Wind energy forecasting method based on trend detector and mathematical morphology operator

A technology of mathematical morphology and forecasting methods, applied in forecasting, instrumentation, calculation, etc., can solve problems such as low forecasting accuracy, stability to be strengthened, and large forecasting limitations.

Active Publication Date: 2015-04-08
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0004] 1) Most of the existing forecasting models are only effective for specific wind farms, which require high prior knowledge of users, and the limitations of forecasting are relatively large
Due to the different conditions of each wind farm, the prediction model of one wind farm may not be ab...

Method used

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  • Wind energy forecasting method based on trend detector and mathematical morphology operator
  • Wind energy forecasting method based on trend detector and mathematical morphology operator
  • Wind energy forecasting method based on trend detector and mathematical morphology operator

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Embodiment 1

[0057] Embodiments of the present invention will be described in further detail below in conjunction with the accompanying drawings.

[0058] Such as figure 1 As shown, the wind energy prediction method based on the trend detector and the mathematical morphology operator of the present embodiment includes the following steps:

[0059] 1) Design an average trend detector, including the following steps:

[0060] Consider the historical wind energy data of the wind farm, recorded as non-stationary time series x(t), the length of the time series is L; using conventional mathematical morphology operators, that is, using the high-hat transformation TH (Top-hat) and low Hat transformation BH (Bottom-hat), to obtain the oscillatory element of the time series x(t), such as figure 2 As shown (the solid line in the figure is the wind energy curve, the dotted line is the average trend line, EO is the oscillating element, M is the weight centroid, and P is the extreme value point), the ...

Embodiment 2

[0086] The main feature of this embodiment is: after the method of Embodiment 1 performs forecasting, the forecast results of the wind power output power of the wind farm are also evaluated, and the average relative error is used to measure the forecast accuracy and the mean square error is used to measure the forecast stability. ;

[0087] The mean relative error is defined as follows:

[0088] MRE = 1 N Σ i = 1 N | y i - y ^ i | y i - - - ( 9 )

[0089] The mean square error is defined as follows:

[...

Embodiment 3

[0093] The main features of this embodiment are: after the method of embodiment 1 forecasts, the forecast results of the wind power output power of the wind farm are also evaluated. For an ideal forecast method, there is no deviation between the forecast results and the actual data. Therefore, the evaluation scheme of this embodiment is based on the similarity between the forecast results and the actual data, so that the forecast results of the wind power output power and the actual data of the wind power output power maintain an identity relationship, as follows:

[0094] y ^ i = y i - - - ( 11 )

[0095] Equation (11) is expressed as a straight line with a slope of 1 and passing through the origin in the Cartesian coordinate system, such as Figure 4 shown. Any small error will lea...

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Abstract

The invention discloses a wind energy forecasting method based on a trend detector and a mathematical morphology operator. The concepts of oscillation element and weight barycenter are introduced in the method, on this basis, an average trend detector is designed in combination with a conventional mathematical morphology operator, and unstable historical wind energy data of a wind power plant are decomposed into two dependent components, namely a low-frequency average trend component and a high-frequency random component, the average trend component reflects the total variation trend of wind energy, and the random component is specific embodiment of mutability and randomness of wind energy; the average trend component is forecast by adopting a sine forecasting operator, and the random component is forecast by adopting a local mathematical morphology operator; the forecasting results of the average trend component and the random component are added to obtain the forecasting result of the wind electricity output power of the wind power plant. The wind energy forecasting method disclosed by the invention makes full use of the advantages of mathematical morphology such as simple operation and high speed, so that the forecasting efficiency is improved, and the method is stable in forecasting result and high in forecasting precision.

Description

technical field [0001] The invention relates to a wind energy forecasting method, in particular to a wind energy forecasting method based on a trend detector and a mathematical morphology operator, and belongs to the technical field of wind energy power forecasting. Background technique [0002] In recent years, affected by the global energy crisis and the environmental problems caused by the combustion of fossil fuels, renewable energy represented by wind power has developed rapidly. Unlike thermal power plants, the output of wind farms is uncontrollable. In order to cope with frequent fluctuations in wind farm output and ensure the safety and stability of the power grid, it is necessary to make corresponding adjustments to the output of other conventional power plants and the system reserve capacity. With the large-scale integration of wind power into the grid, these problems seriously affect the safety, reliability and power quality of the power system and other indicator...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 吴青华吴家樑李梦诗季天瑶
Owner SOUTH CHINA UNIV OF TECH
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