A wind power plant dispatching power limiting state judgment method and system

Abstract

The present application relates to the field of wind power generation technology, and discloses a kind of wind power station dispatching power limiting state judging method and system, first, build global multidimensional data acquisition framework to carry out data acquisition, then the data collected are verified after data cleaning and construct uniform format multidimensional data set;Again, with transmission section rated upper limit power as benchmark, combined with multiple correction coefficient, calculate instantaneous effective sending capacity, and account the implicit capacity occupation caused by cross-section power flow transfer, and then solve the real-time dynamic operation margin of transmission section.This method breaks the traditional limit of considering channel capacity as a fixed value, realizes the full-dimensional quantification of multiple implicit constraints, and at the same time, relies on the margin gap rate to build a dynamic discrimination criterion, completes the wind power output limit determination and limited grade division, effectively solves the problems caused by the traditional scheme that cannot adapt to the dynamic transmission characteristics of power grid and is difficult to identify various implicit sending constraints.

Description

Technical Field

[0001] This invention relates to the field of wind power generation technology, specifically to a method and system for determining the power curtailment status of wind farms. Background Technology

[0002] Wind farm power curtailment, also known as wind curtailment restriction, refers to a situation where, even when wind turbines are in good working order, wind resources are plentiful, and the wind farm has full power generation capacity, the power grid dispatching department proactively issues power control instructions to the wind farm to precisely limit its grid output. This is done to address various factors, including maintaining the safe and stable operation of the entire power grid, addressing transmission capacity bottlenecks, mitigating regional load absorption, achieving dynamic balance in system peak shaving, and regulating power grid connection priority control. Consequently, the actual power generation of the wind farm fails to reach the theoretical exploitable amount of wind energy.

[0003] The transmission capacity of a power grid is not a static, fixed value. Its actual carrying capacity is constrained by multiple implicit factors, such as equipment operating status, real-time grid conditions, and grid topology. These influences cannot be directly reflected by the rated capacity of conventional channels, but they directly determine the full grid-connected transmission capacity of wind power. Therefore, most existing traditional power curtailment judgment logics treat the transmission limit of transmission sections as a constant fixed value, lacking real-time perception and refined quantitative analysis of the grid's dynamic operating margin. When power curtailment judgment is based solely on fixed capacity thresholds, it is impossible to effectively identify the implicit transmission constraints caused by cross-section power flow transfer, equipment health degradation, and changes in grid operation mode. Ultimately, this leads to problems such as one-sided analysis of power curtailment causes, inaccurate division of restricted areas, and large deviations in the calculation of the degree of wind power transmission restriction. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for determining the curtailment status of wind farms. This method utilizes multi-factor coupling correction to calculate the instantaneous effective transmission capacity and quantifies the implicit capacity loss caused by cross-section power flow crowding. It accurately calculates the true real-time dynamic operational margin of the transmission section, breaking the limitation of traditional curtailment judgments that treat transmission section capacity as a constant fixed value. This enables real-time perception and quantitative representation of various implicit transmission constraints such as equipment attenuation, grid changes, and power flow shifts. Then, based on the margin gap rate, a multi-constraint judgment criterion is established to conduct curtailment status identification and hierarchical control. This effectively solves problems such as incomplete constraint identification, one-sided curtailment analysis, inaccurate definition of the restricted area, and large deviations in wind power curtailment calculations caused by the original fixed threshold judgment, achieving precise and dynamic control of wind power grid-connected scheduling.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for determining the power curtailment status of a wind farm, comprising the following steps: Step 1: Build a comprehensive, multi-dimensional data acquisition framework covering power grid physical parameters, equipment basic parameters, grid topology information, real-time power grid operating conditions, power flow data at each section, and real-time wind farm output information. Then, clean and verify the collected data to construct a multi-dimensional data set in a unified format. Step 2: Based on the rated upper limit power of the transmission section, calculate the instantaneous effective transmission capacity by integrating the equipment health attenuation correction coefficient, topology weight coefficient, and real-time operating condition constraint coefficient. Step 3: Calculate the amount of implicit capacity crowding out of this section due to power flow crowding and power transfer from adjacent sections. Step 4: Combine the instantaneous effective transmission capacity and the implicit capacity occupancy to calculate the real-time dynamic operating margin of the remaining transmittable power of the transmission section at the current moment. Step 5: Compare the real-time grid-connected power of the wind farm with the dynamic availability margin of the cross section, establish a multi-constraint dynamic curtailment judgment criterion, and perform real-time identification of the curtailment status of the wind farm and classification of the curtailment level.

