Method, device and equipment for evaluating power generation performance of wind power station and storage medium

Abstract

The invention relates to the technical field of new energy grid connection, and discloses a wind power plant power generation performance evaluation method, device and equipment and a storage medium, and the method comprises the steps: calculating the power generation capability parameters of a target wind power plant according to the operation parameters and configuration parameters of the target wind power plant; calculating an economic effect evaluation parameter according to the availability rate of the target wind power plant, the power grid compatibility comprehensive score, the power generation capacity parameter and the health coefficient; and determining a power generation performance evaluation result of the target wind power plant according to the power generation capacity parameter and the economic effect evaluation parameter. According to the power generation performance evaluation method for the wind power plant provided by the embodiment of the invention, the limitation that evaluation is carried out only from a single index of short-term power generation capacity traditionally is avoided in a mode of combining the power generation capacity parameters and the economic effect evaluation parameters, and comprehensive evaluation of the power generation capacity and the economic benefits is realized.

Description

Technical Field

[0001] This invention relates to the field of new energy power generation technology, specifically to methods, devices, equipment, and storage media for evaluating the power generation performance of wind farms. Background Technology

[0002] Offshore and onshore wind farms, as an important component of energy transition, have developed relatively complete theoretical systems and practical experience in equipment configuration and construction technology. However, due to differences in operating models and maintenance standards, inconsistencies in evaluation standards have arisen in the long-term performance assessment and economic benefit analysis of some wind power plants, leading to disagreements among construction companies, owners, and operators. To resolve this issue, there is an urgent need to establish a scientific, unified, and easily understandable wind farm performance evaluation system.

[0003] Traditional wind farm performance evaluation mainly focuses on the Power Capability Parameter (PCP) index. The calculation of the PCP index mainly considers the unit's power generation indicators such as capacity factor and average power output, but does not take into account factors related to the long-term economic benefits of wind farms, such as grid compatibility and equipment health status. The traditional evaluation method considers relatively one-sided factors, which leads to the failure to balance the power generation capacity of wind farms with the long-term economic benefits of operation in the evaluation of wind farms. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, equipment, storage medium and program product for evaluating the power generation performance of wind farms, in order to solve the problem of failing to balance the power generation capacity and long-term economic benefits of wind farms in the evaluation of wind farms.

[0005] In a first aspect, the present invention provides a method for evaluating the power generation performance of a wind farm, comprising: calculating the power generation capacity parameters of the target wind farm based on the operating parameters and configuration parameters of the target wind farm; calculating economic effect evaluation parameters based on the availability rate, grid compatibility comprehensive score, power generation capacity parameters, and health coefficient of the target wind farm; and determining the power generation performance evaluation result of the target wind farm based on the power generation capacity parameters and economic effect evaluation parameters.

[0006] The wind farm power generation performance evaluation method provided in this invention calculates the power generation capacity parameters and economic effect evaluation parameters of the target wind farm, respectively. The power generation capacity parameters and economic effect evaluation parameters are used together to obtain the power generation performance evaluation result of the target wind farm. The power generation capacity parameters can reflect the instantaneous maximum power output capacity and technical potential of the wind farm, while the economic effect evaluation parameters reflect the ability to transform technical potential into continuous and reliable income. Therefore, the combination of the two avoids the limitations of traditional evaluation based on a single indicator of short-term power generation, and realizes a comprehensive evaluation of power generation and economic benefits.

[0007] In one optional implementation, the operating parameters of the target wind farm include actual capacity, actual average output power, and actual efficiency; the configuration parameters of the target wind farm include target capacity, target average output power, and target efficiency. The power generation capacity parameters of the target wind farm are calculated based on the operating and configuration parameters, including: determining a capacity coefficient based on the ratio of the actual capacity to the target capacity; determining an output power coefficient based on the actual average output power and the target average output power; determining an efficiency coefficient based on the actual and target efficiency values; and calculating the power generation capacity parameters based on the capacity coefficient, output power coefficient, efficiency coefficient, and the weights of each coefficient.

