Power interval predication method based on nucleus limit learning machine model

A kernel extreme learning machine and forecasting method technology, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as unsatisfactory stability and generalization ability, linear inseparability, ELM model output prone to random fluctuations, etc.

Inactive Publication Date: 2015-11-11
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

However, in actual engineering applications, ELM also has some shortcomings: linear inseparability may occur when the calculation data is low-dimensional, whic

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  • Power interval predication method based on nucleus limit learning machine model
  • Power interval predication method based on nucleus limit learning machine model
  • Power interval predication method based on nucleus limit learning machine model

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Embodiment Construction

[0039] The embodiments will be described in detail below in conjunction with the accompanying drawings.

[0040] Such as figure 1 As shown in the flow chart, the actual wind power data collected from SCADA by a wind farm in Huaneng Jiuquan during July-October 2014 is used in the present invention, with a resolution of 15 minutes, including the measured output power and the wind speed of the anemometer tower. Interval forecasts for one-day power. The wind farm has 133 1.5MW pitch-adjustable three-blade horizontal-axis asynchronous generators. The method includes the following steps:

[0041] Step 1: Obtain the actual wind speed and power data in the SCADA of the wind farm, process them according to the correlation between wind speed and power, divide the data into training samples and test samples, and normalize the data;

[0042] Step 101: Data preprocessing: collect the one-to-one correspondence data of wind speed and power in SCADA with an interval of 15 minutes, sort the...

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Abstract

The present invention belongs to the field of power prediction of wind power generation and particularly relates to a method for predicting a wind power interval based on a particle swarm optimization nucleus limit learning machine model. The method comprises: carrying out data preprocessing, i.e. preprocessing historical data in SCADA according to correlation between a wind speed and power; initializing a KELM model parameter and carrying out calculation to obtain an initial output weight betaint; initializing a particle swarm parameter; constructing an optimization criterion F according to an evaluation index and carrying out particle swarm optimization searching to obtain a model optimal output weight betabest; and bringing test data into a KELM model formed by betabest to obtain a wind power prediction interval and evaluating each index of the prediction interval. The method is easy for engineering realization; a good prediction result can be obtained; not only can a future wind power possible fluctuation range be described, but also reliability of the prediction interval is effectively evaluated, possible fluctuation intervals of wind power at different confidence levels are given out and reference is better provided for a power system decision maker.

Description

technical field [0001] The invention belongs to the field of wind power generation power prediction, in particular to a method for wind power interval prediction based on a particle swarm optimization kernel extreme learning machine model. Background technique [0002] As a clean and renewable energy, wind energy has been widely used all over the world. It has the characteristics of safety, cleanness, abundance and huge resources. Wind power generation is the main form of human utilization of wind energy. However, the wind itself has strong randomness and instability, which will lead to violent fluctuations in wind power. Especially in recent years, the wind power industry has developed rapidly, and the installed capacity has continued to increase. Under the background of large-scale wind power access to the grid, the random instability of wind power power has brought severe challenges to the safe and stable operation of the power system. The wind power forecast is used to ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCY04S10/50
Inventor 杨锡运关文渊任杰刘玉奇
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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