers in the current wind farm, matches the rhythm mark status of each wind turbine in the corresponding time period to generate a rhythm adjustment matching table. The node timing allocation submodule extracts the current output target rhythm configuration scheme according to the adjustment category of the wind turbine in the rhythm adjustment matching table, matches the turbines of the delayed adjustment category to the shifted time node of the corresponding time period, and matches the turbines of the synchronous adjustment category to the current rhythm center node, and establishes an output rhythm node allocation table. The output rhythm construction submodule, based on the time node configuration of each unit in the output rhythm node allocation table and combined with the output adjustment window set in the rhythm time structure, sets the output target value for each wind turbine group in stages and arranges the start and end times of execution, generating a wind farm output rhythm adjustment record.

[0014] The beneficial effects of the technical solutions provided by the embodiments of the present invention include at least the following: By constructing a wind speed change trend reversal sequence and introducing statistical data on trend direction inflection points within a time period, combined with the trend reversal frequency, accurate identification of wind speed rhythm fluctuations is achieved. Based on the differentiation characteristics of wind turbine units in rhythm stability, reference objects for regulation are selected and a unified regulation rhythm benchmark is established. Then, by analyzing the differences in trend changes of non-reference units, synchronous and delayed regulation classifications are formed. According to the classification structure, the regulation timing arrangement is matched, and a power output change plan for each wind turbine unit is constructed. This enables unified control of power output rhythm under the perception of wind speed fluctuation structure, improves the response coordination among multiple units and the rhythm consistency of the power output process, and effectively mitigates the impact of overall power output fluctuations in the wind farm. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of a wind farm power output dynamic adjustment system provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the system framework of the present invention; Figure 3 This is a flowchart of the wind speed trend recognition module of the present invention; Figure 4 This is a flowchart of the rhythm fluctuation detection module of the present invention; Figure 5 This is a flowchart of the synchronous benchmark screening module of the present invention; Figure 6 This is a flowchart of the rhythm delay control module of the present invention; Figure 7 This is a flowchart of the output rhythm allocation module of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0018] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0019] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0020] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0021] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0022] This invention provides a dynamic power output adjustment system for wind farms, such as... Figure 1-2 The diagram shown illustrates a dynamic power output adjustment system for a wind farm. The system includes: The wind speed trend recognition module acquires wind speed sensor data from each wind turbine in the wind farm, performs analog-to-digital conversion on the voltage signal, constructs a wind speed change sequence at 5-minute time intervals, calculates the direction of the wind speed difference between every two adjacent points in the sequence and determines the sign change, marks the number of trend inflection points, counts the number of wind speed trend inflection points in each time period, and generates a trend reversal sequence. The rhythm fluctuation detection module compares the number of wind speed trend reversals in each time period with the benchmark value of wind speed trend change frequency based on the trend reversal sequence. If the number of wind speed trend reversals is less than the benchmark value of wind speed trend change frequency, it is marked as a stable period. If the number of wind speed trend reversals is greater than or equal to the benchmark value of wind speed trend change frequency, it is marked as an unstable period, and a rhythm change label sequence is generated. The synchronization benchmark screening module is based on the rhythm change label sequence. It counts the stable and unstable states of wind turbines in the current time period, filters all wind turbines marked as stable, sorts them by wind turbine number, selects the unit with the smallest number as the unified adjustment reference unit, extracts the number of wind speed trend reversals of the corresponding unit as the current rhythm benchmark, and generates unified adjustment benchmark indicators. The rhythm delay control module extracts the number of wind speed trend reversals of non-reference wind turbines based on a unified control benchmark index, performs sign difference judgment with the current rhythm benchmark, and marks the control rhythm of the wind turbines. If the trend change direction is continuously opposite, it is marked as a delayed control category; if they are the same, it is marked as a synchronous control category, and a control rhythm marking sequence is generated. The power output rhythm allocation module matches the number list of all wind turbines in the current wind farm according to the adjustment rhythm mark sequence, and adjusts the rhythm in sequence. It assigns the backward time node in the current power output target rhythm to all wind turbines of the delayed adjustment category, matches the current rhythm center node to all wind turbines of the synchronous adjustment category, and constructs the power output change plan for each wind turbine according to the rhythm time structure, generating a wind farm power output rhythm adjustment record.

[0023] The trend reversal sequence includes inflection point number labels, direction change symbols, and reversal statistics within a time period. The rhythm change label sequence includes stable period identifiers, unstable period identifiers, and wind speed trend change frequency judgment results. The unified adjustment benchmark index includes reference wind turbine number, rhythm benchmark value, and stable unit statistical results. The adjustment rhythm mark sequence includes delayed adjustment identifiers, synchronous adjustment identifiers, and trend difference judgment labels. The wind farm output rhythm adjustment record includes the result of the delayed time node matching, rhythm center node mapping information, and the output change plan arrangement for each wind turbine.

