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The conventional Z-source inverter (ZSI) has the following disadvantages: unidirectional power flow, high startup inrush current, presence of undesirable mode operation, and low boost factor. To solve the aforementioned shortcomings, an enhanced-boost bidirectional quasi-ZSI (QZSI) with novel active switched inductor (SL) cells (named Active-SLs QZSI) is proposed. The proposed active SL cell replaces three diodes in the conventional SL cell of the QZSI with two capacitors and one active switch, thus allowing bidirectional power flow and achieving higher boost factor. In addition, the Z-source network (ZSN) diode is also replaced by an active switch. Compared with the existing ZSIs, the proposed Active-SLs QZSI has less current stress of ZSN, higher efficiency, and higher boost factor. A modified space vector modulation (MSVM) is introduced to reduce the switching frequency of active switches of ZSN. Both the simulation and experimental results validate the theoretical analysis of the proposed Active-SLs QZSI.

Renewable energy generations have been attracting sustained attentions in academic and industry. Current-fed-isolated-bidirectional dc/dc converters (CF-IBDCs) are widely adopted in low-voltage high-current applications such as solar photovoltaic fuel cell with energy storage. This paper gives an overview and a comprehensive comparative evaluation of CF-IBDCs. The active clamped, dual half-bridge, L-L type dual active bridge, resonant-type, naturally clamped, and other type topologies of CF-IBDCs are investigated, analyzed, and compared regarding their circuit topological structures, operation characteristics, modulation methods, and soft-switching technologies. In addition, component cost models and loss models of converters are deduced and presented. On this basis, quantitative and comprehensive performance comparison of seven typical CF-IBDC topologies selected from each type is conducted in terms of costs, losses, weight, volume, and power density for the given system specifications and design constraints. Finally, the application range of different CF-IBDCs and future trend are presented to encourage further development.

Harmonic and electromagnetic interference (EMI) filters are the two important parts in the grid-connected inverter for the harmonic and EMI noise suppression. This article investigates the combined magnetic integration of the harmonic and EMI filters with the volume and weight reduction, which is defined as the integrated LCL–EMI filter in this article. Both common-mode (CM) and differential-mode (DM) models of the integrated inductor with EE-type magnetic core are built and analyzed. By designing the air gap of the center leg and arranging the windings properly, the distribution of magnetic flux on the magnetic branch can be controlled. As a result, harmonic inductors and a CM inductor can be effectively designed in the center leg and side leg, respectively. A symmetrical LCL filter is designed instead of the traditional asymmetrical structure with an integrated magnetic core. The current harmonic distortion analysis and EMI measurement results based on simulation and experiment results verify the effectiveness of the proposed integration method in the silicon carbide (SiC) MOSFET-based grid-connected inverter. Moreover, weight and volume comparisons between the integrated inductor and the traditional discrete inductors also reflect the superiority of the magnetic integrated LCL–EMI filter.

A series of uniform microspheres of spinel-type Mn-doped ferrites (MnxFe3-xO4) with different Fe/Mn ratios were employed for the Fischer-Tropsch-to-olefins reaction (FTO). The highest selectivity of 54.4% and the space-time-yield (STY) of 73.0g·kgcat−1·h−1 to lower olefins (C2-4=) were achieved over an Fe-Mn catalyst with 13 wt% Mn. A panoramic view of structure evolution of MnxFe3-xO4 was revealed using multiple in/ex situ techniques. We demonstrated that the expansion of lattice spacing induced by Mn exerted remarkable effects on the stabilization of MnxFe1-xO and e-Fe2C during activation and reaction. Moreover, Mn endows Fe catalysts with strong surface basicity and consequently enhances the adsorption and dissociation of CO; while weakening the H availability and readsorption of olefinic intermediates. In addition, the impeded carburization of MnxFe1-xO and the reduction in particle size of catalysts jointly contribute to the volcano curve in the catalytic activity. The insights presented could guide the rational design of high performance catalysts via heteroatom doping.

