CN116703249B - Reliability analysis method based on CKL wind power capacity prediction - Google Patents
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Abstract
The invention discloses a reliability analysis method based on CKL wind power capacity prediction, which comprises the following steps: establishing a nested ARIMA wind speed model to obtain a prediction result of original wind speed data; establishing a quadratic Copula model wind power output probability model; building a wind power output prediction model based on CKL to obtain input data of reliability analysis; setting a reliability index of a wind power plant, and taking the reliability index as a precondition of wind power output reliability analysis; and taking a predicted result as input, and adopting a Monte Carlo simulation method to analyze the capacity reliability of the wind power predicted output. The invention can realize the reliability analysis of a period of time in the future on the basis of the original reliability analysis, and solves the problems of insufficient stability, low precision and the like in the prior art.
Description
Technical Field
The invention relates to reliability analysis of wind power generation output capacity, in particular to a reliability analysis method based on CKL wind power capacity prediction.
Background
With the exhaustion of traditional fossil fuel, two major problems of energy shortage and environmental pollution are becoming serious, and the energy structure is becoming more and more advanced. The double-carbon plan greatly promotes the adjustment of the energy structure in China, so that the use of renewable clean energy sources such as wind power, photovoltaics and the like becomes a main energy source.
Because of this certain discontinuity and randomness in wind power generation, it is often treated as an uncontrollable power source. Even if the wind power generation is integrated into the power grid, the wind power generation cannot be used for replacing a certain amount of traditional power generation in an equal amount, and can only be used as an energy supplement, so that the wind power generation is considered as an unstable power generation mode.
Disclosure of Invention
The invention aims to: the invention aims to provide a reliability analysis method based on CKL wind power capacity prediction, so that the problems of insufficient stability, low precision and the like in the prior art are solved, and the reliability analysis method has a prediction function.
The technical scheme is as follows: the invention discloses a reliability analysis method based on CKL wind power capacity prediction, which comprises the following steps:
(1) Establishing a nested ARIMA wind speed model to obtain a prediction result of original wind speed data;
the nested ARIMA wind speed model in the step (1) is specifically:
；
in the method, in the process of the invention,p,d,qis the ending parameter of ARIMA model, and uses ARIMA @ to make the model completep,d,q) Is expressed in terms of (a);
；
in the method, in the process of the invention,for time-lag variable>For all->The method simulates the acceleration distribution by taking into account a gaussian process that produces the fluctuation rate of the acceleration process, and the process is a lognormal distribution.
(2) And establishing a quadratic Copula model wind power output probability model, namely a QC wind power output probability model.
；
In the method, in the process of the invention,x,yrespectively two wind power plant related parameters;the correlation coefficient is QC function; in the case of establishing the wind speed correlation, the time series in which the wind speed can be determined is { t } 1 ,t 2 ,t 3 ,……,t n Establishing a wind farm edge distribution function according to the QC function:
；
in addition, on the basis of considering time sequence, wind speed is a discrete variable, so that a joint distribution function of the discrete variable is also required to be constructed:
；
in the method, in the process of the invention,A、Bthe two electric fields are respectively in a limit state of operation;
building a wind power output model:
；
in the method, in the process of the invention,Vrepresentation oftThe rotation speed of the fan at any moment;V i indicating cut-in wind speed;V a representing a standard wind speed;P a representing rated output power of the fan;
combining edge distribution and Copula functions to establish a Copula wind power output model:
；
in the method, in the process of the invention,Lis a discrete coefficient;v(t)as a function of wind speed over time;P N is rated output.
(3) Building a wind power output prediction model based on CKL to obtain input data of reliability analysis;
the wind power output prediction model based on CKL in the step (3) specifically comprises the following steps:
；
；
in the method, in the process of the invention,IMF m is the mth modal function;R M is the firstMA residual sequence;
；
；
；
in the method, in the process of the invention,xis an environmental variable;Knucleating a matrix for an environmental change;is the contribution rate; />Is the accumulated contribution rate;Pthe dimension-reduced environment variable matrix is the dimension-reduced environment variable matrix.