[0008] In one optional implementation, the calculation method for the comprehensive grid compatibility score includes the following steps: obtaining the voltage imbalance, voltage value, harmonic distortion rate, automatic generation response time, fault absorption degree, fault clearance time, and limit values ​​of each indicator of the target wind farm at the grid connection point; calculating the normalized score of each indicator based on the voltage imbalance, voltage value, harmonic distortion rate, automatic generation response time, and limit values ​​of each indicator of the target wind farm; and calculating the comprehensive grid compatibility score based on the sum of the normalized scores of each indicator, the fault absorption degree, and the fault clearance time.

[0009] In one optional implementation, the indicators include voltage imbalance, voltage deviation, harmonic distortion rate, automatic generation control performance, and automatic voltage control performance. Normalized scores for each indicator are calculated based on voltage imbalance, voltage value, harmonic distortion rate, automatic generation response time, and the limit values ​​for each indicator of the target wind farm. This includes: determining the normalized score for the voltage imbalance indicator based on the ratio of the voltage imbalance at the grid connection point to the limit value of the voltage imbalance indicator; calculating the difference between the voltage value at the grid connection point and the nominal voltage; and calculating the difference between the nominal voltage and the... The normalized score of the voltage deviation index is determined by multiplying the limit values ​​of the voltage deviation index and calculating the ratio of the difference to the product; the normalized score of the harmonic distortion index is determined by the ratio of the harmonic distortion rate at the grid connection point to the limit value of the harmonic distortion index; the normalized score of the automatic generation control performance index is determined by the ratio of the limit value of the automatic generation control performance index to the automatic generation response time; the normalized score of the automatic voltage control performance index is determined by calculating the difference between the limit value of the automatic voltage control performance index and the voltage value at the grid connection point and calculating the ratio of the difference to the operation tracking error.

[0010] In one alternative implementation, the fault clearing time is determined based on the ratio of the fault adjustment time of the target wind farm to the operating cycle duration.

[0011] In one optional implementation, the power generation performance evaluation result of the target wind farm is calculated and determined based on the power generation capacity parameter and the economic effect evaluation parameter, including: if the power generation capacity parameter is greater than a preset value and the economic effect evaluation parameter is greater than the power generation capacity parameter, the power generation performance of the target wind farm is determined to be good.

[0012] Secondly, the present invention provides a wind farm power generation performance evaluation device, comprising: a power generation capacity parameter acquisition module, used to acquire the power generation capacity parameters of the target wind farm based on the operating parameters and configuration parameters of the target wind farm; an economic effect evaluation parameter acquisition module, used to calculate the economic effect evaluation parameters based on the availability rate, grid compatibility comprehensive score, power generation capacity parameters, and health coefficient of the target wind farm; and a power generation performance evaluation module, used to calculate and determine the power generation performance evaluation result of the target wind farm based on the power generation capacity parameters and the economic effect evaluation parameters.

[0013] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the wind farm power generation performance evaluation method of the first aspect or any corresponding embodiment described above.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the wind farm power generation performance evaluation method of the first aspect or any corresponding embodiment thereof.

[0015] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the wind farm power generation performance evaluation method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the wind farm power generation performance evaluation method according to an embodiment of the present invention;

[0018] Figure 2 These are the values ​​of various parameters obtained through a simulation model according to embodiments of the present invention;

[0019] Figure 3These are the values ​​of the power generation capacity and economic effect evaluation parameters obtained from the simulation model according to embodiments of the present invention;

[0020] Figure 4 These are the values ​​of various parameters of the actual wind field according to embodiments of the present invention;

[0021] Figure 5 These are the values ​​of the power generation capacity and economic effect evaluation parameters of the actual wind farm according to embodiments of the present invention;

[0022] Figure 6 This describes the impact of Gc and H on the EEP parameters of a wind farm according to an embodiment of the present invention.

[0023] Figure 7 This describes the changes in PCP and EEP with the overall grid compatibility score when the device health is 0, according to an embodiment of the present invention.

[0024] Figure 8 This describes the changes in PCP and EEP with the overall grid compatibility score when the equipment health level is 1 according to an embodiment of the present invention.

[0025] Figure 9 This describes the changes in PCP and EEP with equipment health when the overall grid compatibility score is 0 according to an embodiment of the present invention.

[0026] Figure 10 This describes the changes in PCP and EEP with equipment health when the overall grid compatibility score is 0.6 according to an embodiment of the present invention.

