CN112035783B - Wind power characteristic evaluation method based on time-frequency analysis - Google Patents
Wind power characteristic evaluation method based on time-frequency analysis Download PDFInfo
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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. performing discrete S conversion on wind power; 2. establishing a time-frequency characteristic index considering wind power fluctuation, randomness and intermittence on a wind power time-frequency domain; 3. and establishing a wind power characteristic evaluation model based on time-frequency analysis by using 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 the time domain or the frequency domain, and make up the defect of researching the intermittent and random indexes of the wind power, thereby providing a certain reference for 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, attention is increasingly paid to the development of renewable energy sources. Wind energy is used as a green renewable energy source in a plurality of renewable energy sources, and the development of the wind energy is rapid. However, wind energy has volatility, randomness and intermittency, so that wind power output by a wind farm also has volatility, randomness and intermittency. As the permeability of wind power in the grid increases, the scheduling impact of grid-connected wind power with volatility, randomness and intermittence on the grid also increases. Therefore, analysis and evaluation are required to be carried out on the wind power characteristics so as to determine the influence degree of wind power grid connection on grid dispatching.
The prior researches mainly carry out characteristic analysis of wind power from the time domain or frequency domain view. The wind power is subjected to characteristic analysis from the time domain, the output of the wind power in each period can be obtained, the change trend and fluctuation characteristics of the wind power are 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 the generator sets with different response speeds cannot be reasonably arranged in wind power grid-connected scheduling. The wind power is subjected to characteristic analysis from the frequency domain, so that the frequency and energy information of wind power fluctuation can be obtained, but the time information of each fluctuation component cannot be obtained, so that the output of the generator set in different time periods cannot be accurately formulated in wind power grid-connected scheduling. In summary, the wind power is only subjected to characteristic analysis from the time domain or frequency domain, and the wind power has great unilateral performance. Therefore, the wind power characteristics are analyzed from the time domain and the frequency domain at the same time, namely, the time-frequency analysis of the wind power is performed, and the characteristic index of the wind power on the time domain and the frequency domain is obtained, so that a foundation is laid for more reasonably performing wind power grid-connected scheduling.
In addition, the characteristic analysis of wind power in the prior art mainly starts from the fluctuation of wind power, but the inherent randomness and intermittence of wind power are not fully studied. The randomness of wind power is represented by uncertainty and unpredictability of wind power output; the intermittence of wind power is represented by zero or very small output of wind power in a certain period of time. The randomness and intermittence of wind power can cause great adverse effect on wind power grid-connected scheduling: the strong randomness of the wind power can cause the increase of wind power prediction errors, thereby affecting the reasonable formulation of a power generation scheduling plan; the intermittence of wind power threatens the power balance of the power grid, and affects the frequency stability of the power grid.
In summary, the traditional wind power characteristic analysis mainly adopts a time domain and frequency domain method, and an analysis result has one-sided property, so that comprehensive and complete information is difficult to provide for wind power characteristic evaluation and wind power scheduling.
Disclosure of Invention
The invention provides a wind power characteristic evaluation method based on time-frequency analysis, aiming at solving the defects existing in the prior art, carrying out time-frequency analysis on wind power and extracting characteristic indexes of wind power fluctuation, intermittence and randomness from time frequency spectrum thereof to carry out characteristic evaluation, obtaining a more comprehensive and reasonable wind power characteristic evaluation result, solving the problem that wind power characteristics cannot be comprehensively evaluated only from time domain or frequency domain, compensating the defects of research on the wind power intermittence and randomness indexes, and providing a certain reference effect for wind power grid-connected dispatching.
