CN112035783A - Wind power characteristic evaluation method based on time-frequency analysis - Google Patents
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Abstract
The invention discloses a wind power characteristic evaluation method based on time-frequency analysis, which comprises the following steps: 1. carrying out discrete S transformation on the wind power; 2. establishing a time-frequency characteristic index considering the volatility, randomness and intermittence of the wind power on a wind power time-frequency domain; 3. and establishing a wind power characteristic evaluation model based on time-frequency analysis by utilizing an analytic hierarchy process according to the wind power time-frequency characteristic index. The method can solve the problem that the wind power characteristics cannot be comprehensively evaluated from a time domain or a frequency domain, and make up for the deficiency of the research on the wind power intermittency and randomness indexes, thereby providing a certain reference for the wind power grid-connected scheduling.
Description
Technical Field
The invention relates to a wind power characteristic evaluation method based on time-frequency analysis, and belongs to the technical field of electrical engineering.
Background
With the increasing exhaustion of traditional energy sources, people pay more attention to the development of renewable energy sources. The wind energy can be used as a green and environment-friendly renewable energy source in a plurality of renewable energy sources, and the development is rapid. However, the wind energy has volatility, randomness and intermittence, so that the wind power output by the wind power plant also has volatility, randomness and intermittence. With the increase of the permeability of the wind power in the power grid, the dispatching influence of the wind power grid connection with volatility, randomness and intermittence on the power grid is increased. Therefore, wind power characteristics need to be analyzed and evaluated to determine the degree of influence of wind power grid connection on power grid dispatching.
The existing research mainly carries out characteristic analysis on wind power from a time domain or frequency domain view. The wind power is subjected to characteristic analysis from a time domain, the output of the wind power in each time period can be obtained, the change trend and fluctuation characteristics of the wind power can be mastered, but the frequency composition of the fluctuation component of the wind power and the energy of each fluctuation component cannot be known, so that the output of generator sets with different response speeds cannot be reasonably arranged in the wind power grid-connected dispatching process. The characteristic analysis of the wind power is carried out from the frequency domain, the frequency of the wind power fluctuation and the energy information thereof can be obtained, but the time information of each fluctuation component can not be obtained, so that the output of the generator set at different time intervals can not be accurately formulated in the wind power grid-connected scheduling. In summary, the characteristic analysis of the wind power from the time domain or the frequency domain only has great one-sidedness. Therefore, the wind power characteristics are urgently needed to be analyzed from the time domain and the frequency domain at the same time, namely, the time-frequency analysis of the wind power is carried out, and the characteristic index of the wind power on the time-frequency domain is obtained, so that a foundation is laid for more reasonably carrying out wind power grid-connected scheduling.
In addition, the existing literature mainly starts with the fluctuation of the wind power for the feature analysis of the wind power, but the research on the inherent randomness and intermittency of the wind power is not sufficient. The randomness of the wind power is represented as uncertainty and unpredictability of the wind power output; the intermittency of the wind power shows that the output of the wind power in a certain period of time is zero or extremely small. The randomness and intermittence of wind power can cause great adverse effects on wind power grid-connected scheduling: the strong randomness of the wind power can cause the prediction error of the wind power to be increased, thereby influencing the reasonable formulation of a power generation scheduling plan; the intermittency of the wind power threatens the power balance of the power grid and influences the frequency stability of the power grid.
In summary, the traditional wind power characteristic analysis mainly adopts a time domain and frequency domain method, the analysis result has one-sidedness, and comprehensive and complete information is difficult to provide for wind power characteristic evaluation and wind power scheduling.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a wind power characteristic evaluation method based on time-frequency analysis, so that a more comprehensive and reasonable wind power characteristic evaluation result is obtained by performing time-frequency analysis on wind power and extracting wind power volatility, intermittence and randomness characteristic indexes from a time frequency spectrum to perform characteristic evaluation, the problem that the wind power characteristics cannot be comprehensively evaluated only from a time domain or a frequency domain is solved, the defects of research on the wind power intermittence and randomness indexes are overcome, and a certain reference function is provided for wind power grid-connected scheduling.
