Wind power plant post-evaluation method and system, medium, program product and computer equipment

Abstract

The invention belongs to the technical field of wind power generation. According to the wind power plant post-evaluation method and system, the medium, the program product and the computer equipment, comprehensive evaluation is conducted on the operation state of a wind turbine generator from the three aspects of wind resources, the power generation amount and the operation condition, problems are found, early warning information is generated, a solution which is feasible in technology, economical, reasonable and practicable is provided, and the wind power plant post-evaluation method and system are provided. The comprehensiveness and the accuracy of wind power plant evaluation are greatly improved; according to the method, all technical improvement schemes are evaluated from the aspects of unit kilowatt-hour cost, technical improvement net profit, generating capacity improvement, cost recovery period and the like, and finally optimization generation of the technical improvement schemes can be guided more accurately and efficiently.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a wind farm post-evaluation method, a wind farm post-evaluation system, a computer device, a computer-readable storage medium and a computer program product. Background Art

[0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art.

[0003] Post-evaluation of wind turbines refers to the analysis and summary of the working conditions of wind turbines that have been put into operation, identifying problems and proposing technically feasible, economically reasonable and practical solutions to improve the economic and social benefits of wind power projects. Although post-evaluation of wind power projects has become a necessary procedure for project construction, a complete post-evaluation indicator system has not yet been established, so that the role of post-evaluation of projects has not been fully exerted, and the evaluation results cannot be effectively used for decision-making of subsequent projects and further improvement of wind farm operation conditions.

[0004] Especially as the years of operation increase, a large number of wind farms have low power generation due to aging equipment and backward technology. For these units, the operating status should be evaluated, technical transformation should be carried out, and their power generation should be increased. However, the cost of technical transformation of wind turbines is high, and the power generation of wind farms built in the early stage involves local subsidies, which need to be reasonably handled before the technical transformation plan is determined; in addition, there are currently few cases of technical transformation of wind turbines, and most of the technical transformation content is a single technical transformation, lacking a representative evaluation method for wind turbines with multiple technical transformation content plans. Summary of the invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a wind farm post-evaluation method, system, medium, program product and computer equipment, establishes a complete post-evaluation indicator system, conducts a comprehensive evaluation of wind turbines, and evaluates various technical transformation plans from various angles such as the unit's cost per kilowatt-hour, net profit from technical transformation, increase in power generation and cost recovery period, ultimately being able to more accurately and efficiently guide the optimization and generation of technical transformation plans.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a wind farm post-evaluation method.

[0007] A post-evaluation method for a wind farm includes the following processes: When the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than the first set warning value, a wind resource warning is issued, and a technical transformation strategy of relocating the machine site or increasing the tower height is generated to increase the wind speed value; When the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than the second set warning value, a power generation warning is issued, and a technical transformation strategy of unit blade modification or model replacement is generated to increase the power generation of the unit; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

[0008] As a further limitation of the first aspect of the present invention, by comparing the cost per kilowatt-hour, net profit from technical transformation, power generation increase and cost recovery period of each unit, the selected technical transformation plans are evaluated to determine the final technical transformation plan.

[0009] As a further limitation of the first aspect of the present invention, the power generation situation is evaluated by verifying the power curve, and the wind speed is cut in. V min, rated wind speed V r, Cut-out wind speed V max partition N1=[ V min, V r] and N2=[ V r, V max] two wind speed intervals, and classify all data points into the two wind speed intervals according to the wind speed; The two wind speed intervals are divided into multiple sub-intervals respectively, and the wind speed statistics in each sub-interval are obtained. Data analysis is performed based on the wind speed statistics. Then, a power curve is fitted using a cubic spline difference algorithm. The power curve is compared with a design curve to obtain an evaluation result of verification compliance or verification non-compliance.

[0010] As a further limitation of the first aspect of the present invention, the unit capacity factor is the ratio of equivalent full-power hours to annual hours, and the wind turbine unit availability includes time-based availability and power generation-based availability; Time-based availability , A represents the number of downtime hours due to failure, and B represents the number of downtime hours caused by reasons other than the responsibility of the assessed object; Availability of power generation , The actual power generation during the statistical period, The lost power generation during the statistical period.