(4) Setting a reliability index of the wind power plant, and taking the reliability index as a premise of wind power output reliability analysis.
The wind farm reliability index in the step (4) is specifically:
probability of loss of power (LOLP):
；
in the method, in the process of the invention,t a time for failure to occur;Lis a power-off state set;Tthe total running time of the system;
loss of power time expectation (ole):
；
power loss capacity expectations (EENS):
；
in the method, in the process of the invention,P i is the average power loss capacity of one year.
(5) And taking a predicted result as input, and adopting a Monte Carlo simulation method to analyze the capacity reliability of the wind power predicted output. The specific formula of the credibility analysis is as follows:
；
；
in the method, in the process of the invention,Rthe reliability of the wind power system at any moment is in the standard reliabilityR 0 On the upper part of the upper part,P W the capacity of the conventional unit is effectively replaced for the wind turbine,C org the initial capacity of the system is set to be,P credit in order to effectively access the wind power capacity of the system,P wind is the total wind power capacity.
A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a CKL wind power capacity prediction based reliability analysis method as described above.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a reliability analysis method based on CKL wind power capacity prediction as described above when executing the computer program.
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
1. the invention provides a QC wind power output probability model in Copula for correlation among wind power plants, and can well describe the relation among wind power plants.
2. CKL predictive models were constructed with 13.3% and 19.1% reduction in RMSE and MAE, respectively, and 2.15% improvement in R2 compared to single LSTM. CKL has a higher accuracy than other common methods.
3. The reliability analysis is provided on the basis of the prediction capacity, so that the reliability assessment of the wind power plant has a prediction function, and the reliability analysis of the time t+1 can be realized; and the reliability of the wind power plant can be improved by analyzing the energy storage equipment.
Drawings
FIG. 1 is a flowchart of a CKL wind power output prediction step.
Fig. 2 is a step flow chart of a reliability analysis method based on CKL wind power capacity prediction according to the present invention.
Fig. 3 is an IEEE RTS-79 system topology.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings.
Fig. 1 is a flowchart of a step of a prediction part in a reliability analysis method based on CKL wind power capacity prediction, which includes the following steps:
(1) And establishing a nested ARIMA wind speed model to obtain a prediction result of the original wind speed data.
(2) On the basis of the steps, a quadratic Copula model wind power output probability model, namely a QC wind power output probability model, is established.
(3) And (3) establishing a wind power output prediction model based on the CKL to obtain input data of reliability analysis.
To verify the high accuracy of the CLK model of the present invention, long-short-term time prediction (LSTM), modal decomposition, and a combination of long-short-term time prediction (EMD-LSTM) were used in comparison to determine that the RMSE of the state values derived from CLK is less than LSTM and EMD-LSTM and has small fluctuations, as seen in Table 1.
Table 1 evaluation index of each model
As shown in fig. 2 and 3, the present embodiment operates on an IEEE RTS-97 system.
(4) Setting a reliability index of the wind power plant, and taking the reliability index as a premise of wind power output reliability analysis.
(5) And taking a predicted result as input, and adopting a Monte Carlo simulation method to analyze the capacity reliability of the wind power predicted output.
In order to verify the reliability analysis diversity based on the CKL prediction model, the comparison is carried out before and after the energy storage equipment (BESS) is added, and the obvious increase of the system prediction output after the BESS and the increase of the prediction reliability can be seen from the table 2.