In order to achieve the purpose, the invention adopts the technical scheme that:
the wind power characteristic evaluation method based on time-frequency analysis is characterized by comprising the following steps of:
step 1, knowing a wind power sequence { p (j) }, j is 0,1, …, N-1 of a certain wind farm with a sampling interval Δ T, performing discrete S transformation on the wind power sequence by using a formula (1) to obtain a wind power time frequency spectrum S [ j, N ] with a time sampling point j and a frequency sampling point N:
in formula (1), j represents a time sampling point, and j is 0,1, …, N-1; n represents a frequency sampling point, N is 0,1, …, N/2; i is an imaginary unit; m is the frequency translation amount; n represents the total number of sampling points and is an even number; p (j) represents the wind power when the time sampling point is j; exp represents an exponential function with a natural constant e as the base;
step 2, aiming at the volatility of the wind power, respectively defining a time-frequency domain mean value m on a wind power time-frequency domain by using the formula (2) and the formula (3)(t,f)Sum time-frequency domain variance
And 3, aiming at the randomness of the wind power, defining the time-frequency spectrum entropy of the wind power on a time-frequency domain by using the formula (4)(t,f)：
Step 4, aiming at the intermittence of the wind power, defining the total intermittence times n of the wind power on a time-frequency domainsumAnd the average intermittent duration t of the wind powermean；
Step 4.1, calculating the intermittent state quantity C (j) when the time sampling point on the time spectrum of the wind power is j by using the formula (5):
in the formula (5), SminThe time-frequency interval value of the wind power is obtained;
and 4.2, calculating an intermittent state transition variable c (j) of the wind power when a time sampling point is j by using the formula (6):
in the formula (6), C (j +1) represents an intermittent state quantity when a time sampling point on a time frequency spectrum of the wind power is j + 1;
step 4.3, calculating the total intermittent times n of the wind power by using the formula (7)sum：
Step 4.4, calculating the average intermittent duration t of the wind power by using the formula (8)mean：
In the formula (8), Δ T is a sampling interval of the wind power;
and 4.5, forming a characteristic index vector x of the wind power by using a formula (9):
x＝[x1,x2,…,xi,…,x5] (9)
in the formula (9), xiIndicates the i-th characteristic index value, x1,x2,x3,x4,x5Time-frequency domain mean values m of wind power respectively(t,f)Time-frequency domain variance
Entropy of time-frequency spectrum(t,f)Intermittent number of times nsumIntermittent average time tmean；i＝1,2,…,5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1, for each wind farm, according to step 1Calculating the characteristic indexes of the wind power in the step 4, and constructing an index vector { x ] of each wind power plant by using the formula (10)d,d＝1,2,…,D}：
xd＝[xd1,xd2,…,xdi,…,xd5] (10)
In the formula (9), xdiRepresenting the ith characteristic index value of the d wind power plant; x is the number ofd1,xd2,xd3,xd4,xd5Respectively representing the mean value of the time-frequency domain, the variance of the time-frequency domain, the entropy of the time-frequency spectrum, the intermittent times and the intermittent average time of the wind power of the d-th wind power plant;
and 5.2, obtaining index matrixes X of the D wind power plants by using the formula (11):
step 6, establishing a wind power characteristic evaluation model based on time-frequency analysis by using an analytic hierarchy process, and performing characteristic evaluation on the grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
a judgment matrix A of the index is constructed by using the formula (12):
in the formula (12), aijThe ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjScale of importance of the comparison; when i is j, let aijWhen i ≠ j, let aij＝1/aji；
Step 6.2, constructing a standard judgment matrix by using the formula (13)
In the formula (13), the reaction mixture is,
the ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjSignificance scale criteria values for comparisons;
and 6.3, calculating an index weight vector w by using the formula (14):
in the formula (14), the superscript T represents the transpose of the vector;
step 6.4, checking consistency of an analytic hierarchy process;
obtaining maximum characteristic root lambda by using formula (15)max：
In formula (15), (Aw)iIs used for judging the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector wiRepresenting the ith element in the indicator weight vector w;
obtaining a consistency index C of the judgment matrix A by using the formula (16)I：
Obtaining the consistency ratio C of the judgment matrix A by using the formula (17)R：
In the formula (17), RIIs an average random consistency index;
when C is presentRIf < then, the judgment matrix A is representedThe consistency is satisfied; otherwise, readjusting the judgment matrix A to meet the consistency; indicating a set threshold of the consistency ratio;
6.5, standardizing the index matrix X;
standardizing the index matrixes X of the D wind power plants in the step 5, and obtaining a standard index matrix by using a formula (18)
In the formula (18), the reaction mixture,
