CN112347655A - Wind power plant theoretical power calculation method based on unit operation performance evaluation - Google Patents

Wind power plant theoretical power calculation method based on unit operation performance evaluation Download PDF

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CN112347655A
CN112347655A CN202011290260.9A CN202011290260A CN112347655A CN 112347655 A CN112347655 A CN 112347655A CN 202011290260 A CN202011290260 A CN 202011290260A CN 112347655 A CN112347655 A CN 112347655A
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杨立滨
李延和
张海宁
李春来
李正曦
张勋
许辉
金卫华
刘庭响
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State Grid Qinghai Electric Power Co Clean Energy Development Research Institute
State Grid Qinghai Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Qianghai Electric Power Co Ltd
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State Grid Qinghai Electric Power Co Clean Energy Development Research Institute
State Grid Qinghai Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Qianghai Electric Power Co Ltd
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Abstract

A wind power plant theoretical power calculation method based on unit operation performance evaluation relates to the technical field of new energy power generation, and comprises the following steps: in the data identification stage, abnormal data caused by faults or interference in links such as data acquisition or communication and the like are identified; the data restoration stage is used for reconstructing the abnormal data of the new energy operation data in three categories according to the characteristics of the abnormal data identified in the data identification stage; in the stage of grouping the non-sample board machines and identifying the output proportionality coefficients of all groups, dynamically identifying the output proportionality coefficient of each group; and in the theoretical power calculation stage of the wind power plant, calculating the theoretical power of the wind power plant according to the calculation formula of the theoretical generating power within the formula statistic time. The invention has the beneficial effects that: the method is easy to operate, simple in modeling and capable of accurately identifying the output proportionality coefficient between each group of non-sample computers and the sample computers, and therefore the calculation accuracy of the theoretical power of the wind power plant is effectively improved.

Description

Wind power plant theoretical power calculation method based on unit operation performance evaluation
Technical Field
The invention relates to the technical field of new energy power generation, in particular to a wind power plant theoretical power calculation method based on unit operation performance evaluation.
Background
With the gradual maturity of wind power generation technology, the attention of wind power renewable energy is greatly improved, however, due to the large-scale rapid unordered operation of wind power plants and the relative lag of the construction of a power grid framework, the wind power receiving capacity of a power grid is limited, and the phenomenon of wind abandonment sometimes occurs. Therefore, the research on theoretical generating power of the wind power plant is necessary, the wind power abandoning amount of the wind power plant can be scientifically and accurately evaluated, the contradiction between the power grid and the plant is relieved, the evaluation of the actual output level of the wind power plant by a dispatching department is facilitated, and a reference is provided for scientific statistics of the power limiting electric quantity of the wind power plant.
At present, a sample board computer method is mainly adopted for calculating theoretical power of a wind power plant, namely single-machine equipment with good performance, stable operation and relatively few faults is selected as the sample board computer, and the theoretical power of the wind power plant is calculated through actual power of the single-machine equipment.
Disclosure of Invention
The invention provides a wind power plant theoretical power calculation method based on unit operation performance evaluation, which aims to overcome the defects of the prior art and solves the problem that the theoretical power calculated by the original sampling machine method has a large error due to the uncertainty of the output proportionality coefficients of the sampling machine and a non-sampling machine caused by factors such as geographic position, arrangement mode and weather.
The invention provides a wind power plant theoretical power calculation method based on unit operation performance evaluation, which specifically comprises the following steps: the method comprises a data identification stage, a data restoration stage, a non-board machine grouping and all groups of output proportion coefficient identification stage and a theoretical power calculation stage of the wind power plant.
And (3) a data identification phase. The method comprises the following steps of identifying the abnormality caused by the fault or interference in the links of data acquisition or communication and the like:
step 1: and identifying null values appearing at a certain time point or time period in the time sequence power data of all the new energy wind turbine generators according to null value identification criteria, and removing the abnormal data.
Step 2: and identifying the out-of-limit values exceeding the reasonable range in the time sequence power data of all the new energy wind turbine generators according to the out-of-limit value identification criterion, and removing the abnormal data.
And step 3: and identifying data values which are not refreshed continuously for a period of time in the time sequence power data of all the new energy wind turbine generators according to a non-refreshing value identification criterion, and removing the abnormal data.
And 4, step 4: and identifying distortion data generated in the time sequence power data of all the new energy wind turbine generators according to a distortion data identification criterion, and removing the abnormal data.
And (5) a data repairing stage. Aiming at the characteristics of the abnormal data identified in the steps 1-4, the abnormal data reconstruction is realized on the new energy running data in three categories including single-point abnormality, multi-point abnormality and continuous abnormality, and the steps are as follows:
and 5: and filling the single-point abnormal data in the time sequence power data of the new energy wind turbine generator by adopting a constant/mean value method.
