As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.

The prediction of wind power plays an indispensable role in maintaining the stability of the entire power grid. In this paper, a deep learning approach is proposed for the power prediction of multiple wind turbines. Starting from the time series of wind power, it is present a two-stage modeling strategy, in which a deep neural network combines spatiotemporal correlation to simultaneously predict the power of multiple wind turbines. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). Herein, the LSTM captures the temporal dependence of the historical power sequence, while the CNN extracts the spatial features among the data, thereby achieving the power prediction for multiple wind turbines. The proposed approach is validated by using the wind power data from an offshore wind farm in China, and the results in comparison with other approaches shows the high prediction preciseness achieved by the proposed approach.

Multiport dc–dc convertersare attracting wide attention in various applications. However, conventional topology derivation methods for multiport dc–dc converters are usually intricate and time-consuming. In this article, a reinforcement learning (RL)-based topology derivation method is proposed, which can derive topologies of complex converters quickly. To apply the RL framework, the topology derivation process is regarded as a Markov decision process. In each step, the agent selects and connects two components until a complete topology is made. To ensure that the derived topologies are feasible and match given voltage specifications, basic circuit constraints and duty cycle constraints are added as hard constraints. Soft constraints are also added to obtain optimal circuits with low voltage stresses or low current stresses. The testing on topology derivation of multiport dc–dc converters shows the great speed of the proposed method. Simulation and experimental results verify that the derived topologies cannot only satisfy given voltage specifications, but also achieve optimization targets of low voltage stresses or current stresses.

Based on multiple reference frames (MRF), this article investigates high-precision harmonic current extraction for permanent magnet synchronous motor, while taking the disturbance of speed harmonics into account. First, the extraction error in MRF caused by speed harmonics is deduced and analyzed, showing that the error is nonignorable and detrimental. Second, a novel preprocessing of fundamental current reconstruction and elimination in MRF is proposed, which effectively suppresses the extraction error. Third, a variable forgetting factor recursive least squares algorithm is proposed for the fundamental current amplitude identification, which guarantees fast and accurate preprocessing. Furthermore, the above method parallelly resolves the conflict between rapid extracting and low steady-state fluctuation in MRF, which significantly enhances harmonic current extraction performance. The superiority and accuracy of the proposed algorithms are proved by experiments.