Wind power plant wind turbine generator power curve self-inspection method

Abstract

At present, wind turbine generators become large gradually, and new problems continuously occur along with updating and iteration of the technology. Wherein the output power curve of the wind turbine generator is crucial to a wind power plant. The power curve is a key index for representing the performance of the unit, and the unit of which the power generation performance does not reach a unit design value and a whole-field average value in a unit power generation state can be found through power curve analysis, so that the power generation loss of the unit hidden in the power generation state is found out. A conventional wind power plant cannot verify a power curve of a wind turbine generator by itself, and a third-party mechanism cannot truly reflect an operation power curve of the wind turbine generator of the wind power plant due to lack of wind turbine limited power flag bit data, inaccurate data cleaning and over-conservative analysis results during verification. The invention aims to provide a self-inspection method for a power curve of a wind turbine generator of a wind power plant. The method has the advantages of being easy to operate, accurate, reliable and high in universality. And a nested combination formula of various functions is developed by using a table, and finally a verification tool suitable for the wind power plant is created according to the characteristics of the wind power plant. The method is not only an innovation of a technical method, but also an innovation of a wind power plant asset management method. The method overcomes the defect of the current wind power plant on the technical management of the fan power curve, and is of great significance to the asset management of the wind power plant.

Description

Technical Field

[0001] The invention relates to the technical field of wind farm power generation verification, and in particular to a method for self-checking power curves of wind turbines in a wind farm. Background Art

[0002] At present, wind turbines are becoming increasingly large-scale, and with the updating and iteration of technology, new problems are constantly occurring. Among them, the output power curve of wind turbines is crucial to wind farms. The power curve is a key indicator to characterize the performance of the unit. Through the power curve analysis, it can be found that the power generation performance of the unit does not reach the unit design value and the average value of the whole field under the power generation state, so as to find out the power generation loss of the unit hidden in the power generation state. In the past, wind farms were unable to verify the power curve of wind turbines by themselves, and third-party institutions lacked wind turbine power limit flag data, inaccurate data cleaning, and overly conservative analysis results during verification, which failed to truly reflect the operating power curve of wind turbines in wind farms. Summary of the invention

[0003] The purpose of the present invention is to provide a method for self-checking the power curve of a wind turbine in a wind farm. The method is simple to operate, accurate and reliable, and has strong versatility. The characteristic of the present invention is that according to the daily work habits of wind farm managers, nested combination formulas of various functions are developed using tables, and finally a verification tool suitable for the wind farm is created according to the characteristics of the wind farm. The present invention is not only an innovation in technical methods, but also an innovation in wind farm asset management methods. The present invention overcomes the shortcomings of the current wind farm in the technical management of wind turbine power curves, and is of great significance to wind farm asset management.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: through the daily operation records of the wind farm, the time periods during which the wind turbine output is abnormal due to external factors such as power outages and ice on the blade surface are accurately counted, and then the time format of the statistical data is converted into the data format consistent with the data format of the wind turbine SCADA system using the TEXT function, so as to facilitate data screening during power curve verification.

[0005] Furthermore, before verifying the wind turbine power curve, it is necessary to export data such as wind turbine name, time, average wind speed, average power, etc., and export a data point every 10 minutes.

[0006] Furthermore, the data corresponding to the wind speed and power of the model to be verified are found according to the technical documents, and the single-machine guaranteed power curve of the wind turbine of this model is fitted.

[0007] Furthermore, the operating data exported from the wind turbine SCADA system and the statistical data of all power-cut periods during the operation, and the time periods when the wind turbine output is abnormal due to external factors such as ice on the blade surface, are put into a table.

[0008] Furthermore, the IF+AND function combination is used for conditional judgment to identify whether the time point of each operating data is in a power-limited period, or in a period of time when the wind turbine output is abnormal due to external factors such as ice on the blade surface.

[0009] Furthermore, the data in the power-limited period is eliminated, and the data in the non-power-limited period is retained. The data in the period where the wind power output is abnormal due to external factors is eliminated.

[0010] Furthermore, the IF function is used to perform conditional judgment to identify whether the wind turbine is in a power generation state at each operating data moment.

