CN111931972B - Wind power plant wind energy resource assessment and prediction method and system based on SWI-RS analysis method - Google Patents
Wind power plant wind energy resource assessment and prediction method and system based on SWI-RS analysis method Download PDFInfo
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Abstract
The invention discloses a wind power plant wind energy resource assessment and prediction method and system based on an SWI-RS analysis method, which comprises the following steps: step 1: fitting an accumulated probability distribution function of the average wind speed by using Gamma distribution according to the rule that the average wind speed follows the skewed distribution, converting the accumulated probability distribution function into a standard normal distribution function to obtain an SWI index sequence, and solving the SWI index sequence to obtain regional wind energy resource estimation; step 2: solving the Hurst index by adopting a de-duplication standard deviation analysis method according to the SWI index sequence to obtain the change trend of the wind energy resource and the future change trend thereof for prediction; and 3, step 3: and judging whether the wind power plant is suitable to be built or not according to the SWI index sequence and the Hurst index of the region where the wind power plant is output.
Description
Technical Field
The invention belongs to the field of wind energy resource assessment and prediction, and particularly relates to a wind power plant wind energy resource assessment and prediction method and system based on an SWI-RS analysis method.
Background
At present, fossil fuels such as coal, petroleum and the like are still main energy sources in the world, but because the fossil fuels are non-renewable and have serious pollution to the environment, people are always searching for substitutes of the fossil fuels, and with the progress of science and technology, wind energy, solar energy and the like have no pollution, and renewable energy sources and the like pay attention to the whole world.
China has rich wind energy resources, the wind energy reserve which can be developed and utilized in China is about 10 hundred million kW according to incomplete calculation, and in order to better use the wind energy resources, the national resource administration and the national oceanic administration jointly release an offshore wind power development and construction management method. And the randomness and the dispersity of wind energy increase the use difficulty of the wind energy. Therefore, effective assessment and prediction of wind energy resources in various regions are needed, which are crucial to site selection and guidance of wind power plants. Accurate site selection can reduce the power production cost and improve the economic benefit of the wind power plant.
At present, the probability density distribution of wind speed is mainly described by using a weibull two-parameter curve, and then parameters need to be solved by a least square method, but the calculation is complex, the requirement on historical data is high, and although the wind energy can be well described, the change trend of the wind energy cannot be predicted. Therefore, a more highly calibrated and compact method for evaluating and predicting wind energy resources is needed.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a wind power plant wind energy resource assessment and prediction method and system based on an SWI-RS analysis method, which take the influence of micro-terrain characteristics on wind speed into consideration, evaluate the wind energy resources of each area by a method which is simpler in calculation and more accurate, predict the size of the future wind energy resources of each area and can become a newer guiding method for site selection of the wind power plant.
The technical scheme adopted by the invention is as follows: a wind power plant wind energy resource evaluation and prediction method based on an SWI-RS analysis method comprises the following steps:
step 1: fitting an accumulative probability distribution function of the average wind speed by adopting Gamma distribution based on the rule that the average wind speed follows the skewed distribution, converting the accumulative probability distribution function into a standard normal distribution function, calculating according to the standard normal distribution function to obtain an SWI (scale of integration) exponential sequence, and solving the average value of the SWI exponential sequence to obtain regional wind energy resource estimation;
step 2: obtaining a Hurst index by adopting a de-duplication standard deviation analysis method according to the SWI index sequence, obtaining the change trend of regional wind energy resources and predicting the change trend of future regional wind energy resources;
and step 3: and judging whether the wind power plant is suitable to be built or not according to the SWI index sequence and the Hurst index of the region where the wind power plant is output.
Further, before the step 1, the method further comprises the steps of solving the micro-terrain features of the area according to the elevation data, and correcting the average wind speed by using the micro-terrain features of the area.
Further, the regional microtopography characteristics comprise gradient, slope direction and elevation difference.
