CN103927597A

CN103927597A - Ultra-short-term wind power prediction method based on autoregression moving average model

Info

Publication number: CN103927597A
Application number: CN201410163064.3A
Authority: CN
Inventors: 汪宁渤; 路亮; 何世恩; 马彦宏; 赵龙; 周强; 马明; 张健美
Original assignee: State Grid Gansu Electric Power Co Ltd; Wind Power Technology Center of Gansu Electric Power Co Ltd; State Grid Corp of China SGCC
Current assignee: State Grid Gansu Electric Power Co Ltd; Wind Power Technology Center of Gansu Electric Power Co Ltd; State Grid Corp of China SGCC
Priority date: 2014-04-22
Filing date: 2014-04-22
Publication date: 2014-07-16

Abstract

The invention discloses an ultra-short-term prediction method of wind power power based on an autoregressive sliding average model, which includes inputting data to obtain parameters of the autoregressive sliding average model; The prediction results were obtained from the autoregressive moving average model. Through the prediction of wind power in the process of wind power generation, key information is provided for real-time scheduling of new energy power generation, day-ahead planning of new energy power generation, monthly plan of new energy power generation, evaluation of new energy power generation capacity and estimation of abandoned wind power. Through the introduction of composite data sources, the ultra-short-term prediction accuracy of wind power can be effectively improved, so as to achieve the purpose of effectively increasing the electricity consumption of new energy on the premise of ensuring the safe, stable and economical operation of the power grid.

Description

Ultra-short-term wind power forecasting method based on autoregressive moving average model

技术领域technical field

本发明涉及新能源发电过程中风电功率预测技术领域，具体地，涉及一种基于复合数据源自回归滑动平均模型的风电功率超短期预测方法。The present invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to an ultra-short-term wind power forecasting method based on composite data derived from a regression moving average model.

背景技术Background technique

我国风电进入规模化发展阶段以后所产生的大型新能源基地多数位于“三北地区”(西北、东北、华北)，大型新能源基地一般远离负荷中心，其电力需要经过长距离、高电压输送到负荷中心进行消纳。由于风、光资源的间歇性、随机性和波动性，导致大规模新能源基地的风电、光伏发电出力会随之发生较大范围的波动，进一步导致输电网络充电功率的波动，给电网运行安全带来一系列问题。Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safety of power grid operation. bring a series of problems.

截至2014年4月，甘肃电网并网风电装机容量已达707万千瓦，约占甘肃电网总装机容量的22％，成为仅次于火电的第二大主力电源。目前，甘肃电网风电、光伏发电装机超过甘肃电网总装机容量的1/3。随着新能源并网规模的不断提高，风电、光伏发电不确定性和不可控性给电网的安全稳定经济运行带来诸多问题。准确预估可利用的发电风资源是对大规模风电优化调度的基础。对风力发电过程中的风电功率进行预测，可为新能源发电实时调度、新能源发电日前计划、新能源发电月度计划、新能源发电能力评估和弃风电量估计提供关键信息。As of April 2014, Gansu grid-connected wind power installed capacity has reached 7.07 million kilowatts, accounting for about 22% of the total installed capacity of Gansu grid, becoming the second largest main power source after thermal power. At present, the installed capacity of wind power and photovoltaic power generation in Gansu Power Grid exceeds 1/3 of the total installed capacity of Gansu Power Grid. With the continuous improvement of the grid-connected scale of new energy, the uncertainty and uncontrollability of wind power and photovoltaic power generation have brought many problems to the safe, stable and economical operation of the power grid. Accurate estimation of available wind resources for power generation is the basis for optimal scheduling of large-scale wind power. The prediction of wind power in the process of wind power generation can provide key information for real-time dispatch of new energy power generation, day-ahead planning of new energy power generation, monthly plan of new energy power generation, evaluation of new energy power generation capacity and estimation of abandoned wind power.

发明内容Contents of the invention

本发明的目的在于，针对上述问题，提出一种基于自回归滑动平均模型的风电功率超短期预测方法，以实现高精度风电功率超短期预测的优点。The purpose of the present invention is to address the above problems, to propose a method for ultra-short-term prediction of wind power based on an autoregressive moving average model, so as to realize the advantages of ultra-short-term prediction of high-precision wind power.