[0011] Furthermore, the data in the non-power generation period is eliminated, and the data in the power generation period is retained.

[0012] Furthermore, the IF+AND function combination is used to perform conditional judgment and divide the wind speed into intervals.

[0013] Furthermore, the IFERROR+VLOOKUP function combination is used for logical judgment to identify the guaranteed power corresponding to the wind speed of each data point.

[0014] Furthermore, the guaranteed power and the actual power are summed using the SUMIFS function.

[0015] Furthermore, the K value is calculated.

[0016] Furthermore, the AVERAGEIFS function is used to calculate the average power of each wind turbine in each wind speed range.

[0017] Furthermore, the COUNTIFS function is used to count the frequency of occurrence of each wind speed range of each wind turbine.

[0018] Furthermore, the guaranteed power curve and the actual operating power curve of the wind turbine are drawn in the table.

[0019] The wind turbine power curve verification according to the method of the present invention can be completed even by non-professionals. The method of the present invention does not require the use of professional software, and the entire process can be performed in spreadsheet software. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A process for using the method of the present invention;

[0021] Figure 2 This is a statistical table of power loss of the present invention.

[0022] Figure 3 Export data table for wind turbine SCADA system of a wind farm.

[0023] Figure 4It is a statistical table of power loss in the present invention with the same time format as the data export table of the wind turbine SCADA system.

[0024] Figure 5 This is a restricted time period screening table for wind turbine operation data of the present invention.

[0025] Figure 6 This is the “wind speed-power analysis” analysis table of the present invention.

[0026] Figure 7 This is the “Model 1 Power Curve” analysis table of the present invention.

[0027] Figure 8 This is an example of drawing a power curve according to the present invention.

[0028] Fig. 9 This is an example of calculating and settling the power curve K value of the present invention. DETAILED DESCRIPTION

[0029] The following describes the application steps of the present invention in detail in conjunction with a certain wind farm power curve self-checking embodiment.

[0030] Step 1: Figure 2 As shown in the figure, the daily operation record of the wind farm requires statistics of all power losses, including the start time and end time, with the time accurate to the minute. It should be noted that external factors such as ice on the blade surface should be counted in combination with wind turbine alarms to ensure the accuracy of the statistics.

[0031] Step 2: Filter out all the data during the power-limited period, blade icing and other external factors that cause abnormal wind turbine processing. Because the data during this period cannot reflect the actual power generation capacity of the wind turbine itself, this part of the data needs to be eliminated when calculating the power curve later.

[0032] Step 3: If Figure 2 , Figure 3 As shown in the figure, since the time format of the wind farm power loss statistics table is different from the time format of the wind turbine SCADA system data export table, the format must be unified. Here, the TEXT function is used for format conversion, such as Figure 4 As shown, taking the 245th row in the wind farm statistics table as an example, two columns are inserted before the elimination date; after the insertion, column B is the start date, column C is the start time, column D is the start time in the same format, column E is the end time in the same format, column F is the end date, and column G is the end time. Enter formula 1 in column D and get the start time after calculation. Enter formula 2 in column E and get the end time after calculation. At this point, the time format of the power loss statistics table is consistent with the time format of the wind turbine SCADA system data export table.

[0033] Formula 1: =TEXT(B245,"yyyy / mm / dd")&""&TEXT(C245,"hh:mm:ss")

[0034] Formula 2: =TEXT(F245,"yyyy / mm / dd")&""&TEXT(G245,"hh:mm:ss")

[0035] Step 4: Select the historical data such as wind turbine name, time, average wind speed, average power, etc. from the wind turbine SCADA system for query and export, and select the interval time as 10 minutes. It should be noted that the tables exported by the wind turbine SCADA system are generally CSV tables, or full-text Excel tables.

[0036] Step 5: For CSV tables, you need to convert the file into Excel, and then convert all the data into digital format to facilitate subsequent operations. For full-text Excel tables, you need to convert all the data into digital format.

[0037] Step 6: Figure 3 In the table shown, the power generation state is entered in cell J1, the restricted start time is entered in cell K1, the restricted end time is entered in cell L1, and the power restriction state is entered in cell M1.

