A method for predicting grid-connected power of regional photovoltaic power station considering system loss

Abstract

A method for predicting grid-connected power of regional photovoltaic power station considering system loss mainly comprises the following steps: A, based on support vector machine method, utilizing multiple meteorological parameters such as solar irradiance, cloud cover temperature and other meteorological parameters, and photovoltaic module surface irradiance, module temperature and other components photovoltaic power generation model. B, according to DC line loss model, bus box model, invert model, AC section loss model and transformer model of that grid-connected power flow component of the photovoltaic module, obtaining a regional photovoltaic power station loss modelC, according to that output prediction of the photovoltaic module and the grid-connected tidal current loss of the regional photovoltaic pow station, providing a grid-connected power prediction method of the regional photovoltaic power station considering the system loss of the component. Compared with other photovoltaic output forecasting methods, the present invention not only exerts the advantages of the support vector machine method in forecasting the small sample data and increases the forecasting speed, butalso further defines each loss in the photovoltaic grid-connected link, which is favorable for the effective operation of the regional photovoltaic power station.

Description

technical field [0001] The invention relates to the field of power system analysis, and more specifically relates to a power system including photovoltaic new energy. Background technique [0002] As an important clean energy, photovoltaic power generation has developed rapidly in recent years. However, its output will fluctuate and intermittent with changes in solar radiation intensity and temperature, so the grid-connected operation of large-scale photovoltaic power generation systems will have a greater impact on the safety and stability of traditional power grids. Accurate photovoltaic power generation grid-connected output prediction technology can reduce this impact and improve the safety and reliability of grid system operation. However, most prediction methods in practical applications only rely on a large amount of historical data for training, which cannot be solved, and do not take into account the impact of the entire regional power grid system on the real-time ...

AI Technical Summary

Problems solved by technology

However, most prediction methods in practical applications only rely on a large amount of historical data for training, which cannot be solved, and do not take into account the impact of the entire regional power grid system on the real-time grid-connected output of photovoltaics.

The effective information provided by the prediction results for the operation of the actual regional photovoltaic power grid is not obvious

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