为实现上述目的，本发明采用的技术方案是：In order to achieve the above object, the technical scheme adopted in the present invention is:

一种基于自回归滑动平均模型的风电功率超短期预测方法，包括输入数据得到自回归滑动平均模型参数；An ultra-short-term prediction method for wind power based on an autoregressive moving average model, including inputting data to obtain autoregressive moving average model parameters;

输入风电功率预测所需输入数据到根据上述自回归滑动平均模型的参数确定的自回归滑动平均模型中得到预测结果。The input data required for wind power prediction is input into the autoregressive moving average model determined according to the parameters of the above-mentioned autoregressive moving average model to obtain the prediction result.

根据本发明的优选实施例，所述输入数据得到自回归滑动平均模型参数包括，步骤101、输入模型训练基础数据；According to a preferred embodiment of the present invention, said input data to obtain autoregressive moving average model parameters includes, step 101, input model training basic data;

步骤102、模型定阶；Step 102, model order determination;

步骤103、采用矩估计方法对定阶的ARMA(p,q)模型参数进行估计。Step 103: Estimate the parameters of the fixed-order ARMA(p,q) model by using the moment estimation method.

根据本发明的优选实施例，所述步骤101输入模型训练基础数据，输入数据包括，风电场基础信息、历史风速数据、历史功率数据和地理信息系统数据。According to a preferred embodiment of the present invention, the step 101 inputs basic data for model training, and the input data includes basic wind farm information, historical wind speed data, historical power data and geographic information system data.

根据本发明的优选实施例，所述步骤102模型定阶：According to a preferred embodiment of the present invention, the step 102 model is ordered:

采用残差方差图法进行模型定阶，具体为设x_t为需要估计的项，x_t-1,x_t-2,...,x_t-n为已知历史功率序列，对于ARMA(p,q)模型，模型定阶即确定模型中参数p和q的值；The residual variance map method is used to determine the order of the model. Specifically, let x _t be the item to be estimated, x _t-1 , x _t-2 ,..., x _tn are known historical power sequences, and for ARMA(p, q) model, the order of the model is to determine the values of the parameters p and q in the model;

用系列阶数逐渐递增的模型拟合原始序列，每次都计算残差平方和然后画出阶数和的图形，当阶数由小增大时，会显著下降，达到真实阶数后的值会逐渐趋于平缓，甚至反而增大，Fits the original series with a model of increasing order of the series, computing the residual sum of squares each time Then plot the order and The graph of , when the order increases from small, will decrease significantly, and after reaching the true order The value will gradually become flat, or even increase instead,

实际观测值个数指拟合模型时实际使用的观察值项数，对于具有N个观察值的序列，拟合AR(p)模型，则实际使用的观察值最多为N-p，模型参数个数指所建立的模型中实际包含的参数个数，对于含有均值的模型，模型参数个数为模型阶数加1，对于N个观测值的序列，ARMA模型的残差估计式为：The number of actual observations refers to the number of observations actually used when fitting the model. For a sequence with N observations, when fitting the AR(p) model, the number of observations actually used is at most N-p, and the number of model parameters refers to The number of parameters actually included in the established model. For a model with a mean value, the number of model parameters is the model order plus 1. For a sequence of N observations, the residual estimation formula of the ARMA model is:

根据本发明的优选实施例，所述步骤103采用矩估计方法对定阶的According to a preferred embodiment of the present invention, the step 103 adopts the method of moment estimation to determine the

ARMA(p,q)模型参数进行估计具体步骤为：The specific steps to estimate the parameters of the ARMA(p,q) model are as follows:

将风电场历史功率数据利用数据序列x₁,x₂,...,x_t表示，其样本自协方差定义为The historical power data of the wind farm is represented by the data sequence x ₁ , x ₂ ,..., x _t , and its sample autocovariance is defined as

${\overset{^^}{γ γ}}_{k k} = = \frac{11}{n no} {Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k},,$

其中，k＝0,1,2,...,n-1，x_t和x_t-k均为数据序列x₁,x₂,...,x_t中的数值；Wherein, k=0,1,2,...,n-1, x _t and x _tk are values in the data sequence x ₁ , x ₂ ,...,x _t ;

则 ${\hat{γ}}_{0} = - \frac{1}{n} Σ_{t = 1}^{n} x_{t}^{2}$ but ${\hat{γ}}_{0} = - \frac{1}{no} Σ_{t = 1}^{no} x_{t}^{2}$