A new three-level bidirectional dc–dc converter configuration for the battery and supercapacitor (SC) energy storage systems in dc microgrids is presented in this paper. The PI-based control method is implemented to operate the proposed converter with hybrid energy storage system. Furthermore, joint control is utilized to obtain rapid dc-link voltage restoration and effective power sharing between battery and SC based on their respective characteristics. The system operating principle, controller design, stability analysis, simulation studies, and experimental studies are presented in this paper. The proposed three-level bidirectional dc–dc converter configuration can effectively integrate the battery and SC with the bidirectional power flow. The proposed control method provides the effective participation of the SC system which reduces the stress on the battery system. The presented converter configuration is effective in reducing the stress on the power semiconductors and provides better performance with smaller passive components. The experimental platform is built to test and validate the three-level bidirectional dc–dc converter and the proposed control method for the HESS.

This article presents a deadbeat-based method for the hybrid energy storage system (HESS) in solar-assisted electric vehicles (EVs), using a new bidirectional three-level cascaded (BTLC) converter. Moreover, photovoltaic (PV) panels are also considered in the system, because the proposed method is designed for solar-assisted EV applications to achieve a longer driving range. The proposed BTLC converter can integrate the battery and the supercapacitor as an HESS to effectively mitigate the power mismatch between the PV power generation and the load power consumption for the solar-assisted EVs. The BTLC converter is superior to the conventional parallel-connected bidirectional battery/supercapacitor converters from the perspectives of related component size reduction and control flexibility. To regulate the BTLC converter, a deadbeat-based method is proposed, which can provide the proper power management for the battery and the supercapacitor, and maintain the bus voltage. The design and implementation of the proposed deadbeat-based method are analyzed in detail. A scaled-down prototype of the proposed BTLC converter is built for the experimental verification. The simulation and experimental results are presented and compared to verify the effectiveness of the deadbeat-based method for the HESS with the proposed BTLC converter.

More and more attention has been gained in the vehicle-to-grid (V2G) system due to its attractive features, such as transferring bidirectional power between electric vehicles and grid, regulating grid voltage/frequency. This paper develops the active electromagnetic interference (EMI) filter (AEF) with significant system volume and weight reduction compared to the traditional passive EMI filter (PEF). While the high-frequency parasitic parameters of the AEF and the restricted loop gain result in limited AEF EMI noise attenuation in the high-frequency range. Thus, an additional passive EMI filter is commonly needed to suppress the EMI noise. For avoiding external passive filter components, the active EMI filter is designed and integrated with a modified LCL-LC filter as a hybrid filter to guarantee EMI noise attenuation. The modified LCL-LC filter can maintain satisfactory harmonic suppression with a higher EMI reduction ability compared to other types of harmonic filters. Based on a single-phase grid-connected inverter, the total harmonic distortion (THD) and the conducted EMI measurement results are compared with the results of traditional LCL, LLCL, LCL-LC filters. The effectiveness of the proposed circuit is verified by simulation and experimental results. The proposed circuit successfully improves the filter design of the inverter in the suppression of harmonics and EMI noise.

Snoring is a common symptom of obstructive sleep apnea (OSA) which is considered to be potential predictors of the obstruction site. Successful treatment of OSA depend on the determination the types of obstruction site. This study aimed to develop a machine learning-based model to detect obstruction site using snoring sound.Patients with OSA underwent drug-induced sleep endoscopy (DISE) and the snoring sounds were recorded simultaneously. We extracted acoustic features based on Mel-frequency cepstral coefficients (MFCC). A k-nearest neighbors (KNN) was used for snore classification.Total 42 patients with OSA were enrolled. The accuracy of model was 85.55 %, F1 score was 85.04. With combined age, gender and Body Mass Index (BMI), the accuracy of model was 87.98 %, and F1 score was 87.96. The model exhibited accuracies of 83 %, 93 % and 92 %; an AUC of 85.88, 89.22 and 88.17 in detecting retropalatal, retrolingual and multilevel obstructions.Our results suggest that combing snoring sound with age, gender and BMI, the machine learning based model can help automatically assess obstruction site. The model may have potential utility as a clinical tool to help for clinical decision-making.

Aiming at the problems of high cost of manual investigation, low accuracy and difficult side-to-end information interaction in the verification of the relationship between distribution transformer terminal and household transformer in low-voltage substation area, a low-voltage substation area topology identification verification method based on DTW algorithm is proposed. According to the time series voltage data of smart meter and distribution transformer terminal, this paper uses DTW algorithm to calculate the similarity of time and voltage series, and checks the user by setting threshold. Then KNN algorithm is used to determine the station area, and the household transformer relationship is determined. Finally, according to the time series voltage data collected by the meter, the DTW algorithm is used to calculate the voltage sequence similarity between users, and the feeder position of users is located combined with the topology. Experiments show that the method proposed in this paper has the characteristics of high accuracy and high efficiency, and it can accurately verify the relationship between household and transformer of distribution transformer terminal equipment in low voltage substation area.