[0011] As a further limitation of the first aspect of the present invention, wind resource indicators include: wind speed and annual utilization hours.

[0012] As a further limitation of the first aspect of the present invention, the power generation indicator includes: power generation and energy availability.

[0013] In a second aspect, the present invention provides a wind farm post-evaluation system.

[0014] A post-evaluation system for a wind farm, comprising: The wind resource assessment and early warning unit is configured to: when the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than a first set warning value, issue a wind resource early warning and generate a technical reform strategy for moving the machine site or increasing the tower height to increase the wind speed value; The power generation evaluation and early warning unit is configured to: when the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than a second set warning value, issue a power generation early warning, and generate a technical transformation strategy for unit blade modification or model replacement to increase the power generation of the unit; The fault assessment and early warning unit is configured to: perform fault detection on the wind turbine according to a preset fault library and a pre-trained machine learning algorithm, and issue a fault alarm when a fault is detected and generate a technical improvement strategy for maintenance and parts replacement; The unit status assessment and early warning unit is configured to calculate the unit capacity coefficient and the wind turbine unit availability, and issue an abnormal unit operating status early warning when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value.

[0015] In a third aspect, the present invention provides a computer device, comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the wind farm post-evaluation method as described in the first aspect of the present invention is implemented.

[0016] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the wind farm post-evaluation method as described in the first aspect of the present invention.

[0017] In a fifth aspect, the present invention provides a computer program product, the computer program product comprising a computer program, and when the computer program is executed by a processor, the wind farm post-evaluation method as described in the first aspect of the present invention is implemented.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention innovatively proposes a complete post-evaluation index system and evaluation method, which comprehensively evaluates the operating status of wind turbines from three aspects: wind resources, power generation, and operating conditions, discovers problems, generates early warning information, and proposes technically feasible, economically reasonable, and practical solutions, which greatly improves the comprehensiveness and accuracy of wind farm evaluation.

[0019] 2. The present invention innovatively proposes a method for optimizing multiple technical transformation plans for wind turbines. This method can select three technical transformation plans: blade transformation, tower transformation and whole-machine displacement. It compares the power generation and cost that can be improved when multiple technical transformations are carried out simultaneously, and obtains evaluation indicators such as the unit's cost per kilowatt-hour, net profit from technical transformation, power generation improvement, and cost recovery period. It fills the gap in the evaluation method for multiple technical transformation plans for wind turbines and provides a reference for subsequent technical transformation of wind turbines.

[0020] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0022] Figure 1 A flowchart of the post-evaluation method for a wind turbine generator system provided in Embodiment 1 of the present invention; Figure 2 A flowchart of a method for optimizing a solution for multiple technical transformation contents of a wind turbine generator set provided in Example 1 of the present invention; Figure 3 A schematic diagram of a wind turbine post-evaluation system provided in Embodiment 2 of the present invention; Figure 4 A schematic diagram of a computer device provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0024] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0025] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0026] Embodiment 1: This implementation proposes a post-evaluation method for wind turbines, such as Figure 1 As shown, specifically, this implementation method comprehensively evaluates the wind resources, power generation conditions, and operating status of the wind farm, diagnoses wind turbine failures, analyzes the causes of the failures, and proposes solutions.

[0027] (1) Wind resource assessment.

[0028] For wind resources in wind farms, evaluate the representativeness and accuracy of the designed wind conditions. Use the maximum likelihood estimation method, mean wind speed and standard deviation estimation method to fit the wind speed Weibull distribution curve, calculate the Weibull parameters, calculate wind speed, wind shear, turbulence intensity and other wind condition indicators, and draw the processed wind condition parameters into a chart, and compare the trends of the parameters before and after to verify the representativeness of the data selected in the design stage.

[0029] Among the wind resource parameters, the wind shear index indicates the change in wind speed in the plane perpendicular to the wind direction, and its magnitude reflects how fast the wind speed increases with height: (1); Where: a represents the wind shear index, Z 1 and Z 2 represents height, V 1 is height Z 1 corresponds to a wind speed of V 2 is height Z 2 corresponds to the wind speed.

[0030] Turbulence intensity is a quantity that characterizes the intensity of turbulence development, that is, the ratio of the standard deviation of wind speed to the average wind speed. Using the same set of measurement data and the specified period for calculation, the 10-minute turbulence intensity is calculated as follows: (2); in, σ is the 10-minute wind speed standard deviation, V The average wind speed in m / s for 10 minutes.

