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Offshore wind farms (OWFs) have received widespread attention for their abundant unexploited wind energy potential and convenient locations conditions. They are rapidly developing towards having large capacity and being located further away from shore. It is thus necessary to explore effective power transmission technologies to connect large OWFs to onshore grids. At present, three types of power transmission technologies have been proposed for large OWF integration. They are: high voltage alternating current (HVAC) transmission, high voltage direct current (HVDC) transmission, and low-frequency alternating current (LFAC) or fractional frequency alternating current transmission. This work undertakes a comprehensive review of grid connection technologies for large OWF integration. Compared with previous reviews, a more exhaustive summary is provided to elaborate HVAC, LFAC, and five HVDC topologies, consisting of line-commutated converter HVDC, voltage source converter HVDC, hybrid-HVDC, diode rectifier-based HVDC, and all DC transmission systems. The fault ride-through technologies of the grid connection schemes are also presented in detail to provide research references and guidelines for researchers. In addition, a comprehensive evaluation of the seven grid connection technologies for large OWFs is proposed based on eight specific indicators. Finally, eight conclusions and six perspectives are outlined for future research in integrating large OWFs.

Lithium-ion batteries (LIBs) are crucial for the large-scale utilization of clean energy. However, because of the complexity and real-time nature of internal reactions, the mechanism of capacity decline in LIBs is still unclear. This has become a bottleneck restricting their promotion and application. Electrochemical impedance spectroscopy (EIS) contains rich electrochemical connotations and significant application prospects, and has attracted widespread attention and research on efficient energy storage systems. Compared to traditional voltage and current data, the state-of-health (SOH) estimation model based on EIS has higher accuracy. This paper categorizes EIS measurement methods based on different principles, introduces the relationship between LIBs aging mechanism and SOH, and compares the advantages of different SOH estimation methods. After a detailed analysis of the latest technologies, a review is given. The insights of this review can deepen the understanding of the relationship between EIS and the aging effect mechanism of LIBs, and promote the development of new energy storage devices and evaluation methods.

Wind power, solar power and water power are technologies that can be used as the main sources of renewable energy so that the target of decarbonisation in the energy sector can be achieved. However, when compared with conventional power plants, they have a significant difference. The share of renewable energy has made a difference and posed various challenges, especially in the power generation system. The reliability of the power system can achieve the decarbonization target but this objective often collides with several challenges and failures, such that they make achievement of the target very vulnerable, Even so, the challenges and technological solutions are still very rarely discussed in the literature. This study carried out specific investigations on various technological solutions and challenges, especially in the power system domain. The results of the review of the solution matrix and the interrelated technological challenges are the most important parts to be developed in the future. Developing a matrix with various renewable technology solutions can help solve RE challenges. The potential of the developed technological solutions is expected to be able to help and prioritize them especially cost-effective energy. In addition, technology solutions that are identified in groups can help reduce certain challenges. The categories developed in this study are used to assist in determining the specific needs and increasing transparency of the renewable energy integration process in the future.

With a high death rate, oral cancer is a major worldwide health problem, particularly in low- and middle-income nations. Timely detection and diagnosis are crucial for effective prevention and treatment. To address this challenge, there is a growing need for automated detection systems to aid healthcare professionals. Regular dental examinations play a vital role in early detection. Transfer learning, which leverages knowledge from related domains, can enhance performance in target categories. This study presents a unique approach to the early detection and diagnosis of oral cancer that makes use of the exceptional sensory capabilities of the mouth. Deep neural networks, particularly those based on automated systems, are employed to identify intricate patterns associated with the disease. By combining various transfer learning approaches and conducting comparative analyses, an optimal learning rate is achieved. The categorization analysis of the reference results is presented in detail. Our preliminary findings demonstrate that deep learning effectively addresses this challenging problem, with the Inception-V3 algorithm exhibiting superior accuracy compared to other algorithms.

High voltage direct current (HVDC) transmission is an economical option for transmitting a large amount of power over long distances. Initially, HVDC was developed using thyristor-based current source converters (CSC). With the development of semiconductor devices, a voltage source converter (VSC)-based HVDC system was introduced, and has been widely applied to integrate large-scale renewables and network interconnection. However, the VSC-based HVDC system is vulnerable to DC faults and its protection becomes ever more important with the fast growth in number of installations. In this paper, detailed characteristics of DC faults in the VSC-HVDC system are presented. The DC fault current has a large peak and steady values within a few milliseconds and thus high-speed fault detection and isolation methods are required in an HVDC grid. Therefore, development of the protection scheme for a multi-terminal VSC-based HVDC system is challenging. Various methods have been developed and this paper presents a comprehensive review of the different techniques for DC fault detection, location and isolation in both CSC and VSC-based HVDC transmission systems in two-terminal and multi-terminal network configurations.