Step 6: for multipoint abnormal data which is not more than 4h in time sequence power data of the new energy wind turbine generator, reconstructing the abnormal data by adopting a reconstruction method based on ARMA model prediction, and establishing average credibility gammapCalculating a forward predicted value of the weighted reconstruction value at the moment k
Figure BDA0002783580260000021
And reverse prediction
Figure BDA0002783580260000022
Thereby improving the reliability of the reconstruction result at the time k; and regarding the multipoint exception exceeding 4h as continuous exception data, and the data reconstruction process goes to step 7.
And 7: for continuous abnormal data in time sequence power data of the new energy wind turbine generator, firstly, a reconstruction method based on sequence time delay correlation is adopted to find out the continuous abnormal data in the time sequence power data of the new energy wind turbine generatoryWind turbine generator system W with maximum time delay correlationx(x is 1,2, N is the number of new energy wind turbine generators), and W is utilizedxForce curve of (d) to WyAnd performing data reconstruction on the continuous abnormal operation data.
Grouping non-sample board machines and identifying each group of force proportion coefficients. In the stage, after the non-sample board machines of the same type in the new energy power station are grouped based on the unit operation performance, the output proportionality coefficient of each group is dynamically identified, so that the relative output condition of the non-sample board machines is accurately mastered, and the method comprises the following steps:
and 8: through the basic output performance characteristic vector W of the ith non-sample board computer in the wind power plantiAnd the average characteristic vector of the basic output performance of the sample plate machine
Figure BDA0002783580260000031
Pearson's correlation coefficient RisDividing the interval unit by taking 0.1 as the interval unit, and dividing the non-sample board machines of each interval unit into one group, marking as the g group, wherein the number of the non-sample board machines is
Figure BDA0002783580260000032
The total number of the groups is 20.
And step 9: calculating the actual output average value sequence of the g group of non-sample plate machines of the k model fan in the statistical time
Figure BDA0002783580260000033
Actual output average value sequence of sample plate machine in statistical time
Figure BDA0002783580260000034
Power difference rate of
Figure BDA0002783580260000035
Obtaining a proportionality coefficient of the g group of non-sample plate machines relative to the sample plate machine
Figure BDA0002783580260000036
Thus, the output proportionality coefficient of each group is dynamically identified.
And (3) calculating theoretical power of the wind power plant.
Step 10: calculating to obtain the output proportionality coefficient of each group of non-sample computers relative to the sample computer in the k model fan of the wind power plant
Figure BDA0002783580260000037
Number of non-sample plate machines
Figure BDA0002783580260000038
And the average value of the actual output of the sample plate machine
Figure BDA0002783580260000039
And calculating the theoretical power of the wind power plant according to a calculation formula of the theoretical generating power within formula statistic time.
The invention has the beneficial effects that: in consideration of data abnormity caused by faults or interference of the new energy wind turbine generator in links such as data acquisition or communication, different types of abnormal data identification criteria are established to identify and remove abnormal data of the time sequence output data of the new energy wind turbine generator; considering that the integrity of the data is damaged and the usability of the data is influenced after the abnormal data are removed, different methods are adopted to reconstruct single-point, multi-point and continuous abnormal data respectively, so that errors caused by subsequent theoretical power calculation of the new energy power station are effectively reduced; in consideration of the fact that the output proportionality coefficient between the original sample board computer method and the non-sample board computer cannot be accurately calculated when the theoretical power is calculated, compared with a grouping method considering terrain and wake effect, the non-sample board computer grouping method based on wind turbine generator running performance evaluation has the advantages of easiness in operation, simplicity in modeling and capability of accurately identifying the output proportionality coefficient between each group of non-sample board computers and the sample board computers, and therefore the calculation accuracy of the theoretical power of the wind power plant is effectively improved.
Drawings
FIG. 1 is a block diagram of a theoretical power calculation process of a wind farm according to the present invention.