则历史功率数据样本自相关函数为：Then the autocorrelation function of historical power data samples is:

${\overset{^^}{ρ ρ}}_{k k} = = \frac{{\overset{^^}{γ γ}}_{k k}}{{\overset{^^}{γ γ}}_{00}} = = \frac{\frac{11}{n no} {Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k}}{\frac{11}{n no} {Σ Σ}_{t t = = 11}^{n no} {x x}_{t t}^{22}} = = \frac{{Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k}}{{Σ Σ}_{t t = = 11}^{n no} {x x}_{t t}^{22}},,$

其中，k＝0,1,2,...,n-1。Wherein, k=0, 1, 2, . . . , n-1.

AR部分的矩估计为，The moments of the AR part are estimated as,

令make

则协方差函数为Then the covariance function is

用的估计代替γ_k，use An estimate of γ instead of _k ,

可得参数 Available parameters

对MA(q)模型系数θ₁,θ₂,...,θ_q采用矩估计有For MA(q) model coefficients θ ₁ , θ ₂ ,..., θ _q are estimated by moments

$γ_{0} (y_{t}) = (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2}) σ_{a}^{2}$ 直到 $γ_{0} ({the y}_{t}) = (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2}) σ_{a}^{2}$ until

${γ γ}_{k k} (({y the y}_{t t})) = = ((- - {θ θ}_{k k} + + {θ θ}_{11} {θ θ}_{k k + + 11} + + . . . . . . + + {θ θ}_{q q - - k k} {θ θ}_{q q})) {σ σ}_{a a}^{22}$

其中k＝1,2,...,m，where k=1,2,...,m,

以上m+1个方程非线性方程，采用迭代法进行求解即得到自回归滑动平均模型参数。The above m+1 equations are nonlinear equations, and the iterative method is used to solve the autoregressive moving average model parameters.

根据本发明的优选实施例，所述输入风电功率预测所需输入数据到根据上述自回归滑动平均模型的参数确定的自回归滑动平均模型中得到预测结果的步骤包括，According to a preferred embodiment of the present invention, the step of inputting the input data required for wind power prediction into the autoregressive moving average model determined according to the parameters of the above-mentioned autoregressive moving average model to obtain the prediction result includes:

步骤201、输入功率预测基础数据；Step 201, input power prediction basic data;

步骤202、对输入的基础数据进行噪声滤波及数据预处理；Step 202, performing noise filtering and data preprocessing on the input basic data;

步骤203、根据确定的参数建立自回归滑动平均模型，并将处理后的数据输入从而得到预测结果；Step 203, establishing an autoregressive moving average model according to the determined parameters, and inputting the processed data to obtain a prediction result;

步骤204、将预测结果输出至数据库中，并通过图表及曲线展示预测结果、并展示预测与实测结果的对比。Step 204, outputting the prediction result to the database, and displaying the prediction result through charts and curves, and displaying a comparison between the prediction and the actual measurement results.

根据本发明的优选实施例，所述输入功率预测基础数据包括资源监测系统数据和运行监测系统数据，所述资源监测系统数据包含风资源监测数据；所述运行监测系统数据包括风机监测数据、升压站监测数据和数据采集与监视控制系统数据。According to a preferred embodiment of the present invention, the input power prediction basic data includes resource monitoring system data and operation monitoring system data, the resource monitoring system data includes wind resource monitoring data; the operation monitoring system data includes fan monitoring data, Pressure station monitoring data and data acquisition and monitoring control system data.

根据本发明的优选实施例，所述噪声滤波及数据预处理具体为：噪声滤波模块对监测系统实时采集得到的带有噪声的数据进行滤波处理，去除坏数据和奇异值；数据预处理模块对数据进行对齐、归一化处理和分类筛选处理。According to a preferred embodiment of the present invention, the noise filtering and data preprocessing specifically include: the noise filtering module performs filtering processing on the data with noise collected by the monitoring system in real time to remove bad data and singular values; the data preprocessing module The data were aligned, normalized and sorted and screened.

根据本发明的优选实施例，所述自回归滑动平均模型为：According to a preferred embodiment of the present invention, the autoregressive moving average model is:

其中，和θ_j(1≤j≤q)是系数，α_t是白噪声序列。in, and θ _j (1≤j≤q) are coefficients, and α _t is a white noise sequence.

