Performance optimization method for wind turbine generator

Abstract

The embodiment of the invention provides a wind turbine generator performance optimization method, and particularly relates to an industrial internet platform wind turbine generator performance optimization method based on new energy station equipment data direct acquisition, which comprises the following steps: a data acquisition step for acquiring direct acquisition data of a wind turbine generator from a state grid new energy cloud; the data selection step is used for selecting the operation data of the wind turbine generator in the latest year from the acquired data, wherein the operation data comprise the wind speed v, the power P, the air temperature T, the atmospheric pressure B, the relative humidity phi, the fan state and the blade pitch angle; the data filtering step is used for only selecting data during normal operation of the fan from the data obtained in the data selection step and the PLC state of the fan; and a wind turbine generator performance optimization step: selecting the wind turbine generator with a relatively poor power curve to carry out targeted performance optimization so as to improve the generating capacity of the wind turbine generator.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a method for optimizing wind turbine performance on an industrial Internet platform based on direct acquisition of new energy site equipment data. Background Art

[0002] Electric energy is an important area involving national security, so State Grid Corporation of China has established an industrial Internet platform; this platform is a new energy industrial Internet platform formed by the deep integration of new generation information technology and new energy business, called the "State Grid New Energy Platform", and wind turbines are part of it.

[0003] Among them, the power curve is a key indicator of wind turbine performance; how to optimize the performance of wind turbines through the State Grid's new energy platform has always been an urgent problem to be solved. Summary of the invention

[0004] In order to improve the effect of wind turbine performance optimization, the present application provides a wind turbine performance optimization method.

[0005] In order to achieve the above object, the embodiment of the present application proposes a method for optimizing the performance of a wind turbine generator set, comprising:

[0006] The beneficial effects of the above-mentioned technical solution of the present application are as follows: The technical solution proposed in the present application can increase the power generation of wind turbines, clearly guide the operation and maintenance of wind turbines, improve the economic benefits of wind farms, and promote better and faster development of the wind power industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 A schematic diagram of a method flow of an embodiment of the present application;

[0008] Figure 2 This is a schematic diagram for comparing power curves. DETAILED DESCRIPTION

[0009] In order to make the technical problems, technical solutions and advantages to be solved by the present application clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0010] The power curve is a key indicator of the performance of a wind turbine. The present invention is based on quasi-real-time data and historical data directly collected from new energy stations. By comparing the average power curves of hundreds of wind turbines of the same model with their own actual power curves, the performance of the running wind turbine is evaluated and a performance optimization process is given. In the process of calculating the power curve, the influence of air pressure and ambient temperature is taken into account through the platform big data analysis method and converted to standard air density conditions for comparison. In addition, unreasonable data during wind abandonment and power rationing are eliminated to ensure the accuracy of the power curve calculation. The wind turbine performance optimization method includes: anemometer inspection, wind vane inspection, wind turbine yaw inspection, blade angle inspection, power control parameter verification, etc. Through the application of the present invention, the power generation of wind turbines can be increased, the operation and maintenance of wind turbines can be clearly guided, the economic benefits of wind farms can be improved, and the wind power industry can be promoted to develop better and faster.

[0011] The power curve is a key indicator of the performance of a wind turbine. The present invention is based on quasi-real-time data and historical data directly collected from new energy stations. By comparing the average power curves of hundreds of wind turbines of the same model with their own actual power curves, the performance of the running wind turbine is evaluated and a performance optimization method is given. In the process of calculating the power curve, the influence of air pressure and ambient temperature is taken into account through the platform big data analysis method and converted to standard air density conditions for comparison. In addition, unreasonable data during wind abandonment and power rationing are eliminated to ensure the accuracy of the power curve calculation. The wind turbine performance optimization method includes: anemometer inspection, wind vane inspection, wind turbine yaw inspection, blade angle inspection, power control parameter verification, etc. Through the application of the present invention, the power generation of wind turbines can be increased, the operation and maintenance of wind turbines can be clearly guided, the economic benefits of wind farms can be improved, and the wind power industry can be promoted to develop better and faster.