Detailed Description
Embodiment 1, as shown in fig. 1, the invention provides a wind farm theoretical power calculation method based on unit operation performance evaluation, which includes four stages, a data identification stage, a data restoration stage, a non-board machine grouping and each group of output proportionality coefficient identification stage, and a wind farm theoretical power calculation stage; the method comprises the following steps:
step 1: according to the vacancy value identification criterion, identifying vacancy values appearing at a certain time point or time period in the time sequence power data of all the new energy wind turbine generators, and removing abnormal data;
step 2: according to the out-of-limit value identification criterion, identifying out-of-limit values which exceed a reasonable range in the time sequence power data of all the new energy wind turbine generators, and removing abnormal data;
and step 3: according to the non-refreshing value identification criterion, identifying data values which are not refreshed continuously for a period of time in the time sequence power data of all the new energy wind turbine generators, and removing the abnormal data;
and 4, step 4: identifying distortion data generated in time sequence power data of all new energy wind turbine generators according to a distortion data identification criterion, and removing abnormal data;
and 5: filling single-point abnormal data in the time sequence power data of the new energy wind turbine generator by adopting a constant/mean value method;
step 6: for multipoint abnormal data which is not more than 4h in time sequence power data of the new energy wind turbine generator, reconstructing the abnormal data by adopting a reconstruction method based on ARMA model prediction, and establishing average credibility gammapCalculating a forward predicted value of the weighted reconstruction value at the moment k
Figure BDA0002783580260000051
And reverse prediction
Figure BDA0002783580260000052
Thereby improving the reliability of the reconstruction result at the time k; regarding the multipoint abnormality exceeding 4h as continuous abnormal data, and transferring the data reconstruction process to step 7;
and 7: for continuous abnormal data in time sequence power data of the new energy wind turbine generator, firstly, a reconstruction method based on sequence time delay correlation is adopted to find out the continuous abnormal data in the time sequence power data of the new energy wind turbine generatoryWind turbine generator system W with maximum time delay correlationx(x is 1,2, N is the number of new energy wind turbine generators), and W is utilizedxForce curve of (d) to WyCarrying out data reconstruction on the continuous abnormal operation data;
and 8: through the basic output performance characteristic vector W of the ith non-sample board computer in the wind power plantiAnd the average characteristic vector of the basic output performance of the sample plate machine
Figure BDA0002783580260000053
Pearson's correlation coefficient RisDividing the interval unit by taking 0.1 as the interval unit, and dividing the non-sample board machines of each interval unit into one group, marking as the g group, wherein the number of the non-sample board machines is
Figure BDA0002783580260000054
20 groups in total;
and step 9: calculating the actual output average value sequence of the g group of non-sample plate machines of the k model fan in the statistical time
Figure BDA0002783580260000055
Actual output average value sequence of sample plate machine in statistical time
Figure BDA0002783580260000056
Power difference rate of
Figure BDA0002783580260000057
Obtaining a proportionality coefficient of the g group of non-sample plate machines relative to the sample plate machine
Figure BDA0002783580260000058
Dynamically identifying the output proportionality coefficient of each group;
step 10: calculating to obtain the output proportionality coefficient of each group of non-sample computers relative to the sample computer in the k model fan of the wind power plant
Figure BDA0002783580260000059
Number of non-sample plate machines
Figure BDA00027835802600000510
And the average value of the actual output of the sample plate machine
Figure BDA00027835802600000511
And calculating the theoretical power of the wind power plant according to a calculation formula of the theoretical generating power within formula statistic time.
The theoretical power improvement calculation of the wind power plant specifically comprises the following steps:
time sequence output data X of all new energy wind turbine generators is { X ═ X1,x2,...,xnAnd (6) identifying and reconstructing abnormal data.
The criteria for identifying the different types of anomalous data are as follows:
(1) identification of a vacancy value
If x is presentiSatisfies the following conditions:
Figure BDA0002783580260000061
then x is judgediThere is a loss of data.
(2) Identification of threshold values
If x is presentiIf the following relation is satisfied, x is determinediOut of the normal range of values.
xi＞xh|xi＜xl (2)
In the formula, xh,xlUpper and lower limits of the normal numerical range, respectively.
(3) Identification of non-refreshed values
If the data is not refreshed for a period of time, the data is identified as not refreshed except for the first data point.
(4) Identification of distorted data
The powers of adjacent wind turbines generally have similar trends, so based on the spatial correlation of the output of the adjacent wind turbines, N power data X of the adjacent wind turbines having spatial correlation with the wind turbine A are utilizedM＝{xM1,xM2,...,xMnFor power data X of observed wind turbine generator (1, 2, …, N) } (M ═ 1,2, …, N)A＝{xA1,xA2,...,xAnChecking is carried out, and abnormal data are identified. The method comprises the following specific steps: normalizing the power based on the formula (3) to obtain power normalization data X of the observation wind turbine generatorA *＝{xA1 *,xA2 *,...,xAn *And power normalization data X of adjacent wind turbine generatorsM *＝{xM1 *,xM2 *,...,xMn *And the difference of the two at each sampling point is represented by formula (4).
Figure BDA0002783580260000062
eAi-Mi＝|xAi *-xMi *| (4)
The power difference value and the average value of the wind turbine generator A and the wind turbine generator M are respectively as follows:
EA-M＝{eA1-M1,eA2-M2,…,eAn-Mn} (5)
Figure BDA0002783580260000064
because power data obtained by different wind turbines in different time periods are different in abnormal conditions, in order to obtain a recognition result with higher reliability, for a data set to be recognized, of which the removed data is missing, not refreshed and the data is out of limit, the average value and the standard difference of the output power difference of the wind turbines and the adjacent wind turbines after normalization need to be calculated and observed. Because the correctness of the output data of the adjacent wind turbines cannot be determined in advance, the method ensures that
Figure BDA0002783580260000071
Determining abnormal data according to the criterion of Lewy-one, i.e. when
Figure BDA0002783580260000072
Then x is considered to beiIs the distorted data.