[0027] Figure 11 This is a structural block diagram of a wind farm power generation performance evaluation device according to an embodiment of the present invention;

[0028] Figure 12 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, 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.

[0030] This embodiment provides a method for evaluating the power generation performance of wind farms. Figure 1 This is a flowchart of a wind farm power generation performance evaluation method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0031] Step S101: Calculate the power generation capacity parameters of the target wind farm based on its operating parameters and configuration parameters.

[0032] In one alternative embodiment, the power generation capacity parameter only reflects the power generation capacity per unit time, without considering economic factors, and often only represents the scale of a wind farm.

[0033] In one optional embodiment, the operating parameters of the target wind farm include parameters such as capacity, output power, and efficiency collected during the operation of the target wind farm, wherein the efficiency is the wind energy capture efficiency or the aerodynamic efficiency of the wind turbine.

[0034] Step S102: Calculate the economic effect assessment parameters based on the availability rate of the target wind farm, the comprehensive grid compatibility score, the power generation capacity parameters, and the health coefficient.

[0035] In an optional embodiment, availability refers to the percentage of time that a wind turbine or wind farm is in a power-generating state. It can be calculated based on the total operating time and downtime of the target wind farm: Availability = (Total time - Downtime) / Total time × 100%. Availability reflects the operation and maintenance level of the wind farm and the reliability of the equipment. The higher the availability, the more healthy the wind turbine is most of the time.

[0036] In one optional embodiment, the grid compatibility comprehensive score is used to quantify the wind farm's adaptability to grid operation requirements. It covers power quality and dynamic control performance. By collecting grid compatibility performance data such as voltage imbalance, voltage deviation, harmonic distortion rate, and AGC / AVC dynamic control at the grid connection point in real time, the actual values ​​of each indicator are compared with the limits specified in national standards or contracts for normalization scoring. The scores are then weighted and summed according to the weight allocation of power quality and dynamic adjustment, which can ultimately reflect the wind farm's adaptability to grid operation requirements.

[0037] In an optional embodiment, the health coefficient can be calculated by weighting the gearbox temperature, blade corrosion degree, and service life / design service life of the wind turbine units in the wind farm after a nonlinear evaluation.

[0038] In one optional embodiment, the gearbox oil temperature is typically controlled between 45°C and 60°C. When the oil temperature exceeds 60°C, the cooling system (such as a fan or water cooling) will activate to lower the temperature. When the oil temperature drops below 45°C, the cooling system will stop operating. When the overall gearbox temperature reaches 70°C-80°C, an alarm threshold will be triggered, and a forced shutdown protection will be implemented at 85°C. Therefore, when the gearbox temperature is between 45°C and 60°C, the temperature index is qualitatively set to "1". When the temperature exceeds 60°C, the score will decrease by 20% for every 5°C change until the gearbox temperature reaches 85°C, at which point the temperature index score drops to 0. When the temperature is below 45°C, the score will decrease by 10% for every 5°C change until the gearbox temperature reaches -5°C, at which point the temperature index score drops to 0.

[0039] In an optional embodiment, the degree of blade corrosion can be divided into three types: mild, moderate, and severe. For different degrees of corrosion, different values ​​can be set for the blade corrosion degree index. For example, if the degree of blade corrosion is mild, the blade corrosion degree index is set to 1; if the degree of blade corrosion is moderate, the blade corrosion degree index is set to 0.6; and if the degree of blade corrosion is severe, the blade corrosion degree index is set to 0.

[0040] In one optional embodiment, if the coating partially peels off or wears off, and the area is less than 5% of the blade surface area without exposing the fiber layer, the blade corrosion is determined to be mild. If the coating peels off to an area of ​​5%-20%, exposing the fiber layer in some areas but without structural damage, the blade corrosion is determined to be moderate. If the peeling area exceeds 20%, and the fiber layer is clearly exposed or shows localized erosion, the blade corrosion is determined to be moderate.

[0041] In one optional embodiment, image data of the wind turbine blades can be acquired, and the degree of blade corrosion can be obtained by analyzing the image data.

[0042] In one alternative embodiment, the design life of onshore wind turbines is typically 20-25 years, and that of offshore wind turbines is 25-30 years. Within the service life, the ratio of service duration to design service life is 1; nearing the end of the service life, the ratio is 0.8; after exceeding the service life and replacing parts, the ratio is 0.5; and after exceeding the service life and with no action taken to prepare for scrapping, the ratio is 0.

