CN106997407A - Wind-resources scene reduction method based on trend fitting - Google Patents
Wind-resources scene reduction method based on trend fitting Download PDFInfo
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Abstract
本发明公开了一种基于趋势拟合的风资源场景缩减方法,其特征在于包括如下步骤:解析风资源场景过程线并进行趋势拟合,之后对于风资源场景过程簇中每个风资源场景过程线均进行趋势拟合,得到相应的全局趋势序列,之后进行场景缩减参数赋值,然后计算本轮场景缩减前风资源场景过程簇中各全局趋势序列之间的距离;最后进行缩减场景及更新场景概率直至得到满足场景缩减个数要求的场景集合及其概率。通过自动化提炼风资源场景过程线的趋势以判断风资源场景之间的相似性,缩减相似场景,进而达到场景缩减目的,避免风资源的随机性干扰,有利于风资源场景相似性的判断,增强风资源典型场景提取效果,对于风能开发利用具有重要意义。
The invention discloses a wind resource scene reduction method based on trend fitting, which is characterized by comprising the following steps: analyzing the wind resource scene process line and performing trend fitting, and then for each wind resource scene process in the wind resource scene process cluster Trend fitting is carried out on the average line to obtain the corresponding global trend sequence, and then the scene reduction parameter assignment is performed, and then the distance between each global trend sequence in the wind resource scene process cluster before the current round of scene reduction is calculated; finally, the reduction scene and the update scene are performed Probabilities until the set of scenes and their probabilities that meet the requirements of reducing the number of scenes are obtained. By automatically extracting the trend of the wind resource scene process line to judge the similarity between the wind resource scenes, reduce similar scenes, and then achieve the purpose of scene reduction, avoid the random interference of wind resources, which is beneficial to the judgment of the similarity of wind resource scenes, and enhance The extraction effect of typical scenes of wind resources is of great significance for the development and utilization of wind energy.
Description
技术领域technical field
本发明属于风资源特性分析技术领域,特别涉及一种基于趋势拟合的风资源 场景缩减方法。The invention belongs to the technical field of wind resource characteristic analysis, in particular to a method for reducing wind resource scenarios based on trend fitting.
背景技术Background technique
风资源场景缩减的研究主要用于风资源典型场景的提取,减少风资源分析的 工作量,提高分析效率,进而为风能开发利用提供技术支持。提高风资源场景缩 减的效果有助于加强风资源典型场景的识别能力,提升风资源分析技术水平。目 前国内外的风资源场景缩减技术均直接对风资源时间序列进行处理,将时间序列 距离较近的场景识别为相似场景,并删减相似场景从而达到场景缩减目的。相似 场景意指趋势相似的场景。风资源同时具有趋势性和随机性。该领域现有的技术 尚未考虑风资源的随机性对风资源时间序列距离计算的干扰,影响了风资源相似 场景的识别效果。The research on wind resource scene reduction is mainly used to extract typical wind resource scenes, reduce the workload of wind resource analysis, improve analysis efficiency, and provide technical support for wind energy development and utilization. Improving the reduction effect of wind resource scenarios will help to strengthen the ability to identify typical scenarios of wind resources and improve the technical level of wind resource analysis. At present, wind resource scene reduction technologies both at home and abroad directly process the time series of wind resources, identify scenes with close time series distances as similar scenes, and delete similar scenes to achieve the purpose of scene reduction. Similar scenarios mean scenarios with similar trends. Wind resources are both trendy and random. The existing technologies in this field have not considered the interference of the randomness of wind resources on the distance calculation of wind resource time series, which affects the recognition effect of similar scenes of wind resources.
发明内容Contents of the invention
本发明要解决的技术问题在于克服现有技术的不足,提供一种基于趋势拟合 的风资源场景缩减方法,针对风资源的趋势进行场景缩减,避免风资源的随机性 干扰,提高风资源典型场景提取效果,更好的为风能开发利用提供技术支持。The technical problem to be solved by the present invention is to overcome the deficiencies of the prior art and provide a wind resource scene reduction method based on trend fitting, which can reduce the scene according to the trend of wind resources, avoid random interference of wind resources, and improve the wind resource typicality. The effect of scene extraction can better provide technical support for the development and utilization of wind energy.