[0024] Specifically, such as Figure 2 , 3 As shown, the wind speed trend recognition module includes: The data stream receiving submodule acquires the voltage signal data collected by the wind speed sensors of each wind turbine in the wind farm, converts the analog voltage signal into the corresponding digital wind speed value through the analog-to-digital converter, and divides the wind speed data obtained by continuous sampling into wind speed change segment sequences according to a fixed time interval to establish wind speed change segment sequence groups. When acquiring voltage signal data collected by the wind speed sensors of each wind turbine in a wind farm, a numbering system is first established for the wind speed sensors associated with each wind turbine. Continuous voltage output signals generated by the wind speed sensors are retrieved from the SCADA system. The data recording frequency is set to 1Hz, meaning one voltage data point is recorded per second to form a structured raw voltage data sequence, such as 2.15V, 2.18V, 2.20V, ... After each turbine completes continuous data acquisition, an analog-to-digital converter (ADC) is used to convert the analog voltage signal. The ADC is set to a reference voltage of 5V and a conversion bit depth of 12 bits. The analog signal at each sampling point is converted into a digital value ranging from 0 to 4095 after linear normalization and quantization. For example, the conversion value corresponding to 2.15V is... (2.15 / 5) × 4095 =1761, 2.18V is 1785, after conversion the resulting digital signal sequence is: 1761, 1785, 1801, ... This conversion process ensures the numerical consistency and processability of subsequent data calculations. Next, according to the State Grid dispatching standards, the wind speed data is reorganized into a statistical cycle of 5 minutes, constructing a wind speed change segment sequence with 300 sampling points as a segment. Each sequence is in the form of: {1761, 1785, 1801, ..., 1995}, and numbered by generator unit. Indexed by number and timestamp, a sequence of wind speed change segments is constructed. Each sequence represents the wind speed change trajectory of a wind turbine over a continuous 5-minute period. If 15 wind turbines are deployed on-site, 15 sets of wind speed change segment data with time sequence identifiers can be obtained. To clarify the wind speed status of each turbine, the minimum, maximum, and average values ​​of each set of data need to be calculated. For example, in a certain 5-minute segment of turbine A, the minimum wind speed value is 1761, the maximum is 1995, and the average is 1878. The statistical structure is shown in the table below: Table 1. Statistics of Wind Speed ​​Variation Periods

[0025] As shown in Table 1, the wind speed data of different units in the wind farm differ within the same time period. The wind speed change segment sequence group constructed after conversion provides the input basic data for subsequent trend judgment. This data not only includes the numerical sequence of wind speed values, but also retains the unit location information and sampling time identifier, which facilitates subsequent data correlation analysis and trend calculation processing. This process generates the wind speed change segment sequence group.

[0026] The trend inflection point identification submodule calculates the direction of the wind speed difference between two consecutive data points based on adjacent data point pairs in each sequence of the wind speed change segment sequence group. It determines whether a change in sign occurs based on the product of the directions of the consecutive differences. Points with a change in direction sign are marked as trend inflection points. The module summarizes the number of trend inflection points in each time period and generates a list of trend inflection point counts. Based on the constructed wind speed variation segment sequence groups, the difference between adjacent data point pairs in each sequence is calculated. Specifically, a data pointer with a sliding window length of 2 is set, and all data point pairs in the sequence are traversed sequentially. Assuming a certain sequence is {1761, 1785, 1801, 1810, 1790}, the difference sequence between adjacent points is Δv=[1785-1761, 1801-1785, 1810-1801]. The difference results [1790-1810] = [24, 16, 9, -20] are recorded in array form. The product of the current difference item and the previous item is calculated sequentially. It is checked whether the product is less than 0. If it is less than 0, it indicates that the direction of wind speed change has changed, i.e., an inflection point has appeared. In the example above, 16 × 24 = 384 > 0, no inflection point; 9 × 16 = 144 > 0, no inflection point; -20 × 9 = -180 < 0, an inflection point exists. The position at index 4 in the original sequence is designated as the trend inflection point and marked with a timestamp "08:00:20". This process iterates through the entire wind speed sequence to obtain the set of inflection point positions P = {t1, t2, ...}, where t is the timestamp of the specific inflection point in seconds. Next, the number of trend inflection points in every 5-minute sequence is counted to determine the frequency of wind speed fluctuations. A threshold range is set to classify the density of trend inflection points: ≤3 inflection points indicate a stable segment, 3 < number ≤ 6 indicate a transitional segment, and >6 indicate a fluctuating segment. For example, if the number of inflection points in a sequence is 7, it is classified as a fluctuating segment. Finally, the statistical values ​​are output in list form as follows: A001-08:00:00-08:05:00:7, A002-08:00:00-08:05:00:4, A003-08:00:00-08:05:00:2. This process yields the list of trend inflection point counts.