[0031] In addition, the graphics for comparing annual wind condition parameters include the daily change curve of wind speed throughout the year, the daily change curve of wind power throughout the year, the annual change curve of wind speed, the annual change curve of wind power, the annual wind speed and wind energy frequency distribution histogram, the annual wind direction rose diagram, the annual wind energy rose diagram, etc. These graphics are the basis for the two-stage wind condition comparison study.

[0032] The annual average wind speed is calculated based on the hourly wind speed for the two-stage wind conditions. The wind speed is taken at the same height, and the deviation and deviation rate are calculated.

[0033] The daily variation of wind speed throughout the year is obtained by filtering and averaging the wind speeds at each time period of the day according to formula (3), and then obtaining the daily variation law of 24 hours: (3); Where: Every day of the year i Average wind speed during the time period, m / s, i =0~23; For the whole year i The number of wind speeds in the time period (usually 365); For the whole year i The highest wind speed among all wind speeds in the time period j Wind speed, m / s.

[0034] By comparing the wind speeds in the corresponding time periods of the two stages, the maximum deviation, minimum deviation, average deviation and change pattern are obtained.

[0035] Daily variation of wind power density throughout the year The wind power density at a certain time period every day throughout the year is averaged to obtain the daily variation pattern of 24 hours. The average wind power density during the set period is calculated according to (4): (4); Where: Every day of the year i Average wind power density during the time period, W / m2, i =0~23, For the whole year i The number of wind speeds in the time period (usually 365); is the air density, kg / m2; For the whole year i The highest wind speed among all the wind speeds in the period j Wind speed, m / s.

[0036] The average wind power density should be the average of the hourly wind power density within the set period. It is the average annual air density of the local area. In order to compare the wind conditions before and after, the density should be the average air density value obtained by averaging the meteorological station data for many years during the design phase.

[0037] The annual change in wind speed is the average of the wind speed data for the whole year counted on a monthly basis, and it is plotted as a line graph to compare the overall change trend of wind speed in the two stages throughout the year.

[0038] The annual change of wind power density is to calculate the average value of wind power density throughout the year on a monthly basis and plot it into a line graph to compare the overall change trend of wind power density in the wind farm area during the two wind measurement periods.

[0039] The annual wind speed and wind energy distribution frequency is based on a wind speed interval of 1 m / s. The frequency of wind speed and wind energy in each wind speed interval is counted, and the wind speed frequency and wind energy frequency at the hub height in the two stages are counted. i The wind energy density in each wind speed interval is: (5); Where: For the i The average wind energy density in each wind speed interval, W / m2, i =0~23; For the i The number of wind speed data in a wind speed interval; is the air density, kg / m2; For the i The wind speed sequence in the wind speed interval j Wind speed, m / s; For the i The wind speed sequence in the wind speed interval j The time when the wind speed value occurs.

[0040] The annual effective wind speed hours are the number of hours in a year when the wind speed is between the cut-in wind speed and the cut-out wind speed, which is used to compare and analyze the quality of wind resources in the two stages.

[0041] When calculating the wind resources of a wind farm, the Weibull distribution model is often used to describe the wind speed distribution. The wind speed probability density function of the two-parameter Weibull distribution is: (6); Where: k is the shape parameter of the Weibull distribution, i.e., the shape factor; c is the scale parameter of the Weibull distribution, i.e., the proportional factor. These two parameters of the Weibull distribution are suggested to be estimated using the mean wind speed and standard deviation estimation method. The specific formula is: (7); visible yes k Function, when the mean and variance of the distribution are known, we can solve k .

[0042] Due to direct use Solution k It is relatively difficult and can usually be solved using the approximate relationship of formula (8) k : (8); Thus: (9); in and The estimate is: (10); (11); Where: is the wind speed observation value for each time in the calculation period, m / s; N is the total number of observations.

[0043] From equations (8) and (9), we can get k and c The estimated value of is, when the frequency of each level of wind speed interval (such as 0 m / s, 1 m / s, 2m / s) is known, and It can also be approximately calculated as: (12); (13); Where: is the value of each wind speed interval (the median of the interval represents the average value of the interval), m / s; is the frequency of occurrence of each interval.