为解决上述技术问题,本发明采用如下技术方案:In order to solve the problems of the technologies described above, the present invention adopts the following technical solutions:
一种基于趋势拟合的风资源场景缩减方法,其特征在于包括如下步骤:A method for reducing wind resource scenarios based on trend fitting, characterized in that it comprises the following steps:
步骤1:在直角坐标系中根据风速与时间的关系,解析风资源场景过程线, 设定解析后设定风资源场景过程线由N个控制点连接组成,N≥2,NN个风资源 场景过程线组成风资源场景过程簇,NN≥3;Step 1: Analyze the wind resource scenario process line according to the relationship between wind speed and time in the Cartesian coordinate system, and set the wind resource scenario process line after analysis to be composed of N control point connections, N≥2, NN wind resource scenarios The process lines form a wind resource scenario process cluster, NN≥3;
步骤2:若N>3,进行风资源场景过程线分割,之后对分割后所在风资源场 景过程线的特定子区间分别进行趋势拟合,得到各自相对应的作为局部趋势的趋 势序列;之后再将局部趋势拟合成所在风资源场景过程线的全局趋势;若N≤3, 无需进行风资源场景过程线分割,直接对风资源场景过程线进行趋势拟合,得到 所在风资源场景过程线的全局趋势序列;Step 2: If N>3, segment the wind resource scene process line, and then perform trend fitting on the specific sub-intervals of the wind resource scene process line after the segmentation, and obtain the corresponding trend sequences as local trends; then Fit the local trend to the global trend of the wind resource scenario process line; if N≤3, there is no need to segment the wind resource scenario process line, and directly carry out trend fitting on the wind resource scenario process line to obtain the wind resource scenario process line global trend sequence;
步骤3:对于风资源场景过程簇中每个风资源场景过程线均按照步骤2的方 式进行趋势拟合,得到相应的所有风资源场景的全局趋势序列,设定共有NN个 全局趋势序列;Step 3: For each wind resource scenario process line in the wind resource scenario process cluster, carry out trend fitting according to the method of step 2, obtain the corresponding global trend sequences of all wind resource scenarios, and set a total of NN global trend sequences;
步骤4:场景缩减参数赋值步骤,设定拟缩减的风资源场景个数为MM,即 场景缩减后的风资源场景集合中场景的个数为NN-MM;Step 4: Scenario reduction parameter assignment step, set the number of wind resource scenarios to be reduced to MM, that is, the number of scenarios in the wind resource scenario set after scenario reduction is NN-MM;
步骤5,计算本轮场景缩减前风资源场景过程簇中每个风资源场景过程线的 风资源全局趋势序列之间的距离;Step 5, calculate the distance between the global trend sequences of wind resources of each wind resource scenario process line in the wind resource scenario process cluster before the current round of scenario reduction;
步骤6,缩减场景及更新场景概率:Step 6, reduce the scene and update the probability of the scene:
根据风资源全局趋势序列之间的概率权重距离确定最小概率的风资源场景, 将该最小概率的风资源场景从概率权重距离的集合中删除并更新未删除的风资 源场景的概率,得到本轮缩减场景后的场景集合及其对应的概率;依次执行场景 缩减,如执行场景替换的次数mm<MM,则转至步骤5开始新一轮缩减,否则 结束步骤5至步骤6的循环,得到满足场景缩减个数要求的场景集合及其概率。Determine the minimum probability wind resource scenario according to the probability weight distance between the global wind resource trend sequences, delete the minimum probability wind resource scenario from the probability weight distance set and update the probability of the undeleted wind resource scenario, and get the current round The set of scenes after reducing the scenes and their corresponding probabilities; perform scene reduction in sequence, if the number of scene replacements mm<MM, then go to step 5 to start a new round of reduction, otherwise end the cycle from step 5 to step 6, and it is satisfied Scenario collections and their probabilities required to reduce the number of scenarios.
进一步的,步骤1解析风资源场景过程线具体如下方式进行:Further, step 1 analyzes the wind resource scenario process line specifically as follows:
所述风资源场景过程线是在直角坐标系中,根据风速的过程,以时间t为横 坐标,以风速w为纵坐标得到;解析过程包括将风资源场景过程线解析为由若 干控制点连接组成,设共有N个控制点,由左至右编号依次为1,2,…,N,第i个 控制点坐标记为(ti,wi),i=1,2,...,N;将风资源场景过程线记为{t,w};设共有NN 个风资源场景过程线,组成风资源场景过程簇,记为{TT,WW},其中风资源场 景过程线编号记为ii=1,2,…,NN。The wind resource scene process line is obtained in the Cartesian coordinate system according to the process of wind speed, with time t as the abscissa and wind speed w as the vertical coordinate; the analysis process includes analyzing the wind resource scene process line as being connected by several control points Assuming that there are N control points in total, numbered from left to right are 1, 2, ..., N, and the coordinates of the i-th control point are marked as (t i , w i ), i=1, 2, ..., N; record the wind resource scenario process line as {t,w}; suppose there are NN wind resource scenario process lines to form a wind resource scenario process cluster, which is denoted as {TT,WW}, where the wind resource scenario process line number is denoted as ii=1,2,...,NN.
