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Short-term wind-speed combination forecasting method

A combined forecasting and wind speed technology, applied in forecasting, data processing applications, instruments, etc., can solve the problems of local minima and long algorithm running time, and achieve the effect of low cost, fast calculation speed, and large engineering application potential.

Inactive Publication Date: 2013-11-20
ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD +1
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AI Technical Summary

Problems solved by technology

However, there are some problems in the traditional artificial neural network method, such as long running time of the algorithm, easy to fall into local minimum, etc.

Method used

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  • Short-term wind-speed combination forecasting method
  • Short-term wind-speed combination forecasting method
  • Short-term wind-speed combination forecasting method

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Embodiment

[0034] First of all, the relevant theoretical basis involved in the present invention is introduced.

[0035] 1. Principle of Empirical Mode Decomposition

[0036] Empirical mode decomposition is essentially an adaptive signal screening method, which can filter out the trend of different characteristics existing in the original sequence step by step, and obtain the intrinsic mode component (intrinsic mode function, IMF) with the same characteristics. The two conditions of (1) and (2) must be met for the modal component: (1) the difference between the number of zeros and the number of poles in the entire eigenmode component sequence is at most one; 2) at any point, the The mean value of the envelope defined by the envelope and local maximum points is 0.

[0037] For a wind speed time series {x(t)}, the steps of empirical mode decomposition are as follows:

[0038] 1) Find all the maximum and minimum values ​​in the sequence {x(t)}. Using cubic spline function to interpolate ...

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Abstract

The invention relates to a short-term wind-speed combination forecasting method. The method comprises the following steps: 1, extracting the history data of wind-speed sequence; 2, allowing the extracted data to be subjected to sequential analysis through an EMO (Empirical Mode Decomposition) method; 3, reconstructing a phase space for each sequence obtained form the EMO; 4, adopting the reconstructed phase spaces of all sequences to train a built improved utmost learning machine predictive model, and stacking the forecasting results of all sequences to obtain the wind-speed forecasting result; and 5, allowing the result to be subjected to error analysis. The forecasting method is simple and practical in modeling process, and can effectively and quickly forecast the wind speed, to effectively forecast the wind power of a grid-connected wind farm. Therefore, the method has an important significance on the safety, stability and scheduling operation of the electric power system under power-wind grid-connection condition.

Description

technical field [0001] The invention relates to a short-term wind speed combined prediction method. Background technique [0002] As an inexhaustible and environmentally friendly renewable energy, wind energy has maintained a relatively high annual growth rate in recent years in terms of installed capacity. However, the chaotic and random characteristics of wind energy make the output power fluctuate faster and in a larger fluctuation range. After wind power is connected to the grid, it will cause difficulties in power system dispatching, voltage and reactive power control. If the wind speed can be predicted in a timely and effective manner, it can not only reduce the reserve capacity of the power system and reduce the operating cost of the system, but also reduce the adverse impact of wind power on the power grid, thereby improving the competitiveness of wind power. [0003] At present, a lot of research has been done on wind speed prediction in China, and the established...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
Inventor 张翌晖王凯陈立胡志坚王贺宁文辉孙结中黄东山周柯奉斌许飞
Owner ELECTRIC POWER RES INST OF GUANGXI POWER GRID CO LTD
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