Photovoltaic output typical scene extraction method combined with spectral clustering algorithm

Abstract

The invention discloses a photovoltaic output typical scene extraction method combined with a spectral clustering algorithm, and relates to the technical field of renewable energy output prediction, a photovoltaic output typical scene is extracted based on the spectral clustering algorithm, clustering analysis can be carried out on data only through a similarity matrix between the data in the calculation process, and the calculation efficiency is improved. The spectral clustering algorithm can realize dimension reduction processing of high-dimensional data, and the result obtained by clustering the high-dimensional data of photovoltaic output is obviously superior to that obtained by a traditional mean value algorithm, so that a typical scene of photovoltaic output can be extracted more efficiently and accurately; the method solves the problem that an existing k-means algorithm is difficult to accurately cluster high-dimensional photovoltaic output data, realizes accurate description of randomness and volatility characteristics of photovoltaic output in a power distribution network, and eliminates the influence of photovoltaic output uncertainty on long-term planning operation and short-term scheduling in a power system. The photovoltaic reasonable planning body condition in the power distribution network is met, and safe, stable and environment-friendly operation and development of a power system are achieved.

Description

technical field [0001] The invention relates to the technical field of renewable energy output forecasting, in particular to a method for extracting typical scenarios of photovoltaic output combined with a spectral clustering algorithm. Background technique [0002] The output of the photovoltaic power generation system is affected by environmental factors such as the intensity of sunlight and temperature, resulting in large fluctuations and intermittency in its output. The foundations of the planning field have important theoretical and practical implications. [0003] For the extraction of typical scenes of photovoltaic output, the existing scene extraction method based on k-means is only suitable for extracting data scenes in low-dimensional space, and it is difficult to extract reasonable typical scenes for high-dimensional space data scenes such as light intensity. Contents of the invention [0004] In order to solve the above-mentioned technical problems, the presen...

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Problems solved by technology

[0003] For the extraction of typical scenes of photovoltaic output, the existing scene extraction method based on k-means is only suitable for extracting low-dimensional space data scenes, and it is difficult to extract reasonable typical scenes for high-dimensional space data scenes such as light intensity

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