进一步的,步骤2按如下三步区别进行:Further, step 2 is carried out according to the following three steps:
步骤2.1,风资源场景过程线分割:Step 2.1, wind resource scene process line segmentation:
若N≤3,无需进行风资源场景过程线分割,直接进入步骤2.3;If N≤3, there is no need to divide the wind resource scene process line, and go directly to step 2.3;
若N>3,将风资源场景过程线{t,w}的第(sg-1)×m+1个控制点至第(sg+1) ×m+1个控制点记为第sg个子区间,其中m为整数,N=4时,m取1,N>4时取 值范围为sg=1,2,…,SG,int(*)表示对“*”取整;当m=1时, SG=N-3;当m>1时,若则 否则至此,可得到SG个子区 间;将{t,w}的第SG×m+1个控制点至第N个控制点记为第SG+1个子区间;至 此,可得到SG+1个子区间,且所得子区间中相邻的两个子区间之间有m+1个 点重叠,即前面子区间的后m+1个点与后面子区间的前m+1个点重叠;前SG 个子区间均有2m+1个控制点,其控制点坐标记为: 第SG+1个子区间含有N-SG×m个控制点, 其控制点坐标记为 If N>3, record the (sg-1)×m+1th control point to the (sg+1)×m+1th control point of the wind resource scenario process line {t,w} as the sg-th subinterval , where m is an integer, when N=4, m takes 1, and when N>4, the value range is sg=1,2,...,SG, int(*) means to round "*"; when m=1, SG=N-3; when m>1, if but otherwise So far, SG subintervals can be obtained; the SG×m+1th control point to the Nth control point of {t,w} is recorded as the SG+1th subinterval; so far, SG+1 subintervals can be obtained, and There are m+1 points overlapping between two adjacent sub-intervals in the obtained sub-interval, that is, the last m+1 points of the previous sub-interval overlap with the first m+1 points of the subsequent sub-interval; the first SG sub-intervals all have 2m+1 control points, whose coordinates are marked as: The SG+1th subinterval contains N-SG×m control points, and the coordinates of the control points are marked as
步骤2.2,风资源局部趋势拟合:Step 2.2, local trend fitting of wind resources:
若N>3,将步骤2.1所得到的SG+1个子区间分别进行趋势拟合,得到各子 区间相对应的趋势序列;前SG个子区间的趋势序列的控制点坐标记为:sg=1,2,…,SG;第SG+1个子区间的 趋势序列的控制点坐标记为: 为便于区分,称当N>3 时在本步骤2.2得到趋势序列为局部趋势;If N>3, carry out trend fitting on the SG+1 subintervals obtained in step 2.1 respectively, and obtain the trend sequence corresponding to each subinterval; the control point coordinates of the trend sequence of the first SG subintervals are marked as: sg=1,2,...,SG; the control point coordinates of the trend sequence of the SG+1th subinterval are marked as: For ease of distinction, when N>3, the trend sequence obtained in step 2.2 is called a local trend;
步骤2.3,风资源全局趋势合成:Step 2.3, wind resource global trend synthesis:
为便于区分,称本步骤2.3得到的趋势为全局趋势;For ease of distinction, the trend obtained in step 2.3 is called the global trend;
若N≤3,直接对风资源场景过程线{t,w}进行趋势拟合,得到其全局趋势序 列{t,glowf},然后进入步骤3;If N≤3, directly perform trend fitting on the wind resource scenario process line {t,w} to obtain its global trend sequence {t, glow w f }, and then proceed to step 3;
若N>3时,须根据局部趋势合成全局趋势;具体合成过程为:取第1个子 区间局部趋势的前m控制点作为风资源全 局趋势序列的前m个控制点,记为取第SG+1 个子区间局部趋势的后N-(SG+1)×m-1个控制点 作为风资源全局趋势序列 的第(SG+1)×m+2个控制点至第N个控制点,即由步骤2.1得到的 SG+1个子区间存在SG个的重叠部分,根据相邻子区间对其重叠部分拟合趋势 的影响程度进行加权处理,将该部分的局部趋势按下式进行加权计算,得到重叠 部分的加权局部趋势序列:If N>3, the global trend must be synthesized according to the local trend; the specific synthesis process is: take the first m control points of the local trend in the first subinterval As the first m control points of the global trend sequence of wind resources, denoted as Take the last N-(SG+1)×m-1 control points of the local trend of the SG+1th subinterval As the global trend sequence of wind resources from the (SG+1)×m+2th control point to the Nth control point, that is The SG+1 subintervals obtained from step 2.1 have SG overlapping parts, and weighting is performed according to the influence of adjacent subintervals on the fitting trend of their overlapping parts, and the local trend of this part is weighted according to the following formula to obtain Weighted local trend series for overlapping parts:
其中:第sg个重叠部分是指第sg个子区间的后m+1个控制点和第sg+1个 子区间的前m+1个控制点重叠;为第sg个重叠部分的加权局部趋势序 列中的第j个控制点的纵坐标,j=1,2,...,m+1,λ1、λ2为权重系数, 为第sg个子区间的局部趋势序列的第j+m个控制点纵坐标, 为第sg+1个子区间的拟合序列中第j个控制点纵坐标;将第sg个重叠 部分的加权局部趋势序列的控制点坐标记为:对SG个重叠部分均进行加权处 理后可得SG个加权局部趋势序列,且前一个加权局部趋势序列的最后一个控制 点坐标与后一个加权局部趋势序列的第一个控制点坐标相同;进行去重操作;去 重的具体处理为将前一个加权局部趋势序列的最后一个控制点坐标保留,去除其 后一个加权局部趋势序列的第一个控制点坐标;如此去重后,第1个加权局部趋 势序列包含m+1个控制点,即其余 加权局部趋势序列均包含m个控制点,即将去重 后的SG个加权局部趋势序列首尾相连,取其作为风资源全局趋势序列的第m+1 个控制点至第(SG+1)×m+1个控制点,即 Among them: the sg-th overlap refers to the overlap between the last m+1 control points of the sg-th subinterval and the first m+1 control points of the sg+1-th subinterval; is the ordinate of the jth control point in the weighted local trend sequence of the sgth overlapping part, j=1,2,...,m+1, λ 1 and λ 2 are weight coefficients, is the ordinate of the j+mth control point of the local trend sequence of the sgth subinterval, is the ordinate of the jth control point in the fitting sequence of the sg+1th subinterval; the coordinates of the control point of the weighted local trend sequence of the sgth overlapping part are marked as: After weighting the SG overlapping parts, SG weighted local trend sequences can be obtained, and the coordinates of the last control point of the previous weighted local trend sequence are the same as the coordinates of the first control point of the latter weighted local trend sequence; Repeat operation; the specific processing of deduplication is to keep the coordinates of the last control point of the previous weighted local trend sequence, and remove the coordinates of the first control point of the subsequent weighted local trend sequence; after such deduplication, the first weighted local trend sequence The trend sequence contains m+1 control points, namely The remaining weighted local trend sequences all contain m control points, namely Connect the SG weighted local trend sequences after deduplication end to end, and take them as the m+1th control point to the (SG+1)×m+1th control point of the global trend sequence of wind resources, that is
至此,可得到包含N个控制点的风资源全局趋势序列,亦记为{t,glowf}。So far, the global trend sequence of wind resources including N control points can be obtained, which is also denoted as {t, glow f }.