The potential value of massive operation data can be tapped by machine learning and other new technologies, and it is important to apply machine learning in the complex environment of distribution network. Therefore, distribution network topology reconfiguration optimization based on machine learning is studied in this paper. In order to reconstruct the topology of distribution network efficiently and economically, a dynamic topology reconstruction algorithm based on LSTM prediction mechanism is proposed. Firstly, LSTM model is constructed to predict the load of each node in each period. Then, the mathematical model of distribution network topology reconfiguration is generated. Finally, the global optimization dynamic reconfiguration strategy is generated by using the minimum comprehensive cost as the optimization objective. The experimental results show that the topology reconstruction algorithm can reduce the line loss cost, and can be used to reconstruct and optimize the topology.

Research studies of vehicle-to-grid (V2G) are gaining more and more attention, because of its attractive features, such as, transferring bidirectional power between electric vehicles and grid, regulating grid voltage/frequency etc. This paper investigates the damping technique of the grid-connected inverter for a V2G application. As the silicon carbide (SiC) mosfet has obvious advantages in high-power-density inverters with a high switching frequency, an LLCL filter-based SiC mosfet inverter is designed to suppress the harmonics and increase the power density in this paper. By analyzing and comparing the suppression effects of four passive damping methods on the resonance peak of the LLCL filter, it is found that passive damping control strategy brings extra loss into the V2G system, and affects the performance of LLCL filter. An active damping control method is proposed to reduce losses and ensure better dynamic performance. The feasibility of the design scheme and performance are verified by simulations and experiments.

Grid-connected inverters are essential equipment for DC-AC energy conversion between renewable energy generation and power grids, and their performance directly affects the stability of the operation of the power grid. Compared to the traditional L or LC filter, LCL filter is widely used in the grid-connected inverter due to its harmonic attenuation performance and system stability. Because of the requirements for higher switching frequency to increase the power density and reduce the cost of the inverter. Compared to the traditional silicon (Si) insulated gate bipolar transistor (IGBT) power device, the silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) has shown apparent advantages in high-power density inverters with a high switching frequency. This study first analyses and compares the suppression effects of passive damping methods on the resonance peak of the LCL filter; then, a double-current closed-loop control strategy as the active damping control is applied in the LCL filter three-phase grid-connected inverter; finally, the experimental platforms of IGBT inverter and SiC MOSFET inverter are built, and the filtering performances of the two inverters are compared. The experimental results show that the double-closed-loop control method significantly reduces the system harmonics and confirms the feasibility of the control strategy.

Load frequency control (LFC) is playing an indispensable role to achieve the secure and economic operation of power grids. However, the existing LFC schemes either may rely on a nonlinear grid model under perfect operating condition with nominal parameters, or they may adopt a complicated control structure of high order. These LFC schemes may have poor control performance or even loss of stability in real-time implementation due to the grid uncertainties and the change of system operation scenarios. Consequently, a hybrid control method with two control loops considering various practical scenarios is originally proposed in this paper. In the inner loop, variable universe fuzzy logic control is applied to mitigate the impact of load disturbances on control performance. In the outer loop, an incremental genetic algorithm is employed to online optimize the control parameters. The performance of the proposed control method is comprehensively tested on a MATLAB/Simulink-based LFC model and a real-time digital simulator-based real-life 49-bus power system. The extensive results show that the proposed hybrid method exhibits comparatively better control performance than an adaptive fuzzy logic controller and an improved proportion integration controller.

This paper presents an electromagnetic interference (EMI) filter design procedure for a single-phase SiC MOSFET inverter. To ensure the EMI filter performance is both accurate and effective, the equivalent noise source impedances of common mode (CM) and differential mode (DM) noise models are extracted. Then the CM and DM EMI filters can be designed based on the known CM and DM noise source impedances, respectively. The EMI filter design based on the proposed method is validated both through simulation and experiments. The simulation and experiment results have verified the effectiveness of the proposed method.