the ith characteristic index standard value of the wind power of the d wind power plant is represented;
6.6, obtaining wind power characteristic evaluation results of the D wind power plants;
according to the index weight vector w and the standard index matrix
Obtaining wind power characteristic evaluation vectors v of D wind power plants by using an equation (19):
compared with the prior art, the invention has the beneficial effects that:
the method solves the problem that the wind power characteristics can not be comprehensively evaluated only from a time domain or a frequency domain, and makes up the defects of research on the randomness and the intermittent indexes of the wind power. The wind power is subjected to time-frequency analysis, characteristic indexes are extracted from the time frequency spectrum, and a wind power characteristic evaluation model is established, so that a more accurate and effective wind power characteristic evaluation result is obtained, and a certain reference function is provided for grid-connected scheduling of the wind power. The concrete effects are shown in the following aspects:
1. according to the method, the wind power is subjected to time-frequency analysis by adopting the discrete S transformation shown in the step 1 to obtain the time-frequency spectrum of the wind power, so that the frequency components and the energy changes of the wind power at all times can be accurately described, and more specific and comprehensive wind power characteristic information is provided for the subsequent wind power characteristic evaluation;
2. the invention adopts the time-frequency spectrum entropy which is used for reflecting the signal random degree and is shown in step 3(t，f)To define the randomness of the wind power on the time frequency spectrum, and the total intermittent times n of the wind power shown in the step 4 are adoptedsumAnd the average intermittent duration t of the wind powermeanThe intermittence of the wind power on a time frequency spectrum is defined, so that the defects of researching the randomness and the intermittence indexes of the wind power are overcome;
3. according to the wind power grid-connected scheduling method, the wind power characteristic evaluation model based on time-frequency analysis is established by adopting the analytic hierarchy process shown in the step 6, and the obtained evaluation result can reflect the wind power characteristics more comprehensively and effectively, so that more effective reference is provided for wind power grid-connected scheduling.
Drawings
FIG. 1 is a flow chart of a wind power characteristic evaluation method based on time-frequency analysis.
Detailed Description
In this embodiment, as shown in fig. 1, a wind power characteristic evaluation method based on time-frequency analysis is performed according to the following steps:
step 1, knowing a wind power sequence { p (j) }, j is 0,1, …, N-1 of a certain wind farm with a sampling interval Δ T, performing discrete S transformation on the wind power sequence by using a formula (1) to obtain a wind power time frequency spectrum S [ j, N ] with a time sampling point j and a frequency sampling point N:
in formula (1), j represents a time sampling point, and j is 0,1, …, N-1; n represents a frequency sampling point, N is 0,1, …, N/2; i is an imaginary unit; m is the frequency translation amount; n represents the total number of sampling points and is an even number; p (j) represents the wind power when the time sampling point is j; exp represents an exponential function with a natural constant e as the base;
step 2, aiming at the volatility of the wind power, respectively defining a time-frequency domain mean value m on a wind power time-frequency domain by using the formula (2) and the formula (3)(t，f)Sum time-frequency domain variance
Mean time-frequency domain m(t，f)The larger the average energy of the wind power is, the larger the influence of grid-connected fluctuation on a power grid is; variance in time and frequency domain
Representing the fluctuation degree and variance of wind power in time-frequency domain
The larger the wind power fluctuation is, the more violent the wind power fluctuation is, and the larger the influence on the power grid is;
and 3, aiming at the randomness of the wind power, defining the time-frequency spectrum entropy of the wind power on a time-frequency domain by using the formula (4)(t，f)
Entropy of time-frequency spectrum(t，f)Representing the randomness of the wind power in the time-frequency domain, the time-frequency spectrum entropy(t，f)The larger the value is, the stronger the randomness of the wind power is, and the weaker the predictability is, so that the reasonable formulation of a power generation dispatching plan is influenced;
and 4. step 4.Aiming at the intermittence of the wind power, the total intermittence times n of the wind power are defined on a time-frequency domainsumAnd the average intermittent duration t of the wind powermean；
Step 4.1, calculating the intermittent state quantity C (j) when the time sampling point on the time spectrum of the wind power is j by using the formula (5):
in the formula (5), SminThe time-frequency interval value is 5% multiplied by m in this embodiment(t，f)(ii) a When C (j) is equal to 1, the wind power at j is in an intermittent state, and when C (j) is equal to 0, the wind power at j is in a non-intermittent state;