进一步的,步骤5按如下步骤进行:Further, step 5 is carried out as follows:
将本轮缩减前的风资源场景集合记为{TT,WW}bef,本轮缩减前风资源场景 的个数记为KK,风资源场景的序号记为kk,即kk=1,2,…,KK,对应风资源场景 的概率记为pkk;当第1次执行本步骤时,KK=NN,视作NN个风资源场景出现 的概率相等,记为pkk=1/NN;根据{TT,WW}bef中每个风资源场景过程线对应的 全局趋势序列计算两两风资源全局趋势序列之间的概率权重距离,记为PDkk, 算式如下:Record the set of wind resource scenes before this round of reduction as {TT,WW} bef , the number of wind resource scenes before this round of reduction as KK, and the sequence number of wind resource scenes as kk, that is, kk=1,2,… , KK, the probability corresponding to the wind resource scene is recorded as p kk ; when this step is executed for the first time, KK=NN, it is considered that the probability of occurrence of NN wind resource scenes is equal, and it is recorded as p kk =1/NN; according to { TT,WW} bef in the global trend sequence corresponding to each wind resource scenario process line to calculate the probability weight distance between two global trend sequences of wind resources, denoted as PD kk , the formula is as follows:
其中kk≠jj,kk=1,2,...,KK,jj=1,2,...,KK;in kk≠jj, kk=1,2,...,KK, jj=1,2,...,KK;
对于第kk个场景,可得到含有KK-1个距离的集合,记为{Dkk-jj},将{Dkk-jj} 中最小值对应的场景序号jj标记为JLkk,对于KK个场景可得到含有KK个JLkk的集合,记为{JLkk};对于本轮缩减前的风资源场景集合{TT,WW}bef,可得到 含有KK个概率权重距离的集合,记为{PDkk},将{PDkk}中最小值对应的场景序 号kk标记为PDmin。For the kkth scene, a set containing KK-1 distances can be obtained, denoted as {D kk-jj }, and the scene number jj corresponding to the minimum value in {D kk-jj } is marked as JL kk , for KK scenes A set containing KK JL kk can be obtained, denoted as {JL kk }; for the wind resource scene set {TT,WW} bef before this round of reduction, a set containing KK probability weight distances can be obtained, denoted as {PD kk }, mark the scene number kk corresponding to the minimum value in {PD kk } as PDmin.
进一步的,步骤6按如下步骤进行:Further, step 6 is carried out as follows:
将序号为PDmin的场景从{TT,WW}bef中删除;在{JLkk}中找出第PDmin 个的元素JLPDmin,然后将序号为PDmin的场景的概率pPDmin加至序号为JLPDmin的场景的概率即至此,可得到本轮缩减场景后的 场景集合{TT,WW}aft及其对应的概率;将执行本步骤的次数记为mm,若 mm<MM,则转至步骤5开始新一轮缩减,否则结束步骤5至步骤6的循环,得 到满足场景缩减个数要求的场景集合及其概率。Delete the scene with the serial number PDmin from {TT,WW} bef ; find the PDminth element JL PDmin in {JL kk }, and then add the probability p PDmin of the scene with the serial number PDmin to the JL PDmin Scenario Probability which is So far, the scene set {TT,WW} aft and its corresponding probability after the current round of reduced scenes can be obtained; record the number of executions of this step as mm, if mm<MM, go to step 5 to start a new round of reduction, Otherwise, end the cycle from step 5 to step 6, and obtain the scene set and its probability that meet the requirement of reducing the number of scenes.