[0038] Step 7: Figure 4 Copy all the data in columns D and E of the table shown to Figure 3 The Kth and Lth columns in the table shown are pasted starting from cell K2. The data in these two columns are the period data for all power-limited periods, blade icing and other external factors that cause abnormal fan operation.

[0039] Step 8: Use the IF+AND function combination to perform conditional judgment to determine whether the data point is in the time period in step 5. Figure 5 As shown, if there are 31 rows of restricted time statistics, enter formula 3 in cell M2. If the data in this row is in a restricted period (i.e., a period when the fan processing is abnormal due to external factors such as power restriction and blade icing), the calculation result is eliminated, otherwise it is retained.

[0040] Formula 3:

[0041] =IF(OR(AND(B2-K$2>=0,B2-L$2<=0),AND(B2-K$3>=0,B2-L$3<=0),AND(B2-K$4>=0,B2-L$4<=0),AND(B2-K$5>=0,B2-L$5<=0),AND(B 2-K$6>=0,B2-L$6<=0),AND(B2-K$7>=0,B2-L$7<=0),AND(B2-K$8>=0,B2-L$8<=0),AND(B2-K$9>=0,B2-L$9<=0),AND(B2-K$10>=0,B2- L$10<=0),AND(B2-K$11>=0,B2-L$11<=0),AND(B2-K$12>=0,B2-L$12<=0),AND(B2-K$13>=0,B2-L$13<=0),AND(B2-K$14>=0,B2-L$14< =0),AND(B2-K$15>=0,B2-L$15<=0),AND(B2-K$16>=0,B2-L$16<=0),AND(B2-K$17>=0,B2-L$17<=0),AND(B2-K$18>=0,B2-L$18<=0),A ND(B2-K$19>=0,B2-L$19<=0),AND(B2-K$20>=0,B2-L$20<=0),AND(B2-K$21>=0,B2-L$21<=0),AND(B2-K$22>=0,B2 -L$22<=0),AND(B2-K$23>=0,B2-L$23<=0),AND(B2-K$24>=0,B2-L$24<=0),AND(B2-K$25>=0,B2-L$25<=0),AND(B2 -K$26>=0,B2-L$26<=0),AND(B2-K$27>=0,B2-L$27<=0),AND(B2-K$28>=0,B2-L$28<=0),AND(B2-K$29>=0,B2-L$29 <=0),AND(B2-K$30>=0,B2-L$30<=0),AND(B2-K$31>=0,B2-L$31<=0),AND(B2-K$32>=0,B2-L$32<=0)),"Eliminate","Keep")

[0042] Step 9: Automatically fill the M2 cell formula into each row. At this point, all calculation results in the restricted time period are eliminated, and the calculation results in the unrestricted time period are retained.

[0043] Step 10: Filter out all the data with calculation results in the Mth column as the retained data as valid data.

[0044] Step 11: Use the IF function to make conditional judgments to determine whether the data point is in the power generation state at that moment. Figure 5 Enter formula 4 into cell J2 in the table shown. If the data in this row is in the power generation state, the calculation result is valid, otherwise it is eliminated.

[0045] Formula 4: =IF(E2>1,"valid","invalid"), column E in the table is the active power on the grid side of the wind turbine (the description of this item may differ from manufacturer to manufacturer).

[0046] Step 12: Filter out all data with valid calculation results in column J as valid data.

[0047] Step 13: Figure 6 As shown, create a new table and name it "Wind Speed-Power Analysis". Copy all the data in the "Wind Turbine Name" column, "Time" column, "Wind Speed" column, and "Active Power" column in the table obtained by executing step 12 to the A, B, C, and D columns in the new table in turn, starting from cell A1.

[0048] Step 14: Figure 6 In the table shown, enter the wind speed interval in cell E1, use the IF+AND function combination to make conditional judgments, and partition the wind speed. Enter formula 5 in cell E2, and after calculation, partition the wind speed of the row of data (with the wind speed of integer multiples of 0.5m / s as the center, and 0.25m / s on each side as the interval segment, to partition the data).