[0027] The reverse sequence generation submodule calculates the number of trend inflection points for each time period based on the list of trend inflection points, arranges them in the order of the time period sequence, constructs the trend change state vector of the corresponding time series, and generates a trend reversal sequence. Based on the trend inflection point count data of each sequence in the trend inflection point count list, the data are combined and arranged in time series order. A corresponding trend state vector V is generated for each group of machine-time period data, where V has three enumeration values: 0 represents a stable segment, 1 represents a transition segment, and 2 represents a fluctuating segment. A numerical mapping is performed based on the aforementioned inflection point count and classification criteria: inflection point count ≤ 3 is mapped to 0, between 4 and 6 is mapped to 1, and greater than 6 is mapped to 2. For example, for A001, the inflection point count during the period from 08:00:00 to 08:05:00 is 7, and the mapping value is 2; for A002, the inflection point count is 4, and the mapping value is 1. During period A003, the value is 2, and the mapping value is 0. This process is applied to multiple time periods to obtain a set of trend change vectors for each time period, such as A001: {2, 1, 2, 0, 1}, A002: {1, 0, 0, 1, 2}. These vectors are arranged according to timestamps to form a continuous trend state sequence. A corresponding state array set is generated for each unit. Finally, all state array sets are combined to form a trend matrix T, where T_{i, j} represents the trend state value of the i-th unit in the j-th time period. This trend matrix can be used to further analyze the wind speed evolution process or model the discriminant calculation structure, ultimately generating a trend reversal sequence.

[0028] Specifically, such as Figure 2 , 4 As shown, the rhythm fluctuation detection module includes: The trend quantity extraction submodule extracts the number of trend states of fluctuation segments in each time period based on the wind speed trend change state vector recorded in the trend reversal sequence, counts the number and binds the corresponding time index information to establish a trend reversal quantity table. Based on the wind speed trend change state vectors recorded in the trend reversal sequence for each time period, the trend state value sequence for each time period is first extracted, and a correspondence between the time index and the trend state value is established. This trend state value originates from the trend reversal sequence generated by the preceding module, and its numerical structure is a three-value code, where 0 represents a stable state, 1 represents a transitional state, and 2 represents a fluctuating state. Therefore, if the state sequence within a certain time period is {2, 1, 2, 2, 0, 2}, the number of trend reversals in that time period is the number of elements with a value equal to 2, which is 4. By setting a sliding time window W=5min and scanning with a step size Δt=5min, the number of occurrences of elements with a value of 2 is counted within each sliding window, and the statistical results are bound to the start timestamp of the current window to construct a record item for the number of wind speed trend reversals. For example, if the trend sequence for unit A001 during the time period 08:00-08:05 is {2, 2, 1, 2, 0, 2}, then the number of reversals is 4, and this is stored as a record item.<A001,08:00,4> After processing all units and all time windows in sequence, a complete list of trend reversal quantities is constructed. In the process of counting trend reversals, each element of the wind speed trend change state vector needs to be matched with a value and a logical equal value comparison is performed. This comparison operation uses a set threshold v=2 as the benchmark judgment condition. Only when the state value is equal to 2 is it counted as a valid reversal behavior. This statistical mechanism makes the wind speed reversal behavior in each time period have a clear numerical expression, and finally establishes a trend reversal quantity table.

[0029] The fluctuation type determination submodule compares the number of trend reversals in each time period in the trend reversal quantity table with the benchmark value of wind speed trend change frequency. If the number of trend reversals is less than the benchmark value of wind speed trend change frequency, the time period is marked as a stable period; otherwise, it is marked as an unstable period, and a stability determination state sequence is generated. Based on the number of trend reversals in each time period from the trend reversal count table, the numerical portion is extracted and compared with the benchmark value for wind speed trend change frequency. The benchmark value is set with reference to the historical wind speed fluctuation frequency distribution of the wind farm. Based on the trend reversal counts of a total of 90 5-minute time periods in the statistical sample, the 70th percentile value after being sorted by frequency is used as the judgment benchmark. That is, the benchmark value for wind speed trend change frequency is set to 5, indicating that if the number of trend reversals in each 5-minute time period is greater than or equal to 5, it can be regarded as an unstable period, and if it is less than 5, it can be regarded as a stable period. For example, if the record item for a certain time period is...<A001,08:00,4> Since 4 < 5, this time period is marked as a stable period. If the record item is...<A002,08:05,6> Since 6 ≥ 5, this time period is marked as an unstable period. All judgment operations are implemented with double-condition logic statements. The structure is as follows: if n ≥ 5, it is marked as unstable; otherwise, it is marked as stable. In addition, the number of wind speed trend reversals involved in the comparison operation is directly called from the result of the previous module, while the benchmark value is generated by historical data statistics and does not involve real-time changes. The judgment results of all time periods are encoded as 0 (stable) or 1 (unstable), and the time period index and unit number are retained to finally obtain the stability judgment state sequence.