[0043] In an optional embodiment, the health coefficient can also be comprehensively evaluated based on indicators such as the wind turbine construction height, the day-night temperature difference of the wind field, the vibration of the wind turbine operating bearings, and the degree of mechanical aging of the wind turbine.

[0044] Step S103: Determine the power generation performance evaluation results of the target wind farm based on the power generation capacity parameters and economic effect evaluation parameters.

[0045] In one optional embodiment, the optimal ranges corresponding to the power generation capacity parameters and economic effect evaluation parameters of a wind farm when its power generation performance is good can be simulated in advance. Then, it is determined whether the actual power generation capacity parameters and economic effect evaluation parameters of the target wind farm are in the above-mentioned optimal ranges, thereby obtaining the power generation performance evaluation result of the target wind farm. For example, if both the actual power generation capacity parameters and economic effect evaluation parameters of the target wind farm are in their respective optimal ranges, the power generation performance of the target wind farm is determined to be excellent. If only one parameter of the actual power generation capacity parameters and economic effect evaluation parameters of the target wind farm is in its optimal range, the power generation performance of the target wind farm is determined to be good. If neither the actual power generation capacity parameters nor the economic effect evaluation parameters of the target wind farm are in their optimal ranges, the power generation performance of the target wind farm is determined to be poor.

[0046] In an optional embodiment, since the economic effect assessment parameters are calculated in combination with the power generation capacity parameters, there is a certain coupling relationship between the economic effect assessment parameters and the power generation capacity parameters. Therefore, the power generation performance assessment result of the target wind farm can also be determined based on the relationship between the economic effect assessment parameters and the power generation capacity parameters.

[0047] The wind farm power generation performance evaluation method provided in this invention calculates the power generation capacity parameters and economic effect evaluation parameters of the target wind farm, respectively. The power generation capacity parameters and economic effect evaluation parameters are used together to obtain the power generation performance evaluation result of the target wind farm. The power generation capacity parameters can reflect the instantaneous maximum power output capacity and technical potential of the wind farm, while the economic effect evaluation parameters reflect the ability to transform technical potential into continuous and reliable income. Therefore, the combination of the two avoids the limitations of traditional evaluation based on a single indicator of short-term power generation, and realizes a comprehensive evaluation of power generation and economic benefits.

[0048] In an optional embodiment, in step S101 above, the operating parameters used when calculating the power generation capacity parameters include the actual capacity value, the actual average output power value, and the actual efficiency value, and the configuration parameters include the target capacity value, the target average output power value, and the target efficiency value.

[0049] In step S101 above, the step of calculating the power generation capacity parameters of the target wind farm based on its operating parameters and configuration parameters specifically includes:

[0050] Step a1: Determine the capacity coefficient based on the ratio of the actual capacity value to the capacity target, determine the output power coefficient based on the actual average output power value and the average output power target value, and determine the efficiency coefficient based on the actual efficiency value and the efficiency target value.

[0051] Step a2: Calculate the power generation capacity parameters based on the capacity factor, output power factor, efficiency factor, and the weight of each factor.

[0052] In this embodiment of the invention, the capacity coefficient is obtained by standardizing the actual capacity value of the target wind farm, which can measure whether the target wind farm has achieved the theoretical power generation target based on local wind resources and equipment performance. The output power coefficient is obtained by standardizing the actual average output power value, which can measure whether the actual average output of the target wind farm has achieved the operation and management target. The efficiency coefficient is obtained by standardizing the actual efficiency of the target wind farm, which can measure whether the equipment health status and technical level of the target wind farm are maintained at the design level. Furthermore, by using the actual values ​​and ratios of each parameter to calculate the power generation capacity parameters, the interference of the target wind farm scale is eliminated, and the obtained parameters can more accurately reflect the power generation capacity of the target wind farm per unit time.

[0053] In an optional embodiment, the power generation capacity parameters can be calculated using the following formula:

[0054]

[0055] Among them, C f P is the capacity factor. avg η is the output power coefficient, and η is the efficiency coefficient.