Photovoltaic (PV)/battery hybrid power units have attracted vast research interests in recent years. For the conventional distributed power generation systems with PV/battery hybrid power units, two independent power converters, including a unidirectional dc-dc converter and a bidirectional converter, are normally required. This paper proposes an energy management and control strategy for the PV/battery hybrid distributed power generation systems with only one integrated three-port power converter. As the integrated bidirectional converter shares power switches with the full-bridge dc-dc converter, the power density and the reliability of the system is enhanced. The corresponding energy management and control strategy are proposed to realize the power balance among three ports in different operating scenarios, which comprehensively takes both the maximum power point tracking (MPPT) benefit and the battery charging/discharging management into consideration. The simulations are conducted using the Matlab/Simulink software to verify the operation performance of the proposed PV/battery hybrid distributed power generation system with the corresponding control algorithms, where the MPPT control loop, the battery charging/discharging management loop are enabled accordingly in different operating scenarios.

As the renewable energy sources (RESs) are being connected to grid through power electronics-based converters without any inertia, the decreasing inertia issue of the distributed power system becomes more and more critical. The quasi-Z source inverter (qZSI) presents many advantages when applied to photovoltaic (PV) power system, such as voltage boost ability and turning the upper and lower switches of one bridge leg without damaging the devices. Thus, this paper proposes a method to emulate the power system inertia based qZSI PV power system. By regulating the voltage of two capacitors in the qZSI and dc-link capacitors according to the grid frequency proportionally, the capacitors can serve as an energy buffer for frequency support. The advantages of the proposed method are its simple implementation process and that the system inertia can be enhanced without adding hardware resources. Simulation and experimental results verify the feasibility of the proposed virtual inertia method and the proposed control scheme.

For reducing mounting space and improving the power density of drive system, it is of great significance to investigate the estimation of the rotation and position of the permanent magnet synchronous motor (PMSM). In this paper, a sensorless control strategy based on the model reference adaptive system (MRAS) has been proposed. Meanwhile, with SiC-MOSFET applied instead of the Si-IGBT device for a higher switching-frequency, that the load-side current can be improved with lower harmonics, and the satisfied performance of the control system based on MRAS can be furtherly promoted. Finally, the proposed strategy will be verified by the simulation and experiment results.

In this work, a two-probe approach is proposed to extract noise source impedance under operating conditions. The proposed two-probe method is introduced and then validated by several passive components. Compared with the previous impedance extraction methods, the proposed approach has no direct electrical contact to equipment under test (EUT). Moreover, the calibration to eliminate the influence of probes and cables is conducted without any contact to the EUT. Finally, experimental results showed EMI filter designed based on the proposed approach can effectively attenuate conducted emissions in single-phase inverter.

Polymers of intrinsic microporosity have attracted comprehensive attention in membrane-mediated gas separation because of their rigid and contorted structure that facilitates well-defined microporosity for fast and selective gas transport. We report a new macromolecular design synthesizes semi-ladder and fully-ladder polymers of intrinsic microporosity containing 9H-xanthene units by superacid-catalyzed Friedel-Crafts polymerization named SACPs. The prepared SACP membranes display high microporosity with amorphous chain packing structure, high FFV, and high BET surfaces areas. In particular, SACP-3 exhibited the most elevated BET surfaces area of 568 m2/g, fractional free volume (FFV) of 0.243, and bimodal micropore size distribution with two maxima at ∼5 and ∼8 A, respectively. Due to its fully ladder architecture, SACP-3 exhibits highly permeable gas transport with CO2 permeability of 6497 Barrer and CO2/CH4 selectivity of 7.8, respectively. The microporosity and gas permeation properties of SACP membranes are also demonstrated to be highly tailorable by employing different monomers. The facile polymerization procedure, excellent solubility and processability, highly diverse tunability, and outstanding gas separation performance render SACP membranes attractive for many membrane-mediated gas separation processes.

The decrease in electrical properties caused by the toughening of polypropylene (PP) is a difficult problem for the modification of PP used for cable insulation. In this research, an isotactic PP, a cross-linked polyethylene (XLPE) and two impact PP copolymers (IPCs) with an ethylene&ndash, propylene rubber phase content of 15 and 30% were prepared to assess the possibility of IPCs to be used as cable insulating material. The tensile properties and breakdown strength were evaluated, meanwhile, the rubber phase content dependence of the crystalline structure, morphology and trap distribution were also investigated. Results show that IPCs with a 15% rubber phase content (IPC15) can achieve the simultaneous improvement of elongation at break and breakdown strength compared with isotactic PP, which can be attributed to the special crystalline structure. According to the results of differential scanning calorimetry (DSC) and FTIR, it is proposed that the lamella thickness of IPC15 is maximal and some ethylene segments exist in PP crystals of IPC15 as crystalline structure defects, which is responsible for this enhanced breakdown strength. The morphology results reveal that rubber microspheres are found to coexist with spherulites, which can promote the relative sliding among lamellas under external force and further results in the increase in the elongation at break.