and 4.2, calculating an intermittent state transition variable c (j) of the wind power when a time sampling point is j by using the formula (6):
in the formula (6), C (j +1) represents an intermittent state quantity when a time sampling point on a time frequency spectrum of the wind power is j + 1; when c (j) is 1, the wind power at j is converted from a non-intermittent state to an intermittent state;
step 4.3, calculating the total intermittent times n of the wind power by using the formula (7)sum：
Step 4.4, calculating the average intermittent duration t of the wind power by using the formula (8)mean：
In the formula (8), Δ T is a sampling interval of the wind power;
using total number of pauses nsumAnd average pause duration tmeanRepresenting intermittency of wind power, total number of intermittencies nsumThe more, the average pause duration tmeanThe longer the frequency is, the stronger the intermittence of the wind power is, the larger the threat to the power balance of the power grid is, and the frequency stability of the power grid is more easily influenced;
and 4.5, putting the 5 indexes together, and forming a characteristic index vector x of the wind power by using a formula (9):
x＝[x1，x2，…，xi，…，x5] (9)
in the formula (9), xiRepresenting the ith characteristic index value; x is the number of1，x2，x3，x4，x5Time-frequency domain mean values m of wind power respectively(t，f)Time-frequency domain variance
Entropy of time-frequency spectrum(t，f)Intermittent number of times nsumIntermittent average time tmean；i＝1，2，…，5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1, calculating the characteristic index of the wind power of each wind power plant according to the steps 1 to 4, and constructing an index vector { x ] of each wind power plant by using the formula (10)d，d＝1，2，…，D}：
xd＝[xd1，xd2，…，xdi，…，xd5] (10)
In the formula (9), xdiRepresenting the ith characteristic index value of the d wind power plant; x is the number ofd1，xd2，xd3，xd4，xd5Respectively representing the mean value of the time-frequency domain, the variance of the time-frequency domain, the entropy of the time-frequency spectrum, the intermittent times and the intermittent average time of the wind power of the d-th wind power plant;
and 5.2, obtaining index matrixes X of the D wind power plants by using the formula (11):
step 6, establishing a wind power characteristic evaluation model based on time-frequency analysis by using an analytic hierarchy process, and performing characteristic evaluation on the grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
a judgment matrix A of the index is constructed by using the formula (12):
in the formula (12), aijThe ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjScale of importance of the comparison; when i is j, let aijWhen i ≠ j, let aij＝1/aji；
Time-frequency domain mean value m of wind power in the embodiment(t，f)Time-frequency domain variance
Number of intermissions nsumIntermittent average time tmeanThe importance degree of the four characteristic indexes is the same, and the time-frequency spectrum entropy of the wind power(t，f)The decision matrix a can take the values in equation (1), which is slightly more important than the other four indicators:
step 6.2, constructing a standard judgment matrix by using the formula (13)
In the formula (13), the reaction mixture is,
the ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjSignificance scale criteria values for comparisons;
and 6.3, calculating an index weight vector w by using the formula (14):
in the formula (14), the superscript T represents the transpose of the vector;
step 6.4, checking consistency of an analytic hierarchy process;
obtaining maximum characteristic root lambda by using formula (15)max：
In formula (15), (Aw)iIs used for judging the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector wiRepresenting the ith element in the indicator weight vector w;
obtaining a consistency index C of the judgment matrix A by using the formula (16)I：
Obtaining the consistency ratio C of the judgment matrix A by using the formula (17)R：
In the formula (17), RIThe index is an average random consistency index, is only related to the order number of a judgment matrix, and the order number of the judgment matrix is 5 and is 1.12;
when C is presentRIf yes, the judgment matrix A meets the consistency; otherwise, it is heavyNewly adjusting the judgment matrix A to meet the consistency; represents a set threshold of the consistency ratio, with default being 0.1;
6.5, standardizing the index matrix X;
standardizing the index matrixes X of the D wind power plants in the step 5, and obtaining a standard index matrix by using a formula (18)
In the formula (18), the reaction mixture,
the ith characteristic index standard value of the wind power of the d wind power plant is represented;
6.6, obtaining wind power characteristic evaluation results of the D wind power plants;
according to the index weight vector w and the standard index matrix
Obtaining wind power characteristic evaluation vectors v of D wind power plants by using an equation (19):
d-th element v in degree of influence vector vdAnd the influence degree of the wind power of the d-th wind power plant accessed to the power grid on the power grid dispatching is represented, and the larger the value of the influence degree is, the larger the influence on the power grid dispatching is represented.
