Photovoltaic power station generating capacity prediction system and method

Abstract

The invention provides a photovoltaic power station generating capacity prediction system and method. The method comprises: collecting environmental influence factors, internal influence factors, power generation historical data and to-be-measured day data; performing denoising processing on the collected data; calculating the actual photoelectric conversion efficiency of the assembly according tothe internal influence factors; using historical data of the environmental influence factors and the actual photoelectric conversion efficiency of the assembly as input vectors; training the elasticadaptive BP neural network model; performing model optimization by repeatedly utilizing the solar radiation intensity, the atmospheric temperature, the actual photoelectric conversion efficiency of the assembly and the generating capacity of each day in a historical time period; using the environmental influence factors of the to-be-measured day and the actual photoelectric conversion efficiency of the assembly as model input. According to the method, the real-time photoelectric conversion efficiency value of the photovoltaic module is obtained through fitting analysis, so that the influence caused by the internal change of the photovoltaic module is reduced, the prediction accuracy of the generating capacity of the photovoltaic power station is greatly improved, and an accurate data reference is provided for power dispatching.

Description

technical field [0001] The present disclosure relates to the technical field of photovoltaic power plants, and in particular to a system and method for predicting power generation of photovoltaic power plants. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] As large-scale photovoltaic power stations are connected to the regional power grid, the operating conditions of each photovoltaic power station are different, which brings difficulties for the power dispatching department to predict the power generation of each photovoltaic power station. The power generation situation provides forecast data of the output power of each photovoltaic power station for power dispatching, and ensures that the power grid energy management system formulates dispatching plans. Most of the existing prediction methods only focus on external environmental con...

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Problems solved by technology

Most of the existing prediction methods only focus on external environmental conditions such as light and weather, and do not consider the reduction in photoelectric conversion efficiency caused by changes in the internal factors of photovoltaic modules, resulting in inaccurate predictions of power generation, especially for new solar cells. , such as polycrystalline PERC or monocrystalline PERC cells, their light attenuation is serious, and their photoelectric conversion efficiency varies greatly with the light. Simply considering environmental factors to estimate the power generation of photovoltaic power plants can only get a rough estimate

[0004] The inventor found in the research that the current generation forecast of photovoltaic power plants has the following problems: (1) The influence of the internal influencing factors of photovoltaic modules on the power generation efficiency is not comprehensively considered in the forecast of power generation of photovoltaic power plants; The photoelectric conversion efficiency of the module decreases with the increase of temperature and the extension of time. At present, only the standard photoelectric conversion efficiency is considered in the forecast of power generation, and the actual photoelectric conversion efficiency of photovoltaic modules is not considered.

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