Abstract
In view of the growing depletion of traditional fossil fuels and their adverse impact on natural environment, wind energy has gained increasing popularity across the globe. Characterized by wide distribution, low cost, and well-rounded technology, it has achieved fast-growing installed capacity in recent years. However, wind power is volatile and random in nature and the power ramping events caused by extreme weather always threaten the safe, stable, and economic operation of the power grid. To address the problems of insufficient sample data and low prediction accuracy in existing ramping prediction methods, a new way of wind power prediction considering ramping events based on Generative Adversarial Network (GAN) is proposed. First of all, the ramping events get identified and separated from the database of historical wind power, and the feature set of historical ramping events is then extracted according to the waveform and meteorological factors. Taking the feature set which integrates similar feature with historical one as the input of GAN, the simulated ramping data are continuously produced through the adversarial training of the generator and discriminator, thus enriching the ramping database. After that, the expanded ramping database can be applied to predict the ramping power through the LSTM model. An experiment based on the wind power dataset in a certain area of northwest China further verifies the effectiveness and superiority of this method compared with traditional ones.
Highlights
Energy powers the progress of human society
In order to verify the effectiveness of the proposed wind power ramping prediction model based on Generative Adversarial Network, a sample dataset of a certain area in Northwest China is selected as a study case
To quantitively evaluate how similar historical ramping event data and Generative Adversarial Network (GAN)-generated data are, a method is adopted to calculate the level of similarity between historical data and simulated ones and to ensure the feasibility of power prediction. e data P are regarded as the set of feature points pi, and the data Q are the set of feature points qj
Summary
Energy powers the progress of human society. It brings people abundant material comforts and enjoyment. Wind energy, featured by large reserves, wide distribution, little pollution, and well-rounded technology, is widely favored among the power system [2]. It can be affected by wind speed, which is naturally random and intermittent, unstable, and difficult to control [3]. Wind power ramping events here refer to the large fluctuation of output power in a short time caused by climate change happening in wind power stations, which may lead to large fluctuation of system frequency and even serious blackouts afterwards [4] For this reason, improving the prediction accuracy of wind power is of great significance to the regular service of power system [5]
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