[0006] Preferably, in step one, data acquisition includes acquiring equipment status data, power grid operating condition data, grid topology data, baseline static data, and wind power output data; The specific equipment status data includes: real-time line load rate, equipment insulation aging attenuation coefficient, equipment remaining current carrying capacity, equipment protection limit, and equipment maintenance downtime duration. The specific power grid operation data includes: real-time active power of transmission sections, power flow transfer amplitude of adjacent sections, and real-time power reserve margin of sections. The specific data of the grid topology structure includes: available capacity of backup channels; quantitative status of line activation / deactivation, quantitative values ​​of switch opening / closing status, grid operation mode coding parameters, and available transmission capacity of backup channels. The specific reference static data are: the rated upper limit power of the transmission section and the conventional static limit threshold; The specific wind power output data includes: real-time active power output of wind farms, predicted wind power output, power output subject to curtailment orders, and power curtailed.

[0007] Preferably, in step two, the formula for calculating the instantaneous effective transmission capacity is: ; In the formula, Represents the instantaneous effective transmission capacity; This represents the rated upper limit power of the transmission section; Represents the equipment health degradation correction factor; Represents the topology weight coefficients; Represents the real-time operating condition constraint coefficient: where: ; In the formula, Represents the remaining current carrying capacity of the equipment; Represents the insulation aging degradation coefficient of the equipment; This represents the quantified downtime of equipment maintenance; ; In the formula, This represents the available transport capacity of the backup channel; The topology adaptability is represented by the weighted summation of the line connection / disconnection quantization status, switch opening / closing status quantization value, and power grid operation mode coding parameters, combined with the corresponding weights. ; In the formula, The estimated value of power flow crowding is calculated by multiplying the sum of the power flow transfer amplitude of adjacent sections and the real-time active power of the transmission section by the power flow crowding influence coefficient. This represents the real-time power reserve margin of the cross-section.

[0008] Preferably, in step three, the formula for calculating the amount of hidden capacity encroachment in this cross-section is: ; In the formula, This represents the amount of hidden capacity encroached upon in this cross-section; Represents the magnitude of tidal current transfer between adjacent sections; The coefficient representing the influence of trend crowding out; This represents the cross-sectional tidal current transmission coefficient.

[0009] Preferably, in step four, the formula for calculating the real-time dynamic operating margin is: ; In the formula, This represents the real-time dynamic operational margin.

[0010] Preferably, in step five, the real-time dynamic operating margin calculated in step four is used as the upper limit constraint for grid acceptance. A multi-constraint coupled dynamic power curtailment judgment logic is constructed by combining this logic with the data collected in step one. The specific steps are as follows: S1. Combining the real-time active power output and wind power prediction values ​​of the wind farm, the real-time active power output and real-time dynamic operating margin of the wind farm are compared simultaneously to determine the real-time operating limit and the future output limit. S2. Calculate the margin gap rate based on the difference between the real-time active power output and the real-time dynamic operating margin of the wind farm. S3. Classify the curtailment level according to the margin gap rate, issue power curtailment control instructions according to the level, and complete the closed-loop calculation of wind curtailment power by combining the wind power output data collected in step one.