本发明的技术方案具有以下有益效果：The technical solution of the present invention has the following beneficial effects:

本发明的技术方案通过对风力发电过程中的风电功率进行预测，为新能源发电实时调度、新能源发电日前计划、新能源发电月度计划、新能源发电能力评估和弃风电量估计提供关键信息。通过引入复合数据源有效提高风电功率超短期预测精度，从而实现在保障电网安全稳定经济运行的前提下有效提高新能源上网电量目的。The technical solution of the present invention provides key information for real-time scheduling of new energy power generation, day-ahead plan of new energy power generation, monthly plan of new energy power generation, evaluation of new energy power generation capacity and estimation of abandoned wind power by predicting wind power in the process of wind power generation. Through the introduction of composite data sources, the ultra-short-term prediction accuracy of wind power can be effectively improved, so as to achieve the purpose of effectively increasing the electricity consumption of new energy on the premise of ensuring the safe, stable and economical operation of the power grid.

下面通过附图和实施例，对本发明的技术方案做进一步的详细描述。The technical solutions of the present invention will be described in further detail below with reference to the accompanying drawings and embodiments.

附图说明Description of drawings

图1为本发明实施例所述的基于自回归滑动平均模型的风电功率超短期预测方法的原理框图。Fig. 1 is a functional block diagram of an ultra-short-term wind power forecasting method based on an autoregressive moving average model according to an embodiment of the present invention.

具体实施方式Detailed ways

以下结合附图对本发明的优选实施例进行说明，应当理解，此处所描述的优选实施例仅用于说明和解释本发明，并不用于限定本发明。The preferred embodiments of the present invention will be described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention.

含大规模风电的电力系统运行依赖庞大的、准确的数据集，而风电功率预测若能将这些数据有效融合利用则可有效提高预测精度。与常规电力系统SCADA监测不同，在各类电气、机械和热力等数据之外，风电监测数据还包含大量的资源监测、运行监测及地理信息等。The operation of power systems including large-scale wind power depends on huge and accurate data sets, and if wind power forecasting can effectively integrate and utilize these data, the prediction accuracy can be effectively improved. Different from conventional power system SCADA monitoring, in addition to various electrical, mechanical and thermal data, wind power monitoring data also includes a large amount of resource monitoring, operation monitoring and geographic information.

如图1所示，本发明技术方案提出的风电功率超短期预测可分为两个阶段：模型训练阶段和功率预测阶段。As shown in FIG. 1 , the ultra-short-term prediction of wind power proposed by the technical solution of the present invention can be divided into two stages: a model training stage and a power prediction stage.

阶段1：模型训练Phase 1: Model Training

步骤1.1：模型训练基础数据输入Step 1.1: Model training basic data input

风功率预报系统模型训练所需输入数据包括，风电场基础信息、历史风速数据、历史功率数据，地理信息系统(GIS)数据(风电场/风机坐标、测风塔坐标、升压站坐标等)。将基础数据输入到预测模型中进行模型训练。The input data required for model training of the wind power forecasting system include basic wind farm information, historical wind speed data, historical power data, and geographic information system (GIS) data (wind farm/wind turbine coordinates, anemometer tower coordinates, booster station coordinates, etc.) . Input the basic data into the predictive model for model training.

步骤1.2：模型定阶Step 1.2: Model Ordering

由于事先无法确定需要使用多少已知时间序列的项来建立估计函数，所以需要对模型进行定阶判断。Since it is impossible to determine in advance how many items of known time series need to be used to establish the estimation function, it is necessary to make an order judgment on the model.

设x_t为需要估计的项，x_t-1,x_t-2,...,x_t-n为已知历史功率序列，对于ARMA(p,q)模型，模型定阶就是确定模型中参数p和q的值。Let x _t be the item to be estimated, x _t-1 , x _t-2 ,..., x _tn are the known historical power sequences, for the ARMA(p,q) model, model order determination is to determine the parameter p in the model and the value of q.