New energy storage devices such as batteries and supercapacitors are widely used in various fields because of their irreplaceable excellent characteristics. Because there are relatively few monitoring parameters and limited understanding of their operation, they present problems in accurately predicting their state and controlling operation, such as state of charge, state of health, and early failure indicators. Poor monitoring can seriously affect the performance of energy storage devices. Therefore, to maximize the efficiency of new energy storage devices without damaging the equipment, it is important to make full use of sensing systems to accurately monitor important parameters such as voltage, current, temperature, and strain. These are highly related to their states. Hence, this paper reviews the sensing methods and divides them into two categories: embedded and non-embedded sensors. A variety of measurement methods used to measure the above parameters of various new energy storage devices such as batteries and supercapacitors are systematically summarized. The methods with different innovative points are listed, their advantages and disadvantages are summarized, and the application of optical fiber sensors is emphasized. Finally, the challenges and prospects for these studies are described. The intent is to encourage researchers in relevant fields to study the early warning of safety accidents from the root causes.

With the rapid development of power electronics technology, microgrid (MG) concept has been widely accepted in the field of electrical engineering. Due to the advantages of direct current (DC) distribution systems such as reduced losses and easy integration with energy storage resources, DC MGs have drawn increasing attentions nowadays. With the increase of distributed generation, a DC MG consisting of multiple sources is a hot research topic. The challenge in such a multi-source DC MG is to provide voltage support and good power sharing performance. As the control strategy plays an important role in ensuring MG’s power quality and efficiency, a comprehensive review of the state-of-art control approaches in DC MGs is necessary. This paper provides an overview of the primary and secondary control methods under the hierarchical control architecture for DC MGs. Specifically, inner loop and droop control approaches in primary control are reviewed. Centralized, distributed, and decentralized approach based secondary control is discussed in details. Key findings and future trends are also presented at last.

The industrial load sample data categories are unbalanced, resulting in low classification performance for a few sample categories. An imbalanced industrial load identification method based on optimized CatBoost with entropy features is proposed. Firstly, multiple original samples of industrial load data and their corresponding switch states are selected from the dataset. The original samples are segmented in the time domain, dividing each sample into three time-domain intervals. The 27 time-domain features containing 8 types of entropy features are extracted from different time-domain intervals, resulting in the construction of an 81-dimensional original feature set. Next, the feature importance is calculated and sorted based on the Prediction Value Change method. The optimal subset of classification features for the corresponding device in the original sample is determined through forward feature selection, with the CatBoost classification accuracy being used as the decision variable. Secondly, the Borderline-SMOTE method is used to synthesize the sample data for balancing processing to obtain balanced switching sample data. Finally, the CatBoost classifier with Bayesian optimization and hyperBand hyperparameter optimization is constructed to identify industrial loads. The experimental results show that this method has the advantages of high feature extraction efficiency and high accuracy in identifying imbalanced small sample data.

A novel single-phase nine-level switched-capacitor inverter (9LSCI) with quadruple-boost ability and reducing the component counts is proposed. Only one DC source, nine switches, two diodes and two switched capacitors (SCs) are employed in the basic unit of the proposed topology to realize nine-level output. Due to the passive voltage balancing of each capacitor maintains a constant voltage without additional control. A simple logic-gate-based pulse width-modulation scheme is developed for gating switches of the proposed topology. The working principle of the basic unit topology, the voltage/current stress on the switch, the determination of the capacitance and the simulation and the experiment at 400 W output power are introduced in this paper. In addition, an extended form of the basic unit is provided. A detailed analysis of the proposed topology has been carried out to show the superiority of the proposed converter with respect to the other existing MLI topologies. Various simulations are carried out in Matlab/Simulink R2021b and the feasibility is verified in experiments.

This paper presents the formulation of the simultaneous planning of distributed generations (DGs) and automatic distribution according to the goals of reliability, operation, and economy of the distribution system. The objective function aims at minimizing the total costs of construction, maintenance, and operation of distribution automation resources and devices, plus the cost of voltage deviations and expected energy not supplied in this paper. This scheme limits to AC optimal power flow, the planning constraints of DGs and distribution automation devices, and reliability equations. The mentioned scheme has an integer nonlinear optimization format. In the following, a linear approximation model is extracted for it to reach the unique response. Finally, by applying the proposed problem to the standard distribution grid by GAMS optimization software, the numerical results highlight the capability of the proposed scheme in improving the technical and economic conditions of the distribution network with optimal DGs and distribution automation planning.