[0012] Implementation scheme of the prior art: The standard test method for the power curve of a wind turbine is generally to establish a wind tower according to the power characteristic test standard IEC61400-12-1 of the International Electrotechnical Commission (IEC), and to establish a power curve by the bin method. This standard specifies the test method for the power characteristics of a single wind turbine, which is applicable to all types and sizes of grid-connected wind turbines. The test method can be used to evaluate the performance of a specific wind turbine in a specific geographical location. Similarly, when considering the influence of specific site conditions and data screening, the method can be used to make a general comparison of wind turbines of different types or different settings. The power characteristics of a wind turbine are determined by the measured power curve and the annual energy production (AEP). The measured power curve is defined as the relationship between wind speed and wind turbine output power, which is determined by the meteorological parameters (including wind speed) and wind turbine signals (including output power) collected synchronously within a certain time period. The time period should be long enough to establish a statistical database under certain wind speed ranges and atmospheric conditions. AEP is calculated using the measured power curve and the frequency distribution of the reference wind speed, and assumes that the availability of the wind turbine is 100%.

[0013] The existing technical solutions have the following problems: The IEC61400-12-1 power characteristic test method is a very accurate and rigorous test method, but there are strict location regulations for the establishment of wind towers. In actual testing, it is impossible to build a dedicated wind tower for each wind turbine. Some wind power stations measure the power curve and only conduct theoretical analysis. They do not conduct cause analysis for wind turbines with poor power curves and low power generation to find the possible causes of power generation loss, thereby guiding the operation and maintenance work. Or, they do the operation and maintenance work but do not form a closed loop and cannot evaluate the effect of the operation and maintenance work.

[0014] The disclosed embodiment provides a method for optimizing the performance of wind turbines based on an industrial Internet platform that directly collects data from new energy stations and equipment. The method is currently used on the State Grid New Energy Cloud Platform, and has calculated the power curves of wind turbines from some typical manufacturers to obtain an average power curve. For wind turbine components whose power generation is lower than the power curve, repair and maintenance suggestions are proposed. By checking anemometers, wind vanes, wind turbine yaw, blade angles, and power control parameter calibration, the power generation of wind turbines is improved, the economic benefits of wind farms are improved, and the wind power industry is promoted to develop better and faster.

[0015] The scheme of the disclosed embodiment is to optimize the performance of wind turbines connected to the State Grid New Energy Cloud. The main content is to select the wind turbine operation data of the most recent year for new energy stations that can provide direct data, filter out unreasonable data according to the wind turbine status and blade pitch angle value, normalize the wind speed data, and uniformly convert it into wind speed under standard air density, calculate the average power curve according to the same manufacturer and the same model of wind turbines, and after completing the calculation of the power curve of a single wind turbine, compare the power curve of the single wind turbine with the average power curve, find out the wind turbine with poor power generation performance and provide targeted operation and maintenance guidance, and calculate the power curve of the wind turbine again one month after the completion of the operation and maintenance, and evaluate the operation and maintenance effect.

[0016] Specifically, the method of the embodiment of the present disclosure is as follows: Figure 1 The steps shown include:

[0017] The first step is the data acquisition step. Select the State Grid New Energy Cloud, which can provide new energy station equipment that can provide direct data acquisition. The new energy cloud is based on cloud resources and cloud service applications. It brings together key data resources such as the production and manufacturing, operation and maintenance status of various new energy equipment. It is a typical example of the application of industrial Internet in the new energy industry. Since the official launch of the new energy cloud in April 2021, it has been deployed and applied in all 27 provincial power grid companies in the operating area of ​​​​the State Grid Corporation of China, connecting to 2.83 million new energy stations with an installed capacity of 550 million kilowatts.

[0018] The second step is data selection: select the wind turbine operation data of the most recent year from the acquired data, including wind speed v, power P, temperature T, atmospheric pressure B, relative humidity φ, wind turbine status, and blade pitch angle; the data directly collected from the New Energy Cloud is the 15-minute average data, 96 points per day;

[0019] The third step is the data filtering step: it is used to obtain the data from the data selection step, and only select the data when the fan is operating normally from the wind turbine PLC status, and filter out the data when the fan is faulty, under maintenance and under power-limited operation; in the data when the fan is operating normally, when the wind speed is less than 95% of the rated wind speed, the blade pitch angle should be 0 degrees ± 0.5 degrees. If the pitch angle is greater than 5 degrees, it may be power-limited or data damaged; similarly, the wind turbine operation data with a wind speed less than 95% of the rated wind speed and a pitch angle greater than 5 degrees is filtered out.