采用残差方差图法进行模型定阶。假定模型是有限阶自回归模型，如果设置的阶数小于真实阶数，则是一种不足拟合，因而拟合残差平方和必定偏大，此时通过提高阶数可以显著降低残差平方和。反之，如果阶数已经达到真实值，那么再增加阶数，就是过度拟合，此时增加阶数不会令残差平方和显著减小，甚至会略有增加。The order of the model was determined using the residual variogram method. Assuming that the model is a finite-order autoregressive model, if the set order is smaller than the true order, it is a kind of underfitting, so the sum of the squares of the fitting residuals must be too large. At this time, the residual squares can be significantly reduced by increasing the order and. Conversely, if the order has reached the true value, then increasing the order is overfitting. At this time, increasing the order will not significantly reduce the residual sum of squares, or even increase slightly.

这样用系列阶数逐渐递增的模型来拟合原始序列，每次都计算残差平方和然后画出阶数和的图形。当阶数由小增大时，会显著下降，达到真实阶数后的值会逐渐趋于平缓，有时甚至反而增大。残差方差的估计式为：In this way, the original series is fitted with a model with a series of increasing orders, and the residual sum of squares is calculated each time Then plot the order and graphics. When the order increases from small to small, will decrease significantly, and after reaching the true order The value of will gradually level off, and sometimes even increase instead. The estimator of the residual variance is:

“实际观测值个数”是指拟合模型时实际使用的观察值项数，对于具有N个观察值的序列，拟合AR(p)模型，则实际使用的观察值最多为N-p。"Number of actual observations" refers to the number of observations actually used when fitting the model. For a sequence with N observations, when fitting the AR(p) model, the number of observations actually used is at most N-p.

“模型参数个数”是指所建立的模型中实际包含的参数个数，对于含有均值的模型，模型参数个数为模型阶数加1。对于N个观测值的序列，相应ARMA模型的残差估计式为："Number of model parameters" refers to the number of parameters actually included in the established model. For a model with a mean value, the number of model parameters is the model order plus 1. For a sequence of N observations, the residual estimation formula of the corresponding ARMA model is:

(式1)， (Formula 1),

其中，公式中，Q为拟合误差的平方和，和θ_j(1≤j≤q)是模型系数，N是观测序列长度，是模型参数中的常数项，的常识值是根据不同的和θ_j(1≤j≤q)进行变化的常数项，不同的和θ_j(1≤j≤q)对照不同的值。Among them, in the formula, Q is the sum of squares of the fitting error, and θ _j (1≤j≤q) are the model coefficients, N is the observation sequence length, is a constant term in the model parameters, The common sense value of is based on different and θ _j (1≤j≤q) to change the constant term, the different different from θ _j (1≤j≤q) value.

步骤1.3：模型参数估计Step 1.3: Model parameter estimation

采用矩估计方法对ARMA(p,q)的模型参数进行估计。首先，将风电场历史功率数据利用数据序列x₁,x₂,...,x_t表示，其样本自协方差定义为The model parameters of ARMA(p,q) are estimated by moment estimation method. First, the historical power data of the wind farm is represented by the data sequence x ₁ , x ₂ ,..., x _t , and its sample autocovariance is defined as

${\hat{γ}}_{k} = \frac{1}{n} Σ_{t = k + 1}^{n} x_{t} x_{t - k}$ (式2) ${\hat{γ}}_{k} = \frac{1}{no} Σ_{t = k + 1}^{no} x_{t} x_{t - k}$ (Formula 2)

其中，k＝0,1,2,...,n-1，x_t和x_t-k均为数据序列x₁,x₂,...,x_t中的数值。Wherein, k=0,1,2,...,n-1, x _t and x _tk are values in the data sequence x ₁ , x ₂ ,...,x _t .

特别的，special,

${\hat{γ}}_{0} = - \frac{1}{n} Σ_{t = 1}^{n} x_{t}^{2}$ (式3) ${\hat{γ}}_{0} = - \frac{1}{no} Σ_{t = 1}^{no} x_{t}^{2}$ (Formula 3)

${\hat{ρ}}_{k} = \frac{{\hat{γ}}_{k}}{{\hat{γ}}_{0}} = \frac{\frac{1}{n} Σ_{t = k + 1}^{n} x_{t} x_{t - k}}{\frac{1}{n} Σ_{t = 1}^{n} x_{t}^{2}} = \frac{Σ_{t = k + 1}^{n} x_{t} x_{t - k}}{Σ_{t = 1}^{n} x_{t}^{2}}$ (式4) ${\hat{ρ}}_{k} = \frac{{\hat{γ}}_{k}}{{\hat{γ}}_{0}} = \frac{\frac{1}{no} Σ_{t = k + 1}^{no} x_{t} x_{t - k}}{\frac{1}{no} Σ_{t = 1}^{no} x_{t}^{2}} = \frac{Σ_{t = k + 1}^{no} x_{t} x_{t - k}}{Σ_{t = 1}^{no} x_{t}^{2}}$ (Formula 4)

其中，k＝0,1,2,...,n-1。Wherein, k=0, 1, 2, . . . , n-1.

