Method of predicating ultra-short-term wind power based on self-learning composite data source

Patent metadata

Field	Value
Publication number	US-2015302313-A1
Application number	US-201514682121-A
Country	US
Kind code	A1
Filing date	Apr 9, 2015
Priority date	Apr 22, 2014
Publication date	Oct 22, 2015
Grant date	—

Abstract

A method of predicating ultra-short-term wind power based on self-learning composite data source includes following steps. Model parameters of an autoregression moving average model are obtained by inputting data. A predication result is obtained by inputting data required by wind power predication into the autoregression moving average model. A post-evaluation is performed to the predication result by analyzing error between the predication result and measured values, and performing model order determination and model parameters estimation again while the error is greater than an allowable maximum error.

First claim

What is claimed is:

1 . A method of predicating ultra-short-term wind power based on self-learning composite data source, the method comprising:
obtaining model parameters of an autoregression moving average model by inputting data; obtaining a predication result by inputting data required by wind power predication into the autoregression moving average model; and performing post-evaluation to the predication result by analyzing error between the predication result and measured values, and performing model order determination and model parameters estimation again while the error is greater than an allowable maximum error.

2 . The method of claim 1 , wherein the model parameters of the autoregression moving average model is obtained by:
inputting basic data of model training; determining a model order; and estimating the model parameters via moment estimation method.

3 . The method of claim 2 , wherein the basic data of model training comprises wind farm's basic information, historical wind speed data, historical power data, and geographic information system data.

4 . The method of claim 2 , wherein the model order is determined by:
determining the model order by using a residual variance map, wherein x t is assumed as an item to be estimated, and x t-1 , x t-2 , . . . , x t-n is the known historical power sequence; for a ARMA (p, q) model, the determining model order is to determine values of the model parameters p and q; fitting an original sequence with a series of progressively increasing order model, calculating residual sum of squares {circumflex over (σ)} a 2 , and drawing the order and graphics of {circumflex over (σ)} a 2 , wherein while the order increase, {circumflex over (σ)} a 2 decreases dramatically; while the order reaches actual order, {circumflex over (σ)} a 2 is gradually leveled off, or even increase, {circumflex over (σ)} a 2 =Squares of fitting error/((number of actually observed values)−(number of model parameters)); wherein a number of actually observed values are observed values which applied in the fitting model; in a sequence with N observed values, the maximum number of observed values is N−p in fitting AR(p) model; the number of model parameters is the number of parameters applied in constructing model; while the model comprises mean values, the number of model parameters equals to the number of order plus one; In the sequence with N observed values, the ARMA model residuals estimator is:

σ
^

a
2



(

p
,
q

)

=

Q


(

μ
^

,



ϕ
^

1

,
…


,

ϕ
^

p

,

θ
^

1

,
…


,

θ
^

q

)

(

N
-
p

)

-

(

p
+
q
+
1

)

;

wherein Q is a sum of squares of fitting error; φ 1 (1≦i≦p) and θ j (1≦j≦q) are model coefficients; N is a length of observed sequence; {circumflex over (μ)} is a constant of model parameters, and determined by φ 1 (1≦i≦p) and θ j (1≦j≦q).

5 . The method of claim 4 , wherein the estimating the parameters of ARMA (p,q) model via moment estimation method comprises:
defining the historical power data of wind farm as a data sequence x 1 , x 2 , . . . , x t , and autocovariance of x 1 , x 2 , . . ., x t is defined as:

γ
^

k

=

1
n



∑

t
=

k
+
1

n





x
t



x

t
-
k

,

wherein k=0, 1, 2, . . . , n−1, x t and x t-k are values in the sequence x 1 , x 2 , . . . , x t ; then

γ
^

0

=

1
n



∑

t
=
1

Assignees

• State Grid Corp China
• Gansu Electric Power Company of State Grid
• Wind Power Technology Ct Of Gansu Electric Power Company

Inventors

• Wang ning-bo
• Lu Liang
• Han Xu-Shan
• Han zi-fen
• Jia huai-sen
• Wang Xiao-Yong
• Huang Rong
• Zhang Jin-Ping

Classifications

• G06F17/18 Primary

  for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} · CPC title

• G06F30/20

  Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) · CPC title

• G06N20/00

  Machine learning · CPC title

• G06F2119/06

  Power analysis or power optimisation · CPC title

• G06F30/27 Primary

  using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model · CPC title