[0030] The rhythm tag generation submodule generates mapping tag values ​​based on the stability determination state sequence and the determination results of stability and instability. Stable periods are marked as 0 and unstable periods are marked as 1. The time-series mapping matrix is ​​constructed by arranging the values ​​in chronological order to generate a rhythm change tag sequence. Based on the stability state code value of each time period in the stability determination state sequence, a rhythm label vector is constructed by arranging the values ​​in chronological order. A state code of 0 indicates a stable rhythm segment, and a state code of 1 indicates an unstable rhythm segment. Further mapping rules are constructed to group each time period and its state code into structured data items. Each item has the format <unit number, time period start, label value>. For example, if the stability code of A001 during the time period 08:00-08:05 is 0, then a label item is generated.<A001,08:00,0> If the period from 08:05 to 08:10 is unstable, then it is...<A001,08:05,1> The continuous time period labels are grouped into a sequence to construct a complete rhythm change label structure. This structure is then organized into multiple label sequences according to the wind turbine unit classification, recording the rhythm fluctuation label status of each wind turbine unit within multiple time periods. A matrix-form data structure T_{i, j} is then established, where i represents the wind turbine unit number, j represents the time period index, and the value of T_{i, j} is the corresponding rhythm change label value. Partial rhythm label data is shown in the table below: Table 2. Rhythm Change Labels

[0031] As shown in Table 2, the rhythm label values ​​for each time period form a fluctuating vector sequence in time sequence. Its structure clearly reflects the rhythm state change process of each unit, and finally generates a rhythm change label sequence.

[0032] Specifically, such as Figure 2 , 5 As shown, the synchronous benchmark screening module includes: The stable unit screening submodule extracts records with values ​​of 0 based on the tag values ​​of each wind turbine in the rhythm change tag sequence for the corresponding time period and establishes a stable unit set. It divides the set according to the time period dimension, counts the stable unit number information in each time period, and obtains a stable unit number list. Based on the rhythm label values ​​of each wind turbine in the rhythm change label sequence for the current time period, all records with a label value of 0 are extracted, indicating that the corresponding wind turbine is in a stable state during that time period. First, the rhythm label table is grouped by time period, and all records are categorized by time period key to ensure temporal consistency in subsequent filtering operations. Then, within each time period, all records with a label value of 0 are filtered out, and their corresponding turbine numbers are extracted. For example, in the time period 08:00-08:05, the rhythm label sequence is {A001-0, A002-1, A003-0}, so stable turbine numbers A001 and A003 are filtered out. Finally, the extracted set of numbers is normalized. The process involves removing duplicates and validating the numbering format to ensure uniformity. Then, the stable turbine numbers corresponding to all time periods are recorded and a data structure is created as follows: the key is the time period, and the value is an array of numbers, resulting in the following structure: {08:00-08:05: [A001, A003], 08:05-08:10: [A002], 08:10-08:15: [A001, A004]}. All stable turbine numbers are managed by time dimension within this structure. This structure is used for subsequent reference turbine selection, and the statistical results can also be used to determine the stability distribution of wind farms across different time periods. Examples of some selection results are shown in the table below. Table 3 Stabilized Unit Numbering Table

[0033] As shown in Table 3, the number of stable unit numbers varies in each time period. The numbers in the table will be used as input conditions for subsequent reference unit sorting, and finally a list of stable unit numbers will be obtained.