In order to achieve the above purpose, the invention adopts the following technical scheme:
the invention discloses a wind power characteristic evaluation method based on time-frequency analysis, which is characterized by comprising the following steps of:
step 1, a wind power sequence { P (j) } with a sampling interval of deltaT of a certain wind power plant is known, j=0, 1, … and N-1, discrete S transformation is carried out on the wind power sequence { P (j) }, j=0, 1, … and N-1 by using the formula (1), and a frequency spectrum S [ j, N ] of wind power with a time sampling point of j and a frequency sampling point of N is obtained:
in the formula (1), j represents a time sampling point, j=0, 1, …, N-1; n represents a frequency sampling point, n=0, 1, …, N/2; i is an imaginary unit; m is the frequency translation; n represents the total number of sampling points, and N is an even number; p (j) represents wind power when the time sampling point is j; exp represents an exponential function based on a natural constant e;
step 2, aiming at the fluctuation of wind power, utilizing the formula (2) and the formula (3) to respectively perform wind powerDefinition of time-frequency domain mean m on time-frequency domain of rate (t,f) Sum of time-frequency domain variance
Step 3, defining the time-frequency spectrum entropy epsilon of the wind power on a time-frequency domain by using a formula (4) according to the randomness of the wind power (t,f) ：
Step 4, defining total intermittent times n of wind power on a time-frequency domain according to the intermittent performance of the wind power sum Average intermittent duration t of wind power mean ；
Step 4.1, calculating an intermittent state quantity C (j) of the wind power when a time sampling point of the wind power on a time spectrum is j by using the formula (5):
in the formula (5), S min The time-frequency intermittent value of the wind power is the time-frequency intermittent value of the wind power;
step 4.2, calculating an intermittent state transition quantity c (j) of the wind power at a time sampling point j by using the formula (6):
in the formula (6), C (j+1) represents an intermittent state quantity when a time sampling point of wind power on a time spectrum is j+1;
step 4.3, calculating the total intermittent times n of wind power by using the step (7) sum ：
Step 4.4. Calculating the average intermittent duration t of the wind power by using the method (8) mean ：
In the formula (8), delta T is the sampling interval of wind power;
step 4.5, forming a characteristic index vector x of the wind power by using the formula (9):
x＝[x 1 ,x 2 ,…,x i ,…,x 5 ] (9)
in the formula (9), x i Represents the i-th characteristic index value, x 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively the time-frequency domain mean value m of wind power (t,f) Time-frequency domain varianceTemporal spectral entropy ε (t,f) Number of pauses n sum Time t of intermittent average mean ；i＝1,2,…,5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1. Calculating characteristic indexes of wind power according to the steps 1 to 4 for each wind power plant, and constructing an index vector { x) of each wind power plant by using the formula (10) d ,d＝1,2,…,D}：
x d ＝[x d1 ,x d2 ,…,x di ,…,x d5 ] (10)
In the formula (9), x di An ith characteristic index value representing a d-th wind farm; x is x d1 ,x d2 ,x d3 ,x d4 ,x d5 Respectively the time-frequency domain mean value of the wind power of the d-th wind power plant,Time-frequency domain variance, time-frequency spectrum entropy, intermittent times and intermittent average time;
step 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 grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
constructing a judgment matrix A of the index by using the formula (12):
in the formula (12), a ij The i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A scale of importance of the phase comparison; when i=j, let a ij When i is equal to j, let a =1 ij ＝1/a ji ；
Step 6.2. Constructing a standard judgment matrix by using the method (13)
In the formula (13), the amino acid sequence of the compound,the i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A comparative importance scale standard value;
step 6.3. Calculating an index weight vector w using equation (14):
in the formula (14), the superscript T represents a transpose of the vector;
step 6.4, consistency test of the analytic hierarchy process;
obtaining the maximum characteristic root lambda by using the formula (15) max ：
In the formula (15), (Aw) i To determine the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector w i Representing the ith element in the index weight vector w;
obtaining a consistency index C of the judgment matrix A by using a formula (16) I ：
Obtaining the consistency ratio C of the judgment matrix A by using the formula (17) R ：
In the formula (17), R I Is an average random consistency index;
when C R When delta is less than delta, the judgment matrix A meets consistency; otherwise, readjusting the judgment matrix A to enable the judgment matrix A to meet consistency; delta represents the set consistency ratio threshold;