The reconstruction method of different abnormal data comprises the following steps:
(1) single point exception
For the single-point abnormal data, the filling method can be adopted to carry out engineering reconstruction on the abnormal data, namely an empirical constant is selected or the abnormal data is replaced according to a certain rule, and the process is simple and time-saving and has considerable accuracy.
1) Constant filling: and based on manual experience, filling the single-point abnormal data by adopting the same specified constant value.
2) Mean value filling: the single point abnormal data may be filled in using the mean of the data sequence or the mean of the data before and after the abnormal data.
(2) Multipoint exception
For multipoint abnormity, due to the fact that complete actual operation data exist on two sides of the abnormal data, the abnormal data can be reconstructed by a reconstruction method based on ARMA model prediction according to the actual operation data on the two sides.
1) Assumed time period tm,tn]Operating data for missing data periods based on intact new energy unit
Figure BDA0002783580260000073
M-step prediction was performed from both sides using the ARMA model.
Pt＝β1Pt-1+β2Pt-2+…+βpPt-p+Zt (7)
Zt＝εt+λ1εt-1+λ2εt-2+…+λ1εt-1 (8)
In the formula, betaiIs an autoregressive parameter; lambda [ alpha ]iIs a moving average parameter; epsiloniIs the prediction error.
2) For the prediction result of the p-th step, if the prediction error is less than the error threshold epsilon0And if so, the prediction result is considered to be credible. Determining the average reliability gamma of the prediction of the p-th step of the data sequence by predicting a large amount of perfect operation datap：
Figure BDA0002783580260000081
In the formula, Count (·) is the frequency statistics; epPredicting error for the p step; n is the total number of times.
3) Calculating a forward predicted value of the weighted reconstruction value at the moment k
Figure BDA0002783580260000082
Has a prediction time length of k-tm+1, acceptance rate of
Figure BDA0002783580260000083
And reverse predict the value
Figure BDA0002783580260000084
Is tn-k +1, acceptability of
Figure BDA0002783580260000085
Thus, the final reconstruction result at time k is:
Figure BDA0002783580260000086
the reconstruction method based on the self-output rule considers the prediction effect of ARMA, and the reconstruction time length is generally limited within 4 h.
(3) Continuous abnormality
As the time scale of the abnormal data increases, the accuracy of the reconstruction method based on the ARMA model prediction will be significantly reduced. Because a certain correlation exists among various new energy operation data, for continuous abnormal data, a reconstruction method based on sequence time delay correlation can be adopted to reconstruct the abnormal data.
1) Time delay correlation
Suppose there are two time series X ═ X1,x2,…,xnY ═ Y1,y2,…,ynThe correlation coefficient R (l) of Y relative to X delay time l is calculated as:
Figure BDA0002783580260000087
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000091
the maximum value of l is n/2, and when l is changed from 0 to n/2, R (0), R (1), … and R (n/2) are obtained.
The delay corresponding to the maximum value is (maximum delay correlation point), if (is correlation threshold), then X and Y have delay correlation. (three-point prediction exploration can be adopted, the 1 st exploration point (determined by the trigonometry) is arranged from the position where the maximum time delay related point is most likely to appear, and other exploration points are respectively arranged on the left side and the right side of the exploration point in a geometric progressive mode.
2) Reconstruction based on new energy wind turbine generator output time delay correlation
Assume missing data period is [ t ]m,tn]The wind turbine generator to be reconstructed is W0The rest wind turbine generators are W1,W2…. Taking each wind turbine generator at time interval [ tm-t0,tm]Internal force data. First calculate W0And W1The maximum delay correlation point of the output force in the time interval and the corresponding delay correlation coefficient if R (l)1')＝max(R(l1'),R(l2') …), then W0And W1The corresponding maximum delay correlation point is l1', the corresponding delay correlation coefficient is R (l)1') to a host; then respectively calculating W according to the calculated values0And W2,W3… the maximum delay correlation point and corresponding delay correlation coefficient of the force output during the time period; if there is W0And W1When the delay correlation coefficient is maximum, W is used1Force curve of (d) to W0And reconstructing the historical operating data. I.e. W0And W1The force relationships of (d) can be fit using linear regression as:
Figure BDA0002783580260000092
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000093
and
Figure BDA0002783580260000094
are respectively W0And W1At the t thmAnd tm+l1' output value at time; a and b are coefficients to be determined, and can be obtained by a least square method.