[0034] The reference unit identification submodule performs a character sorting operation on the unit numbers according to the set of stable units corresponding to each time period in the stable unit number list, selects the first number in the sorting result, extracts the wind turbine identification value corresponding to the number, and establishes a unified regulation reference identifier. Based on the set of stable units for each time period in the stable unit number list, the numbers are sorted lexicographically. Each set of numbers is character data, and the sorting uses ASCII order comparison, that is, the number strings are compared character by character from the first character. For example, the sorting result of the number set [A003, A001, A002] is [A001, A002, A003]. After sorting, the first number in the sorting result for each time period is selected as the reference unit identifier for that time period. This number is bound to the current time period and stored as a structured data item with the record format <time period, reference unit number>. Continuing with the data in Table 3 as an example, the stable units in the 08:00-08:05 time period... Units A001 and A003 are sorted, and the reference unit is A001; in the 08:10-08:15 segment, A001 and A004 are sorted, and the reference unit is still A001; in the 08:05-08:10 segment, there is only one stable unit A002, which is directly selected without sorting. The reference unit identification operation processes each time period separately, and the generated structure sequence is as follows: {08:00-08:05: A001, 08:05-08:10: A002, 08:10-08:15: A001}. The number of each reference unit will be used in the next stage to extract the wind speed trend reversal value of that unit, and finally establish a unified regulation reference identifier.

[0035] The benchmark index extraction submodule generates unified regulation benchmarks by binding the reference unit number and time index to the number of wind speed trend reversals of the unit corresponding to the unified regulation reference identifier during the time period. Based on the unit number corresponding to each time period in the unified adjustment reference identifier, and combined with the wind speed trend reversal quantity data recorded in the trend reversal quantity table, the reversal quantity value of that unit number within the corresponding time period is extracted. The extraction method is to use <time period, unit number> as a dual index key to retrieve the corresponding record item from the trend reversal quantity table, and read the trend reversal quantity field value from that item as the adjustment benchmark index value for that time period. For example, if the unified adjustment reference identifier is...<A001,08:00> Search the trend reversal quantity table<A001,08:00> If the number of inversions for an item is 4, then the baseline index value is 4. This value, along with the reference unit number and time period, is recorded as a structure item.<A001,08:00,4> Repeat the above steps to extract indicator values ​​for all time periods, ultimately constructing a complete adjustment benchmark indicator sequence. Each item in the sequence is recorded in the form of a ternary structure, forming the following matrix: {<A001,08:00,4> ,<A002,08:05,3> ,<A001,08:10,2> This sequence will serve as a unified reference in the regulation and control process, ultimately generating a unified regulation benchmark index.

[0036] Specifically, such as Figure 2 , 6 As shown, the rhythm delay control module includes: The non-reference unit extraction submodule is based on the reference wind turbine number information of each time period in the unified adjustment benchmark index. It filters out all wind turbines that are inconsistent with the reference wind turbine number in the corresponding time period, extracts the number of wind speed trend reversals of each non-reference unit in the corresponding time period, and generates a non-reference unit trend dataset. After obtaining the reference turbine number information for each time period in the unified regulation benchmark index, a reference index set S = {t1: r1, t2: r2, ...} is constructed, where t is the time period and r is the reference turbine number. Within each time period t, the set M of all participating wind turbine numbers is called. t And perform set difference operation M t '=M t -{r t To obtain the set of non-reference unit numbers, for each non-reference unit number, retrieve the trend reversal quantity value for the corresponding time period from the trend reversal quantity table. For example, within the time period 08:00–08:05, if the reference unit is A001 and the participating units are A001, A002, A003, and A004, then the set of non-reference units is {A002, A003, A004}. Assuming their corresponding trend reversal quantities are 4, 6, and 2 respectively, construct the structure record item as follows:<A002,08:00,4> ,<A003,08:00,6> ,<A004,08:00,2> This operation is repeated for every non-reference unit in all time periods, resulting in a two-dimensional structure list with time as the first index and unit number as the second index. This records the trend status behavior of all non-reference units in each time period and provides the basic data structure for subsequent directional comparison operations. The result is the non-reference unit trend dataset.

[0037] The trend sign difference determination submodule performs difference calculations item by item based on the number of wind speed trend reversals of each unit in the non-reference unit trend dataset, combined with the benchmark values ​​of the same time period in the unified adjustment benchmark index, and judges the directional relationship based on the positive or negative difference. If the trend directions of the time period are opposite, it is marked as a delay type; if they are the same, it is marked as a synchronization type, and a trend direction difference label group is generated. According to each record in the non-reference unit trend dataset, extract the unit number, time period, and trend reversal quantity value item by item, and call the reference value in the unified adjustment reference index for the corresponding time period to construct an operation pair in the form of <non-reference value, reference value>. Calculate the difference Δv = non-reference value - reference value, and perform a logical judgment on the sign of Δv. The set rule is: Δv > 0 is a positive difference, Δv < 0 is a negative difference, and Δv = 0 is no difference. Then, combine the sign sequences of each unit in three consecutive time periods to form a trend difference trajectory sequence. For example, if the Δv sequence of a certain unit in three consecutive time periods is [-1, -2, -3], since they are all negative and the directions are the same, it is judged that the trend continues to be opposite and is marked as the delay type. If it is [1, 1, 1], then the trend continues in the same direction and is marked as the synchronous type. If there are alternating changes such as [1, -1, 1], it is not marked. This judgment is implemented by means of logical screening. Traverse all unit time period sequences and record the judgment results. The judgment condition is that if the directions in three time periods are all the same and opposite to the reference direction, it is classified as a delay. If they are the same and in the same direction, it is classified as synchronous. Generate judgment label values and bind the time period and unit number, and record them in the structural form such as <A002, 08:00, delay>, <A003, 08:00, synchronous>, etc., to form a trend direction difference label group.

