Photovoltaic scene generation method and system based on random matrix theory enhancement

By introducing random matrix theory and an alternating training strategy to optimize the generative adversarial network, the problems of interpretability and relevance assessment of photovoltaic power scenario generation are solved. The generated photovoltaic power time series scenarios are highly consistent with real data in terms of statistical characteristics and physical laws, meeting the high requirements of new energy systems.

CN121614977APending Publication Date: 2026-03-06XI AN JIAOTONG UNIV
0 Cites 0 Cited by

Patent Information

Application Number
CN202511721788.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-21
Publication Date
2026-03-06

AI Technical Summary

Technical Problem

Existing methods for generating photovoltaic power scenarios are insufficient in terms of generation accuracy and interpretability. Traditional generative adversarial network models lack clear correlation evaluation indicators, making them difficult to apply in power systems.

Method used

By introducing the theory of random matrices and constructing a generative adversarial network framework, the generated photovoltaic power time series scene and the real scene are constructed into an augmented matrix. The spectral distribution is calculated and transformed into a correlation metric, which is used as the loss term of the discriminator. An alternating training strategy is adopted to optimize the model. Combined with a physical constraint generator and a dynamic gating function, the statistical authenticity and physical rationality of the generated scene are ensured.

Benefits of technology

The system achieves interpretability and relevance assessment of the generated scenarios. The generated photovoltaic power time series scenarios are highly consistent with real data in terms of statistical characteristics and physical laws, providing a reliable data foundation and high-quality input for new energy system planning and power system analysis.

✦ Generated by Eureka AI based on patent content.
Patent Text Reader

Abstract

The invention discloses a photovoltaic scene generation method and system based on random matrix theory enhancement, and the method comprises the steps: constructing a training sample set based on historical data, and constructing a generative adversarial network framework comprising a generator and a discriminator; on the basis of a random matrix theory, time sequence data of a generated photovoltaic power time sequence scene and a real photovoltaic power time sequence scene are constructed into an augmented matrix, spectral distribution of the augmented matrix is calculated, the spectral distribution is converted into a correlation measurement index, and the correlation measurement index serves as an additional loss item to be introduced into a loss function of a discriminator; adopting an alternate training strategy to perform joint training on the generator and the discriminator into which the correlation loss item is introduced, and optimizing a generative adversarial network framework; and inputting the random noise vector to the generator, and generating a photovoltaic power time sequence scene. Spectral distribution analysis is carried out on a generated scene and a real scene through a random matrix theory, a correlation index is used as a discriminator loss item, and the problems of insufficient interpretability, lack of correlation evaluation and the like of the generative adversarial network in photovoltaic scene generation are solved.
Need to check novelty before this filing date? Find Prior Art