[0011] Preferably, in step S1, the real-time power over-limit determination rule is as follows: When the wind farm has real-time active power output Real-time dynamic runtime margin At this time, it means that the wind power is operating in real time without exceeding the limit and is maintaining full grid connection. When the wind farm has real-time active power output Real-time dynamic runtime margin When this occurs, it indicates that the real-time operation has exceeded the limit, and S2 will be executed; The rules for determining future output ahead of schedule are as follows: When wind power forecast value Real-time dynamic runtime margin This indicates that there is no risk of exceeding the power limit in the future, and the grid capacity is sufficient and does not require regulation. When wind power forecast value Real-time dynamic runtime margin When this occurs, it indicates a potential risk of exceeding future limits in future output, triggering an alert and prompting proactive control measures.

[0012] Preferably, in step S2, the formula for calculating the margin gap ratio is: .

[0013] Preferably, in S3, the principle for classifying the restricted levels is as follows: when When there is no capacity shortage, wind power is fully connected to the grid and operating without power curtailment, the corrected wind curtailment power is 0; when This indicates moderate power restriction, triggering routine power rationing and control measures. when At this time, it indicates severe restrictions and strict power rationing measures are being implemented.

[0014] A system for determining the power curtailment status of wind farms using a wind farm scheduling curtailment status judgment method includes an edge acquisition unit, a local service unit, and a scheduling terminal unit; The edge acquisition unit is used to connect with various sensors and SCADA systems on site to complete the acquisition of raw data in multiple dimensions across the entire domain, and to initially complete data cleaning and format normalization verification to build a unified format multidimensional data set. The local service unit has a complete set of core computing models built in, which sequentially completes the calculation of instantaneous effective transmission capacity, the solution of implicit capacity occupancy, and the calculation of real-time dynamic operating margin of transmission sections. At the same time, it is responsible for the database storage and data retrieval of all multi-dimensional data. The dispatch terminal unit has built-in multi-constraint dynamic power curtailment judgment criteria, which realizes real-time identification of wind farm power curtailment status, over-limit warning, and restricted level classification. It also supports dispatchers to view operating parameters, verify alarm information, issue power curtailment instructions, and update wind curtailment power data in a closed loop.

[0015] Compared with the prior art, the present invention provides a method for determining the power curtailment status of wind farms, which has the following beneficial effects: 1. This invention comprehensively covers various implicit influencing factors affecting the actual transmission capacity of the power grid by collecting equipment status data, power grid operation data, grid topology data, benchmark static data, and wind power output data across the entire domain. It also completes data standardization, cleaning, and integration, thus overcoming the shortcomings of traditional methods with their single parameters from the data source level. It incorporates all implicit factors such as equipment attenuation, topology changes, and operating condition fluctuations that could not be reflected by rated capacity into the analysis system, providing a comprehensive and reliable data foundation for subsequent dynamic quantitative calculations.

[0016] 2. This invention obtains the instantaneous effective transmission capacity through multi-factor coupling correction and separately quantifies the implicit capacity loss caused by cross-section power flow crowding. Finally, it calculates the real-time dynamic operating margin that fits the actual situation of the power grid. This breaks through the limitation of the traditional view that the transmission section capacity is a constant fixed value, realizes the real-time perception and accurate quantification of the power grid's actual remaining transmission capacity, effectively identifies various implicit transmission constraints caused by power flow transfer, equipment attenuation, and grid adjustment, and solves the core problems of rigid capacity estimation and lack of dynamic margin in the original method.

[0017] 3. This invention constructs a multi-constraint dynamic discrimination criterion based on real-time dynamic operating margin, combines real-time output with future output prediction to complete the identification of power curtailment status, and implements hierarchical control and closed-loop accounting of wind curtailment based on margin gap rate. This improves the problems of one-sided analysis, inaccurate scope definition, and large deviation in the calculation of the degree of limitation caused by traditional fixed threshold judgment, realizes proactive and precise scheduling, and makes the power curtailment judgment basis more in line with the actual carrying capacity of the power grid, taking into account both the safe operation of the power grid and the efficient consumption of wind power. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. It is worth noting that this application also relates to prior art. Since prior art is well known to those skilled in the art, it will not be described in detail in this application.

