In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean, renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind power has been developing in the direction of digitization and intelligence. It is of great significance to carry out research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit for the reduction of the operation and maintenance costs, the improvement of the power generation efficiency, improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms. This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of “offshore wind power engineering and biological and environment”, the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored, especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of power equipment, and digital platforms.

In this study, we propose a novel 2D lag-complex logistic map (LCLM). The 2D-LCLM is derived from the conventional 2D logistic map by extending its variables from real numbers to complex field. The bifurcation diagrams, Lyapunov exponents, and the evolution of chaotic attractors have been exploited for evaluating the chaotic behavior. Results show that the 2D-LCLM has extensive chaotic intervals, good ergodicity, and unpredictable trajectories. For further studying its practical significance, we then construct a color image cryptosystem based on 2D-LCLM. Bit-level permutation and one-time keys are used for speeding up the computational speed and defending chosen plaintext attack. Simulation results and corresponding analysis reveal that the cryptosystem has large key-space and good key sensitivity, and its information entropy approaches to the ideal value. Hence, the cryptosystem based on 2D-LCLM can strongly resist traditional cipher attacks.

In communication, the signal at the receiver end at time t is the signal from the transmitter side at time t −Τ (Τ ≥ 0 and it is the lag time) as the time lag of transmission. Therefore, lag synchronization (LS) is more accurate than complete synchronization to design communication scheme. Taking complex Lorenz system as an example, we design the LS controller according to error feedback. Using chaotic masking, we propose a communication scheme based on LS and independent component analysis (ICA). It is suitable to transmit multiple messages with all kinds of amplitudes and it has the ability of anti-noise. Numerical simulations verify the feasibility and effectiveness of the presented schemes.

In order to realize the economic dispatch and safety stability of offshore wind farms, and to address the problems of strong randomness and strong time correlation in offshore wind power forecasting, this paper proposes a combined model of principal component analysis (PCA), sparrow algorithm (SSA), variational modal decomposition (VMD), and bidirectional long- and short-term memory neural network (BiLSTM). Firstly, the multivariate time series data were screened using the principal component analysis algorithm (PCA) to reduce the data dimensionality. Secondly, the variable modal decomposition (VMD) optimized by the SSA algorithm was applied to adaptively decompose the wind power time series data into a collection of different frequency components to eliminate the noise signals in the original data; on this basis, the hyperparameters of the BiLSTM model were optimized by integrating SSA algorithm, and the final power prediction value was obtained. Ultimately, the verification was conducted through simulation experiments; the results show that the model proposed in this paper effectively improves the prediction accuracy and verifies the effectiveness of the prediction model.

The collected data of a pumping unit contain environmental noise, which significantly reduces the precision of fault diagnosis. The previous fault detection approach depends on manual feature extraction, which is time-consuming and laborious, and it cannot cope with high-noise conditions. Therefore, we propose a dual-branch time–frequency fusion deep learning model for fault diagnosis of the pumping unit. One branch extracts time-domain information, while the other branch extracts frequency-domain information by employing the fast Fourier transform. The branch information of these two branches is concatenated, and the gate-controlled channel transfer unit module automatically learns the competitive and cooperative relationships between each branch, making the key features more prominent in information fusion. Consequently, an accurate fault diagnosis of the pumping unit can be achieved under high-noise conditions. The results demonstrate that the proposed model outperforms the traditional schemes in terms of noise, with different signal-to-noise ratios.