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Unscented Kalman filtering and neural network-based photovoltaic power generation prediction method

An unscented Kalman and Kalman filter technology, which is applied in the prediction field of photovoltaic power generation models, can solve problems such as the influence of identification results

Inactive Publication Date: 2016-12-07
STATE GRID QINGHAI ELECTRIC POWER +2
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Problems solved by technology

Using the improved wavelet neural network to identify the nonlinear model of the photovoltaic power generation system, and achieved good results, but the severity of weather fluctuations has a certain impact on the identification results

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  • Unscented Kalman filtering and neural network-based photovoltaic power generation prediction method

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Embodiment Construction

[0031] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than for limiting the protection scope of the present invention.

[0032] The photovoltaic power generation system is a nonlinear system. According to the nonlinear characteristics of the neuron excitation function, the unscented Kalman filter is used to realize the adaptive adjustment of the neural network weight coefficient and threshold, so as to adaptively simulate the complex nonlinear system.

[0033] The neural network adopts a multi-layer feedforward neural network BP neural network:

[0034] For an N-layer BP network, the number of neurons in each layer is H k (k=1,2,...,N) input, connection weights of neurons in the kth layer in order to The calculation of is transformed into the form of improved Kalman filter, and the ...

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Abstract

The invention discloses an unscented Kalman filtering and neural network-based photovoltaic power generation prediction method. The method comprises the steps of 1: taking illumination and temperature as input signals u(k) and taking active power and reactive power as output yk; 2: establishing a BP neural network and taking a weight coefficient and a threshold of the neural network as state variables xk; 3: initializing the state variables; 4: calculating a Sigma point; 5: improving state updates and error variance updates of time updates in Kalman filtering; 6: calculating covariances of the state variables and measurement variables; 7: measuring updated state updates and error covariance updates in the Kalman filtering; and 8: judging whether an updated state variance matrix is converged or not. The method has high calculation speed and high prediction precision, and can be adaptive to dynamic changes under different weather conditions; and a prediction model built in the method has wider adaptability to the weather conditions.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a method and technology for predicting a photovoltaic power generation model. Background technique [0002] In recent years, photovoltaic power generation, as a clean and renewable energy source, has been applied and developed on a large scale. The design of large-scale photovoltaic power generation grid-connected system and the analysis of the impact of grid-connection have become the research hotspots in recent years. When the photovoltaic power station is connected to the power grid, it has a certain impact on the system power grid, which is mainly reflected in the change of the actual output power of the solar photovoltaic power station with the change of the light intensity: when the light intensity is the strongest during the day, the output power of the power generation device is the largest, and when there is almost no light at night, The output pow...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045Y04S10/50
Inventor 张海宁李春来杨立滨杨军李正曦梁英王平杜炜谢解解江金洋李娜李刚健
Owner STATE GRID QINGHAI ELECTRIC POWER
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