[0020] Please see Figure 1 A method for determining the power curtailment status of a wind farm, comprising the following steps: Step 1: Build a comprehensive, multi-dimensional data acquisition framework covering power grid physical parameters, equipment basic parameters, grid topology information, real-time power grid operating conditions, power flow data at each section, and real-time wind farm output information. Then, clean and verify the collected data to construct a multi-dimensional data set in a unified format. Data acquisition includes collecting equipment status data, power grid operating condition data, grid topology data, baseline static data, and wind power output data. It can comprehensively cover transmission constraints of transmission sections, equipment operating health levels, power flow distribution characteristics of the power grid, available transmission capacity of the grid, and real-time power generation information of wind farms. It provides comprehensive, accurate, and complete underlying data support for subsequent calculation of instantaneous effective transmission capacity, solution of implicit capacity crowding, real-time dynamic operating margin calculation, and multi-constraint dynamic power curtailment judgment. It avoids the judgment bias caused by single parameter calculation and ensures that subsequent power curtailment status identification and level classification are consistent with the actual operating status of the power grid. The specific equipment status data includes: real-time line load rate, equipment insulation aging attenuation coefficient, equipment remaining current carrying capacity, equipment protection limits, and equipment maintenance downtime duration; the specific grid operation status data includes: real-time active power of transmission sections, power flow transfer amplitude of adjacent sections, and real-time power reserve margin of sections; the specific grid topology data includes: available capacity of backup channels; quantitative status of line commissioning / decommissioning, quantitative values ​​of switch opening / closing status, grid operation mode coding parameters, and available transmission capacity of backup channels; the specific baseline static data includes: rated upper limit power of transmission sections and conventional static limit thresholds; the specific wind power output data includes: real-time active power output of wind farms, predicted wind power, power issued by curtailment orders, and wind curtailment power; Data cleaning and verification specifically includes: first, using the nearest-neighbor time-series interpolation method to fill in missing values ​​in the collected data; then, using threshold limitation combined with trend deviation analysis to remove abnormal data; using extreme value standardization to perform dimensional normalization; and using the power grid operation logic cross-verification method to perform data consistency verification, eliminating data errors, redundant information, and format differences, and finally integrating them into a standard, unified format multidimensional data set; enabling the interoperability and timing synchronization of various parameters, improving the stability and accuracy of subsequent algorithm operations, facilitating local service units to quickly retrieve data for operations, ensuring continuous and reliable computation throughout the entire process, and simultaneously achieving full-process data traceability and closed-loop storage for scheduling; Step 2: Based on the rated upper limit power of the transmission section, calculate the instantaneous effective transmission capacity by integrating the equipment health attenuation correction coefficient, topology weight coefficient, and real-time operating condition constraint coefficient. This step uses the rated upper limit power of the transmission section as the calculation basis. Through a multi-factor coupled superposition correction calculation formula, it integrates three dynamic influencing factors—equipment health degradation correction coefficient, topology weight coefficient, and real-time operating condition constraint coefficient—for comprehensive correction calculation, thereby solving for the instantaneous effective transmission capacity of the transmission section in real time. This calculation method fully considers the characteristic that the actual transmission capacity of the power grid is not a static fixed value. It quantifies and characterizes implicit constraint factors that are difficult to directly reflect through the rated upper limit power of the transmission section, such as real-time line load rate, equipment insulation aging degradation coefficient, changes in grid topology, and power flow transfer amplitude between adjacent sections. This effectively compensates for the limitations of traditional methods. The new method for determining power curtailment treats the upper limit of transmission section as a constant fixed value, which is a drawback of the previous method. It solves the defects of the original method, which lacks real-time dynamic operational margin perception and relies solely on conventional static limit thresholds to determine the power curtailment status. It can accurately identify the implicit transmission constraints caused by power flow transfer occupation, equipment operation attenuation, and grid operation mode adjustment. This makes the subsequent section transmission capacity calculation more in line with the actual operation status of the power grid. It fundamentally improves the problems of one-sided power curtailment cause analysis, inaccurate division of restricted range, and large deviation in the calculation of wind power transmission restriction degree. It lays an accurate and reliable foundation for subsequent implicit capacity crowding-out calculation and real-time dynamic operational margin calculation. The formula for calculating the instantaneous effective transmission capacity is: ; In the formula, Represents the instantaneous effective transmission capacity; This represents the rated upper limit power of the transmission section; Represents the equipment health degradation correction factor; Represents the topology weight coefficients; Represents the real-time operating condition constraint coefficient; where: ; In the formula, Represents the remaining current carrying capacity of the equipment; Represents the insulation aging degradation coefficient of the equipment; The quantitative duration of equipment maintenance and downtime is represented by the equipment health attenuation correction coefficient. The equipment health attenuation correction coefficient is quantitatively solved by combining the equipment's remaining current carrying capacity, the equipment insulation aging attenuation coefficient, and the quantitative duration of equipment maintenance and downtime. This accurately restores the actual usable transmission capacity of the equipment, eliminates the capacity error caused by aging and downtime, and makes the instantaneous effective transmission capacity calculation more in line with the actual operating conditions of the equipment, avoiding overestimation of the transmission capacity. ; In the formula, This represents the available transport capacity of the backup channel; Representing topology adaptability, it is calculated by combining the quantified status of line commissioning / decommissioning, the quantified values ​​of switch opening / closing status, and the grid operation mode coding parameters with corresponding weights. The topology structure weight coefficient is integrated with the available transmission capacity of the backup channel and various topology status parameters to solve the topology adaptability. It is also combined with the rated upper limit power of the transmission section to complete the normalization limit processing. This can quantify the changes in transmission capacity caused by dynamic changes in the grid structure, so that the instantaneous effective transmission capacity calculation is consistent with the actual grid structure conditions, and improve the accuracy of subsequent dynamic margin calculation and power curtailment judgment. ; In the formula, The estimated value of power flow crowding is calculated by multiplying the sum of the power flow transfer amplitude of adjacent sections and the real-time active power of the transmission section by the power flow crowding influence coefficient. It represents the real-time power reserve margin of the cross section; the real-time operating condition constraint coefficient is combined with the real-time power reserve margin of the cross section and the power flow crowding estimate to quantify the solution, which can accurately characterize the transmission constraint impact of the real-time power flow distribution of the power grid and the remaining reserve capacity of the cross section, quantify the capacity occupancy loss caused by the power flow transfer of adjacent cross sections, correct the available transmission boundary of the cross section, make the instantaneous effective transmission capacity calculation fit the real-time operating condition of the power grid, avoid the problem of inflated capacity caused by power flow crowding, and ensure that the subsequent dynamic margin calculation is more realistic and reliable. Step 3: Calculate the implicit capacity encroachment of this section caused by power flow encroachment and power transfer from adjacent sections. The calculation formula is as follows: ; In the formula, This represents the amount of hidden capacity encroached upon in this cross-section; Represents the magnitude of tidal current transfer between adjacent sections; The coefficient representing the influence of trend crowding out; Represents the cross-sectional power flow transmission coefficient; This step calculates the implicit capacity occupancy of the local section caused by the power flow transfer between adjacent sections using a formula. It comprehensively quantifies the transmission capacity loss caused by cross-section power flow conduction and power flow occupancy, which can fully reflect the implicit capacity occupancy problem caused by power flow interaction in the power grid. It makes up for the shortcomings of traditional methods that ignore cross-section power transfer constraints and only consider the transmission limit of the local section itself, thereby further improving the fit of subsequent wind power curtailment judgment and level classification. Step 4: Combining the instantaneous effective transmission capacity and the implicit capacity occupancy, calculate the real-time dynamic operating margin of the remaining transmittable power of the transmission section at the current moment. The calculation formula is as follows: ; In the formula, Represents real-time dynamic operational margin; This step calculates the real-time dynamic operating margin, comprehensively integrates all influencing factors such as equipment attenuation, grid topology, grid operating conditions constraints, and cross-section power flow crowding, and accurately determines the true remaining transmittable power of the transmission section. This effectively breaks the traditional static judgment mode of fixed capacity thresholds and realizes a full-dimensional dynamic quantitative assessment of the grid section's transmission capacity, providing a true and reliable core judgment basis for subsequent wind farm curtailment status identification, curtailment level classification, and wind curtailment correction and control. Step 5: Using the real-time dynamic operating margin calculated in Step 4 as the upper limit constraint for grid acceptance, and combining it with the data collected in Step 1, construct a multi-constraint coupled dynamic power curtailment judgment logic. The specific steps are as follows: S1. Combining the real-time active power output and wind power prediction values ​​of the wind