[0038] Based on the direction difference types corresponding to each wind turbine in the trend direction difference label group, the adjustment marking generation sub-module performs label encoding on the original time series index structure, constructs an adjustment control matrix according to the time period order, and generates an adjustment rhythm marking sequence; According to the adjustment type marking of each record item in the trend direction difference label group, and in combination with its time period and unit number information, perform label embedding on the original time axis structure. Set the encoding rule as: the synchronous adjustment category is marked as 0, and the delay adjustment category is marked as 1. Records that cannot be classified or have inconsistent differences are not processed, and empty positions are reserved or recorded as null values. Subsequently, form a matrix of the adjustment category labels of all wind turbines in all time periods, with rows representing time periods and columns representing wind turbine numbers. The matrix element value is the corresponding adjustment category encoding value. The example structure is as follows: If in the time period from 08:00 to 08:05, A002 is marked as delayed, A003 is synchronous, and A004 has no judgment result, then the matrix row is [1, 0, null]. Serialize and construct the entire row according to the time period, and construct a multi-time period matrix row by row to obtain the following result: Table 4 Adjustment Rhythm Marking Table

[0039] As shown in Table 4, the adjustment rhythm marking sequence structure visualizes the synchronous or delay attribute classification of each wind turbine in the time dimension, and finally generates an adjustment rhythm marking sequence.

[0040] Specifically, such as Figure 2 、7 As shown in the figure, the output rhythm allocation module includes: Based on the adjustment category tags of each wind turbine in each time period recorded in the adjustment rhythm mark sequence, and combined with the complete list of wind turbine numbers in the current wind farm, the rhythm category matching sub-module matches the rhythm mark status of each wind turbine in the corresponding time period one by one, and generates a rhythm adjustment matching table; After obtaining the adjustment rhythm mark sequence, first extract the rhythm mark values of each wind turbine in each time period, and call the list M of all the wind turbine numbers currently online in the wind farm, where the number format is uniformly in the form of Axxx. Subsequently, in each time period, perform a one-by-one matching operation on each number in M, extract the corresponding rhythm category status in the adjustment rhythm mark sequence. If the rhythm mark value is 0, the unit belongs to the synchronous adjustment category; if it is 1, it belongs to the delayed adjustment category; if it is a null value, it does not participate in the adjustment. Establish a number-rhythm type mapping dictionary T, and the record form is: <time period, unit number, adjustment category>. For example, in the time period from 08:00 to 08:05, if A001 = 0, A002 = 1, A003 = null, then record as <A001, synchronous>, <A002, delayed>, <A003, null>. Then execute the above steps in sequence according to the time period to construct a rhythm adjustment structure table with multiple time series. The design of this structure corresponds the number with the category status one by one, and has good indexing and data expansion capabilities. Part of the generated results are shown in the following table: Table 5 Rhythm Adjustment Matching Table

[0041] As shown in Table 5, this structure completes the two-way mapping between the wind turbine number and the adjustment category, and finally generates a rhythm adjustment matching table.

[0042] The node time series allocation sub-module extracts the current output target rhythm configuration plan according to the adjustment category of the wind turbine in the rhythm adjustment matching table, matches the units in the delayed adjustment category to the later time nodes in the corresponding time period, and matches the units in the synchronous adjustment category to the current rhythm center node, and establishes an output rhythm node allocation table; Adjust the adjustment category tags of each unit in the matching table according to the rhythm, and call the current output target rhythm configuration table in the time period dimension. In this configuration table, a rhythm center node tc and a shifted node td are defined for each time period. The interval value between the two is defined as the rhythm shift offset time Δt = 5 minutes. Subsequently, within each time period, if the wind turbine is of the synchronous adjustment category, its adjustment target time node is set to tc; if it is of the delayed adjustment category, it is set to td = tc + Δt. For example, in the time period from 08:00 to 08:05, if tc = 08:02:30 is set, then the delayed node td = 08:07:30. Match A001 and A002 to tc and td respectively, and record this information as the adjustment time node mapping structure items <A001, 08:02:30> and <A002, 08:07:30>. Construct the adjustment time sequence table items for all wind turbines in all time periods according to this logic, and output the structure table. Part of the mapping structure is shown in Table 6: Table 6 Output Rhythm Node Allocation Table

[0043] As shown in Table 6, wind turbines of different rhythm categories are assigned to corresponding time nodes, and finally an output rhythm node allocation table is established.

