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Spatial correlation and genetic algorithm (GA) based wind power forecast method for wavelet-BP neural network

A technology for wind power prediction and spatial correlation, which is applied in biological neural network models, predictions, neural architectures, etc., to achieve the effect of satisfying prediction accuracy, improving prediction accuracy, and making up for insufficient wind speed monitoring data.

Active Publication Date: 2016-05-11
南京鼎竹电力设备工程有限公司
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Problems solved by technology

[0003] Due to the randomness, variability and uncertainty of wind energy itself, the power quality and stable operation of wind power grid-connected systems will face certain challenges

Method used

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  • Spatial correlation and genetic algorithm (GA) based wind power forecast method for wavelet-BP neural network
  • Spatial correlation and genetic algorithm (GA) based wind power forecast method for wavelet-BP neural network
  • Spatial correlation and genetic algorithm (GA) based wind power forecast method for wavelet-BP neural network

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Embodiment

[0066] Step 1, wavelet-BP neural network and genetic algorithm parameter initialization, initialize the wavelet-BP neural network structure, parameter initialization includes the initial population number, crossover probability, mutation probability and maximum evolution algebra of genetic algorithm.

[0067] The neural network adopts a three-layer structure, and its topology is as follows: image 3 As shown, it is a 4-10-1 structure, that is, the input layer is X 1 、X 2 、X 3 、X 4 Four input nodes represent the wind speed time series at four time points before the predicted target time; the hidden layer contains ten neuron nodes, and the hidden layer transfer function uses the Morlet wavelet basis function h f (f=1,2,...,10), representing the training state and mode of the neural network; the output layer is an output node Y 1 , representing the predicted result value. w ij is the connection weight between the input layer and the hidden layer, w jm is the connection wei...

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Abstract

The invention proposes a spatial correlation and genetic algorithm (GA) based wind power forecast method for a wavelet-BP neural network. The wind power forecast method comprises the following steps of acquiring wind speeds or power time sequences of wind power plants at a windward position and a leeward position according to target time and sampling time; calculating a correlation coefficient among the acquired time sequences of the wind power plants at the windward position and the leeward position, and extracting a data set with high correlation; inputting the time sequences of the data set with high correlation into the wavelet-BP neural network, and carrying out global optimization on weight and threshold parameters of a neural network structure by a GA; deeply optimizing the weight and the threshold of the neural network by combining an L-M algorithm and an additional momentum method during the training process of the wavelet-BP neural network, and correcting and optimizing shift factor and scaling factor parameters of a wavelet base function by adopting a gradient descent method; and forecasting wind power of the wind power field at the leeward position by using the trained wavelet-BP neural network. By the wind power forecast method, the leaning capability of the neural network is improved, and the defect that the wind power forecast cannot be directly carried out due to wind speed monitoring data deficiency of the wind power plant is overcome.

Description

technical field [0001] The invention belongs to the field of electric power system automation, in particular to a wavelet-BP neural network wind power prediction method based on spatial correlation and GA. Background technique [0002] In order to achieve sustainable energy development, the development and utilization of renewable energy will become one of the mainstream research directions in the field of electric power. From the analysis of existing wind energy utilization technology and economic practicability, the competitiveness of wind power generation has been greatly enhanced. Large-scale wind energy utilization is also conducive to adjusting the energy structure of the power grid and promoting the sustainable development of local and social economies. [0003] Due to the randomness, variability and uncertainty of wind energy itself, the power quality and stable operation of wind power grid-connected systems will face certain challenges. Wind power forecasting is a...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/12
CPCG06N3/04G06N3/126G06Q10/04G06Q50/06Y04S10/50
Inventor 解坤张俊芳陈鸿亮齐浩宇王惟怡花雨洁沈烨
Owner 南京鼎竹电力设备工程有限公司
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