本发明所提供的基于趋势拟合的风资源场景缩减技术方案,通过自动化提炼 风资源场景过程线的趋势以判断风资源场景之间的相似性,提供了新的判断方 法,结果简单明了,实施简便易行。在风能资源分析应用中,将风资源场景过程 和需要缩减的场景个数作为输入,即可自动判断场景之间的相似程度,缩减相似 场景,进而达到场景缩减目的。对比现有技术,首次提出以风资源的趋势为判别 依据进行场景相似识别,从而避免风资源的随机性干扰,是本技术领域的重要创 新,有利于风资源场景相似性的判断,增强风资源典型场景提取效果,对于风能 开发利用具有重要意义,具有重要的推广使用价值。The trend-fitting-based wind resource scene reduction technical solution provided by the present invention judges the similarity between wind resource scenes by automatically extracting the trend of the wind resource scene process line, and provides a new judgment method. The result is simple and clear, and the implementation Simple and easy. In the application of wind energy resource analysis, the process of wind resource scenarios and the number of scenarios to be reduced can be used as input to automatically judge the similarity between scenarios, reduce similar scenarios, and achieve the purpose of scenario reduction. Compared with the existing technology, it is proposed for the first time to use the trend of wind resources as the basis for the identification of similar scenes to avoid the random interference of wind resources. The extraction effect of typical scenes is of great significance to the development and utilization of wind energy, and has important promotion and use value.
附图说明Description of drawings
图1是根据本发明实施的风资源场景过程线的子区间分割示意图。图2是本 发明实施例的场景合成示意图。Fig. 1 is a schematic diagram of sub-interval division of a wind resource scenario process line implemented according to the present invention. Fig. 2 is a schematic diagram of scene synthesis according to the embodiment of the present invention.
图3是本发明实施例的缩减前初始场景集合示意图。Fig. 3 is a schematic diagram of an initial scene set before reduction according to an embodiment of the present invention.
图4是使用现有的场景缩减技术——同步回代缩减法进行场景缩减的过程 (图中场景个数为缩减后的场景个数)。Fig. 4 is the process of scene reduction using the existing scene reduction technology - synchronous back generation reduction method (the number of scenes in the figure is the number of reduced scenes).
图5是本发明实施例的使用本发明技术方案的场景缩减过程(图中场景个数 为缩减后的场景个数)。Fig. 5 is the scene reduction process using the technical solution of the present invention according to the embodiment of the present invention (the number of scenes in the figure is the number of scenes after reduction).
具体实施方式detailed description
为了使本发明实施例的目的、技术方案、优点更加清晰,下面将结合本发明 实施例和附图来介绍本发明的技术方案。In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be introduced below in conjunction with the embodiments of the present invention and the accompanying drawings.
本发明提供一种基于趋势拟合的风资源场景缩减方法,包括如下步骤:The present invention provides a wind resource scenario reduction method based on trend fitting, comprising the following steps:
步骤1,解析风资源场景过程线Step 1, analyze the wind resource scene process line
所述风资源场景过程线是在直角坐标系中,根据风速的过程,以时间t为横 坐标,以风速w为纵坐标得到;解析过程包括将风资源场景过程线解析为由若 干控制点连接组成,设共有N个控制点,由左至右编号依次为1,2,…,N,第i个 控制点坐标记为(ti,wi),i=1,2,...,N;将风资源场景过程线记为{t,w}。设共有NN 个风资源场景过程线,组成风资源场景过程簇,记为{TT,WW},其中风资源场 景过程线编号记为ii=1,2,…,NN。The wind resource scene process line is obtained in the Cartesian coordinate system according to the process of wind speed, with time t as the abscissa and wind speed w as the vertical coordinate; the analysis process includes analyzing the wind resource scene process line as being connected by several control points Assuming that there are N control points in total, numbered from left to right are 1, 2, ..., N, and the coordinates of the i-th control point are marked as (t i , w i ), i=1, 2, ..., N; record the wind resource scenario process line as {t,w}. It is assumed that there are NN wind resource scenario process lines in total to form a wind resource scenario process cluster, denoted as {TT,WW}, and the numbers of the wind resource scenario process lines are recorded as ii=1,2,...,NN.
步骤2.1,风资源场景过程线分割Step 2.1, wind resource scene process line segmentation
若N≤3,无需进行风资源场景过程线分割,直接进入步骤2.3。If N≤3, there is no need to divide the wind resource scene process line, and go directly to step 2.3.