[0056] In an alternative embodiment, the economic effect assessment parameters can be calculated using the following formula:

[0057]

[0058] Where A is the availability rate, G c The comprehensive score for grid compatibility is calculated, where PCP is the power generation capacity parameter and H is the health coefficient.

[0059] In one optional embodiment, the calculation method for the comprehensive power grid compatibility score includes the following steps:

[0060] Step b1: Obtain the voltage imbalance, voltage value, harmonic distortion rate, automatic generation response time, fault absorption degree, fault elimination time, and limit values ​​of various indicators of the target wind farm at the grid connection point.

[0061] Step b2: Calculate the normalized scores of various indicators based on voltage imbalance, voltage value, harmonic distortion rate, automatic generation response time, and the limit values ​​of various indicators of the target wind farm.

[0062] Step b3: Calculate the comprehensive grid compatibility score based on the normalized scores of various indicators, the degree of fault absorption, and the sum of fault elimination time.

[0063] In an optional embodiment, the indicators include voltage imbalance, voltage deviation, harmonic distortion rate, automatic generation control performance, and automatic voltage control performance. The step b2 above, which calculates the normalized scores for each type of indicator, includes:

[0064] Step b21: Determine the normalized score of the voltage imbalance index based on the ratio of the voltage imbalance at the grid connection point to the limit value of the voltage imbalance index.

[0065]

[0066] Step b22: Calculate the difference between the voltage value at the grid connection point and the nominal voltage, and the product of the nominal voltage and the voltage deviation index limit. Determine the normalized score of the voltage deviation index based on the ratio of the difference to the product.

[0067]

[0068] Step b23: Determine the normalized score of the harmonic distortion index based on the ratio of the harmonic distortion rate at the grid connection point to the limit value of the harmonic distortion index.

[0069]

[0070] Step b24: Determine the normalized score of the automatic generation control performance index based on the ratio of the limit value of the automatic generation control performance index to the automatic generation response time.

[0071]

[0072] Step b25: Calculate the difference between the limit value of the automatic voltage control performance index and the voltage value at the grid connection point. Determine the normalized score of the automatic voltage control performance index based on the ratio between the difference and the operation tracking error.

[0073]

[0074] In an optional embodiment, the fault clearing time is determined based on the ratio of the fault adjustment time of the target wind farm to the operating cycle duration:

[0075]

[0076] In one optional embodiment, the fault absorption degree (FE) adopts a fuzzy control method to divide the fault absorption degree into: no absorption, low absorption, medium absorption and almost complete absorption, and the index values ​​corresponding to different fault absorption degrees are different.

[0077] In an optional embodiment, the overall grid compatibility score can be calculated using the following formula:

[0078]

[0079] In an optional embodiment, step S103 above, which involves calculating and determining the power generation performance evaluation result of the target wind farm based on power generation capacity parameters and economic effect evaluation parameters, specifically includes:

[0080] If the power generation capacity parameter is greater than the preset value, and the economic effect evaluation parameter is greater than the power generation capacity parameter, the power generation performance of the target wind farm is determined to be good.

[0081] In one optional embodiment, the power generation capacity parameter represents the power generation capacity per unit time. Generally, the actual wind farm's power generation capacity is between 0.4 and 0.6 (mainly limited by the wind farm's power generation efficiency). A value higher than 0.45 indicates that the model / wind farm has a relatively good power generation level. The economic effect evaluation parameter represents the wind farm's economic effect over a longer time period. When the economic effect evaluation parameter is higher than the power generation capacity parameter, the wind farm can be considered to be operating stably at that moment, and the higher the economic effect evaluation parameter, the better the wind farm's benefits. However, when the economic effect evaluation parameter is lower than the power generation capacity parameter, the wind farm may have experienced significant power generation problems or certain operational risks, requiring maintenance personnel to promptly investigate the wind farm's problems to avoid major accidents in the future.

[0082] In one specific embodiment, by simulating a wind farm, the values ​​of capacity factor, average power factor, efficiency factor, availability, grid compatibility score, and health factor are obtained as follows: Figure 2 As shown, based on Figure 2 The parameters shown yielded the following PCP and EEP values: Figure 3 As shown in the bar chart, PCP is greater than 0.45, indicating that the overall power generation level of the wind farm is in an ideal state, and EEP = 0.68, which is greater than PCP.