farm, simultaneously compare the real-time active power output with the real-time dynamic operating margin to determine real-time operating limits and future power output limits; among which, the real-time power limit determination rules are as follows: When the wind farm has real-time active power output Real-time dynamic runtime margin At this time, it means that the wind power is operating in real time without exceeding the limit and is maintaining full grid connection. When the wind farm has real-time active power output Real-time dynamic runtime margin When this occurs, it indicates that the real-time operation has exceeded the limit, and S2 will be executed; The rules for determining future output ahead of schedule are as follows: When wind power forecast value Real-time dynamic runtime margin This indicates that there is no risk of exceeding the power limit in the future, and the grid capacity is sufficient and does not require regulation. When wind power forecast value Real-time dynamic runtime margin When this occurs, it indicates a potential risk of exceeding future limits in terms of output, triggering an alert and prompting proactive control measures. By combining the real-time active power output of the wind farm with the predicted wind power, S1 can perform real-time limit exceedance judgment and future output exceedance prediction. Relying on the real-time dynamic operating margin, it can complete the grid connection boundary verification. It can accurately identify whether the current wind power grid connection power exceeds the remaining transmission capacity of the section, and accurately judge the real-time operating status. It can also predict the potential for wind power output exceedance in advance, and realize early warning and advanced control, making wind power dispatch and control more proactive and precise. S2. Based on the difference between the real-time active power output and the real-time dynamic operating margin of the wind farm, calculate the margin gap rate. The calculation formula is as follows: ; S2, through the margin gap ratio calculation formula, reflects the proportion of power gap where the real-time active power output of the wind farm exceeds the dynamic available margin of the section. It intuitively represents the degree of wind power grid connection exceeding the limit and the actual size of the restriction. It can accurately quantify the relative extent of wind power exceeding the transmission boundary, providing a quantitative basis for subsequent classification of restriction levels and issuance of differentiated power curtailment instructions. This ensures that the power curtailment control scale is in line with the actual gap size, avoids the problem of excessive power curtailment or insufficient control, and improves the level of precision in wind power dispatch and management. S3. Based on the margin gap ratio, classify the curtailment level, issue power curtailment control instructions according to the level, and complete the closed-loop calculation of wind curtailment power by combining the wind power output data collected in step one. The principle for classifying the curtailment level is as follows: when When there is no capacity shortage, wind power is fully connected to the grid and operating without power curtailment, the corrected wind curtailment power is 0; when This indicates moderate power restriction, triggering routine power rationing and control measures. when At this time, it indicates severe restrictions and strict power rationing measures are being implemented. S3 quantifies the curtailment level of wind farms based on the margin gap rate, issues differentiated curtailment control instructions according to the curtailment level, and performs closed-loop calculation of wind curtailment power in combination with the original data related to wind power output. This achieves precise graded control of curtailment level, avoids excessive wind curtailment or grid overload caused by blindly uniform curtailment, and maximizes the safety constraints of wind power grid connection and the consumption of new energy. A system for determining the power curtailment status of wind farms using a wind farm scheduling curtailment status judgment method includes an edge acquisition unit, a local service unit, and a scheduling terminal unit; The edge acquisition unit is used to connect with various sensors and SCADA systems on site to complete the acquisition of raw data in multiple dimensions across the entire domain. It also performs preliminary data cleaning and format normalization verification to build a unified format multidimensional data set. At the same time, it is equipped with a data transmission gateway, edge communication module and anti-interference protection equipment to ensure stable data upload on site. The local service unit has a complete set of core computing models built in, which sequentially completes the calculation of instantaneous effective transmission capacity, the solution of implicit capacity occupancy, and the calculation of real-time dynamic operating margin of transmission sections. At the same time, it is responsible for the database storage and data retrieval of all multi-dimensional data. It is also equipped with a local server, a large-capacity storage hard drive and an uninterruptible power supply to meet the needs of continuous computing and secure data storage. The dispatch terminal unit has built-in multi-constraint dynamic power curtailment judgment criteria, which enables real-time identification of wind farm power curtailment status, over-limit warning, and restricted level classification. It also supports dispatchers to view operating parameters, verify alarm information, issue power curtailment commands, and update wind curtailment power data in a closed loop. In addition, it is equipped with a human-machine interaction display screen, operation terminal peripherals, and alarm sound and light prompts to facilitate daily operation by dispatchers.