若N>3,将风资源场景过程线{t,w}的第(sg-1)×m+1个控制点至第(sg+1) ×m+1个控制点记为第sg个子区间,其中m为整数,其取值范围为 sg=1,2,…,SG,int(*)表示对“*”取整。当m=1时,SG=N-3;当m>1时,若 则否则 至此,可得到SG个子区间。将{t,w}的第SG×m+1个控制 点至第N个控制点记为第SG+1个子区间。至此,可得到SG+1个子区间,且所 得子区间中相邻的两个子区间之间有m+1个点重叠,即前面子区间的后m+1个 点与后面子区间的前m+1个点重叠。前SG个子区间均有2m+1个控制点,其控 制点坐标记为:第SG+1个子区间含有 N-SG×m个控制点,其控制点坐标记为 子区间分割示意图可参见图1, 子区间1包括第1个控制点到第2m+1个控制点,坐标记为 子区间2包括第m+1个控制点到第3m+1个控 制点,坐标记为子区间sg包括第(sg-1)×m+1 个控制点到第(sg+1)×m+1个控制点,坐标记为子区间SG包括第(SG-1)×m+1个控制点 到第(SG+1)×m+1个控制点,坐标记为子区 间SG+1包括第SG×m+1个控制点到第N个控制点,坐标记为 本步骤中m的具体取值由本领域 技术人员设定。If N>3, record the (sg-1)×m+1th control point to the (sg+1)×m+1th control point of the wind resource scenario process line {t,w} as the sg-th subinterval , where m is an integer, and its value range is sg=1,2,...,SG, int(*) means to round "*". When m=1, SG=N-3; when m>1, if but otherwise So far, SG subintervals can be obtained. The SG×m+1th control point to the Nth control point of {t,w} is recorded as the SG+1th subinterval. So far, SG+1 subintervals can be obtained, and m+1 points overlap between two adjacent subintervals in the obtained subintervals, that is, the last m+1 points of the previous subinterval and the first m+1 points of the subsequent subinterval 1 point overlaps. There are 2m+1 control points in the first SG subintervals, and the coordinates of the control points are marked as: The SG+1th subinterval contains N-SG×m control points, and the coordinates of the control points are marked as See Figure 1 for the schematic diagram of sub-interval division. Sub-interval 1 includes the first control point to the 2m+1th control point, and the coordinates are marked as Subinterval 2 includes the m+1th control point to the 3m+1th control point, and the coordinates are marked as The subinterval sg includes the (sg-1)×m+1th control point to the (sg+1)×m+1th control point, and the coordinates are marked as The subinterval SG includes the (SG-1)×m+1th control point to the (SG+1)×m+1th control point, and the coordinates are marked as The subinterval SG+1 includes the SG×m+1th control point to the Nth control point, and the coordinates are marked as The specific value of m in this step is set by those skilled in the art.
步骤2.2,风资源局部趋势拟合Step 2.2, wind resource local trend fitting
若N>3,将步骤2.1所得到的SG+1个子区间分别进行趋势拟合,得到其相 对应的趋势序列;前SG个子区间的趋势序列的控制点坐标记为:sg=1,2,…,SG;第SG+1个子区间的 趋势序列的控制点坐标记为: 为便于区分,称当N>3 时在本步骤得到趋势序列为局部趋势。本步骤的趋势拟合采取多项式拟合方法,拟合阶数由本领域技术人员设定。If N>3, carry out trend fitting on the SG+1 subintervals obtained in step 2.1 to obtain the corresponding trend sequence; the control point coordinates of the trend sequence of the first SG subintervals are marked as: sg=1,2,...,SG; the control point coordinates of the trend sequence of the SG+1th subinterval are marked as: For ease of distinction, when N>3, the trend sequence obtained in this step is called a local trend. The trend fitting in this step adopts a polynomial fitting method, and the fitting order is set by those skilled in the art.
步骤2.3,风资源全局趋势合成Step 2.3, wind resource global trend synthesis
为便于区分,称本步骤得到的趋势为全局趋势。若N≤3,直接对风资源场景 过程线{t,w}进行趋势拟合,得到其全局趋势序列{t,glowf},然后进入步骤3。 若N>3时,须根据局部趋势合成全局趋势。具体合成过程为:取第1个子区间 局部趋势的前m控制点作为风资源全局趋 势序列的前m个控制点,记为取第SG+1个 子区间局部趋势的后N-(SG+1)×m-1个控制点 作为风资源全局趋势序列 的第(SG+1)×m+2个控制点至第N个控制点,即由步骤2.1得到的 SG+1个子区间存在SG个的重叠部分,将该部分的局部趋势按下式进行加权计 算,得到重叠部分的加权局部趋势序列。For ease of distinction, the trend obtained in this step is called the global trend. If N≤3, directly perform trend fitting on the wind resource scenario process line {t,w} to obtain its global trend sequence {t, glow w f }, and then proceed to step 3. If N>3, the global trend must be synthesized according to the local trend. The specific synthesis process is: take the first m control points of the local trend of the first subinterval As the first m control points of the global trend sequence of wind resources, denoted as Take the last N-(SG+1)×m-1 control points of the local trend of the SG+1th subinterval As the global trend sequence of wind resources from the (SG+1)×m+2th control point to the Nth control point, that is The SG+1 subintervals obtained from step 2.1 have SG overlapping parts, and the local trends of this part are weighted according to the following formula to obtain the weighted local trend sequence of the overlapping parts.
其中:第sg个重叠部分是指第sg个子区间的后m+1个控制点和第sg+1个 子区间的前m+1个控制点重叠;为第sg个重叠部分的加权局部趋势序 列中的第j个控制点的纵坐标,j=1,2,...,m+1,λ1、λ2为权重系数, 为第sg个子区间的局部趋势序列的第j+m个控制点纵坐标, 为第sg+1个子区间的拟合序列中第j个控制点纵坐标。本算式的作用为 根据相邻子区间对其重叠部分拟合趋势的影响程度进行加权处理。Among them: the sg-th overlap refers to the overlap between the last m+1 control points of the sg-th subinterval and the first m+1 control points of the sg+1-th subinterval; is the ordinate of the jth control point in the weighted local trend sequence of the sgth overlapping part, j=1,2,...,m+1, λ 1 and λ 2 are weight coefficients, is the ordinate of the j+mth control point of the local trend sequence of the sgth subinterval, It is the ordinate of the jth control point in the fitting sequence of the sg+1th subinterval. The function of this formula is to carry out weighting according to the degree of influence of adjacent subintervals on the fitting trend of their overlapping parts.