[0044] The annual wind rose diagram compares the annual wind rose diagrams of two stages to determine the difference in the main wind direction between the two stages.

[0045] The wind energy in each sector is calculated according to the wind direction area, and the wind energy frequency of each sector is obtained by comparing it with the total wind energy. A wind energy rose diagram is made to analyze and compare the differences in wind energy distribution between the two stages.

[0046] (2) Post-evaluation of power generation.

[0047] The power generation status of the unit is evaluated to verify the power curve, review the designed power generation of the wind farm, calculate the reduction factor at the location of each wind turbine, count the parameters such as the grid-connected power and energy availability, analyze the reasons for the differences, and provide a reference for future calculations of wind farm power generation.

[0048] When fitting the power curve, a mathematical model can be established based on the parameter method to fit the power curve. Here, the improved dbin algorithm is used to clean the cubic spline interpolation method to correctly calculate, test, correct and evaluate the power curve according to the specific wind farm environmental conditions.

[0049] The bin algorithm cuts in wind speed ( V min) and cut-out wind speed ( Vmax), and divide the actual wind speed data into these intervals to obtain the wind speed statistics in each interval. The statistical values ​​are used for data analysis. However, the bin algorithm cannot take into account the situation where the power remains unchanged when the wind speed is between the rated wind speed and the cut-out wind speed, which may lead to inaccurate fitting.

[0050] The dbin algorithm is an improvement on the bin algorithm. It uses the district bin algorithm to perform regional calculations to avoid the problem of inaccurate fitting of the wind speed-power curve at rated power. V min), rated wind speed ( V r), cut-out wind speed ( V max) divides N1=[ V min, V r] and N2=[ V r, V max] two wind speed intervals, firstly all data points are classified into these two wind speed intervals according to their wind speeds, and then the bin algorithm is used to perform statistical analysis on the data in the N1 wind speed interval to obtain the statistical value.

[0051] The dbin algorithm retains the advantages of data reduction, data smoothing and outlier elimination achieved by the bin algorithm, and can effectively avoid the phenomenon of inaccurate fitting when the wind power is rated power. The fitting process is as follows: 1) Define cut-in wind speed , cut out wind speed , rated power , rated wind speed , divide the interval , ; 2) Calculate N1 and N2; (14); (15); Wherein, N1 and N2 are positive integers; 3) Calculate the step size and take , the wind speed data is and Divide the intervals, where the step length is calculated as follows: (16); (17); 4) Yes Interval Wind speed data in sub-intervals To conduct statistics, Divided into Grid, refer to formula (18) to calculate the Expected wind speed in the subinterval : (18); 5) Similarly, calculate the power corresponding to the wind speed: (19); 6) Statistics Interval Wind speed data in the sub-interval ,Will Divided into Then use the formula to calculate the Expected wind speed within the sub-range ; 7) The power corresponding to the wind speed in the N2 interval is: (20); This will enable the cleaning of wind speed data.

[0052] Based on the above wind speed data cleaning, the two wind speed intervals are divided into Small area , and perform cubic spline interpolation on the wind speed data in each small interval. The cubic spline equation satisfies the following conditions: In each segment , is a cubic equation; Satisfy the interpolation condition, that is , the curve is smooth, that is Continuous, this cubic equation can be constructed as follows: , this equation is a cubic spline function .

[0053] The cubic spline difference algorithm after dbin cleaning can better fit the power curve to accurately evaluate the power generation of wind turbines.

[0054] When reviewing the designed power generation of a wind farm, the power generation compliance rate is calculated, the power generation in the design stage and the operation stage is counted and a bar chart is drawn, and the power generation of the wind turbines is visually compared through images.

[0055] When verifying the power generation situation, the actual power generation and the designed power generation are used to verify the actual reduction factor. The power curve of the actual operation period and the wind resource data of the design stage are used to calculate the design stage power generation under the operation period power curve. The power generation calculation method is: (twenty one); in, nDis the number of wind direction sectors; nU is the number of wind speed intervals; nT is the number of wind turbines; Fijk It is a wind turbine k In the wind sector i Wind speed is in the range j The probability of Pijk Under the corresponding power curve, the wind turbine is in the wind direction sector i Wind speed range j The power generation power (unit is kW), the annual power generation hours are 8766, that is, the average annual hours of four years considering leap years. The total power generation is the average power generation for one year, the unit is kWh.