[0044] Based on the time node configuration of each unit in the output rhythm node allocation table, and combined with the output adjustment window set in the rhythm time structure, set the output target value for each wind turbine in stages and arrange the start and end times of execution, generating a wind farm output rhythm adjustment record; According to the adjustment time node value of each wind turbine in the output rhythm node allocation table, and combined with the output control window parameters in the rhythm time structure, set the window length W = 10 minutes. The window is divided into a climbing stage in the first half and a stable stage in the second half. Among them, the first 5 minutes is a linear climbing section, and the target output value rises from the starting output P0 to the target output P1. The next 5 minutes keeps P1 unchanged. The setting of the output target value is mapped and converted according to the target load curve in the current dispatching plan. Take the A001 unit in a certain time period as an example. Suppose its adjustment node is 08:02:30, the starting output P0 = 1.8 MW, and the target output P1 = 2.2 MW. Then the adjustment plan structure is: keep P0 from 08:00 to 08:02:30, linearly rise to P1 from 08:02:30 to 08:07:30, and keep P1 from 08:07:3 to 08:12:30. The record structure is described by a three-segment curve, and a complete output adjustment plan record item <A001, start time, rhythm node, P0, P1, climbing rate> is formed. After sorting out the record items of all units in all time periods, a two-dimensional time sequence structure table is constructed, and finally a wind farm output rhythm adjustment record is generated.

[0045] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A wind farm power output dynamic adjustment system, characterized in that, The system comprises: The wind speed trend identification module obtains wind speed sensor data of each wind turbine of a wind farm, constructs a wind speed change sequence at a fixed time interval, calculates the wind speed difference direction of each two adjacent points, judges the sign change, counts the number of wind speed trend inflection points in each time period, and generates a trend reversal sequence; The rhythm fluctuation detection module compares and judges the number of wind speed trend reversals in each time period with a wind speed trend change frequency reference value according to the trend reversal sequence, marks stable periods and unstable periods, and generates a rhythm change label sequence; The synchronization reference screening module screens all wind turbines marked as stable based on the rhythm change label sequence, selects the wind turbine with the smallest number as a unified adjustment reference unit, extracts the number of wind speed trend reversals of the corresponding wind turbine as a current rhythm reference, and generates a unified adjustment reference index; The rhythm delay regulation module extracts the number of wind speed trend reversals of non-reference wind turbines based on the unified adjustment reference index, respectively performs a sign difference judgment with the current rhythm reference, marks the adjustment rhythm of the wind turbines, and generates an adjustment rhythm label sequence; The output rhythm distribution module matches the number list of all wind turbines in the current wind farm according to the adjustment rhythm label sequence, sequentially performs rhythm adjustment, and constructs the output change plan arrangement of each wind turbine, and generates a wind farm output rhythm adjustment record.

2. The wind farm power output dynamic regulation system according to claim 1, characterized in that: The stable period and the unstable period specifically refer to that if the number of wind speed trend reversals is less than the wind speed trend change frequency reference value, it is marked as a stable period, and if the number of wind speed trend reversals is greater than or equal to the wind speed trend change frequency reference value, it is marked as an unstable period.

3. The wind farm power output dynamic regulation system according to claim 1, characterized in that, During the adjustment rhythm marking process, if the trend change direction is continuously opposite, it is marked as a delay adjustment category, and if it is the same, it is marked as a synchronous adjustment category.

4. The wind farm power output dynamic regulation system according to claim 3, characterized in that, During the sequential rhythm adjustment process, the delayed adjustment category wind turbines are assigned a time node of the current output target rhythm, and the synchronous adjustment category wind turbines are matched with a current rhythm center node.

5. The wind farm power output dynamic regulation system of claim 1, wherein, The trend reversal sequence includes an inflection point label, a direction change sign, and a reversal statistical value in a time period. The rhythm change label sequence includes a stable period identifier, an unstable period identifier, and a wind speed trend change frequency judgment result. The unified adjustment reference index includes a reference wind turbine number, a rhythm reference value, and a stable unit statistical result. The adjustment rhythm label sequence includes a delay adjustment identifier, a synchronous adjustment identifier, and a trend difference judgment label. The wind farm output rhythm adjustment record includes a time node matching result, a rhythm center node mapping information, and a wind turbine output change plan arrangement.