Grouping of wind power plant non-sample board machines, dynamic identification of each group of output proportional coefficients and calculation of theoretical power are as follows:
(1) non-sample board machine grouping based on unit operation performance evaluation
Firstly, establishing a basic power generation performance index (non-power-limiting condition) of the wind turbine generator set as follows:
a) maximum power of the moon w1
Maximum power of the moon w1(kW) is the actual power P of the wind turbine generator 30 days before the observation dayr＝{p1,p2,…,pnThe maximum of, i.e.:
w1＝max{pi},i＝1,2,…,n (13)
in the formula, n is the number of sample sampling points.
b) Monthly average generated power w2
Figure BDA0002783580260000101
In the formula, Pr,j,tTo observe the actual power (kW) of the sample at jth day 30 days before the day.
c) Annual rate of change w of average generated power3
The annual change rate of the average generating power of the unit reflects the trend of the generating level of the unit changing along with time:
Figure BDA0002783580260000102
Figure BDA0002783580260000103
Figure BDA0002783580260000104
in the formula, Pr,av,yObserving the average actual generated power (kW) for the unit in the first 365 days before the day; p'r,av,yFor the unit to observe the average in the second 365 days before the dayActual generated power (kW); p'r,j,tObserving the actual power (kW) of the jth sampling point on the jth day in the first 365 days before the day; p'r,j,tTo observe the actual power (kW) of the jth sampling point on the jth day in the 365 th day before the day; m is the number of sampling points per day.
d) Number of full hair4
Number of full hair4(h) In order to observe the ratio of the actual power generation amount of the wind turbine generator to the rated power 30 days before the day, the method can be used for comparing power generation systems with different installed capacities, and the calculation formula is as follows:
Figure BDA0002783580260000105
Figure BDA0002783580260000106
in the formula, PnFor observing the rated power (kW) of the wind turbine 30 days before the day, ErTo observe the actual power generation 30 days before the day.
Establishing a characteristic vector W of the basic output performance of the wind turbine generator within statistical time by using the basic generation performance index of the wind turbine generator1,w2,w3,w4}. Setting the basic output performance characteristic vector of the ith non-sample plate machine of the k model fan in the statistical time as Wi k＝{wi1,wi2,wi3,wi4The average characteristic vector of the basic output performance of the k model sample plate machine in the statistical time is
Figure BDA0002783580260000111
Wherein M iskThe total number of the full wind power fields of the k model sample machines;
Figure BDA0002783580260000112
the characteristic vector of the basic output performance of the mth sample board machine of the k model fan in the statistical time is obtained.
The basic output performance characteristic of the ith non-sample plate machine within the statistical timeVector quantity
Figure BDA0002783580260000113
And the average characteristic vector of the basic output performance of the sample plate machine
Figure BDA0002783580260000114
The Pearson correlation coefficient of (a) is:
Figure BDA0002783580260000115
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000116
let 0.1 be an interval unit pair RisIs divided, usually RisE [0.1 (g-11),0.1 (g-10)), g is an integer, and g e [1,20 ]]. Dividing the non-sample plate machines of each interval unit into a group, and setting the number of the g-th group of non-sample plate machines as
Figure BDA0002783580260000117
(the length of the interval unit may be reduced in order to improve the calculation accuracy).
(2) Dynamic identification of groups of output proportionality coefficients
And setting the actual force output average value sequence of the g group of non-sample plate machines in the statistical time as follows:
Figure BDA0002783580260000118
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000119
and the actual output sequence of the ith group of non-sample plate machines of the k model fan in the statistical time is obtained.
Setting the actual output average value sequence of the k model fan sample plate machine in the statistical time as follows:
Figure BDA00027835802600001110
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000121
the actual output sequence of the ith sample plate machine under the k model within the statistical time is shown.
Then within the statistical time of the time period,
Figure BDA0002783580260000122
and
Figure BDA0002783580260000123
the average of the power difference rates is:
Figure BDA0002783580260000124
wherein n is the number of sampling points in the statistical time.
Thus, the proportionality coefficient of the g-th set of non-sample plates with respect to the plate-out force of the sample plates may be taken as
Figure BDA0002783580260000125
In an ideal situation, when the theoretical output of all the units in the wind power plant is equal,
Figure BDA0002783580260000126
at this time, g is 10, the proportional coefficient of the non-sample plate machine relative to the output force of the sample plate machine is 1, and the theoretical power calculation formula of the power station completely accords with the current ideal calculation formula.
(3) The theoretical power of the wind farm is calculated as follows:
and respectively calculating theoretical generating power of each group of non-sample machines by using the actual output of the sample machines, and finally obtaining a calculation formula of the theoretical generating power of the new energy power station within the statistical time:
Figure BDA0002783580260000127
Figure BDA0002783580260000128
in the formula (I), the compound is shown in the specification,
Figure BDA0002783580260000129
calculating the improved theoretical power of all the units of the new energy power station model k within the time;
Figure BDA00027835802600001210
calculating theoretical power of the new energy power station within the time; k is the model number of the new energy power station unit.