Table 2 system reliability index and trusted capacity
Claims (7)
1. A reliability analysis method based on CKL wind power capacity prediction is characterized by comprising the following steps:
(1) Establishing a nested ARIMA wind speed model to obtain a prediction result of original wind speed data;
(2) According to the prediction result of wind speed data, a secondary Copula model wind power output probability model, namely a QC wind power output probability model, is established;
(3) Building a wind power output prediction model CEEMD-KPCA-LSTM model based on CKL to obtain input data of reliability analysis; the wind power output prediction model based on CKL in the step (3) specifically comprises the following steps:
；
；
in the method, in the process of the invention,IMF m is the mth modal function;R M is the firstMA residual sequence;
；
；
；
in the method, in the process of the invention,xis an environmental variable;Knucleating a matrix for an environmental change;is the contribution rate; />Is the accumulated contribution rate;Pthe dimension-reduced environment variable matrix is the dimension-reduced environment variable matrix;
(4) Setting a reliability index of a wind power plant, and taking the reliability index as a precondition of wind power output reliability analysis;
(5) And (3) taking a predicted result obtained by a wind power output prediction model CEEMD-KPCA-LSTM in the step (3) as input, and carrying out capacity reliability analysis on wind power predicted output by adopting a Monte Carlo simulation method.
2. The reliability analysis method based on CKL wind power capacity prediction according to claim 1, wherein the nested ARIMA wind speed model in step (1) specifically comprises:
；
in the method, in the process of the invention,p, d, qis the ending parameter of ARIMA model, and uses ARIMA @ to make the model completep, d, q) Is expressed in terms of (a);
；
in the method, in the process of the invention,for time-lag variable>For all->The method simulates the acceleration distribution by taking into account a gaussian process that produces the fluctuation rate of the acceleration process, and the process is a lognormal distribution.
3. The reliability analysis method based on CKL wind power capacity prediction according to claim 1 is characterized in that the QC wind power output probability model in the step (2) specifically comprises the following steps:
；
in the method, in the process of the invention,x, yrespectively two wind power plant related parameters;the correlation coefficient is QC function; in the case of establishing the wind speed correlation, the time series in which the wind speed can be determined is { t } 1 ,t 2 ,t 3 ,……,t n Establishing a wind farm edge distribution function according to the QC function:
；
in addition, on the basis of considering time sequence, wind speed is a discrete variable, so that a joint distribution function of the discrete variable is also required to be constructed:
；
in the method, in the process of the invention,A、Bthe two electric fields are respectively in a limit state of operation;
building a wind power output model:
；
in the method, in the process of the invention,Vrepresentation oftThe rotation speed of the fan at any moment;V i indicating cut-in wind speed;V a representing a standard wind speed;P a representing rated output power of the fan;
combining edge distribution and Copula functions to establish a Copula wind power output model:
；
in the method, in the process of the invention,Lis a discrete coefficient;v(t)as a function of wind speed over time;P N is rated output.
4. The reliability analysis method based on CKL wind power capacity prediction according to claim 1, wherein the wind power plant reliability index in step (4) specifically comprises:
loss of power probability LOLP:
；
in the method, in the process of the invention, t a time for failure to occur;Lis a power-off state set;Tthe total running time of the system;
the loss of power time is expected to be LOLE:
；
the loss of power capacity expects EENS:
；
in the method, in the process of the invention,P i is the average power loss capacity of one year.
5. The reliability analysis method based on the CKL wind power capacity prediction according to claim 1, wherein the formula of the reliability analysis in step (5) is specifically:
；
；
in the method, in the process of the invention,Rthe reliability of the wind power system at any moment is in the standard reliabilityR 0 On the upper part of the upper part,P W the capacity of the conventional unit is effectively replaced for the wind turbine,C org the initial capacity of the system is set to be,P credit in order to effectively access the wind power capacity of the system,P wind is the total wind power capacity.
6. A computer storage medium having stored thereon a computer program which, when executed by a processor, implements a CKL wind power capacity prediction based reliability analysis method as claimed in any one of claims 1 to 5.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a CKL wind power capacity prediction based reliability analysis method as claimed in any one of claims 1 to 5 when the computer program is executed.
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