[0049] Formula 5:

[0050] =IF(AND(D2<0.25,D2>=0),0,IF(AND(D2<0.75,D2>=0.25),0.5,IF(AND(D2<1.25,D2>=0.75),1,IF(AND(D2<1.75,D2>=1.25),1.5,IF(AND(D2<2.25,D2>=1.75),2,IF(AND(D2<2.75,D2>=2.25),2.5,IF(AND(D2<3.25,D2>=2.75),3,IF(AND(D2<3.75,D2>=3.25),3.5,IF(AND(D2<4.25,D2>=3.75),4,IF(AND(D2<4.75,D2>=4.25),4.5,IF(AND(D2<5.25,D2>=4.75),5,IF(AND(D2<5.75,D2>=5.25),5.5,IF(AND(D2<6.25,D2>=5.75),6,IF(AND(D2<6.75,D2>=6.25),6.5,IF(AND(D2<7.25,D2>=6.75),7,IF(AND(D2<7.75,D2>=7.25),7.5,IF(AND(D2<8.25,D2>=7.75),8,IF(AND(D2<8.75,D2>=8.25),8.5,IF(AND(D2<9.25,D2>=8.75),9,IF(AND(D2<9.75,D2>=9.25),9.5,IF(AND(D2<10.25,D2>=9.75),10,IF(AND(D2<10.75,D2>=10.25),10.5,IF(AND(D2<11.25,D2>=10.75),11,IF(AND(D2<11.75,D2>=11.25),11.5,IF(AND(D2<12.25,D2>=11.75),12,IF(AND(D2<12.75,D2>=12.25),12.5,IF(AND(D2<13.25,D2>=12.75),13,IF(AND(D2<13.75,D2>=13.25),13.5,IF(AND(D2<14.25,D2>=13.75),14,IF(AND(D2<14.75,D2>=14.25),14.5,IF(AND(D2<15.25,D2>=14.75),15,IF(AND(D2<15.75,D2>=15.25),15.5,IF(AND(D2<16.25,D2>=15.75),16,IF(AND(D2<16.75,D2>=16.25),16.5,IF(AND(D2<17.25,D2>=16.75),17,IF(AND(D2<17.75,D2>=17.25),17.5,IF(AND( D2<18.25,D2>=17.75),18,IF(AND(D2<18.75,D2>=18.25),18.5,IF(AND(D2<19.25, D2>=18.75),19,IF(AND(D2<19.75,D2>=19.25),19.5,IF(AND(D2<20.25,D2>=19.75 ),20,IF(AND(D2<20.75,D2>=20.25),20.5,IF(AND(D2<21.25,D2>=20.75),21,IF(AN D(D2<21.75,D2>=21.25),21.5,IF(AND(D2<22.25,D2>=21.75),22,IF(AND(D2<22.75,D2>=2 2.25),22.5,IF(AND(D2<23.25,D2>=22.75),23,IF(AND(D2<23.75,D2>=23.25),23.5,IF(AND (D2<24.25,D2>=23.75),24,IF(AND(D2<24.75,D2>=24.25),24.5,IF(AND(D2<25.25,D2>=24 .75),25,IF(D2>=25.25,25.5,))))))))))))))))))))))))))))))))))))))))))))))).

[0051] Step 15: Figure 6 In the table shown, start from column G, row 3 and enter each wind speed range in sequence.

[0052] Step 16: Figure 6 In the table shown, enter the name of each fan in sequence, starting from row 2, column H.

[0053] Step 17: Use the AVERAGEIFS function to average the actual power of each wind turbine in each wind speed range. Figure 6 In the table shown, enter formula 6 in cell H3, and after calculation, the average actual active power of the wind turbine in the wind speed range of 0 m / s is obtained.

[0054] Formula 6: =AVERAGEIFS($C:$C,$E:$E,$G3,$A:$A,H$2)

[0055] Step 18: Fill the formula in cell H3 into each wind speed interval cell corresponding to each wind turbine, and after calculation, obtain the average actual active power of each wind speed interval of each wind turbine.

[0056] Step 19: Figure 7 As shown, the average actual active power data corresponding to each wind speed range of all wind turbines in the table in step 18 is copied to a new worksheet "Power Curve of Model 1", including the data in row 2 and column G, so that the wind speed-power curve can be drawn later.

