With the continuous increase of installed capacity of wind power, the influence of large-scale wind power integration on the power grid is becoming increasingly apparent. Ultra-short-term wind power prediction is conducive to the dispatching management of the power grid, and improves the operating efficiency and economy of the power system. In order to overcome the intermittency and uncertainty of wind power generation, this paper proposes the DE-BP (Dfferential Evolution-Back Propagation) algorithm to predict wind power, and addresses such shortcomings of BP neural network as its falling into local optimality and slow training speed when predicting. In this paper, the differential evolution algorithm is used to find the optimal value of the initial weight and threshold of the BP neural network, and the DE-BP neural network prediction model is obtained. According to the data of a wind farm in Northwest China, the short-term wind power is predicted. Compared with the application of the BP model in wind power prediction, the results show that the accuracy of the DE-BP algorithm is improved by about 5%; Compared with the GA-BP(Genetic Algorithm-Back Propagation) model, the prediction time is shortened by 23.1%.
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as the graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many studies on extending deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks, graph recurrent neural networks, graph attention networks, graph generative networks, spatial-temporal graph convolutional networks, and hybrid forms of GNNs) are summarized, and key applications in power systems such as fault diagnosis, power prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed.
Ice is the enemy of turbines everywhere. Some wind farms report energy production losses of up to 20 percent due to icing, according to Canadian wind-industry consultancy firm TechnoCentre Éolien (TCE), and that's not the worst of it. Over time, ice shedding from blades can damage other blades or overstress internal components, necessitating costly repairs. There's a clear and present use case, then, for an AI system that detects wind turbine icing. Fortunately, that's just what a team of researchers recently described in a paper published on the preprint server Arxiv.org