Further, according to the elevation data, the micro-terrain features of the area are solved, and the calculation method is as follows:
in the formula, slope we And slope sn Respectively representing the gradient in the east-west direction and the gradient in the south-north direction, wherein delta h is an elevation difference, and delta x and delta y are horizontal distances in the east-west direction and the south-north direction;
the calculation formula of the gradient and the slope direction is as follows:
in the formula, slope represents the gradient, aspect represents the slope direction, and the meanings of the rest parameters are consistent with the formula (1).
Further, the step of correcting the average wind speed by using the regional micro-terrain features comprises:
the upwind correction coefficient k is as follows:
in the formula, Δ h is an elevation difference, c is a hill inclination angle coefficient, and the calculation formula is as follows:
the correction coefficient k of the wind speed under the narrow tube effect is as follows:
in the formula, theta is an airflow wind direction angle;
when no topographic effect exists, k is 1;
therefore, the terrain-corrected wind speed w is:
w＝k·V (6)。
further, the step 1 specifically comprises:
fitting a cumulative probability distribution function of the average wind speed by using Gamma distribution:
F(w)＝q+(1-q)G(w) (10)
in the formula, q is the probability that the average wind speed is 0, Γ (α) is a Gamma function, α is a shape parameter, p is w/β, w is the average wind speed, and β is a scale parameter;
converting the cumulative probability distribution function into a standard normal distribution function, and calculating according to the standard normal distribution function to obtain an SWI (scale of inertia) exponential sequence:
in the formula: when the content of F is more than 0 and (w) is less than or equal to 0.5,
when 0.5 < F (w) < 1,
obtaining regional wind energy resource estimation by solving the average value of the SWI index sequence:
the exponent sequence { SWI) is calculated according to equation (17) i Mean value of }
Wherein, SWI i Represents the ith SWI index, and tau represents the total number of SWIs;
when in use
When the wind energy is larger than the first threshold value, the wind energy resource in the area is rich, and when the wind energy resource is larger than the first threshold value
And when the wind energy is not greater than the first threshold value, the shortage of the wind energy resources in the area is indicated.
Further, the step 2 specifically comprises:
solving according to equation (19) to obtain the Hurst index:
ln[R(τ)/S(τ)]＝H lnτ+H lnε (19)
wherein H represents the Hurst parameter and can be obtained by least square regression, and epsilon represents the error.
Further, the step 3 specifically includes:
judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is smaller than a first threshold, if so, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; and if not, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold value, if so, recommending to build the wind power plant, otherwise, not recommending to build the wind power plant.
The invention also discloses a wind power plant wind energy resource evaluation and prediction system based on the SWI-RS analysis method, which comprises
The SWI exponential sequence calculation module is used for fitting an accumulated probability distribution function of the average wind speed by adopting Gamma distribution based on the rule that the average wind speed obeys the skewed distribution, converting the accumulated probability distribution function into a standard normal distribution function, and calculating according to the standard normal distribution function to obtain an SWI exponential sequence;
the Hurst index calculating module is used for solving and obtaining the Hurst index by adopting a de-duplication standard polar difference analysis method according to the SWI index sequence obtained by the SWI index sequence calculating module;
the judging module is used for judging whether the current area is suitable for building a wind power plant or not according to the SWI index sequence obtained by the SWI index sequence calculating module and the Hurst index obtained by the Hurst index calculating module; judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is smaller than a first threshold, if so, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; and otherwise, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, recommending to build the wind power plant, and otherwise, not recommending to build the wind power plant.
The system further comprises an area micro-terrain feature acquisition module, a data acquisition module and a data processing module, wherein the area micro-terrain feature acquisition module is used for solving according to the elevation data to obtain area micro-terrain features; and the average wind speed correction module is used for correcting the average wind speed by utilizing the micro-terrain characteristics of the area.
Has the beneficial effects that: the method considers the influence of the micro-terrain characteristics on the wind speed, evaluates the wind energy resources of each area by a method which is simpler in calculation and more accurate, predicts the size of the future wind energy resources of each area, and can become a newer guiding method for site selection of the wind power plant.
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FIG. 1 is a wind power plant wind energy resource assessment and prediction process of SWI-RS analysis.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further explained with reference to the following embodiments.