[0057] Step 20: Following step 16, use the COUNTIFS function to count the frequency of each wind speed interval for each wind turbine. Figure 6 In the table shown, enter formula 7 in cell H3, and after calculation, the frequency of the wind turbine's 0m / s wind speed range is obtained.

[0058] Formula 7: =COUNTIFS($E:$E,$G3,$A:$A,H$2)

[0059] Step 21: Automatically fill the formula in cell H3 into each wind speed interval cell corresponding to each wind turbine, and obtain the frequency of each wind speed interval of each wind turbine after calculation. It should be noted that if the calculation result is less than 3, the data point is considered incomplete and the corresponding average actual power may be distorted.

[0060] Step 22: For the points in the new worksheet created in step 19 where the corresponding data is distorted or the intermediate data is discontinuous, the interpolation method can be used to calculate and supplement. Assume that the wind speed corresponding to the data point is X. The wind speed corresponding to the previous valid data point is X1, and the active power is P1. The wind speed corresponding to the next valid data point is X2, and the power is P2. Then you can enter formula 8 to calculate and deduce the average active power P of this point.

[0061] Formula 8: P = P1 + (P2-P1) * (X-X1) / (X2-X1)

[0062] Step 23: Insert a column to the left of column B in the "Power Curve of Model 1" worksheet, enter the guaranteed power in cell B1, and enter the guaranteed power generation under the corresponding wind speed in this column by referring to the technical data of Model 1.

[0063] Step 24: Use the chart tool - scatter plot - smooth line in the table, select the data in the "Model 1 Power Curve" worksheet, draw the guaranteed power curve and the actual operating power curve, and analyze the problems with the actual operating power curve. Figure 8 This is an example of drawing a curve.

[0064] Step 25: In the above "Wind Speed-Power" worksheet, insert multiple blank cells on the right side of column E, enter the guaranteed power in cell F1, use the IFERROR+VLOOKUP function combination for logical judgment, and calculate the guaranteed active power of each wind turbine in each wind speed range. Enter formula 9 in cell F2, and after calculation, get the guaranteed power of the wind turbine in this row corresponding to the wind speed.

[0065] Formula 9: =IFERROR(VLOOKUP(@E:E,Model 1 Power Curve!A:B,2,FALSE),0)

[0066] Step 26: Automatically fill the formula in cell F2 into each row, and after calculation, obtain the guaranteed active power corresponding to each row of data.

[0067] Step 27: In column G, enter the name of each wind turbine starting from row 5. Enter the converted power generation in cell H4, the guaranteed power generation in cell I4, and the K value in cell J4.

[0068] Step 28: Use the SUMIFS function to sum the guaranteed power and actual power of each wind turbine to calculate the guaranteed converted power generation and the actual converted power generation. Enter formula 10 in cell H5 and get the converted power generation of the first wind turbine after calculation.

[0069] Formula 10: =SUMIFS(C:C,A:A,G5) / 60000

[0070] Step 29: Automatically fill the formula in cell H5 into the cells corresponding to the converted power generation and guaranteed power generation of each wind turbine. After calculation, the converted power generation and guaranteed power generation of each wind turbine are obtained.

[0071] Step 30: Enter formula 11 in cell J5 to calculate the K value. Fig. 9 This is an example of calculation results.

[0072] Formula 11:=H5 / I5

[0073] The above steps are preferred implementation modes of the present invention, but it should be understood that the implementation modes of the present invention are not limited to the above embodiments. For ordinary technicians in this field, they can be improved or transformed according to the above description, and all these improvements and transformations should fall within the scope of protection of the present invention.

Claims

1. A method for self-checking power curve of wind turbines in a wind farm, the characteristics of which are: Through the daily operation records of the wind farm, accurate statistics are collected on the power-cut-off periods, periods of abnormal wind turbine output caused by external factors such as ice on the blade surface, etc. The nested combination formulas of various functions are developed using tables, and finally a verification tool suitable for the wind farm is created.

2. According to claim 1, in combination with the method flow and the formulas involved in the present invention, data verification can be performed using only spreadsheet software without the need to use other professional software.

Images