[0020] Step 4: Data processing: The wind turbines used to access the new energy cloud platform are located in different geographical locations and have different wind speed environments. In order to make the wind speeds comparable, they are uniformly converted to the wind speed V under standard air density according to the IEC61400-12-1 standard. n ;

[0021]

[0022] Where:

[0023] V n is the normalized wind speed;

[0024] V 15min The 15-minute average wind speed of the wind turbine direct data;

[0025] ρ 15min It is the average value of air density over 15 minutes;

[0026] ρ0 is the standard air density, 1.225kg / m 3 ;

[0027] ρ 15min The calculation method is as follows:

[0028]

[0029] Where:

[0030] T 15min It is the 15-minute average value of the absolute temperature of the fan direct data, in K;

[0031] B 15min It is the 15-minute average pressure of the wind turbine direct data, in Pa;

[0032] R0 is the gas constant of dry air 287.05;

[0033] Φ is the relative humidity of the fan direct data (0-100%);

[0034] P w is the water vapor pressure, equal to 0.0000205exp(0.0631846T 15min );

[0035] R w The gas constant for water vapor is 461.6.

[0036] The fifth step is the calculation step: used to calculate the power curve according to the wind turbine model; the new energy cloud platform has access to a large number of wind turbines, and has been classified by model. There are thousands of wind turbines of the same model on the calculation platform, and the sampling data is larger, which can offset the positive and negative effects of power and make the calculation results more accurate. According to the IEC61400-12-1 standard, the interval method is used to determine the test power curve for the normalized wind speed and power data group. The interval of 0.5m / s is used. According to formula (3) and formula (4), the normalized wind speed average value and output power average value are calculated for each wind speed interval:

[0037]

[0038]

[0039] Where:

[0040] V i is the normalized average wind speed in the ith interval;

[0041] V n,i,j Normalized wind speed of the jth data set in the i-th interval;

[0042] P i is the average output power in the i-th interval;

[0043] P n,i,j The average output power of the jth data group in the i-th interval;

[0044] N i The number of 15-minute data groups in the i-th interval.

[0045] Apply all normalized wind speeds and powers of the same type of wind turbine to calculate V i and P i , and draw the corresponding curves with them as horizontal and vertical coordinates, which is the average power curve of this model of wind turbine.

[0046] The sixth step is to calculate the power curve of a single wind turbine. The power curve calculation method is the same as the previous step. The normalized wind speed and power of a single wind turbine are used to calculate V i and P i , and draw the corresponding curves with them as horizontal and vertical coordinates, which is the power curve of the wind turbine.

[0047] The seventh step is the screening step: used to find out the wind turbines with poor performance based on the power curve of a single wind turbine; specifically, it includes: comparing the power curve of a single wind turbine with the average power curve, taking 95% of the power value of the average power curve as the standard, if more than half of the interval of the power curve of a single wind turbine is lower than 95% of the average power curve, it is identified as a wind turbine with poor performance.

[0048] Step 8: Guide the operation and maintenance inspection. The main reasons for the poor wind turbine power curve are as follows:

[0049] The wind speed and wind direction meter is damaged or has problems with installation, and there are problems with the wind turbine yaw system, such as unreasonable encoder or yaw margin, inaccurate blade angle zero position, inaccurate power control system parameters, etc. Guide the operation and maintenance personnel to check the anemometer, wind vane, wind turbine yaw, blade angle, and power control parameter, and record the operation and maintenance time and corrective measures.

[0050] Step 9: Operation and maintenance verification. One month later, use the data of a single month to recalculate the power curve of a single wind turbine, and compare the power curve of a single wind turbine with the average power curve (such as Figure 2 to verify the operation and maintenance effect.

[0051] 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 programs. When the computer program is loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer program may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage medium such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0052] A person skilled in the art may understand that the various numerical numbers such as first and second involved in the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application, and also indicate the order of precedence.

[0053] At least one in the present application can also be described as one or more, and a plurality can be two, three, four or more, which is not limited in the present application. In the embodiments of the present application, for a technical feature, the technical features in the technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no order of precedence or size between the technical features described by the "first", "second", "third", "A", "B", "C" and "D".

[0054] The corresponding relationships shown in each table in the present application can be configured or predefined. The values ​​of the information in each table are only examples and can be configured as other values, which are not limited by the present application. When configuring the corresponding relationship between the information and each parameter, it is not necessarily required to configure all the corresponding relationships illustrated in each table. For example, in the table in the present application, the corresponding relationships shown in some rows may not be configured. For another example, appropriate deformation adjustments can be made based on the above table, such as splitting, merging, etc. The names of the parameters shown in the titles of the above tables can also use other names that can be understood by the communication device, and the values ​​or representations of the parameters can also be other values ​​or representations that can be understood by the communication device. When implementing the above tables, other data structures can also be used, such as arrays, queues, containers, stacks, linear lists, pointers, linked lists, trees, graphs, structures, classes, heaps, hash tables or hash tables.