[0083] In one specific embodiment, by simulating a wind farm, the values ​​of capacity factor, average power factor, efficiency factor, availability, grid compatibility score, and health factor are obtained as follows: Figure 2 As shown, based on Figure 4 The parameters shown yielded the following PCP and EEP values: Figure 5 As shown.

[0084] The calculations show that the wind farm's capacity factor is 0.42, and the average power output is close to the capacity factor, meeting the actual power generation standards and grid connection requirements of a wind farm. The efficiency of 0.32 indicates low mechanical losses in the wind turbines, demonstrating high-quality energy conversion capabilities. Since there is currently no downtime maintenance, the availability rate is 1. However, in actual wind farm operation and maintenance, downtime typically occurs once a week to once a month; the lack of downtime maintenance could affect equipment health. The PCP parameter is 0.47, greater than 0.45, and the EEP parameter is greater than PCP. The actual wind farm performance is superior.

[0085] Based on the simulation model and actual wind farm data, after verifying the PCP and EEP indices, we can preliminarily conclude that under normal operating conditions, the PCP index is generally greater than 0.45, providing a relatively intuitive evaluation parameter for wind farm design. In terms of performance, when the EEP index is greater than the PCP index, the wind farm can be considered to be in a healthy operating state. A higher EEP index indicates better long-term economic benefits for the wind farm. However, when the EEP is less than the PCP, it may indicate a sub-healthy state for the wind farm, requiring timely investigation.

[0086] After obtaining preliminary conclusions, a detailed analysis of the relevant data was conducted. Using simulation model parameters as a baseline design, the influence of Gc and H on the EEP parameters of the wind farm was investigated. Three-dimensional plots were used for analysis, and the results were presented. Figure 6 As shown, by Figure 6 It is evident that, in wind farm construction, without considering grid compatibility and equipment health scores, the installed performance indicators will determine the overall PCP parameters of the wind farm. In other words, the power generation capacity of a wind farm is essentially only related to the limited parameters at the time of turbine installation. Introducing these parameters allows for a more intuitive analysis of the economic benefits of sustainable wind farm operation.

[0087] When both the grid compatibility comprehensive score (Gc) and equipment health (H) are set to 0, the EEP index can reach 0.3. However, since the EEP is less than the PCP at this point, the wind farm can be directly judged as unqualified (equipment aging or unable to connect to the grid safely). When the grid compatibility comprehensive score is 0, because the strict constraints are not met, no matter how high the equipment health is, the EEP parameter cannot be raised to match the PCP. This means the wind farm cannot meet the economic standards for long-term operation and has encountered a major problem that needs immediate resolution. When the equipment health is 0, an extremely high grid compatibility comprehensive score can allow the wind farm to operate normally for a short period. However, due to potential equipment safety hazards, this period is only for grid adaptability preparation before shutdown. Therefore, it is necessary to comprehensively consider both grid compatibility indicators and equipment health to determine whether the EEP parameter is better than the PCP parameter and whether the wind farm is in a good operating condition.

[0088] from Figure 6 From this, we can see that the PCP parameter is basically only related to the limitations set at the time of wind turbine installation, with its capacity factor accounting for the largest share; half of the power generation capacity depends on the capacity factor set at the time of installation. However, the power generation capacity of a wind farm parameter considers the unit power generation capacity, not the absolute value of power generation. Therefore, for C... f The design does not use absolute values, but rather the ratio of actual values ​​to design values. This parameter decreases when the actual value of the wind farm capacity factor is less than the design value. avg This refers to the actual output efficiency during operation, not an absolute value. Efficiency is primarily limited by the Betz limit and the mechanical losses of the wind turbine. Therefore, a larger wind farm or a higher installed capacity does not necessarily mean better PCP parameters. Instead, it's based on the actual / target values ​​of the wind farm to accurately measure the power generation performance of the turbines. In its design, PCP avoids the one-sided judgment of prioritizing power generation in traditional wind power assessments. The stronger the power generation per unit area, the higher the actual / target value, the higher the PCP parameters of the wind farm, and the better the power generation performance of the wind turbines. Conversely, when the actual value deviates significantly from the designed target value, it indicates that the PCP parameters of the wind farm need optimization, and the power generation performance of the wind turbines is unsatisfactory.