Claims (5)

1. A wind power plant theoretical power calculation method based on unit operation performance evaluation is characterized by comprising the following steps: the method comprises four stages, namely a data identification stage, a data restoration stage, a non-sample board machine grouping and output proportional coefficient identification stage and a theoretical power calculation stage of the wind power plant;
in the data identification stage, abnormal data caused by faults or interference in links such as data acquisition or communication and the like are identified, and the steps are as follows:
step 1: according to the vacancy value identification criterion, identifying vacancy values appearing at a certain time point or time period in the time sequence power data of all the new energy wind turbine generators, and removing abnormal data;
step 2: according to the out-of-limit value identification criterion, identifying out-of-limit values which exceed a reasonable range in the time sequence power data of all the new energy wind turbine generators, and removing abnormal data;
and step 3: according to the non-refreshing value identification criterion, identifying data values which are not refreshed continuously for a period of time in the time sequence power data of all the new energy wind turbine generators, and removing the abnormal data;
and 4, step 4: identifying distortion data generated in time sequence power data of all new energy wind turbine generators according to a distortion data identification criterion, and removing abnormal data;
and in the data restoration stage, aiming at the characteristics of the abnormal data identified in the steps 1-4, the abnormal data is reconstructed by three categories including single-point abnormality, multipoint abnormality and continuous abnormality, and the steps are as follows:
and 5: filling single-point abnormal data in the time sequence power data of the new energy wind turbine generator by adopting a constant/mean value method;
step 6: for multipoint abnormal data which is not more than 4h in time sequence power data of the new energy wind turbine generator, reconstructing the abnormal data by adopting a reconstruction method based on ARMA model prediction, and establishing average credibility gammapCalculating a forward predicted value of the weighted reconstruction value at the moment k
Figure FDA0002783580250000021
And reverse prediction
Figure FDA0002783580250000022
Thereby improving the reliability of the reconstruction result at the time k; regarding the multipoint abnormality exceeding 4h as continuous abnormal data, and transferring the data reconstruction process to step 7;
and 7: for continuous abnormal data in time sequence power data of the new energy wind turbine generator, firstly, a reconstruction method based on sequence time delay correlation is adopted to find out the continuous abnormal data in the time sequence power data of the new energy wind turbine generatoryWind turbine generator system W with maximum time delay correlationxN, N is the number of new energy wind turbine generators, and W is usedxForce curve of (d) to WyCarrying out data reconstruction on the continuous abnormal operation data;
grouping non-sample board machines and identifying each group of output proportionality coefficients, grouping the non-sample board machines of the same model in the new energy power station based on the unit operation performance, and then dynamically identifying the output proportionality coefficients of each group, thereby more accurately mastering the relative output conditions of the non-sample board machines, and the method comprises the following steps:
and 8: through the basic output performance characteristic vector W of the ith non-sample board computer in the wind power plantiAnd the average characteristic vector of the basic output performance of the sample plate machine
Figure FDA0002783580250000023
Pearson's correlation coefficient RisDividing the interval unit by taking 0.1 as the interval unit, and dividing the non-sample board machines of each interval unit into one group, marking as the g group, wherein the number of the non-sample board machines is
Figure FDA0002783580250000024
20 groups in total;
and step 9: calculating the actual output average value sequence of the g group of non-sample plate machines of the k model fan in the statistical time
Figure FDA0002783580250000025
Actual output average value sequence of sample plate machine in statistical time
Figure FDA0002783580250000026
Power difference rate of
Figure FDA0002783580250000027
Obtaining a proportionality coefficient of the g group of non-sample plate machines relative to the sample plate machine
Figure FDA0002783580250000028
Dynamically identifying the output proportionality coefficient of each group;
the theoretical power calculation stage of the wind power plant comprises the following steps:
step 10: calculating to obtain the output proportionality coefficient of each group of non-sample computers relative to the sample computer in the k model fan of the wind power plant
Figure FDA0002783580250000029
Number of non-sample plate machines
Figure FDA00027835802500000210
And the average value of the actual output of the sample plate machine
Figure FDA00027835802500000211
According to the formula, the theoretical generated power in time is countedThe theoretical power of the wind farm is calculated by the calculation formula (2).