Claims (1)
1. A wind power characteristic evaluation method based on time-frequency analysis is characterized by comprising the following steps:
step 1, knowing a wind power sequence { p (j) }, j is 0,1, …, N-1 of a certain wind farm with a sampling interval Δ T, performing discrete S transformation on the wind power sequence by using a formula (1) to obtain a wind power time frequency spectrum S [ j, N ] with a time sampling point j and a frequency sampling point N:
in formula (1), j represents a time sampling point, and j is 0,1, …, N-1; n represents a frequency sampling point, N is 0,1, …, N/2; i is an imaginary unit; m is the frequency translation amount; n represents the total number of sampling points and is an even number; p (j) represents the wind power when the time sampling point is j; exp represents an exponential function with a natural constant e as the base;
step 2, aiming at the volatility of the wind power, respectively defining a time-frequency domain mean value m on a wind power time-frequency domain by using the formula (2) and the formula (3)(t,f)Sum time-frequency domain variance
And 3, aiming at the randomness of the wind power, defining the time-frequency spectrum entropy of the wind power on a time-frequency domain by using the formula (4)(t,f)：
Step 4, aiming at the intermittence of the wind power, defining the total intermittence times n of the wind power on a time-frequency domainsumAnd the average intermittent duration t of the wind powermean；
Step 4.1, calculating the intermittent state quantity C (j) when the time sampling point on the time spectrum of the wind power is j by using the formula (5):
in the formula (5), SminThe time-frequency interval value of the wind power is obtained;
and 4.2, calculating an intermittent state transition variable c (j) of the wind power when a time sampling point is j by using the formula (6):
in the formula (6), C (j +1) represents an intermittent state quantity when a time sampling point on a time frequency spectrum of the wind power is j + 1;
step 4.3, calculating the total intermittent times n of the wind power by using the formula (7)sum：
Step 4.4, calculating the average intermittent duration t of the wind power by using the formula (8)mean：
In the formula (8), Δ T is a sampling interval of the wind power;
and 4.5, forming a characteristic index vector x of the wind power by using a formula (9):
x＝[x1,x2,…,xi,…,x5] (9)
in the formula (9), xiIndicates the i-th characteristic index value, x1,x2,x3,x4,x5Time-frequency domain mean values m of wind power respectively(t,f)Time-frequency domain variance
Entropy of time-frequency spectrum(t,f)Intermittent number of times nsumIntermittent average time tmean；i＝1,2,…,5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1, calculating the characteristic index of the wind power of each wind power plant according to the steps 1 to 4, and constructing an index vector { x ] of each wind power plant by using the formula (10)d,d＝1,2,…,D}：
xd＝[xd1,xd2,…,xdi,…,xd5] (10)
In the formula (9), xdiRepresenting the ith characteristic index value of the d wind power plant; x is the number ofd1,xd2,xd3,xd4,xd5Respectively representing the mean value of the time-frequency domain, the variance of the time-frequency domain, the entropy of the time-frequency spectrum, the intermittent times and the intermittent average time of the wind power of the d-th wind power plant;
and 5.2, obtaining index matrixes X of the D wind power plants by using the formula (11):
step 6, establishing a wind power characteristic evaluation model based on time-frequency analysis by using an analytic hierarchy process, and performing characteristic evaluation on the grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
a judgment matrix A of the index is constructed by using the formula (12):
in the formula (12), aijThe ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjScale of importance of the comparison; when i is j, let aijWhen i ≠ j, let aij＝1/aji；
Step 6.2, constructing a standard judgment matrix by using the formula (13)
In the formula (13), the reaction mixture is,
the ith index x in the feature index vector x represented by the expression (9)iAnd the jth index xjSignificance scale criteria values for comparisons;
and 6.3, calculating an index weight vector w by using the formula (14):
in the formula (14), the superscript T represents the transpose of the vector;
step 6.4, checking consistency of an analytic hierarchy process;
obtaining maximum characteristic root lambda by using formula (15)max：
In formula (15), (Aw)iIs used for judging the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector wiRepresenting the ith element in the indicator weight vector w;
obtaining a consistency index C of the judgment matrix A by using the formula (16)I：
Obtained by the formula (17)Determining the consistency ratio C of the matrix AR：
In the formula (17), RIIs an average random consistency index;
when C is presentRIf yes, the judgment matrix A meets the consistency; otherwise, readjusting the judgment matrix A to meet the consistency; indicating a set threshold of the consistency ratio;
6.5, standardizing the index matrix X;
standardizing the index matrixes X of the D wind power plants in the step 5, and obtaining a standard index matrix by using a formula (18)
In the formula (18), the reaction mixture,
the ith characteristic index standard value of the wind power of the d wind power plant is represented;
6.6, obtaining wind power characteristic evaluation results of the D wind power plants;
according to the index weight vector w and the standard index matrix
Obtaining wind power characteristic evaluation vectors v of D wind power plants by using an equation (19):
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