Photovoltaic power station power prediction method and system based on grid-connected inverter operation data

Abstract

The invention provides a photovoltaic power station power prediction method and system based on grid-connected inverter operation data, and the method comprises the steps: building a photovoltaic module model according to the parameters of a photovoltaic module in a photovoltaic power station; constructing a power prediction model based on an artificial neural network algorithm; collecting outputdata of the photovoltaic array under the shielding of static shadows with different thicknesses and different shielding sizes, constructing a training set, training the power prediction model, and obtaining a trained power prediction model; the output power of the real-time operation data of the inverter of the photovoltaic array under the sunny sky working condition is acquired; classification and normalization are carried out, the output power of the whole photovoltaic power station is predicted through the trained power prediction model, and power prediction comprises rolling prediction ofthe output power of the photovoltaic power station under the sunny sky working condition and minute-level power prediction of the photovoltaic power station under dynamic cloud cluster shielding. According to the method, the equipment cost is reduced, and the defect that cloud clusters with different thicknesses influence the photovoltaic array power prediction precision is overcome.

Description

technical field [0001] The present disclosure relates to the technical field of power prediction, in particular to a method and system for power prediction of a photovoltaic power station based on grid-connected inverter operation data. Background technique [0002] As an inexhaustible renewable energy source, solar energy is an ideal substitute for traditional energy sources. The output power of photovoltaic arrays is highly dependent on meteorological parameters, including irradiance, ambient temperature, relative humidity, transportation volume, wind speed, etc. When the meteorological parameters change greatly, the output power of the photovoltaic array will fluctuate more violently. When a large-capacity photovoltaic power station is disturbed by cloud cover or other disturbance factors, its power fluctuations will pose severe challenges to the stability of the grid operation. Therefore, it is necessary to study the power prediction technology of photovoltaic power pl...

AI Technical Summary

Problems solved by technology

However, this method has certain limitations. As a commonly used meteorological satellite cloud image, its spatial resolution is low, and each pixel corresponds to a large area, so it is impossible to accurately predict the cloud movement in a small area.

If the ground-based cloud image is used and the ground equipment is used to continuously shoot and analyze the cloud layer, only the cloud cluster information in a small range can be obtained. When the cloud cluster moves fast, the forecast time scale will be greatly reduced, and the cloud layer thickness information is difficult to obtain , it is difficult to accurately judge the amount of change in irradiance, which will increase the prediction error, and when the scale of the photovoltaic power station is large, the number of sky imagers required is large, which greatly increases the cost

[0005] To sum up, the inability to accurately predict the output power fluctuations of photovoltaic arrays caused by clouds or other disturbance factors has become a bottleneck restricting the power prediction accuracy of high-time resolution photovoltaic power plants.

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