2. The wind power plant theoretical power calculation method based on unit operation performance evaluation according to claim 1, characterized in that: the abnormal data identification specifically includes:
identification of the vacancy value: let X be { X ═ X1,x2,...,xnThe power is the actual power of the wind turbine generator in the statistical time, if x existsiSatisfies the following conditions:
Figure FDA0002783580250000031
then x is judgediThere is a loss of data;
identification of the threshold value: if x is presentiIf the following relation is satisfied, x is determinediOut of normal numerical ranges:
xi＞xh|xi＜x1 (2)
in the formula, xh,x1Upper limit and lower limit of the normal numerical range respectively;
identification of non-refreshed values: if the data is not refreshed continuously for a period of time, identifying the data as not refreshed except the first data point;
identification of distorted data: the powers of adjacent wind turbines generally have similar trends, so based on the spatial correlation of the output of the adjacent wind turbines, N power data X of the adjacent wind turbines having spatial correlation with the wind turbine A are utilizedM＝{xM1,xM2,...,xMn1,2, …, N, for observing power data X of the wind turbineA＝{xA1,xA2,...,xAnChecking is carried out, and abnormal data are identified, which specifically comprises the following steps:
normalizing the power based on the formula (3) to obtain power normalization data X of the observation wind turbine generatorA *＝{xA1 *,xA2 *,...,xAn *And the power of the adjacent wind turbine generator setNormalized data XM *＝{xM1 *,xM2 *,...,xMn *The difference value of the two at each sampling point is shown as formula;
Figure FDA0002783580250000041
eAi-Mi＝|xAi *-xMi *| (4)
the power difference value and the average value of the wind turbine generator A and the wind turbine generator M are respectively as follows:
EA-M＝{eA1-M1,eA2-M2,…,eAn-Mn} (5)
Figure FDA0002783580250000042
3. the wind power plant theoretical power calculation method based on unit operation performance evaluation according to claim 1, characterized in that: the abnormal data reconstruction specifically includes:
single point exception: for the single-point abnormal data, a filling method can be adopted to carry out engineering reconstruction on the abnormal data, namely an experience constant is selected or the abnormal data is replaced and filled according to a certain rule; constant filling: filling single-point abnormal data by adopting the same specified constant value based on manual experience; mean value filling: filling single-point abnormal data by adopting the average value of the data sequence or the average values of the data before and after the abnormal data;
multipoint exception: for multipoint abnormity, due to the fact that complete actual operation data exist on two sides of the abnormal data, the abnormal data can be reconstructed by a reconstruction method based on ARMA model prediction according to the actual operation data on the two sides;
first assume a period tm,tn]For the period of missing data, the method is based on the intact wind turbine generator operation data
Figure FDA0002783580250000043
Predicting from two sides by adopting an ARMA model;
Pt＝β1Pt-1+β2Pt-2+…+βpPt-p+Zt (7)
Zt＝εt+λ1εt-1+λ2εt-2+…+λ1εt-1 (8)
in the formula, betaiIs an autoregressive parameter; lambda [ alpha ]iIs a moving average parameter; epsiloniIs a prediction error;
secondly, for the prediction result of the p step, if the prediction error is less than the error threshold value epsilon0If so, the prediction result is considered to be credible; determining an average confidence gamma of a prediction of a data sequence by predicting a large amount of good operating datap：
Figure FDA0002783580250000058
In the formula, Count (·) is the frequency statistics; epPredicting an error for the data sequence; n is the total number of times;
finally, calculating a forward predicted value of the weighted reconstruction value at the moment k
Figure FDA0002783580250000051
Has a prediction time length of k-tm+1, acceptance rate of
Figure FDA0002783580250000052
And reverse predict the value
Figure FDA0002783580250000053
Is tn-k +1, acceptability of
Figure FDA0002783580250000054
Thus, the final reconstruction result at time k is:
Figure FDA0002783580250000055
continuous anomalies: with the increase of the time scale of the abnormal data, the accuracy of the reconstruction method based on ARMA model prediction is obviously reduced, and as the running data of the wind turbine generator has certain correlation, the reconstruction method based on the sequence time delay correlation can be adopted to reconstruct the abnormal data for the continuous abnormal data;
delay dependence, assuming that there are two time sequences X ═ X1,x2,…,xnY ═ Y1,y2,…,ynThe correlation coefficient R (l) of Y relative to X delay time l is calculated as:
Figure FDA0002783580250000056
in the formula (I), the compound is shown in the specification,
Figure FDA0002783580250000057
the maximum value of l is n/2, and when l is changed from 0 to n/2, R (0), R (1), … and R (n/2) are obtained;
based on reconstruction of output time delay correlation of new energy wind turbine generator, supposing that the time period of missing data is [ t ]m,tn]The wind turbine generator to be reconstructed is W0The rest wind turbine generators are W1,W2…, taking each wind turbine at time interval [ tm-t0,tm]Internal output data, first calculate W0And W1The maximum delay correlation point of the output force in the time interval and the corresponding delay correlation coefficient if R (l)1')＝max(R(l1'),R(l2') …), then W0And W1The corresponding maximum delay correlation point is l1', the corresponding delay correlation coefficient is R (l)1') to a host; then respectively calculating W according to the calculated values0And W2,W3… the maximum delay correlation point and corresponding delay correlation coefficient of the force output during the time period; if there is W0And W1When the delay correlation coefficient is maximum, W is used1Force curve of (d) to W0Is reconstructed, i.e. W0And W1The force relationships of (d) can be fit using linear regression as:
Figure FDA0002783580250000061
in the formula (I), the compound is shown in the specification,
Figure FDA0002783580250000062
and
Figure FDA0002783580250000063
are respectively W0And W1At the t thmAnd tm+l1' output value at time; a and b are coefficients to be determined, and can be obtained by a least square method.