[0089] In EEP parameter design, it is mainly related to PCP, Gc and H. Each variable can reflect the problem of its corresponding module. If the EEP parameter is low and does not meet the requirement of being greater than PCP, the poor performance can be determined by checking the situation of these three parameters. It can be determined whether the poor performance is caused by poor grid connection standardization of the wind turbine or by low health of the wind turbine equipment.

[0090] In EEP (Electrical Energy Utilization), the grid compatibility score accounts for 25% to 40% of the overall evaluation index, depending on the PCP (Power Consumption Performance) of the wind turbine. The calculation model is tested using the controlled variable method, with the equipment health score set to 0. The resulting EEP as a function of Gc is shown below. Figure 7 As shown, when the device health score is set to 1, the change of EEP with Gc is as follows: Figure 8 As shown.

[0091] Depend on Figure 7 It can be seen that, when the equipment health level is limited to 0, Gc needs to reach 0.749 to ensure that the EEP parameter is greater than the PCP parameter, thus ensuring that the wind farm's performance is at a normal level. Figure 8It is known that when the equipment health level is limited to 1, Gc needs to reach 0.149 to ensure that the wind farm performance is at a normal level. However, since the equipment health level cannot reach 1 in reality, and the wind farm cannot operate for a long time when the equipment health level is 0, the range of Gc from 0.15 to 0.75 is taken as the normal range for reference by the staff, that is, 0.3 < Gc < less than 0.6. In the evaluation, if Gc is less than 0.3, or in the lower half of the range of 0.3 to 0.6, it may be that the wind turbine has a fault such as inaccurate control parameters of the AGC-AVC control loop or abnormal load reduction, and the control parameters of AGC-AVC need to be optimized.

[0092] In EEP (Electrical Equipment Health), equipment health indicators account for 15% to 25% of the overall evaluation indicators, depending on the different PCP (Power Processing Capacity) conditions of the wind turbine. When the grid compatibility score is set to 0, the EEP changes with H as follows: Figure 9 As shown, when the grid compatibility score is set to 0.6, the EEP changes with H as follows: Figure 10 As shown.

[0093] Depend on Figure 9 and Figure 10 It can be seen that when the grid compatibility is limited to 0, H cannot make the EEP meet the standard no matter what. However, when the grid compatibility is at the normal level of 0.6, H of 0.416 can make the EEP meet the standard. Therefore, when H is less than 0.416, it can be judged that there is a problem with the health of the wind turbine, such as inaccurate weather sensors, inaccurate yaw, etc.

[0094] In summary, by analyzing the parameters in detail, wind farm staff can narrow down the scope of troubleshooting wind turbine faults, dividing the overall troubleshooting into grid connection compliance checks and equipment health checks, which greatly improves work efficiency.

[0095] This embodiment also provides a wind farm power generation performance evaluation device, which is used to implement the above embodiments and preferred embodiments, and will not be repeated as already described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0096] This embodiment provides a wind farm power generation performance evaluation device, such as... Figure 11 As shown, it includes:

[0097] The power generation capacity parameter acquisition module 201 is used to calculate the power generation capacity parameters of the target wind farm based on the operating parameters and configuration parameters of the target wind farm.

[0098] The economic effect assessment parameter acquisition module 202 is used to calculate the economic effect assessment parameters based on the availability rate of the target wind farm, the comprehensive score of grid compatibility, the power generation capacity parameters, and the health coefficient.

[0099] The power generation performance evaluation module 203 is used to calculate and determine the power generation performance evaluation results of the target wind farm based on the power generation capacity parameters and economic effect evaluation parameters.

[0100] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0101] In this embodiment, the wind farm power generation performance evaluation device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0102] This invention also provides a computer device having the above-described features. Figure 11 The device shown is for evaluating the power generation performance of a wind farm.

[0103] Please see Figure 12 , Figure 12 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 12 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 12 Take a processor 10 as an example.

[0104] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0105] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0106] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0107] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0108] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 12 Taking the example of a connection between China and Israel via a bus.

[0109] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0110] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0111] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0112] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for evaluating the performance of a wind farm station, characterized in that, The method comprises: calculating a power generation capability parameter of the target wind farm according to operation parameters and configuration parameters of the target wind farm; calculating an economic effect evaluation parameter according to an availability, a grid compatibility comprehensive score, the power generation capability parameter and a health coefficient of the target wind farm; determining a power generation performance evaluation result of the target wind farm according to the power generation capability parameter and the economic effect evaluation parameter.