将第sg个重叠部分的加权局部趋势序列的控制点坐标记为:对SG个重叠部分均进行加权处 理后可得SG个加权局部趋势序列,且前一个加权局部趋势序列的最后一个控制 点坐标与后一个加权局部趋势序列的第一个控制点坐标相同。进行去重操作。去 重的具体处理为将前一个加权局部趋势序列的最后一个控制点坐标保留,去除其 后一个加权局部趋势序列的第一个控制点坐标。如此去重后,第1个加权局部趋 势序列包含m+1个控制点,即其余 加权局部趋势序列均包含m个控制点,即将去重 后的SG个加权局部趋势序列首尾相连,取其作为风资源全局趋势序列的第m+1 个控制点至第(SG+1)×m+1个控制点,即 Label the control point coordinates of the weighted local trend sequence for the sg-th overlap as: SG weighted local trend sequences can be obtained after SG overlapping parts are weighted, and the coordinates of the last control point of the previous weighted local trend sequence are the same as the first control point coordinates of the latter weighted local trend sequence. Perform deduplication. The specific processing of deduplication is to keep the coordinates of the last control point of the previous weighted local trend sequence, and remove the coordinates of the first control point of the subsequent weighted local trend sequence. After deduplication, the first weighted local trend sequence contains m+1 control points, namely The remaining weighted local trend sequences all contain m control points, namely Connect the SG weighted local trend sequences after deduplication end to end, and take them as the m+1th control point to the (SG+1)×m+1th control point of the global trend sequence of wind resources, that is
至此,可得到包含N个控制点的风资源全局趋势序列,亦记为{t,glowf}。 步骤3,所有风资源场景的全局趋势序列So far, the global trend sequence of wind resources including N control points can be obtained, which is also denoted as {t, glow f }. Step 3, Global Trend Sequence for All Wind Resource Scenarios
根据步骤2.1至步骤2.3,对于风资源场景过程簇中每个风资源场景过程线 均可得到相应的风资源全局趋势序列,记为{t,glowf}ii,共NN个。According to steps 2.1 to 2.3, for each wind resource scenario process line in the wind resource scenario process cluster, the corresponding global trend sequence of wind resources can be obtained, denoted as {t, glow f } ii , NN in total.
步骤4,场景缩减参数赋值Step 4, scene reduction parameter assignment
设定拟缩减的风资源场景个数为MM,即场景缩减后的风资源场景集合中场 景的个数为NN-MM。Set the number of wind resource scenes to be reduced as MM, that is, the number of scenes in the set of wind resource scenes after scene reduction is NN-MM.
步骤5,计算本轮场景缩减前风资源全局趋势序列之间的概率权重距离Step 5. Calculate the probability weight distance between the global trend sequences of wind resources before the current round of scene reduction
将本轮缩减前的风资源场景集合记为{TT,WW}bef,本轮缩减前风资源场景 的个数记为KK,风资源场景的序号记为kk,即kk=1,2,…,KK,对应风资源场景 的概率记为pkk;当第1次执行本步骤时,KK=NN,视作NN个风资源场景出现 的概率相等,记为pkk=1/NN;根据{TT,WW}bef中每个风资源场景过程线对应的 全局趋势序列计算两两风资源全局趋势序列之间的概率权重距离,记为PDkk, 算式如下:Record the set of wind resource scenes before this round of reduction as {TT,WW} bef , the number of wind resource scenes before this round of reduction as KK, and the sequence number of wind resource scenes as kk, that is, kk=1,2,… , KK, the probability corresponding to the wind resource scene is recorded as p kk ; when this step is executed for the first time, KK=NN, it is considered that the probability of occurrence of NN wind resource scenes is equal, and it is recorded as p kk =1/NN; according to { TT,WW} bef in the global trend sequence corresponding to each wind resource scenario process line to calculate the probability weight distance between two global trend sequences of wind resources, denoted as PD kk , the formula is as follows:
其中kk≠jj,kk=1,2,...,KK,jj=1,2,...,KK;in kk≠jj, kk=1,2,...,KK, jj=1,2,...,KK;
对于第kk个场景,可得到含有KK-1个距离的集合,记为{Dkk-jj},将{Dkk-jj} 中最小值对应的场景序号jj标记为JLkk,对于KK个场景可得到含有KK个JLkk的集合,记为{JLkk};对于本轮缩减前的风资源场景集合{TT,WW}bef,可得到 含有KK个概率权重距离的集合,记为{PDkk},将{PDkk}中最小值对应的场景序 号kk标记为PDmin。For the kkth scene, a set containing KK-1 distances can be obtained, denoted as {D kk-jj }, and the scene number jj corresponding to the minimum value in {D kk-jj } is marked as JL kk , for KK scenes A set containing KK JL kk can be obtained, denoted as {JL kk }; for the wind resource scene set {TT,WW} bef before this round of reduction, a set containing KK probability weight distances can be obtained, denoted as {PD kk }, mark the scene number kk corresponding to the minimum value in {PD kk } as PDmin.