4. The wind power plant theoretical power calculation method based on unit operation performance evaluation according to claim 1, characterized in that: the non-sample board computers are grouped, and firstly, under the condition of non-limited power, basic power generation performance indexes of the wind turbine generator are established;
maximum power of the moon w1(kW) is the actual power P of the wind generating set within 30 days before the observation dayr＝{p1,p2,…,pnThe maximum of, i.e.:
w1＝max{pi},i＝1,2,…,n (13)
in the formula, n is the number of sample sampling points;
monthly average generated power w2
Figure FDA0002783580250000064
In the formula, Pr,j,tObserving actual power (kW) of the jth sampling point on the jth day in 30 days before the day;
annual rate of change w of average generated power3The annual change rate of the average generating power of the unit reflects the trend of the generating level of the unit changing along with time:
Figure FDA0002783580250000071
Figure FDA0002783580250000072
Figure FDA0002783580250000073
in the formula, Pr,av,yObserving the average actual generated power (kW) for the unit in the first 365 days before the day; p'r,av,yObserving the average actual generated power (kW) for the unit in the second 365 days before the day; p'r,j,tObserving the actual power (kW) of the jth sampling point on the jth day in the first 365 days before the day; p ″)r,j,tTo observe the actual power (kW) of the jth sampling point on the jth day in the 365 th day before the day; m is the number of sampling points per day;
number of full hair4(h) In order to observe the ratio of the actual power generation amount to the rated power of the wind generating set in 30 days before the day, the method can be used for comparing power generation systems with different installed capacities, and the calculation formula is as follows:
Figure FDA0002783580250000074
Figure FDA0002783580250000075
in the formula, PnFor observing the rated power (kW) of the wind generating set within 30 days before the day, ErThe actual power generation within 30 days before the observation day;
establishing a characteristic vector W of the basic output performance of the wind turbine generator within statistical time by using the basic generation performance index of the wind turbine generator1,w2,w3,w4And setting a basic output performance characteristic vector of the ith non-sample board machine of the k-type fan in the statistical time as
Figure FDA0002783580250000076
The average characteristic vector of the basic output performance of the k model sample plate machine in the statistical time is
Figure FDA0002783580250000081
Wherein M iskThe total number of the full wind power fields of the k model sample machines;
Figure FDA0002783580250000082
the characteristic vector of the basic output performance of the mth sample board machine of the k model fan in the statistical time is obtained;
the basic output performance characteristic vector of the ith non-sample plate machine in the statistical time
Figure FDA0002783580250000083
And the average characteristic vector of the basic output performance of the sample plate machine
Figure FDA0002783580250000084
The Pearson correlation coefficient of (a) is:
Figure FDA0002783580250000085
in the formula (I), the compound is shown in the specification,
Figure FDA0002783580250000086
let 0.1 be an interval unit pair RisIs divided, usually RisE [0.1 (g-11),0.1 (g-10)), g is an integer, and g e [1,20 ]]Dividing the non-sample board machines of each interval unit into a group, and setting the number of the g-th group of non-sample board machines as
Figure FDA0002783580250000087
And dynamically identifying the force proportion coefficients of all groups, and setting the actual force output average value sequence of the g group of non-sample plate machines in the statistical time as follows:
Figure FDA0002783580250000088
in the formula (I), the compound is shown in the specification,
Figure FDA0002783580250000089
the actual output sequence of the ith group of non-sample plate machines of the k model fan in the statistical time is obtained;
setting the actual output average value sequence of the k model fan sample plate machine in the statistical time as follows:
Figure FDA00027835802500000810
in the formula (I), the compound is shown in the specification,
Figure FDA00027835802500000811
the actual output sequence of the ith sample board machine under the k model within the statistical time is obtained;
then within the statistical time of the time period,
Figure FDA0002783580250000091
and
Figure FDA0002783580250000092
the average of the power difference rates is:
Figure FDA0002783580250000093
wherein n is the number of sampling points in the statistical time.
5. The wind power plant theoretical power calculation method based on unit operation performance evaluation according to claim 1, characterized in that: the theoretical power of the wind farm is calculated as follows:
and respectively calculating theoretical generating power of each group of non-sample machines by using the actual output of the sample machines, and finally obtaining a calculation formula of the theoretical generating power of the new energy power station within the statistical time:
Figure FDA0002783580250000094
Figure FDA0002783580250000095
in the formula (I), the compound is shown in the specification,
Figure FDA0002783580250000096
calculating the improved theoretical power of all the units of the new energy power station model k within the time;
Figure FDA0002783580250000097
calculating theoretical power of the new energy power station within the time; k is the model number of the new energy power station unit.
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