Aiming at the need of smart electric grid as a schedulable energy storage system to realize energy optimization management of power grid in the future, this paper studies a multi-port DC charging station with G2V and V2G modes. The proposed DC charging station adopts cascaded multi-level power electronic transformer to connect to the 10kV distribution network, the isolation stage adopts multiple dual active inverter parallel structures to form a DC bus. The EV charge modules connected in parallel to the DC bus to form a multi-port charging station. The control method of the high-voltage stage and isolation stage of the power electronic transformer and the control methods of the charging module are analyzed in details, and the results of the proposed charging station and the control method are verified by simulation experiments.

In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems. The key idea is to introduce a novel label encoding function, mapping the ground-truth labels into latent embedding, acting as an auxiliary intermediate supervision to the detection backbone during training. Our approach mainly involves a two-step training procedure. First, we optimize the label encoding function via an AutoEncoder defined in the label space, approximating the “desired” intermediate representations for the target object detector. Second, taking advantage of the learned label encoding function, we introduce a new auxiliary loss attached to the detection backbones, thus benefiting the performance of the derived detector. Experiments show our method improves a variety of detection systems by around 2% on COCO dataset, no matter one-stage or two-stage frameworks. Moreover, the auxiliary structures only exist during training, i.e. it is completely cost-free in inference time.

Network traffic acquirement is an important problem in power telecommunications for remote smart managements of optical fiber cores, because the power scheduling task requires the real-time operation. To the end, network operators need to perform the accurate and real-time acquirement for network traffic. However, to quickly and accurately estimate network traffic is an open problem in current communication networks including power communication networks. This paper propose an accurate acquirement approach to obtain network traffic in power telecommunications for these applications. Firstly, we used the short-time Fourier transform (STFT) theory to describe end-to-end network traffic in power telecommunications. Secondly, based on the STFT method, network traffic is divided into two parts of low frequency and high frequency. The two parts are predicted by ARMA and linear prediction model, respectively. We propose a dynamic reconstruction model to model network traffic. The corresponding model method is used to construct and capture network traffic in next time. Thirdly, we propose a detailed reconstruct algorithm to obtain network traffic. Simulation results show that our approach is promising.

What the thesis studies is the active and reactive decoupling control method of photovoltaic grid-connected inverter, and then the reactive power of grid-connected inverter is optimized by genetic algorithm with the objective of minimizing network loss, So as to realize reactive power optimization control of the photovoltaic power generation system. An algorithm based on genetic algorithm for reactive power optimization of photovoltaic power generation and distribution network is proposed. A mathematical model for reactive power optimization of distribution network has been established. The mathematical model includes those of photovoltaic power generation system Considering the limits of not only minimum network loss but also node voltage, what is used to optimize the reactive power of photovoltaic power generation is genetic algorithm. The example simulation results indicate the designed optimization algorithm is effective and correct in reducing power loss and improving voltage quality.

With new network technologies and applications fast emerging in power telecommunications, network flow traffic exhibits many novel behaviors. However, to find abnormal part in network traffic is difficult. In this paper, we propose a new anomaly detection method to recognize abnormal network traffic for remote managements of optical fiber cores. Firstly, we use the Back Propagation Neural Network (BPNN) model to describe network traffic in these applications. Secondly, the BPNN training process is proposed to build network traffic model. The detailed detection method is proposed to find out the anomalous part in network traffic. Thirdly, the detection algorithm is proposed to recognize anomalous traffic. Simulation results shows that our approach is feasible and effective.