AR部分的矩估计为The moments of the AR part are estimated as

(式5) (Formula 5)

令make

(式6) (Formula 6)

则协方差函数为Then the covariance function is

用的估计代替γ_k，有use The estimate of γ instead of _k , has

(式8) (Formula 8)

可得参数 Available parameters

$γ_{0} (y_{t}) = (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2}) σ_{a}^{2}$ (式9) $γ_{0} ({the y}_{t}) = (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2}) σ_{a}^{2}$ (Formula 9)

……...

$γ_{k} (y_{t}) = (- θ_{k} + θ_{1} θ_{k + 1} + . . . + θ_{q - k} θ_{q}) σ_{a}^{2}$ (式10) $γ_{k} ({the y}_{t}) = (- θ_{k} + θ_{1} θ_{k + 1} + . . . + θ_{q - k} θ_{q}) σ_{a}^{2}$ (Formula 10)

其中k＝1,2,...,m。where k=1,2,...,m.

以上共包含m+1个方程，对其参数而言，方程为非线性，采用迭代法进行求解。The above contains a total of m+1 equations. For its parameters, the equations are nonlinear, and the iterative method is used to solve them.

具体步骤如下，将方程变形为：The specific steps are as follows, transforming the equation into:

$σ_{a}^{2} = γ_{0} / (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2})$ (式11) $σ_{a}^{2} = γ_{0} / (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2})$ (Formula 11)

$θ_{k} = - \frac{γ_{k}}{σ_{a}^{2}} + θ_{1} θ_{k + 1} + . . . + θ_{q - k} θ_{q}, k = 1,2, . . ., m$ (式12) $θ_{k} = - \frac{γ_{k}}{σ_{a}^{2}} + θ_{1} θ_{k + 1} + . . . + θ_{q - k} θ_{q}, k = 1,2, . . ., m$ (Formula 12)

给定θ₁,θ₂,...,θ_q和的一组初始值，如Given θ ₁ ,θ ₂ ,...,θ _q and A set of initial values, such as

$θ_{1} = θ_{2} = . . . = θ_{q} = 0, σ_{a}^{2} = γ_{0}$ (式13) $θ_{1} = θ_{2} = . . . = θ_{q} = 0, σ_{a}^{2} = γ_{0}$ (Formula 13)

代入以上两式右边，左边所得到的值为第一步迭代值，记为再将该值依次代入上两式的右侧，便得到第二步迭代值，依次类推，直到相邻两次迭代结果小于给定阈值时，取所得的结果作为参数的近似解。Substituting the right side of the above two equations, the value obtained on the left side is the value of the first iteration, denoted as Then substitute this value into the right side of the above two formulas in turn to get the second iteration value, By analogy, until the results of two adjacent iterations are less than the given threshold, the obtained results are taken as the approximate solution of the parameters.

通过上述求解过程发现，要求解时间序列模型的阶数，就要得到时间序列的预测值；要得到时间序列的预测值，必须先建立具体的预测函数；要建立具体的预测函数，必须知道模型的阶数。Through the above solution process, it is found that in order to solve the order of the time series model, the predicted value of the time series must be obtained; to obtain the predicted value of the time series, a specific prediction function must be established first; to establish a specific prediction function, the model must be known of order.