[0021] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for determining the power curtailment status of a wind farm, characterized in that, Includes the following steps: A comprehensive, multi-dimensional data acquisition framework covering power grid physical parameters, equipment basic parameters, grid topology information, real-time power grid operating conditions, power flow data at various sections, and real-time power output information of wind farms is established for data acquisition. After data cleaning and verification, a multi-dimensional data set in a unified format is constructed. Based on the rated upper limit power of the transmission section, the instantaneous effective transmission capacity is calculated by integrating the equipment health attenuation correction coefficient, topology weight coefficient, and real-time operating condition constraint coefficient. Calculate the amount of implicit capacity crowding out of this section due to power flow crowding out and power transfer from adjacent sections; By combining the instantaneous effective transmission capacity and the implicit capacity occupancy, the real-time dynamic operating margin of the remaining transmittable power of the transmission section at the current moment is calculated. By comparing the real-time grid-connected power of wind farms with the dynamic availability margin of cross sections, a multi-constraint dynamic curtailment judgment criterion is established to identify the curtailment status of wind farms in real time and classify the curtailment level.

2. The method for determining the power curtailment status of a wind farm as described in claim 1, characterized in that, The data acquisition includes collecting equipment status data, power grid operating condition data, grid topology data, baseline static data, and wind power output data; The specific equipment status data includes: real-time line load rate, equipment insulation aging attenuation coefficient, equipment remaining current carrying capacity, equipment protection limit, and equipment maintenance downtime duration. The specific power grid operation data includes: real-time active power of transmission sections, power flow transfer amplitude of adjacent sections, and real-time power reserve margin of sections. The specific data of the grid topology structure includes: available capacity of backup channels; quantitative status of line activation / deactivation, quantitative values ​​of switch opening / closing status, grid operation mode coding parameters, and available transmission capacity of backup channels. The specific reference static data are: the rated upper limit power of the transmission section and the conventional static limit threshold; The specific wind power output data includes: real-time active power output of wind farms, predicted wind power output, power output subject to curtailment orders, and power curtailed.

