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Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better. [ABSTRACT FROM AUTHOR]

To cope with the problems of cumbersome modeling and strongly disturbances in the control of floating offshore wind turbines (FOWTs), an improved model-free adaptive control (MFAC) strategy considering both wind speed disturbance and floating platform motion response to wave disturbance is proposed. In this strategy, a detailed mathematical model of the FOWT is avoided and the feedback controller is designed based on improved MFAC using only the input–output data to dynamically linearize the FOWT. Strongly disturbances caused by random wind and floating platform motion response to wave are compensated by a feedforward controller. The proposed method and the comparison method including the baseline controller are simulated under different wind scenarios using FAST. The results show that the proposed controller regulates the generator speed more smoothly and closer to the rated value, suppresses the motion of the floating platform more effectively, and can significantly improve the performance of FOWTs. [ABSTRACT FROM AUTHOR]

Wind turbines located in high humidity and high altitude areas are often accompanied by blade icing, which adverses the operating efficiency and even causes safety accidents. Early identification of blade icing will help improve the operating efficiency of the wind turbine. This paper proposes an icing diagnosis method for wind turbine blades based on feature optimization and the one-dimensional convolutional neural network (1D-CNN). First, feature optimization is achieved by feature selection and feature reconstruction. The XGBoost algorithm is used to calculate the importance of each feature and select the features comprehensively that reflect blade icing. Second, the important features related to blade icing are reconstructed by using the deviation principle to extract the deviation information of features accurately when blades ice. Finally, the features screened by XGBoost and the reconstructed features are combined into the final feature set as the input of the 1D-CNN, which takes the temporal and spatial characteristics of data into account, to diagnose the icing state of blades. The method is validated on the data set collected from a real wind farm. The experimental results show that the proposed icing diagnosis method for wind turbine blades is superior to the traditional deep learning methods. It is favorable to improve the efficiency of wind turbine operation and maintenance. [ABSTRACT FROM AUTHOR]

For wind farms to participate in the combined energy-frequency regulation (E-FR) market, wind farms are considered as a combination of both power generation and frequency regulation capability, and wind farms bid in both the energy market and the FR market. In this paper, the impact of different bidding decisions on the distribution of wind farm revenues is analyzed in a process where the interest of two markets is played against each other. A wind power probability density prediction model of kernel extreme learning machine (KELM)-particle swarm optimization (PSO)-adaptive diffusion kernel density estimation (AKDE) is established using an improved extreme learning machine (KELM) with good fitting ability and the AKDE method, wind farm bidding is carried out on the premise of wind power probability prediction, which is optimally solved by the multi-objective quantum genetic algorithm, and the optimization results are filtered using entropy-fuzzy C-means clustering. Based on the actual wind farm operation data for simulation analysis, the model analyzes the benefits of wind farm participation in the joint market from different preference perspectives, which is a reference for wind farm participation in bidding decisions. [ABSTRACT FROM AUTHOR]

Blade icing of a wind turbine will affect the startup performance of the blades, resulting in the loss of power generation of the wind turbine, and even affect the safety of production and operation. In order to reflect the blade icing state of wind turbines as truthfully and objectively as possible, this paper proposes a wind turbine blade icing state recognition model based on the combination of vine-Copula network model and Long Short-Term Memory (LSTM)-Autoencoder algorithm. First, the vine-Copula model is used to analyze the correlation between the various parameters in supervisory control and data acquisition (SCADA) system and the blade icing state, thereby constructing a high-dimensional vine-Copula structure. Then, removing the features that are not directly related to the blade icing state, the final vine-Copula model and related features are obtained. The filtered features are input into the LSTM-Autoencoder algorithm, then the "memory" function and non-linear feature extraction capabilities of the LSTM-Autoencoder algorithm are used to obtain the evaluation results of the blade icing state of wind turbines. The experimental results show that the indicators of the wind turbine blade icing state recognition based on this method are overall better than the indicators of the Recurrent Neural Network-Autoencoder algorithm without feature reduction and the LSTM-Autoencoder algorithm without feature reduction and traditional classification algorithms. [ABSTRACT FROM AUTHOR]

Timely and accurate assessment of wind turbine operating condition can save the maintenance cost effectively, because equipment repair and replacement for large wind turbine are expensive. Therefore, an efficient operating condition assessment strategy for wind turbine is proposed based on fuzzy synthesis method. First, the traditional fuzzy assessment process is introduced briefly a foundation for comparison. Second, a wind turbine assessment model is established and assessed in detail, with an improved fuzzy prediction strategy and de-fuzzy strategy. Finally, a comparison between the improved strategy and the traditional strategy is studied based on wind turbine operation data, and the results validate that the strategy proposed in this paper can alarm operation fault more promptly and efficiently, which is better for wind turbine operation safety and maintenance. [ABSTRACT FROM AUTHOR]