step 6.5, normalizing the index matrix X;
normalizing the index matrix 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 amino acid sequence of the compound,an ith characteristic index standard value for representing the wind power of the (d) th wind power plant;
step 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 matrixObtaining wind power characteristic evaluation vectors v of the D wind power plants by using the formula (19):
compared with the prior art, the invention has the beneficial effects that:
the method solves the problem that the wind power characteristics cannot be comprehensively evaluated only from the time domain or the frequency domain, and overcomes the defect of researching the randomness and intermittence indexes of the wind power. By carrying out time-frequency analysis on wind power, extracting characteristic indexes from a time frequency spectrum of the wind power, and establishing a wind power characteristic evaluation model, a more accurate and effective wind power characteristic evaluation result is obtained, and a certain reference effect is provided for wind power grid-connected scheduling. The specific effects are shown in the following aspects:
1. according to the invention, the discrete S transformation shown in the step 1 is adopted to perform time-frequency analysis on the wind power, so that a wind power time spectrum is obtained, frequency components and energy changes of the wind power at each moment can be accurately described, and more specific and comprehensive wind power characteristic information is provided for subsequent wind power characteristic evaluation;
2. the invention adopts the time spectrum entropy epsilon used for reflecting the randomness degree of the signal and shown in the step 3 (t，f) To define the wind power over time spectrumMechanically, adopting the total intermittent times n of the wind power shown in the step 4 sum Average intermittent duration t of wind power mean The intermittence of wind power on a time frequency spectrum is defined, so that the defect of researching wind power randomness and intermittence indexes is overcome;
3. according to the method, a time-frequency analysis-based wind power characteristic evaluation model is established by adopting the analytic hierarchy process shown in the step 6, and the obtained evaluation result can reflect 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 method for evaluating wind power characteristics based on time-frequency analysis is performed according to the following steps:
step 1, a wind power sequence { P (j) } with a sampling interval of deltaT of a certain wind power plant is known, j=0, 1, … and N-1, discrete S transformation is carried out on the wind power sequence { P (j) }, j=0, 1, … and N-1 by using the formula (1), and a frequency spectrum S [ j, N ] of wind power with a time sampling point of j and a frequency sampling point of N is obtained:
in the formula (1), j represents a time sampling point, j=0, 1, …, N-1; n represents a frequency sampling point, n=0, 1, …, N/2; i is an imaginary unit; m is the frequency translation; n represents the total number of sampling points, and N is an even number; p (j) represents wind power when the time sampling point is j; exp represents an exponential function based on a natural constant e;
step 2, defining a time-frequency domain mean value m on the time-frequency domain of the wind power by using the formula (2) and the formula (3) respectively aiming at the fluctuation of the wind power (t，f) Sum of time-frequency domain variance
Time-frequency domain mean value m (t，f) The larger the average energy of the wind power is, the larger the influence of grid-connected fluctuation on the power grid is; time-frequency domain varianceRepresenting the fluctuation degree of wind power in the time-frequency domain, the time-frequency domain variance +.>The larger the wind power fluctuation is, the more severe the influence on the power grid is;
step 3, defining the time-frequency spectrum entropy epsilon of the wind power on a time-frequency domain by using a formula (4) according to the randomness of the wind power (t，f)
Temporal spectral entropy ε (t，f) Representing randomness of wind power on time-frequency domain, and time-frequency spectrum entropy epsilon (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 scheduling plan is affected;
step 4, defining total intermittent times n of wind power on a time-frequency domain according to the intermittent performance of the wind power sum Average intermittent duration t of wind power mean ；
Step 4.1, calculating an intermittent state quantity C (j) of the wind power when a time sampling point of the wind power on a time spectrum is j by using the formula (5):
in the formula (5), S min The time-frequency intermittent value of wind power is 5% m in the embodiment (t，f) The method comprises the steps of carrying out a first treatment on the surface of the When C (j) =1, the wind power at j is in an intermittent state, and when C (j) =0, the wind power at j is in a non-intermittent state;