[0056] The theoretical grid-connected power of each wind turbine in the design year is obtained. E After that, by comparing it with the theoretical power generation, the comprehensive reduction factor at each unit location can be obtained. δi , the calculation method is: (twenty two); Where: E0i Indicates i Theoretical power generation of a typhoon turbine during the design phase.

[0057] This method can be used to review the designed power generation of the wind farm, analyze the reasons for the differences in the reduction factors at the locations of each wind turbine, and provide a reference for future calculations of wind farm power generation.

[0058] Post-operation status evaluation. To evaluate the operating status and fault conditions of wind turbine equipment, it is necessary to collect statistics and analyze wind turbine faults and unit output in the wind farm. First, a fault library is established, and machine learning algorithms are used to diagnose unit faults, and the causes of faults are analyzed based on the fault library. In addition, by collecting statistics on the failure time, maintenance time, and failure time of each system in the wind farm for each unit throughout the year and drawing them into graphs, it is not only convenient to judge and compare the failure conditions of different units, but also to intuitively show the failure rate and severity of different systems in the wind farm.

[0059] For the analysis of the operating status, refer to the provisions of the national standard GB 18451.1 "Safety Requirements for Wind Turbines", process the statistical data, define appropriate evaluation indicators, so that it can better reflect the actual operating level of the wind turbine, and select the unit capacity factor and wind turbine availability as the main evaluation indicators of this part: (twenty three); Where: 8760 is the number of hours in a year.

[0060] Time-based availability calculation method: (twenty four); In the formula: 8760 represents the number of hours in a year, A represents the number of downtime hours due to failure, and B represents the number of downtime hours caused by reasons other than the assessed object. Including: grid failure time; downtime caused by force majeure; owner-initiated downtime; time when the site cannot be entered due to safety reasons; time for the manufacturer's staff to apply for work tickets or other documents to enter and exit the wind farm; downtime caused by external sensors or external system signals and instructions provided by a third party; unplanned maintenance and the arrival time of the spare parts to be replaced due to reasons other than the assessed object exceeds 24 hours, etc. When the above situations occur at the same time, the longer time is selected for calculation.

[0061] Calculation method of availability based on power generation: (25); Where: is the actual power generation during the statistical period, It is the lost power generation during the statistical period.

[0062] (4) Comprehensive post-evaluation Finally, based on the previous evaluation, a comprehensive evaluation of the entire site is conducted. First, the wind resources, power generation, fault diagnosis and operating status evaluation indicators and evaluation results of each unit are displayed; then, based on the unit evaluation results, the unit operating status is analyzed and the corresponding solutions are associated: Wind resource indicators select wind speed and annual utilization hours, and set a warning value for each indicator (i.e., the first set warning value). When the ratio of the indicator values ​​in the design stage to the operating stage is less than the warning value, consider relocating the machine site or increasing the tower height during technical transformation to increase the wind speed.

[0063] The power generation index selects power generation and energy availability, and sets a warning value (i.e., the second set warning value) for each index. When the ratio of the index value in the design stage to the index value in the operation stage is less than the warning value, consider modifying the unit blades or replacing the model to increase the unit power generation; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

[0064] (5) Technological transformation.

[0065] At present, the technical transformation of wind turbines is basically divided into three types: blade transformation, tower transformation and whole machine displacement. Figure 2 As shown in the figure, the optimization method for the scheme of multiple technical transformation contents of the wind turbine set is to superimpose the technical transformation costs of these three methods and the total profit brought by the increase in power generation. By comparing the cost per kilowatt-hour of each unit, the net profit of technical transformation, the increase in power generation, and the cost recovery period, each technical transformation scheme is evaluated to determine the final technical transformation scheme.

