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Wind speed sequence forecasting method based on Kalman filtering

A Kalman filter and wind speed sequence technology, applied in special data processing applications, instruments, biological neural network models, etc.

Inactive Publication Date: 2014-02-26
TIANJIN POLYTECHNIC UNIV
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Problems solved by technology

However, due to the fluctuating and intermittent characteristics of wind speed, wind power has become an intermittent energy source. In the process of wind power grid integration, it brings new problems to the safety, stability and normal dispatch of the grid, which has become a constraint on the utilization of wind energy. one of the key issues

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  • Wind speed sequence forecasting method based on Kalman filtering
  • Wind speed sequence forecasting method based on Kalman filtering
  • Wind speed sequence forecasting method based on Kalman filtering

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Embodiment Construction

[0010] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0011] Time series predictive analysis models include autoregressive model (AR model), moving average model (MA model) and autoregressive moving average model (ARMA model). The present invention uses an AR model or a high-order AR model.

[0012] When using the ARMA model for time series forecasting analysis, the time series is required to be a stationary series. If the time series is non-stationary, the method of difference can be used for smoothing, and the obtained model is the differential autoregressive moving average model (ARIMA model).

[0013] In the AR model, for the smoothed wind speed time series {x t}'s current moment value can be regarded as a linear combination of the previous p moment values ​​and random interference signals, and its equation is:

[0014] x t =φ 1 x t-1 +φ 2 x t-2 +...φ p x t-p +ε t (1)

[0015] In this way, the...

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Abstract

The invention belongs to the field of time sequence forecasting analysis and in particular relates to a wind speed sequence forecasting method based on Kalman filtering. A high-order AR (auto-regressive) model of a wind speed time sequence is constructed by virtue of a time sequence analysis method, so that a state equation and a measurement equation of Kalman filtering are constructed; a training sample pair of the wind speed time sequence is constructed by the time sequence analysis method; a wind speed sequence is forecasted and analyzed by adopting a time delay neural network; a forecasting result of the time delay neural network is used as a measurement value of Kalman filtering; diagonal covariance matrixes of the state equation and the measurement equation of the Kalman filtering method are determined according to the AR model and a forecasting error of the time delay neural network, so that the wind speed sequence can be forecasted and analyzed by the Kalman filtering method. The wind speed sequence forecasting method can be applied to an on-line wind speed forecasting analysis system of a wind power plant.

Description

technical field [0001] The invention belongs to the field of time series prediction and analysis, and relates to a method for wind speed time series prediction, in particular to a method for realizing the prediction and analysis of the wind speed series of a wind farm by using a Kalman filtering method and a neural network method. Background technique [0002] With the increasingly serious environmental pollution and the depletion of fossil fuels, wind energy, as a pollution-free and renewable energy, has been highly valued by countries all over the world. Wind power generation is the main form of utilizing wind energy. However, due to the fluctuating and intermittent characteristics of wind speed, wind power has become an intermittent energy source. In the process of wind power grid integration, it brings new problems to the safety, stability and normal dispatch of the grid, which has become a constraint on the utilization of wind energy. one of the key issues. [0003] Cu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00G06N3/02
Inventor 修春波万蓉凤王柳
Owner TIANJIN POLYTECHNIC UNIV
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