Example 1:
as shown in fig. 1, the wind energy resource assessment and prediction method for a wind farm based on the SWI-RS analysis method in the present embodiment includes the following steps:
step 1: solving micro-terrain features of the area according to the elevation data, wherein the micro-terrain features comprise a slope, a slope direction and an elevation difference, and correcting the average wind speed by using the micro-terrain features of the area; the method comprises the following specific steps:
calculating the micro-terrain features according to the elevation data, wherein the calculation method comprises the following steps:
in the formula, slope we And slope sn Respectively, the gradients in the east-west (X-axis) and north-south (Y-axis) directions, Δ h being the elevation difference, and Δ X and Δ Y being the horizontal distances in the east-west (X-axis) and north-south (Y-axis) directions. The calculation formula of the gradient and the slope direction is
In the formula, slope represents the gradient, aspect represents the slope direction, and the meanings of the rest parameters are consistent with the formula (1);
the wind speed is corrected according to the micro-terrain features, and the method comprises the following aspects:
(1) the upwind correction coefficient k is as follows:
in the formula, Δ h is an elevation difference, c is a hill inclination angle coefficient, and the calculation formula is as follows:
(2) the correction coefficient k of the wind speed under the narrow tube effect is as follows:
in the formula, θ is an airflow wind direction angle.
When no topographic effect exists, k is 1;
therefore, the terrain-corrected wind speed w is:
w＝k·V (6)
step 2: fitting the cumulative probability distribution function of the average wind speed by using Gamma distribution according to the rule that the average wind speed follows the skewed distribution, and converting the cumulative probability distribution function into a standard normal distribution function standardized high wind index (SWI); solving SWI to obtain regional wind energy resources; the method specifically comprises the following steps:
the change of the gale is expressed by using Gamma distribution, which is concretely as follows:
in the formula: alpha is a shape parameter; beta is a scale parameter; w is the average wind speed; x is the instantaneous wind speed; Γ (α) is a Gamma function, and g (w) represents the probability of an average wind speed w.
The formula that gives the corresponding cumulative probability (given time scale) is:
order to
Equation (8) becomes an incomplete Gamma equation:
since the Gamma equation does not include the case where x is 0, and the actual average wind speed may be 0, the cumulative probability distribution function may be expressed as:
F(w)＝q+(1-q)G(w) (10)
in the formula: q is the probability that the average wind speed is 0, if m represents the number of average wind speeds of 0 in the time series of average wind speeds, then:
where n is the total number of time series of average wind speeds.
Converting the cumulative probability distribution function into a standard normal distribution function, and solving the problem of adding SWI, wherein the formula is as follows:
in the formula: when F (w) is more than 0 and less than or equal to 0.5,
when 0.5 < F (w) < 1,
when the temperature is higher than the set temperature
The wind energy resource is rich when
Illustrating the lack of wind energy resources here;
and step 3: solving a Hurst index by using a de-weighting standard range analysis method (RS analysis method), analyzing the change trend of the wind energy resource, and predicting the future change trend of the wind energy resource; the method specifically comprises the following steps:
solving the range of the SWI index sequence, wherein the formula is as follows:
wherein Z (t, τ) represents SWI relative to its average value
The calculation formula of the accumulated dispersion is as follows:
wherein, SWI i Denotes the ith SWI index, τ denotes the total number of SWIs,
is a sequence { SWI i The average value of (f) is calculated by:
solving the standard deviation of the SWI index sequence, wherein the formula is as follows:
in the formula, the meaning of the parameter is the same as in the formula (16).
Solving the Hurst index, the formula is as follows:
ln[R(τ)/S(τ)]＝H lnτ+H lnε (19)
wherein, H represents the Hurst parameter and can be obtained by least square regression, and epsilon represents the error and is a constant.
If H is 0.5, the SWI exponential sequence is standard random walk, namely, no correlation exists between the past and the future; if H is more than 0.5 and less than or equal to 1, the future overall trend is related to the past characteristics; if 0 ≦ H < 0.5, this indicates that the overall trend in the future will be the opposite of the past.