2. The method of claim 1, wherein, The operation parameters of the target wind farm comprise a capacity actual value, an average output power actual value and an efficiency actual value, and the configuration parameters of the target wind farm comprise a capacity target value, an average output power target value and an efficiency target value; The calculating of the power generation capability parameter of the target wind farm according to the operation parameters and the configuration parameters of the target wind farm comprises: determining a capacity coefficient according to a ratio of the capacity actual value to the capacity target value, determining an output power coefficient according to a ratio of the average output power actual value to the average output power target value, and determining an efficiency coefficient according to a ratio of the efficiency actual value to the efficiency target value; calculating the power generation capability parameter according to the capacity coefficient, the output power coefficient, the efficiency coefficient and weights of the respective coefficients.

3. The method of claim 1, wherein, The calculating of the grid compatibility comprehensive score comprises the following steps: obtaining a voltage unbalance degree, a voltage value, a harmonic distortion rate, an automatic power generation response time, a fault absorption degree, a fault elimination time of a grid connection point of the target wind farm and a limit value of each index of the target wind farm; calculating a normalized score of each type of index according to the voltage unbalance degree, the voltage value, the harmonic distortion rate, the automatic power generation response time and the limit value of each index of the target wind farm; calculating the grid compatibility comprehensive score according to a sum of the normalized scores of each type of index, the fault absorption degree and the fault elimination time.

4. The method according to claim 3, wherein the indexes comprise a voltage unbalance degree index, a voltage deviation index, a harmonic distortion rate index, an automatic power generation control performance index and an automatic voltage control performance index, the calculating of the normalized score of each type of index according to the voltage unbalance degree, the voltage value, the harmonic distortion rate, the automatic power generation response time and the limit value of each index of the target wind farm comprises: determining the normalized score of the voltage unbalance degree index according to a ratio of the voltage unbalance degree of the grid connection point to the limit value of the voltage unbalance degree index; calculating a difference between the voltage value of the grid connection point and a nominal voltage and a product of the nominal voltage and the limit value of the voltage deviation index, and determining the normalized score of the voltage deviation index according to a ratio of the difference to the product; determining the normalized score of the harmonic distortion rate index according to a ratio of the harmonic distortion rate of the grid connection point to the limit value of the harmonic distortion rate index; determining the normalized score of the automatic power generation control performance index according to a ratio of the limit value of the automatic power generation control performance index to the automatic power generation response time; calculating a difference between the limit value of the automatic voltage control performance index and the voltage value of the grid connection point, and determining the normalized score of the automatic voltage control performance index according to a ratio between the difference and a tracking error.

5. The method of claim 3, wherein the fault clearing time is determined according to a ratio of a fault regulation time of the target wind farm and a length of an operating cycle. The fault clearing time is determined according to a ratio of a fault regulation time of the target wind farm and a length of an operating cycle.

6. The method of claim 1, wherein, The generating performance evaluation result of the target wind farm is determined according to the generating capacity parameter and the economic effect evaluation parameter. If the generating capacity parameter is greater than a preset value, and the economic effect evaluation parameter is greater than the generating capacity parameter, it is determined that the generating performance of the target wind farm is good.

7. A wind farm station power generation performance evaluation device characterized by comprising: The device comprises: a generating capacity parameter acquisition module configured to acquire a generating capacity parameter of the target wind farm according to operating parameters and configuration parameters of the target wind farm; an economic effect evaluation parameter acquisition module configured to acquire an economic effect evaluation parameter according to an availability rate, a power grid compatibility comprehensive score, the generating capacity parameter, and a health coefficient of the target wind farm; a generating performance evaluation module configured to determine a generating performance evaluation result of the target wind farm according to the generating capacity parameter and the economic effect evaluation parameter.

8. A computer device, comprising: The device comprises: a memory and a processor in communication connection with each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the wind farm station generating performance evaluation method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer instructions for causing a computer to perform the wind farm station generating performance evaluation method according to any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer instructions are configured to cause a computer to perform the wind farm station generating performance evaluation method according to any one of claims 1 to 6.

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