Open Access

ARTICLE

Photovoltaic Power Generation Power Prediction under Major Extreme Weather Based on VMD-KELM

Yuxuan Zhao^1,2,*, Bo Wang¹, Shu Wang¹, Wenjun Xu², Gang Ma²

1 National Key Laboratory of Renewable Energy Grid Integration, China Electric Power Research Institute, Beijing, 100192, China
2 School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing, 210023, China

* Corresponding Author: Yuxuan Zhao. Email:

Energy Engineering 2024, 121(12), 3711-3733. https://doi.org/10.32604/ee.2024.054032

Received 16 May 2024; Accepted 24 September 2024; Issue published 22 November 2024

Abstract

The output of photovoltaic power stations is significantly affected by environmental factors, leading to intermittent and fluctuating power generation. With the increasing frequency of extreme weather events due to global warming, photovoltaic power stations may experience drastic reductions in power generation or even complete shutdowns during such conditions. The integration of these stations on a large scale into the power grid could potentially pose challenges to system stability. To address this issue, in this study, we propose a network architecture based on VMD-KELM for predicting the power output of photovoltaic power plants during severe weather events. Initially, a grey relational analysis is conducted to identify key environmental factors influencing photovoltaic power generation. Subsequently, GMM clustering is utilized to classify meteorological data points based on their probabilities within different Gaussian distributions, enabling comprehensive meteorological clustering and extraction of significant extreme weather data. The data are decomposed using VMD to Fourier transform, followed by smoothing processing and signal reconstruction using KELM to forecast photovoltaic power output under major extreme weather conditions. The proposed prediction scheme is validated by establishing three prediction models, and the predicted photovoltaic output under four major extreme weather conditions is analyzed to assess the impact of severe weather on photovoltaic power station output. The experimental results show that the photovoltaic power output under conditions of dust storms, thunderstorms, solid hail precipitation, and snowstorms is reduced by 68.84%, 42.70%, 61.86%, and 49.92%, respectively, compared to that under clear day conditions. The photovoltaic power prediction accuracies, in descending order, are dust storms, solid hail precipitation, thunderstorms, and snowstorms.

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Keywords

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