And 4, step 4: according to the SWI and the Hurst index of the region where the wind power plant is output, effective evaluation and prediction are carried out on wind energy resources, and the method specifically comprises the following steps: judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is less than 0.2, if so, judging whether the Hurst index of the region where the output wind power plant is located is greater than 0.5, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; otherwise, judging whether the Hurst index of the region where the output wind power plant is located is larger than 0.5, if so, recommending to build the wind power plant, and otherwise, not recommending to build the wind power plant.
Example 2:
the embodiment also discloses a wind power plant wind energy resource assessment and prediction system based on the SWI-RS analysis method, which comprises the following steps:
the regional micro-terrain feature acquisition module is used for solving according to the elevation data to obtain regional micro-terrain features;
the average wind speed correction module is used for correcting the average wind speed by utilizing the micro-terrain characteristics of the area;
the SWI index sequence calculation module is used for fitting the corrected cumulative probability distribution function of the average wind speed by adopting Gamma distribution based on the rule that the average wind speed obeys the skewed distribution, converting the cumulative probability distribution function into a standard normal distribution function, and calculating according to the standard normal distribution function to obtain the SWI index sequence;
the Hurst index calculating module is used for solving and obtaining the Hurst index by adopting a de-duplication standard polar difference analysis method according to the SWI index sequence obtained by the SWI index sequence calculating module;
the judging module is used for judging whether the current area is suitable for building a wind power plant or not according to the SWI index sequence obtained by the SWI index sequence calculating module and the Hurst index obtained by the Hurst index calculating module; judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is smaller than a first threshold, if so, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; and otherwise, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, recommending to build the wind power plant, and otherwise, not recommending to build the wind power plant.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (9)
1. A wind power plant wind energy resource assessment and prediction method based on an SWI-RS analysis method is characterized by comprising the following steps: the method comprises the following steps:
step 1: based on the rule that the average wind speed obeys the skewed distribution, fitting a cumulative probability distribution function of the average wind speed by adopting Gamma distribution, converting the cumulative probability distribution function into a standard normal distribution function, calculating according to the standard normal distribution function to obtain an SWI (wind energy infrastructure) exponential sequence, and solving the average value of the SWI exponential sequence to obtain regional wind energy resource estimation;
step 2: obtaining a Hurst index by adopting a de-duplication standard deviation analysis method according to the SWI index sequence, obtaining the change trend of regional wind energy resources and predicting the change trend of future regional wind energy resources;
and 3, step 3: judging whether the wind power plant is suitable to be built or not according to the SWI index sequence and the Hurst index of the region where the wind power plant is output;
the step 1 specifically comprises the following steps:
fitting the cumulative probability distribution function of the average wind speed by using Gamma distribution:
F(w)＝q+(1-q)G(w) (10)
wherein q is the probability that the average wind speed is 0, Γ (α) is a Gamma function, α is a shape parameter, p is w/β, w is the average wind speed, and β is a scale parameter;
converting the cumulative probability distribution function into a standard normal distribution function, and calculating according to the standard normal distribution function to obtain an SWI (scale of inertia) exponential sequence:
in the formula: when the content of F is more than 0 and (w) is less than or equal to 0.5,
when 0.5 < F (w) < 1,
obtaining regional wind energy resource estimation by solving the average value of the SWI index sequence:
calculating the exponential sequence { SWI) according to equation (17) i Mean value of }
Wherein, SWI i Represents the ith SWI index, and tau represents the total number of SWIs;
when in use
When the wind energy is larger than the first threshold value, the wind energy resource in the area is rich, and when the wind energy resource is larger than the first threshold value
And if the wind energy is not greater than the first threshold value, the shortage of wind energy resources in the region is indicated.
2. A wind farm wind energy resource assessment and prediction method based on SWI-RS analysis method according to claim 1, characterized in that: before the step 1, the method further comprises the steps of solving the micro-terrain features of the area according to the elevation data, and correcting the average wind speed by using the micro-terrain features of the area.