[0055] The predefined in the present application may be understood as defined, predefined, stored, pre-stored, pre-negotiated, pre-configured, solidified, or pre-burned.

[0056] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein 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. Professional and technical personnel 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.

[0057] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0058] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for optimizing wind turbine performance, characterized in that: include: The data acquisition step is used to obtain the direct data of the wind turbine from the State Grid New Energy Cloud; Data selection step: used to select the wind turbine operation data of the most recent year from the acquired data, including wind speed v, power P, temperature T, atmospheric pressure B, relative humidity φ, wind turbine status, and blade pitch angle; among them, the direct data collected from the State Grid New Energy Cloud is 15-minute average data, 96 points per day; Data filtering step: for the data obtained from the data selection step, from the wind turbine PLC status, only the data when the wind turbine is in normal operation is selected, and the data when the wind turbine is in failure, maintenance and power-limited operation are filtered out; in the data when the wind turbine is in normal operation, when the wind speed is less than 95% of the rated wind speed, the blade pitch angle should be 0 degrees ± 0.5 degrees. If the pitch angle is greater than 5 degrees, it may be power-limited or data damaged; similarly, the wind turbine operation data with a wind speed less than 95% of the rated wind speed and a pitch angle greater than 5 degrees is filtered out; Data processing steps: The wind turbines used to access the new energy cloud platform are located in different geographical locations and have different wind speed environments. In order to make the wind speed comparable, they are uniformly converted to the wind speed V under standard air density according to the IEC61400-12-1 standard. n ; Where: V n is the normalized wind speed; V 15min The 15-minute average wind speed of the wind turbine direct data; ρ 15min It is the average value of air density over 15 minutes; ρ0 is the standard air density, 1.225kg / m 3 ; ρ 15min The calculation method is as follows: Where: T 15min It is the 15-minute average value of the absolute temperature of the fan direct data, in K; B 15min It is the 15-minute average pressure of the wind turbine direct data, in Pa; R0 is the gas constant of dry air 287.05; Φ is the relative humidity of the fan direct data (0-100%); P w is the water vapor pressure, equal to 0.0000205exp(0.0631846T 15min ); R w The gas constant for water vapor is 461.6; Calculation steps: used to calculate the power curve according to the wind turbine model; China Grid New Energy Cloud has access to a large number of wind turbines, and has been classified according to the model; according to the IEC61400-12-1 standard, the interval method is used to determine the test power curve for the normalized wind speed and power data group, using an interval of 0.5m / s, and according to formulas (3) and (4), the normalized wind speed average value and output power average value are calculated for each wind speed interval: Where: V i is the normalized average wind speed in the ith interval; V n,i,j Normalized wind speed of the jth data set in the i-th interval; P i is the average output power in the i-th interval; P n,i,j The average output power of the jth data group in the i-th interval; N i The number of 15-minute data groups in the i-th interval; Apply all normalized wind speeds and powers of the same type of wind turbine to calculate V i and P i , draw the corresponding curves of horizontal and vertical coordinates respectively, which is the average power curve of this type of wind turbine; The power curve calculation steps for a single wind turbine are as follows: The power curve calculation method is the same as the previous step, and the normalized wind speed and power of a single wind turbine are used to calculate V i and P i , draw the corresponding curves of horizontal and vertical coordinates respectively, which is the power curve of the wind turbine; Screening step: used to find out the wind turbines with poor performance based on the power curve of a single wind turbine; specifically including: comparing the power curve of a single wind turbine with the average power curve, taking 95% of the power value of the average power curve as the standard, if more than half of the interval of the power curve of a single wind turbine is lower than 95% of the average power curve, it is identified as a wind turbine with poor performance.

2. The method for optimizing wind turbine performance according to claim 1, further comprising: Guide operation and maintenance inspection steps: used to determine the cause of the poor wind turbine power curve; The reasons include at least: The wind speed and direction meter is damaged or has problems with installation, the wind turbine yaw system has problems such as unreasonable encoder or yaw margin, inaccurate blade angle zero position, and inaccurate power control system parameters; The operation and maintenance personnel shall check the anemometer, wind vane, wind turbine yaw, blade angle, power control parameter calibration, and record the operation and maintenance time and corrective measures.

3. The method for optimizing wind turbine performance according to claim 1, further comprising: The operation and maintenance verification step is used to recalculate the power curve of a single wind turbine using the data of a single month after a predetermined time; And compare it with the single wind turbine power curve and average power curve determined last time to determine the inspection and operation and maintenance effect.

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