In this research, a one-pot prepared modified sludge bio-hydrochar (IBHC) was prepared to activate persulfate (PS) for the degradation of tetracycline (TC) antibiotic. The obtained IBHC bearing defect structure, dispersed iron and large amounts of surface organic functional groups, acts as an outstanding modified biomass carbonaceous material for catalyzing PS to improve the removal efficiency of TC as high as 99.72%. The IBHC + PS system can remove TC effectively with a relative low IBHC dosage (0.2 g·L−1), limited PS consumption (5 mmol·L−1) and wide pH values (2–10). In addition, the degradation of TC still keep in 94.70% after 5 rounds reuse proving that IBHC possesses excellent stability and practicability. During the activation, both and •OH were generated and the contribution of each component on the TC degradation in IBHC + PS system was explored. Furthermore, the degradation pathways of TC were proposed based on the results of LC-MS.

The planning of charging facilities (CFs) for electric vehicles (EVs) plays an important role for the extensive applications of EVs. Uncertainties existing in the development of future EV technology should be properly modeled to ensure the robustness of the planning scheme. The uncertainties concerned include EV development types, growth rate of load and traffic flow, and distributions of load and traffic flow in the smart grid. The existing single-stage planning model cannot fully evaluate the risks brought by all kinds of uncertainties. Given this background, the multistage CF planning problem considering uncertainties is studied in this work. First, several typical uncertainties in future smart grid with a high penetration of EVs are considered to generate development scenarios for multistage planning. Then, the well-established data envelopment analysis is utilized to evaluate the planning schemes while the novel EV expected energy not supplied cost is defined to measure the service ability of CFs. The final planning result obtained by the proposed framework will not only have good performance in the current stage but also exhibit robustness for all the considered scenarios in the future stage with respect to uncertainties. The application potential of the designed multistage planning framework is proved by an example with both the distribution network and traffic network included.

This paper aims to solve wind energy integrated electric power dispatch (EPD) problem from the perspectives of stochastic modelling and multiobjective optimization. At first, the competing objectives in modern electric power and energy system is analyzed and a new interval optimization model for each objective is proposed based on the uncertainties with respect to wind power. Then, a novel strength differential evolution (SDE) algorithm is developed to address the interval optimization model. The SDE adopts a population selection process based on chaotic sequence and Boltzmann distribution to balance the tradeoff between local exploitation and global exploration. Afterwards, a multiobjective EPD model is established and a novel generalized piecewise normal boundary intersection (GPNBI) method is mooted to transform multiobjective EPD into a series of single-objective sub-problems which can be effectively solved by SDE. In order to deal with the highly constrains in GPNBI, a new heuristic constraint handling strategy is proposed accordingly to accelerate the convergence speed. At last, a hyper-plane based decision-making strategy is originally developed to identify the best compromise solution in the obtained Pareto frontiers (PFs). The feasibility and effectiveness of the interval optimization model and GPNBI inspired multiobjective SDE (MOSDE) have been comprehensively evaluated on a modified IEEE 30-bus system and a 118-bus system. The statistical results confirm that the proposed interval optimization model can approximately quantify the potential uncertainty in each objective and also demonstrate that the proposed MOSDE exhibits better performance than the algorithms of the state. Therefore, it is convinced that the proposed optimization model and method have high potentials to address the practical implementation problems in electric power and energy systems with wind energy penetration.

For power converters with inductive loads, a freewheeling path is needed for the current due to reactive power. The MOSFET synchronous rectification (SR) is widely used to reduce the conduction loss during the freewheeling period. Due to the wide band gap of silicon carbide (SiC), the intrinsic body diode of SiC MOSFET exhibits a high voltage drop. Hence, an antiparallel SiC Schottky diode is normally implemented to eliminate its conduction. However, the external SiC Schottky diode is not fully utilized as it only works during the dead time. In this paper, the hard-switching SR is investigated in an SiC three-phase inverter and compared with a conventional inverter using freewheeling diode (FWD). An improved power loss model for the two inverters has been developed. It is found that the inverter using SR has higher efficiency due to the smaller switching loss. A 7-kW prototype of SiC three-phase inverter is built, which achieves a peak efficiency of 98.8% (±0.15%) and 98.5% (±0.15%) at 40 kHz using SR and FWD, respectively. This paper confirms that the SiC MOSFET is an ideal candidate for the SR. NRF (Natl Research Foundation, S’pore)

This article takes China’s jurisdiction over foreign-related divorce cases as an entry point, and systematically expounds the provisions of China’s foreign-related divorce jurisdiction. According to my country’s regulations, my country’s jurisdiction over a foreign-related divorce is vertically divided into direct jurisdiction and indirect jurisdiction, and horizontally divided into personal Jurisdiction, territorial jurisdiction, exclusive jurisdiction, and jurisdiction by agreement. In my country’s Civil Procedure Law and related judicial interpretations, the domicile of the “plaintiff is the defendant” and the location of the plaintiff under certain circumstances is the main focus. The general solution path of the case; At the same time, my country's regulations on foreign-related divorce cases still have shortcomings, and there are still many areas that need to be improved. This article analyzes the shortcomings and the areas to be improved.