步骤6:缩减场景及更新场景概率Step 6: Reduce the scene and update the probability of the scene
将序号为PDmin的场景从{TT,WW}bef中删除;在{JLkk}中找出第PDmin 个的元素JLPDmin,然后将序号为PDmin的场景的概率pPDmin加至序号为JLPDmin的场景的概率即至此,可得到本轮缩减场景后的 场景集合{TT,WW}aft及其对应的概率;将执行本步骤的次数记为mm,若 mm<MM,则转至步骤5开始新一轮缩减,否则结束步骤5至步骤6的循环,得 到满足场景缩减个数要求的场景集合及其概率。Delete the scene with the serial number PDmin from {TT,WW} bef ; find the PDminth element JL PDmin in {JL kk }, and then add the probability p PDmin of the scene with the serial number PDmin to the JL PDmin Scenario Probability which is So far, the scene set {TT,WW} aft and its corresponding probability after the current round of reduced scenes can be obtained; record the number of executions of this step as mm, if mm<MM, go to step 5 to start a new round of reduction, Otherwise, end the cycle from step 5 to step 6, and obtain the scene set and its probability that meet the requirement of reducing the number of scenes.
图2为场景过程簇的生成示意图,由正弦趋势和阶梯趋势分别叠加服从标准 正态分布的随机过程线以对应合成场景过程线1和场景过程线2。将前述两种趋 势分别与随机生成且均服从标准正态分布的200个过程线叠加,可得到含有400 个场景过程线的初始场景集合(缩减前初始场景集合),如图3所示。Figure 2 is a schematic diagram of the generation of scene process clusters. The sinusoidal trend and the step trend are respectively superimposed on random process lines that obey the standard normal distribution to correspond to the synthetic scene process line 1 and scene process line 2. By superimposing the above two trends with 200 process lines randomly generated and subject to standard normal distribution, an initial scene set containing 400 scene process lines (initial scene set before reduction) can be obtained, as shown in Figure 3.
图4为使用现有的场景缩减技术——同步回代缩减法进行场景缩减的过程。 图5为使用本发明技术方案进行场景缩减的过程。对比图4和图5可知,现有的 场景缩减技术在场景缩减中并未识别出正弦和阶梯两种不同的趋势,而本发明所 提技术方案则识别出了正弦和阶梯两种不同趋势,验证了本发明所提技术方案的 有效性。FIG. 4 shows the process of scene reduction using the existing scene reduction technology—the synchronous back-generation reduction method. Fig. 5 is a process of scene reduction using the technical solution of the present invention. Comparing Figure 4 and Figure 5, it can be seen that the existing scene reduction technology does not recognize two different trends of sinusoidal and ladder in scene reduction, but the technical solution proposed by the present invention recognizes two different trends of sinusoidal and ladder, The effectiveness of the technical solution proposed in the present invention has been verified.
本发明具体实施时可采用计算机软件技术实现自动运行。When the present invention is specifically implemented, computer software technology can be used to realize automatic operation.
通过实施例成果可知,本发明所提技术方案识别出了不同的趋势,说明了本 发明的有效性。可知本发明可以自动有效地提取风资源场景的趋势并基于此进行 场景缩减,为风能开发利用提供决策支持。As can be seen from the results of the examples, the technical solutions proposed in the present invention have identified different trends, which demonstrates the effectiveness of the present invention. It can be seen that the present invention can automatically and effectively extract the trend of the wind resource scene and reduce the scene based on it, so as to provide decision support for the development and utilization of wind energy.
本发明主要应用于风资源场景缩减,在风能资源分析应用中,将风资源场景 过程和需要缩减的场景个数作为输入,即可自动判断场景之间的相似程度,缩减 相似场景,进而达到场景缩减目的。与现有的相关技术相比,本发明的创新在于 通过趋势以判断场景之间的相似性。鉴于此,将本发明与现有的技术同时应用于 由不同的趋势与相同分布的随机叠加得到的场景过程簇的场景缩减中,可用以验 证本发明技术方案的合理性。The present invention is mainly applied to the reduction of wind resource scenarios. In the application of wind energy resource analysis, the wind resource scenario process and the number of scenarios to be reduced are used as input to automatically judge the similarity between the scenarios, reduce similar scenarios, and then achieve the scenario Reduced purpose. Compared with the existing related technologies, the innovation of the present invention lies in judging the similarity between scenes through the trend. In view of this, applying the present invention and the existing technology to the scene reduction of the scene process cluster obtained by the random superposition of different trends and the same distribution can be used to verify the rationality of the technical solution of the present invention.
需要强调的是,本发明所述的实施例是说明性的,而不是限定性的,因此本 发明并不限于具体实施方式中所述的实施例,凡是由本领域技术人员根据本发明 的技术方案得出的其他实施方式,同样属于本发明保护的范围。It should be emphasized that the embodiments described in the present invention are illustrative rather than restrictive, so the present invention is not limited to the embodiments described in the specific implementation, and those skilled in the art according to the technical solutions of the present invention Other obtained implementation modes also belong to the protection scope of the present invention.
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