3. The method for determining the power curtailment status of a wind farm as described in claim 2, characterized in that, The formula for calculating the instantaneous effective transmission capacity is as follows: ； In the formula, Represents the instantaneous effective transmission capacity; This represents the rated upper limit power of the transmission section; Represents the equipment health degradation correction factor; Represents the topology weight coefficients; Represents the real-time operating condition constraint coefficient: where: ； In the formula, Represents the remaining current carrying capacity of the equipment; Represents the insulation aging degradation coefficient of the equipment; This represents the quantified downtime of equipment maintenance; ； In the formula, This represents the available transport capacity of the backup channel; The topology adaptability is represented by the weighted summation of the line connection / disconnection quantization status, switch opening / closing status quantization value, and power grid operation mode coding parameters, combined with the corresponding weights. ； In the formula, The estimated value of power flow crowding is calculated by multiplying the sum of the power flow transfer amplitude of adjacent sections and the real-time active power of the transmission section by the power flow crowding influence coefficient. This represents the real-time power reserve margin of the cross-section.

4. The method for determining the power curtailment status of a wind farm as described in claim 2, characterized in that, The formula for calculating the amount of hidden capacity encroachment in this cross-section is as follows: ； In the formula, This represents the amount of hidden capacity encroached upon in this cross-section; Represents the magnitude of tidal current transfer between adjacent sections; The coefficient representing the influence of trend crowding out; This represents the cross-sectional tidal current transmission coefficient.

5. The method for determining the power curtailment status of a wind farm as described in claim 4, characterized in that, The formula for calculating real-time dynamic runtime margin is: ； In the formula, This represents the real-time dynamic operational margin.

6. The method for determining the power curtailment status of a wind farm as described in claim 5, characterized in that, Using the calculated real-time dynamic operating margin as the upper limit constraint for grid acceptance, and combining it with the collected data, a multi-constraint coupled dynamic power curtailment judgment logic is constructed. The specific steps are as follows: S1. Combining the real-time active power output and wind power prediction values ​​of the wind farm, the real-time active power output and real-time dynamic operating margin of the wind farm are compared simultaneously to determine the real-time operating limit and the future output limit. S2. Calculate the margin gap rate based on the difference between the real-time active power output and the real-time dynamic operating margin of the wind farm. S3. Classify the curtailment level according to the margin gap rate, issue power curtailment control instructions according to the level, and complete the closed-loop calculation of wind curtailment power by combining the wind power output data collected in step one.

7. The method for determining the power curtailment status of a wind farm as described in claim 6, characterized in that, The real-time power limit exceeding judgment rule is as follows: When the wind farm has real-time active power output Real-time dynamic runtime margin At this time, it means that the wind power is operating in real time without exceeding the limit and is maintaining full grid connection. When the wind farm has real-time active power output Real-time dynamic runtime margin When this occurs, it indicates that the real-time operation has exceeded the limit, and S2 will be executed; The rules for determining future output ahead of schedule are as follows: When wind power forecast value Real-time dynamic runtime margin This indicates that there is no risk of exceeding the power limit in the future, and the grid capacity is sufficient and does not require regulation. When wind power forecast value Real-time dynamic runtime margin When this occurs, it indicates a potential risk of exceeding future limits in future output, triggering an alert and prompting proactive control measures.

8. The method for determining the power curtailment status of a wind farm as described in claim 6, characterized in that, The formula for calculating the margin gap ratio is: 。 9. The method for determining the power curtailment status of a wind farm as described in claim 6, characterized in that, The principle for classifying restricted levels is as follows: when When there is no capacity shortage, wind power is fully connected to the grid and operating without power curtailment, the corrected wind curtailment power is 0; when This indicates moderate power restriction, triggering routine power rationing and control measures. when At this time, it indicates severe restrictions and strict power rationing.

10. A system for determining the power curtailment status of a wind farm using the method for determining the power curtailment status of a wind farm as described in any one of claims 1 to 9, characterized in that, It includes an edge acquisition unit, a local service unit, and a scheduling terminal unit; The edge acquisition unit is used to connect with various sensors and SCADA systems on site to complete the acquisition of raw data in multiple dimensions across the entire domain, and to initially complete data cleaning and format normalization verification to build a unified format multidimensional data set. The local service unit has a complete set of core computing models built in, which sequentially completes the calculation of instantaneous effective transmission capacity, the solution of implicit capacity occupancy, and the calculation of real-time dynamic operating margin of transmission sections. At the same time, it is responsible for the database storage and data retrieval of all multi-dimensional data. The dispatch terminal unit has built-in multi-constraint dynamic power curtailment judgment criteria, which realizes real-time identification of wind farm power curtailment status, over-limit warning, and restricted level classification. It also supports dispatchers to view operating parameters, verify alarm information, issue power curtailment instructions, and update wind curtailment power data in a closed loop.

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