The penetration of photovoltaic (PV) energy into modern electric power and energy systems has been gradually increased in recent years due to its benefits of being abundant, inexhaustible and clean. In order to reduce the negative impacts of PV energy on electric power and energy systems, advanced forecasting approach with high-accuracy is a pressing need. Aimed at this, a novel hybrid method for deterministic PV power forecasting based on wavelet transform (WT) and deep convolutional neural network (DCNN) is firstly proposed in this paper. WT is used to decompose the original signal into several frequency series. Each frequency has better outlines and behaviors. DCNN is employed to extract the nonlinear features and invariant structures exhibited in each frequency. Then, a probabilistic PV power forecasting model that combines the proposed deterministic method and spine quantile regression (QR) is originally developed to statistically evaluate the probabilistic information in PV power data. The proposed deterministic and probabilistic forecasting methods are applied to real PV data series collected from PV farms in Belgium. Numerical results presented in the case studies demonstrate that the proposed methods exhibit the ability of improving forecasting accuracies in terms of seasons and various prediction horizons, when compared to conventional forecasting models.

With the rapid growth of penetration of distributed generations and the strong interdependencies of multiple energy sources, the optimal operation of integrated energy systems (IESs) is becoming more important than ever before. However, current optimal operation strategies for IESs may be impractical because their potential negative impacts on utility grids are generally not considered. Therefore, in this study, an original multiobjective optimization model for IES operation is formulated to minimize the operational cost, primary energy consumption, and carbon dioxide emission of IESs and to optimally reduce the power loss and voltage magnitude deviation of the utility grid. Then, a novel Pareto optimization algorithm, called multiobjective strength firefly algorithm (MOSFA), is proposed to solve the multiobjective operation problem of IES. The strength firefly algorithm (SFA) uses a Boltzmann distribution and a chaotic-sequence-based population selection process to facilitate local exploitation and global exploration. In addition, a generalized piecewise normal boundary intersection (GPNBI) method is developed to transform the multiobjective operation problem into a series of highly constrained single-objective optimization sub-problems that can be effectively solved by the SFA. Finally, a hyper-plane-based decision making strategy is introduced to identify the best compromise solution for the obtained Pareto frontiers. The GPNBI-inspired MOSFA was comprehensively evaluated on a novel IES powered by natural gas, wind and photovoltaic generators. The standard IEEE 39-bus system is considered as the utility grid. The numerical results demonstrated that the proposed MOSFA exhibits competitive performance when compared to the algorithms of the state, which means that the optimized operation strategy provides a better trade-off between all objectives considered in this study.

Highly dispersed FeMn bimetallic nanoparticles prepared by a low-temperature co-precipitation method were employed for direct CO hydrogenation for light olefins (C2∼C4=). An outstanding selectivity (60.6 %) for light olefins at a CO conversion level of 7.6 % was obtained over FeMn (4:1) catalyst activated by syngas (5%CO/5%H2). Characterization indicated that a strong interaction between Fe and Mn resulted in significant effects on stabilization of Fe1-xMnxO phase during activation period. Responding to the morphology effect, Mn decreased the surface energy of nanocrystals and weakened the agglomeration of nanoparticles, thus resulted in a decrease in particle size of Mn-promoted catalysts. The combined effects of inhibited carburization and improved metal dispersion contributed to the volcano pattern in the catalytic activity of Mn-promoted catalysts. In addition, Mn may act as an effective electron donor to Fe, enhancing CO dissociative adsorption while inhibiting secondary hydrogenation of olefins, thus giving rise to high olefin selectivity in CO hydrogenation.