根据实践得出，时间序列模型阶数一般不超过5阶。所以在该算法具体实现时，可以首先假设模型为1阶，利用步骤1.3中的参数估计方法得到一阶模型的参数，进而建立估计函数便可以求得一阶模型时间序列模型估计得到各个项的预测值，从而求得一阶模型的残差方差；之后，假设模型为二阶，用上述方法求得二阶模型的残差；以此类推，可以得到1到5阶模型的残差，选残差最小的模型的阶数作为最终模型的阶数。确定模型阶数后，便可计算得到参数θ₁,θ₂,...,θ_q的值。According to practice, the order of the time series model generally does not exceed 5. Therefore, when implementing the algorithm, we can first assume that the model is first-order, use the parameter estimation method in step 1.3 to obtain the parameters of the first-order model, and then establish an estimation function to obtain the first-order model time series model and estimate the parameters of each item predicted value, so as to obtain the residual variance of the first-order model; then, assuming that the model is second-order, use the above method to obtain the residual error of the second-order model; The order of the model with the smallest residual is taken as the order of the final model. After the order of the model is determined, the values of the parameters θ ₁ , θ ₂ ,...,θ _q can be calculated.

阶段2：功率预测Phase 2: Power Prediction

步骤2.1：功率预测基础数据输入Step 2.1: Power Prediction Basic Data Input

风电功率预测所需输入数据包括资源监测系统数据和运行监测系统数据两部分，其中，资源监测系统数据包含风资源监测数据；运行监测系统数据包括风机监测数据、升压站监测数据和数据采集与监视控制系统(SCADA)数据等。The input data required for wind power prediction includes resource monitoring system data and operation monitoring system data. Among them, resource monitoring system data includes wind resource monitoring data; operation monitoring system data includes wind turbine monitoring data, booster station monitoring data and data acquisition and monitoring data. Supervisory control system (SCADA) data, etc.

步骤2.2：噪声滤波及数据预处理Step 2.2: Noise filtering and data preprocessing

噪声滤波模块对实时监测系统采集得到的带有噪声的进行滤波处理，去除坏数据和奇异值；数据预处理模块对数据进行对齐、归一化处理和分类筛选等操作，以便使得输入的数据可以为模型所用。The noise filtering module filters the noisy data collected by the real-time monitoring system to remove bad data and singular values; used by the model.

步骤2.3：超短期功率预测Step 2.3: Ultra-short-term power forecasting

将模型参数估计出来之后，结合已估计的模型阶数，便可得到用于风电功率超短期预测的时间序列方程。根据上述步骤2和步骤3得出的p和q值，以及θ₁,θ₂,...,θ_q的值建立自回归滑动平均模型；After estimating the model parameters, combined with the estimated model order, the time series equations for ultra-short-term forecasting of wind power can be obtained. p and q values derived from steps 2 and 3 above, and The value of θ ₁ , θ ₂ ,..., θ _q establishes an autoregressive moving average model;

自回归滑动平均模型如下：The autoregressive moving average model is as follows:

(式14) (Formula 14)

步骤2.4：预测结果输出及展示Step 2.4: Output and display of prediction results

将预测结果输出至数据库中，并通过图表及曲线展示预测结果、展示预测与实测结果的对比。Output the forecast results to the database, and display the forecast results through charts and curves, and show the comparison between the forecast and actual measurement results.

最后应说明的是：以上所述仅为本发明的优选实施例而已，并不用于限制本发明，尽管参照前述实施例对本发明进行了详细的说明，对于本领域的技术人员来说，其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换。凡在本发明的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。Finally, it should be noted that: the above is only a preferred embodiment of the present invention, and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, it still The technical solutions recorded in the foregoing embodiments may be modified, or some technical features thereof may be equivalently replaced. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A wind power ultra-short-term prediction method based on autoregressive moving average model, is characterized in that, comprises input data and obtains autoregressive moving average model parameter;

The input data required for wind power prediction is input into the autoregressive moving average model determined according to the parameters of the above-mentioned autoregressive moving average model to obtain the prediction result.

2. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 1, is characterized in that, described input data obtains autoregressive moving average model parameter and comprises, step 101, input model training basic data;

Step 102, model order determination;

Step 103: Estimate the parameters of the fixed-order ARMA(p,q) model by using the moment estimation method.

3. the ultra-short-term prediction method of wind power based on autoregressive moving average model according to claim 2, is characterized in that, described step 101 input model training basic data, input data comprises, wind farm basic information, historical wind speed data, Historical power data and GIS data.

4. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 3, is characterized in that, described step 102 model order:

The residual variance map method is used to determine the order of the model. Specifically, let x _t be the item to be estimated, x _t-1 , x _t-2 ,..., x _tn are known historical power sequences, and for ARMA(p, q) model, the order of the model is to determine the values of the parameters p and q in the model;

Fits the original series with a model of increasing order of the series, computing the residual sum of squares each time Then plot the order and The graph of , when the order increases from small, will decrease significantly, and after reaching the true order The value will gradually become flat, or even increase instead,

The number of actual observations refers to the number of observations actually used when fitting the model. For a sequence with N observations, when fitting the AR(p) model, the number of observations actually used is at most N-p, and the number of model parameters refers to The number of parameters actually included in the established model. For a model with a mean value, the number of model parameters is the model order plus 1. For a sequence of N observations, the residual estimation formula of the ARMA model is:

Among them, Q is the sum of squares of the fitting error, and θ _j (1≤j≤q) are the model coefficients, N is the observation sequence length, is a constant term in the model parameters.

5. the ultra-short-term prediction method of wind power based on autoregressive moving average model according to claim 4, is characterized in that, described step 103 adopts moment estimation method to estimate the ARMA (p, q) model parameter of fixed order. The steps are:

The historical power data of the wind farm is represented by the data sequence x ₁ , x ₂ ,..., x _t , and its sample autocovariance is defined as

{\overset{^^}{γ γ}}_{k k} = = \frac{11}{n no} {Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k},,

Wherein, k=0,1,2,...,n-1, x _t and x _tk are values in the data sequence x ₁ , x ₂ ,...,x _t ;

but

{\hat{γ}}_{0} = - \frac{1}{no} Σ_{t = 1}^{no} x_{t}^{2}

Then the autocorrelation function of historical power data samples is:

{\overset{^^}{ρ ρ}}_{k k} = = \frac{{\overset{^^}{γ γ}}_{k k}}{{\overset{^^}{γ γ}}_{00}} = = \frac{\frac{11}{n no} {Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k}}{\frac{11}{n no} {Σ Σ}_{t t = = 11}^{n no} {x x}_{t t}^{22}} = = \frac{{Σ Σ}_{t t = = k k + + 11}^{n no} {x x}_{t t} {x x}_{t t - - k k}}{{Σ Σ}_{t t = = 11}^{n no} {x x}_{t t}^{22}},,

Among them, k=0,1,2,...,n-1;

The moments of the AR part are estimated as,

make

Then the covariance function is

use An estimate of γ instead of _k ,

Available parameters

For MA(q) model coefficients θ ₁ , θ ₂ ,..., θ _q are estimated by moments

γ_{0} ({the y}_{t}) = (1 + θ_{1}^{2} + θ_{2}^{2} + . . . + θ_{q}^{2}) σ_{a}^{2}

until

{γ γ}_{k k} (({y the y}_{t t})) = = ((- - {θ θ}_{k k} + + {θ θ}_{11} {θ θ}_{k k + + 11} + + . . . . . . + + {θ θ}_{q q - - k k} {θ θ}_{q q})) {σ σ}_{a a}^{22}

where k=1,2,...,m,

The above m+1 equations are nonlinear equations, and the iterative method is used to solve the autoregressive moving average model parameters.

6. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 5, is characterized in that, the required input data of described input wind power prediction is to the autoregressive moving average model according to the parameter determination autoregressive The steps to obtain the forecast results in the regression moving average model include,

Step 201, input power prediction basic data;

Step 202, performing noise filtering and data preprocessing on the input basic data;

Step 203, establishing an autoregressive moving average model according to the determined parameters, and inputting the processed data to obtain a prediction result.

7. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 6, is characterized in that, also comprises,

Step 204, outputting the prediction result to the database, and displaying the prediction result through charts and curves, and displaying a comparison between the prediction and the actual measurement results.

8. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 7, is characterized in that, described input power prediction basic data comprises resource monitoring system data and operation monitoring system data, and described resource monitoring system The data includes wind resource monitoring data; the operation monitoring system data includes fan monitoring data, booster station monitoring data and data acquisition and monitoring control system data.

9. The ultra-short-term prediction method of wind power based on the autoregressive moving average model according to claim 7, wherein the noise filtering and data preprocessing are specifically: the noise filtering module collects the real-time information with The noisy data is filtered to remove bad data and singular values; the data preprocessing module performs alignment, normalization, and classification screening on the data.

10. the wind power ultra-short-term prediction method based on autoregressive moving average model according to claim 7, is characterized in that, described autoregressive moving average model is:

in, and θ _j (1≤j≤q) are coefficients, and α _t is a white noise sequence.