[0066] When calculating the cost, the construction auxiliary projects, equipment and installation projects, construction projects, other expenses, basic reserve, price difference reserve, and construction period interest of the technical transformation content are counted to obtain the total investment of the project. Among them, the construction auxiliary projects should include construction traffic projects, construction power supply projects, construction water supply projects and other construction auxiliary projects; equipment and installation projects include power plant equipment and installation projects, step-up substation equipment and installation projects, control and protection equipment and installation projects and other equipment and installation projects; construction projects include power plant projects, step-up substation projects, housing construction projects, transportation projects and other projects; other expenses include project construction land fees, project construction management fees, production preparation fees, survey and design fees and other taxes and fees.

[0067] When calculating profits, we first calculate the amount of power generated by the technical transformation. We select the content of the technical transformation, and calculate the power generation based on the annual wind speed of the wind farm representative. For wind turbines with longer blades, we recalculate their power curves; for turbines with increased tower heights, we recalculate the wind resources; for turbines with displaced positions, we select the position of the turbine using an intelligent algorithm, and finally calculate the power generation after the technical transformation by adding the above operations. The profit is the annual power generation increased by the technical transformation * the remaining life of the wind turbine * the electricity price.

[0068] After the calculation is completed, the plan is saved, the technical transformation content is changed and recalculated, and after the plan is output multiple times, the unit's cost per kilowatt-hour, technical transformation net profit, power generation increase, and cost recovery period are compared for each plan, and each technical transformation plan is evaluated to determine the final technical transformation plan.

[0069] Embodiment 2: like Figure 3 As shown, this implementation provides a wind farm post-evaluation system, including: The wind resource assessment and early warning unit is configured to: when the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than a first set warning value, issue a wind resource early warning and generate a technical reform strategy for moving the machine site or increasing the tower height to increase the wind speed value; The power generation evaluation and early warning unit is configured to: when the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than a second set warning value, issue a power generation early warning, and generate a technical transformation strategy for unit blade modification or model replacement to increase the power generation of the unit; The fault assessment and early warning unit is configured to: perform fault detection on the wind turbine according to a preset fault library and a pre-trained machine learning algorithm, and issue a fault alarm when a fault is detected and generate a technical improvement strategy for maintenance and parts replacement; The unit status assessment and early warning unit is configured to calculate the unit capacity coefficient and the wind turbine unit availability, and issue an abnormal unit operating status early warning when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value.

[0070] The specific working methods of the above-mentioned units are described in Example 1 and will not be repeated here.

[0071] It is understandable that the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (some) of the units can be further divided into multiple smaller units in function to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In practical applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0072] According to another embodiment of the present application, the system described in this embodiment can be constructed, and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0073] Embodiment 3: like Figure 4 As shown, this implementation provides an electronic device, which includes a processor 1001, a communication interface 1002, and a computer-readable storage medium 1003. The processor 1001, the communication interface 1002, and the computer-readable storage medium 1003 may be connected via a bus or other means.

[0074] Among them, the communication interface 1002 is used to receive and send data, the computer-readable storage medium 1003 can be stored in the memory of the electronic device, the computer-readable storage medium 1003 is used to store a computer program, the computer program includes program instructions, and the processor 1001 is used to execute the program instructions stored in the computer-readable storage medium 1003.

[0075] The processor 1001 (or CPU (Central Processing Unit)) is the computing core and control core of the electronic device, which is suitable for implementing one or more instructions, and specifically suitable for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0076] The processor 1001 is configured to execute the following process: When the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than the first set warning value, a wind resource warning is issued, and a technical transformation strategy of relocating the machine site or increasing the tower height is generated to increase the wind speed value; When the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than the second set warning value, a power generation warning is issued, and a technical transformation strategy of unit blade modification or model replacement is generated to increase the power generation of the unit; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

[0077] The specific working method is described in Example 1 and will not be repeated here.

[0078] Embodiment 4: This implementation provides a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides a storage space that stores the processing system of the electronic device.

[0079] In addition, the storage space also stores one or more instructions suitable for being loaded and executed by the processor, and these instructions may be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here may be a high-speed RAM memory, or a non-volatile memory, such as at least one disk memory; optionally, it may also be at least one computer-readable storage medium located away from the aforementioned processor.

[0080] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: When the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than the first set warning value, a wind resource warning is issued, and a technical transformation strategy of relocating the machine site or increasing the tower height is generated to increase the wind speed value; When the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than the second set warning value, a power generation warning is issued, and a technical transformation strategy of unit blade modification or model replacement is generated to increase the power generation of the unit; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

[0081] The specific working method is described in Example 1 and will not be repeated here.