3. The SWI-RS analysis method-based wind farm wind energy resource assessment and prediction method according to claim 2, wherein: the regional microtopography characteristics comprise gradient, slope direction and elevation difference.
4. A wind farm wind energy resource assessment and prediction method based on SWI-RS analysis according to claim 3, characterized in that: solving the micro-terrain features of the area according to the elevation data, wherein the calculation method comprises the following steps:
in the formula, slope we And slope sn Respectively representing the gradient in the east-west direction and the gradient in the south-north direction, wherein delta h is an elevation difference, and delta x and delta y are horizontal distances in the east-west direction and the south-north direction;
the calculation formula of the gradient and the slope direction is as follows:
in the formula, slope represents the gradient, aspect represents the slope direction, and the meanings of the rest parameters are consistent with the formula (1).
5. The SWI-RS analysis method-based wind farm wind energy resource assessment and prediction method according to claim 2, wherein: the step of correcting the average wind speed by using the regional micro-terrain characteristics comprises the following steps:
the upwind correction coefficient k is as follows:
in the formula, Δ h is an elevation difference, c is a slope inclination angle coefficient, and the calculation formula is as follows:
the correction coefficient k of the wind speed under the narrow tube effect is as follows:
in the formula, theta is an airflow wind direction angle;
when no terrain influence exists, k is 1;
therefore, the terrain-corrected wind speed w is:
w＝k·V (6)。
6. the SWI-RS analysis method-based wind farm wind energy resource assessment and prediction method according to claim 1, wherein: the step 2 specifically comprises the following steps:
solving according to the formula (19) to obtain the Hurst index:
ln[R(τ)/S(τ)]＝Hlnτ+Hlnε (19)
wherein H represents the Hurst parameter and can be obtained by least square regression, and epsilon represents the error.
7. The SWI-RS analysis method-based wind farm wind energy resource assessment and prediction method according to claim 1, wherein: the step 3 specifically comprises the following steps:
judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is smaller than a first threshold, if so, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; and if not, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold value, if so, recommending to build the wind power plant, otherwise, not recommending to build the wind power plant.
8. Wind power plant wind energy resource evaluation and prediction system based on SWI-RS analysis method is characterized in that: comprises that
The SWI exponential sequence calculation module is used for fitting an accumulated probability distribution function of the average wind speed by adopting Gamma distribution based on the rule that the average wind speed obeys the skewed distribution, converting the accumulated probability distribution function into a standard normal distribution function, and calculating according to the standard normal distribution function to obtain an SWI exponential sequence;
the Hurst index calculating module is used for solving and obtaining a Hurst index by adopting a de-duplication standard polar difference analysis method according to the SWI index sequence obtained by the SWI index sequence calculating module;
the judging module is used for judging whether the current area is suitable for building a wind power plant or not according to the SWI index sequence obtained by the SWI index sequence calculating module and the Hurst index obtained by the Hurst index calculating module; judging whether the average value of the SWI index sequence of the region where the output wind power plant is located is smaller than a first threshold, if so, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold, if so, not recommending to build the wind power plant, and otherwise, recommending to build the wind power plant; and if not, judging whether the Hurst index of the region where the output wind power plant is located is larger than a second threshold value, if so, recommending to build the wind power plant, otherwise, not recommending to build the wind power plant.
9. A wind farm wind energy resource assessment and prediction system based on SWI-RS analysis according to claim 8, characterized in that: also comprises
The regional micro-terrain feature acquisition module is used for solving according to the elevation data to obtain regional micro-terrain features;
and the average wind speed correction module is used for correcting the average wind speed by utilizing the micro-terrain characteristics of the area.
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| CN108205713A (en) * | 2016-12-16 | 2018-06-26 | 中国电力科学研究院 | A kind of region wind power prediction error distribution determination method and device |
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| CN108205713A (en) * | 2016-12-16 | 2018-06-26 | 中国电力科学研究院 | A kind of region wind power prediction error distribution determination method and device |
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