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.

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Transformers are playing an increasingly significant part in energy conversion, transmission, and distribution, which link various resources, including conventional, renewable, and sustainable energy, from generation to consumption. Power transformers and their components are vulnerable to various operational factors during their entire life cycle, which may lead to catastrophic failures, irreversible revenue losses, and power outages. Hence, it is crucial to investigate transformer condition assessment to grasp the operating state accurately to reduce the failures and operating costs and enhance the reliability performance. In this context, comprehensive data mining and analysis based on intelligent algorithms are of great significance for promoting the comprehensiveness, efficiency, and accuracy of condition assessment. In this article, in an attempt to provide and reveal the current status and evolution of intelligent algorithms for transformer condition assessment and provide a better understanding of research perspectives, a unified framework of intelligent algorithms for transformer condition assessment and a survey of new findings in this rapidly-advancing field are presented. First, the failure statistics analysis is outlined, and the developing mechanism of the transformer internal latent fault is investigated. Then, in combination with intelligent demands of the tasks in each stage of transformer condition assessment under big data, we analyze the data source in-depth and redefine the concept and architecture of transformer condition assessment. Furthermore, the typical methods widely used in transformer condition assessment are mainly divided into rule, information fusion, and artificial intelligence. The new findings for intelligent algorithms are also elaborated, including differentiated evaluation, uncertainty methods, and big data analysis. Finally, future research directions are discussed.

Cognitive dysfunction is common in multiple sclerosis (MS), yet few studies have examined effects of treatment on neuropsychological (NP) performance. To evaluate the effects of interferon beta-1a (IFNbeta-1a, 30 microg administered intramuscularly once weekly [Avonex]) on cognitive function, a Comprehensive NP Battery was administered at baseline and week 104 to relapsing MS patients in the phase III study, 166 of whom completed both assessments. A Brief NP Battery was also administered at 6-month intervals. The primary NP outcome measure was 2-year change on the Comprehensive NP Battery, grouped into domains of information processing and learning/memory (set A), visuospatial abilities and problem solving (set B), and verbal abilities and attention span (set C). NP effects were most pronounced in cognitive domains vulnerable to MS: IFNbeta-1a had a significant beneficial effect on the set A composite, with a favorable trend evident on set B. Secondary outcome analyses revealed significant between-group differences in slopes for Brief NP Battery performance and time to sustained deterioration in a Paced Auditory Serial Addition Test processing rate, favoring the IFNbeta-1a group. These results support and extend previous observations of significant beneficial effects of IFNbeta-1a for relapsing MS.

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The information security has an irreplaceable position in the smart grid (SG). In order to avoid the malicious attack and ensure the information security, the cryptographic techniques are essential. This paper focuses on the encryption techniques to ensure the information security of SG. Firstly, an unusual 7-dimensional complex chaotic system (7D-CCS) combined with the cubic memristor is introduced. Besides its phase portraits, Lyapunov exponent, 0–1 test, complexity, and bifurcation diagram are investigated. Then, with the proposed 7D-CCS, we design a data encryption algorithm to ensure the encryption security. Finally, the data and monitoring images in SG are encrypted by the designed encryption scheme. Besides, the encryption performance is given in detailed. The experimental results show that the proposed encryption scheme has quite good encryption performance. Therefore, it can ensure the information security of SG.