6. The wind farm power output dynamic regulation system of claim 1, wherein, The wind speed trend identification module comprises: The data stream receiving submodule obtains the voltage signal data collected by the wind speed sensor of each wind turbine of a wind farm, converts the analog voltage signal into corresponding digital wind speed values through an analog-to-digital conversion device, divides the wind speed data obtained by continuous sampling into a wind speed change segment sequence at a fixed time interval, and establishes a wind speed change segment sequence group; The trend inflection point identification submodule calculates the wind speed difference value direction of the adjacent data points of each sequence in the wind speed change segment sequence group, judges whether a sign change occurs according to the product of the difference value directions, marks the point with a sign change as a trend inflection point, collects the number of trend inflection points in each time segment, and generates a trend inflection point number list; The reverse sequence generation submodule counts the number of trend inflection points in each time segment according to the trend inflection point number list, arranges the time segments in sequence, constructs a trend change state vector corresponding to the time sequence, and generates a trend reversal sequence.

7. The wind farm power output dynamic regulation system of claim 1, wherein, The rhythm fluctuation detection module comprises: The trend number extraction submodule extracts the number of trend states of the fluctuation segment in each time segment based on the trend change state vector of the wind speed in each time segment recorded in the trend reversal sequence, counts the number and binds the corresponding time index information, and establishes a trend reversal number table; The fluctuation type determination submodule compares the number of trend reversals in each time segment in the trend reversal number table with a wind speed trend change frequency reference value, and if the number of trend reversals is less than the wind speed trend change frequency reference value, the time segment is marked as a stable time segment, otherwise it is marked as a non-stable time segment, and a stability determination state sequence is generated; The rhythm label generation submodule generates a mapping label value according to the determination results of stability and instability based on the stability determination state sequence, marks the stable time segment as 0 and the non-stable time segment as 1, arranges the time sequence to construct a time sequence mapping matrix, and generates a rhythm change label sequence.

8. The wind farm power output dynamic regulation system of claim 1, wherein, The synchronization reference screening module comprises: The stable unit screening submodule extracts the record items with a value of 0 from the rhythm change label sequence based on the label values of each wind turbine in the corresponding time segment, establishes a stable unit set, divides it by time segment, and counts the stable unit number information in each time segment to obtain a stable unit number list; The reference unit identification submodule performs a character sorting operation on the stable unit set corresponding to each time segment in the stable unit number list, selects the first number in the sorting result, extracts the wind turbine identifier corresponding to the number, and establishes a unified adjustment reference identifier; The reference index extraction submodule binds the reference unit number and the time index according to the number of wind speed trend reversals of the unit corresponding to the unified adjustment reference identifier in the time segment, constructs a structured data item, and generates a unified adjustment reference index.

9. The wind farm power output dynamic regulation system of claim 1, wherein, The rhythm delay control module comprises: The non-reference unit extraction submodule excludes all wind turbines inconsistent with the reference wind turbine number in the corresponding time segment based on the reference wind turbine number information in each time segment in the unified adjustment reference index, extracts the number of wind speed trend reversals of each non-reference unit in the corresponding time segment, and generates a non-reference unit trend data set; The trend symbol difference determination submodule performs difference operation item by item according to the number of wind speed trend reversals of each unit in the non-reference unit trend data set, in combination with the reference value of the same time period in the unified adjustment benchmark index, and judges the direction relationship according to the positive and negative difference values. If the trend directions of the time periods are opposite, it is marked as a delay type; if they are consistent, it is marked as a synchronization type, to generate a trend direction difference label group; The adjustment label generation submodule generates an adjustment rhythm label sequence by performing label coding on the original time sequence index structure based on the direction difference type corresponding to each wind turbine in the trend direction difference label group, and constructing an adjustment control matrix in the order of time periods.

10. The wind farm power output dynamic regulation system of claim 1, wherein, The output rhythm distribution module includes: The rhythm category matching submodule generates a rhythm adjustment matching table by matching the rhythm label state of each wind turbine in the corresponding time period based on the adjustment category label of each wind turbine in each time period recorded in the adjustment rhythm label sequence, in combination with the complete number list of wind turbines in the current wind farm; The node time sequence distribution submodule extracts the current output target rhythm configuration scheme according to the adjustment category of the wind turbine in the rhythm adjustment matching table, matches the units of the delay adjustment category to the moved time node of the corresponding time period, matches the units of the synchronization adjustment category to the current rhythm center node, and establishes an output rhythm node distribution table; The output rhythm construction submodule sets the output target value for each wind turbine in stages and arranges the execution start and end time based on the time node configuration of each unit in the output rhythm node distribution table, in combination with the output adjustment window set in the rhythm time structure, to generate a wind farm output rhythm adjustment record.

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