[0082] Embodiment 5: The present implementation provides a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device performs the following process: When the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than the first set warning value, a wind resource warning is issued, and a technical transformation strategy of relocating the machine site or increasing the tower height is generated to increase the wind speed value; When the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than the second set warning value, a power generation warning is issued, and a technical transformation strategy of unit blade modification or model replacement is generated to increase the power generation of the unit; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

[0083] The specific working method is described in Example 1 and will not be repeated here.

[0084] A person skilled in the art can appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0085] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by the computer or a data processing device such as a server, a data center, etc. that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A post-evaluation method for a wind farm, characterized in that: The process includes: When the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than the first set warning value, a wind resource warning is issued, and a technical transformation strategy of relocating the machine site or increasing the tower height is generated to increase the wind speed value; When the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than the second set warning value, a power generation warning is issued, and a technical transformation strategy of unit blade modification or model replacement is generated to increase the power generation of the unit; Detect wind turbine faults based on a preset fault database and pre-trained machine learning algorithms, issue fault alarms when faults are detected, and generate technical improvement strategies for repairs and parts replacement; The computer unit capacity coefficient and the wind turbine unit availability are calculated, and when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value, an abnormal unit operation status warning is issued.

2. The wind farm post-evaluation method according to claim 1, characterized in that: By comparing the cost per kilowatt-hour, net profit from technical transformation, power generation increase and cost recovery period of each unit, the selected technical transformation plans are evaluated and the final technical transformation plan is determined.

3. The wind farm post-evaluation method according to claim 1, characterized in that: Power generation evaluation through power curve verification, wind speed cut-in V min, rated wind speed V r, Cut-out wind speed V max partition N1=[ V min, V r] and N2=[ V r, V max] two wind speed intervals, and classify all data points into the two wind speed intervals according to the wind speed; The two wind speed intervals are divided into multiple sub-intervals respectively, and the wind speed statistics in each sub-interval are obtained. Data analysis is performed based on the wind speed statistics. Then, a power curve is fitted using a cubic spline difference algorithm. The power curve is compared with a design curve to obtain an evaluation result of verification compliance or verification non-compliance.

4. The wind farm post-evaluation method according to claim 1, characterized in that: The unit capacity factor is the ratio of equivalent full-power hours to annual hours. The wind turbine unit availability includes availability based on time and availability based on power generation. Time-based availability , A represents the number of downtime hours due to failure, and B represents the number of downtime hours caused by reasons other than the responsibility of the assessed object; Availability of power generation , The actual power generation during the statistical period, The lost power generation during the statistical period.

5. The wind farm post-evaluation method according to claim 1, characterized in that: Wind resource indicators include: wind speed and annual utilization hours.

6. The wind farm post-evaluation method according to claim 1, characterized in that: Power generation indicators, including: power generation and energy availability.

7. A post-evaluation system for a wind farm, characterized in that: include: The wind resource assessment and early warning unit is configured to: when the ratio of the wind resource index in the design phase to the wind resource index in the operation phase is less than a first set warning value, issue a wind resource early warning, and generate a technical reform strategy for moving the machine site or increasing the tower height to increase the wind speed value; The power generation evaluation and early warning unit is configured to: when the ratio of the power generation index in the design phase to the power generation index in the operation phase is less than a second set warning value, issue a power generation early warning, and generate a technical transformation strategy for unit blade modification or model replacement to increase the power generation of the unit; The fault assessment and early warning unit is configured to: perform fault detection on the wind turbine according to a preset fault library and a pre-trained machine learning algorithm, and issue a fault alarm when a fault is detected and generate a technical improvement strategy for maintenance and parts replacement; The unit status assessment and early warning unit is configured to: calculate the unit capacity coefficient and the wind turbine unit availability, and issue an abnormal unit operating status early warning when the unit capacity coefficient is less than the third set warning value, or when the wind turbine unit availability is less than the fourth set warning value.

8. A computer device, characterized in that: include: A processor and a computer readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the wind farm post-evaluation method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the wind farm post-evaluation method according to any one of claims 1 to 6.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the wind farm post-evaluation method according to any one of claims 1 to 6 is implemented.

Images