step 4.2, calculating an intermittent state transition quantity c (j) of the wind power at a time sampling point j by using the formula (6):
in the formula (6), C (j+1) represents an intermittent state quantity when a time sampling point of wind power on a time spectrum is j+1; when c (j) =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 wind power by using the step (7) sum ：
Step 4.4. Calculating the average intermittent duration t of the wind power by using the method (8) mean ：
In the formula (8), delta T is the sampling interval of wind power;
by total intermittent times n sum Average intermittent duration t mean Indicating the intermittence of wind power and the total intermittence times n sum The more the average intermittent duration t mean The longer the wind power is, the stronger the intermittence of the wind power is, the greater the threat on the power balance of the power grid is, and the frequency stability of the power grid is easier to influence;
step 4.5, putting the 5 indexes together, and forming a characteristic index vector x of the wind power by using a formula (9):
x＝[x 1 ，x 2 ，…，x i ，…，x 5 ] (9)
in the formula (9), x i Representing an i-th feature index value; x is x 1 ，x 2 ，x 3 ，x 4 ，x 5 Respectively the time-frequency domain mean value m of wind power (t，f) Time-frequency domain varianceTemporal spectral entropy ε (t，f) Number of pauses n sum Time t of intermittent average mean ；i＝1，2，…，5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1. Calculating characteristic indexes of wind power according to the steps 1 to 4 for each wind power plant, and constructing an index vector { x) of each wind power plant by using the formula (10) d ，d＝1，2，…，D}：
x d ＝[x d1 ，x d2 ，…，x di ，…，x d5 ] (10)
In the formula (9), x di An ith characteristic index value representing a d-th wind farm; x is x d1 ，x d2 ，x d3 ，x d4 ，x d5 Respectively obtaining a time-frequency domain mean value, a time-frequency domain variance, a time-frequency spectrum entropy, intermittent times and intermittent average time of wind power of the d-th wind power plant;
step 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 grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
constructing a judgment matrix A of the index by using the formula (12):
in the formula (12), a ij The i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A scale of importance of the phase comparison; when i=j, let a ij When i is equal to j, let a =1 ij ＝1/a ji ；
In this embodiment, the time-frequency domain mean value m of the wind power is taken (t，f) Time-frequency domain varianceNumber of pauses n sum Time t of intermittent average mean The importance degree between every two of the four characteristic indexes is the same, and the time spectrum entropy epsilon of wind power is the same (t，f) The decision matrix a may take the values in equation (1):
step 6.2. Constructing a standard judgment matrix by using the method (13)
In the formula (13), the amino acid sequence of the compound,the i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A comparative importance scale standard value;
step 6.3. Calculating an index weight vector w using equation (14):
in the formula (14), the superscript T represents a transpose of the vector;
step 6.4, consistency test of the analytic hierarchy process;
obtaining the maximum characteristic root lambda by using the formula (15) max ：
In the formula (15), (Aw) i To determine the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector w i Representing the ith element in the index weight vector w;
obtaining a consistency index C of the judgment matrix A by using a formula (16) I ：
Obtaining the consistency ratio C of the judgment matrix A by using the formula (17) R ：
In the formula (17), R I The average random consistency index is only related to the order of the judgment matrix, and the order of the judgment matrix is 5, delta=1.12;
when C R When delta is less than delta, the judgment matrix A meets consistency; otherwise, readjusting the judgment matrix A to enable the judgment matrix A to meet consistency; δ represents the set consistency ratio threshold, default δ=0.1;
step 6.5, normalizing the index matrix X;
normalizing the index matrix 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 amino acid sequence of the compound,an ith characteristic index standard value for representing the wind power of the (d) th wind power plant;
step 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 matrixObtaining wind power characteristic evaluation vectors v of the D wind power plants by using the formula (19):
the d-th element v in the influence degree vector v d The influence degree of the wind power of the d-th wind power plant on the power grid dispatching is represented, and the larger the value 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, a wind power sequence { P (j) } with a sampling interval of deltaT of a certain wind power plant is known, j=0, 1, … and N-1, discrete S transformation is carried out on the wind power sequence { P (j) }, j=0, 1, … and N-1 by using the formula (1), and a frequency spectrum S [ j, N ] of wind power with a time sampling point of j and a frequency sampling point of N is obtained:
in the formula (1), j represents a time sampling point, j=0, 1, …, N-1; n represents a frequency sampling point, n=0, 1, …, N/2; i is an imaginary unit; m is the frequency translation; n represents the total number of sampling points, and N is an even number; p (j) represents wind power when the time sampling point is j; exp represents an exponential function based on a natural constant e;
step 2, defining a time-frequency domain mean value m on the time-frequency domain of the wind power by using the formula (2) and the formula (3) respectively aiming at the fluctuation of the wind power (t,f) Sum of time-frequency domain variance
Step 3, defining the time-frequency spectrum entropy epsilon of the wind power on a time-frequency domain by using a formula (4) according to the randomness of the wind power (t,f) ：
Step 4, defining total intermittent times n of wind power on a time-frequency domain according to the intermittent performance of the wind power sum Average intermittent duration t of wind power mean ；
Step 4.1, calculating an intermittent state quantity C (j) of the wind power when a time sampling point of the wind power on a time spectrum is j by using the formula (5):
in the formula (5), S min The time-frequency intermittent value of the wind power is the time-frequency intermittent value of the wind power;
step 4.2, calculating an intermittent state transition quantity c (j) of the wind power at a time sampling point j by using the formula (6):
in the formula (6), C (j+1) represents an intermittent state quantity when a time sampling point of wind power on a time spectrum is j+1;
step 4.3, calculating the total intermittent times n of wind power by using the step (7) sum ：
Step 4.4. Calculating the average intermittent duration t of the wind power by using the method (8) mean ：
In the formula (8), delta T is the sampling interval of wind power;
step 4.5, forming a characteristic index vector x of the wind power by using the formula (9):
x＝[x 1 ,x 2 ,…,x i ,…,x 5 ] (9)
in the formula (9), x i Represents the i-th characteristic index value, x 1 ,x 2 ,x 3 ,x 4 ,x 5 Respectively the time-frequency domain mean value m of wind power (t,f) Time-frequency domain varianceTemporal spectral entropy ε (t,f) Number of pauses n sum Time t of intermittent average mean ；i＝1,2,…,5；
Step 5, constructing index matrixes X of the D wind power plants;
step 5.1. Calculating characteristic indexes of wind power according to the steps 1 to 4 for each wind power plant, and constructing an index vector { x) of each wind power plant by using the formula (10) d ,d＝1,2,…,D}：
x d ＝[x d1 ,x d2 ,…,x di ,…,x d5 ] (10)
In the formula (9), x di An ith characteristic index value representing a d-th wind farm; x is x d1 ,x d2 ,x d3 ,x d4 ,x d5 Respectively obtaining a time-frequency domain mean value, a time-frequency domain variance, a time-frequency spectrum entropy, intermittent times and intermittent average time of wind power of the d-th wind power plant;
step 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 grid-connected wind power of each wind power plant in the D wind power plants;
step 6.1, constructing a judgment matrix A;
constructing a judgment matrix A of the index by using the formula (12):
in the formula (12), a ij The i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A scale of importance of the phase comparison; when i=j, let a ij When i is equal to j, let a =1 ij ＝1/a ji ；
Step 6.2. Constructing a standard judgment matrix by using the method (13)
In the formula (13), the amino acid sequence of the compound,the i-th index x in the characteristic index vector x shown in expression (9) i And the j-th index x j A comparative importance scale standard value;
step 6.3. Calculating an index weight vector w using equation (14):
in the formula (14), the superscript T represents a transpose of the vector;
step 6.4, consistency test of the analytic hierarchy process;
obtaining the maximum characteristic root lambda by using the formula (15) max ：
In the formula (15), (Aw) i To determine the ith element, w, in the vector Aw formed by the product of the matrix A and the index weight vector w i Representing the ith element in the index weight vector w;
obtaining a consistency index C of the judgment matrix A by using a formula (16) I ：
Obtaining the consistency ratio C of the judgment matrix A by using the formula (17) R ：
In the formula (17), R I Is an average random consistency index;
when C R When < delta, the judgment is indicatedThe matrix A meets consistency; otherwise, readjusting the judgment matrix A to enable the judgment matrix A to meet consistency; delta represents the set consistency ratio threshold;
step 6.5, normalizing the index matrix X;
normalizing the index matrix 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 amino acid sequence of the compound,an ith characteristic index standard value for representing the wind power of the (d) th wind power plant;
step 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 matrixObtaining wind power characteristic evaluation vectors v of the D wind power plants by using the formula (19):
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