The cumulative prospect theory quantitatively fulfills user experience evaluation.We study the influence of affective states through shaping affective parameters.We develop a hierarchical Bayesian model via Markov chain Monte Carlo technique.We demonstrate the proposed method via a aircraft cabin interior design. User Experience (UX) design has become an important factor of product success. One of the important issues involved in UX design is how to evaluate UX. In this research, UX evaluation is quantitatively fulfilled by the cumulative prospect theory, in which UX is perceived from the perspective of the decision making procedure of two alternative design profiles. Furthermore, we study the influence of affective states on UX prospect evaluation through shaping affective parameters involved in UX design. To account for multiple sources of uncertainties, we develop a hierarchical Bayesian model via Markov chain Monte Carlo technique for parameter estimation under three affective states. Also, aircraft cabin interior design is studied as a case study to demonstrate the potential and feasibility of the proposed method.

This paper presents a design methodology for a high power density converter, which emphasizes weight minimization. The design methodology considers various inverter topologies and semiconductor devices with application of cold plate cooling and LCL filter. Design for a high-power inverter is evaluated with demonstration of a 50 kVA 2-level 3-phase SiC inverter operating at 60 kHz switching frequency. The prototype achieves high gravimetric power density of 6.49 kW/kg.

Due to the economic and environmental benefits, wind power is becoming one of the more promising supplements for electric power generation. However, the uncertainty exhibited in wind power data is generally unacceptably large. Thus, the data should be accurately evaluated by operators to effectively mitigate the risks of wind power on power system operations. Recognizing this challenge, a novel deep learning based ensemble approach is proposed for probabilistic wind power forecasting. In this approach, an advanced point forecasting method is originally proposed based on wavelet transform and convolutional neural network. Wavelet transform is used to decompose the raw wind power data into different frequencies. The nonlinear features in each frequency that are used to improve the forecast accuracy are later effectively learned by the convolutional neural network. The uncertainties in wind power data, i.e., the model misspecification and data noise, are separately identified thereafter. Consequently, the probabilistic distribution of wind power data can be statistically formulated. The proposed ensemble approach has been extensively assessed using real wind farm data from China, and the results demonstrate that the uncertainties in wind power data can be better learned using the proposed approach and that a competitive performance is obtained.

Mass customization has been well recognized as an effective means of providing product variety while keeping mass production efficiency. As a critical aspect of fulfilling mass customization, assembly system design is facing the challenge of handling high product and process variety. This paper presents a variety-driven clustering method for assembly system layout design. Key issues include variety modeling and modularization of assembly process flows. Based on the minimization process of total coordination cost, the cluster analysis is formulated using the design structure matrix (DSM). A case study of automobile connector assembly system design is reported to demonstrate the feasibility and potential of DSM clustering analysis for dealing with product and process variety.

With the rapid growth of wind power penetration into modern power grids, wind speed forecasting (WSF) plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the wind speed time series always exhibits nonlinear and non-stationary features, making it very difficult to be predicted accurately. Recognizing this challenge, a novel deep learning based approach is proposed for deterministic and probabilistic WSF. The approach is a hybrid of wavelet transform (WT), deep belief network (DBN) and spine quantile regression (QR). WT is employed to decompose raw wind speed data into different frequency series with better behaviors. The nonlinear features and invariant structures of each frequency are completely extracted by layer-wise pre-training based DBN. Then, the uncertainties in wind speed are statistically synthesized via the QR method. Case studies using real wind farm data from China and Australia have been presented. The comparative results demonstrate that the high-level nonlinear and non-stationary feature in the wind speed series can be learned better, and competitive performance can thus be obtained. It is therefore convinced that the proposed method has a high potential for practical applications in electric power and energy systems.

In order to better realize the plug and play function of various distributed energy sources (photovoltaic, storage batteries, etc.) in the DC micro-grid, improve energy conversion efficiency and improve DC side bus voltage performance. The layered control strategy introduced contains two layers: the new energy in the inner layer of the micro-grid is maximized by the maximum power tracking control, and the energy storage unit uses the voltage leveling control to ensure the stability of the DC side bus voltage; The bidirectional vector decoupling control includes voltage and current double closed loop control, and uses d-q coordinate axis transformation to control DC voltage and power factor to solve the problem of bidirectional flow of power during grid connection. This paper gives a detailed theoretical analysis and design method for each layer control. Simulation results demonstrate the feasibility and effectiveness of the proposed method.

Emerging infectious diseases (EIDs) have been acknowledged as a major public health concern worldwide. Unfortunately, most protective respirators used to prevent EID transmission suffer from the disadvantage of lacking antimicrobial activity, leading to an increased risk of cross-contamination and post-infection. Herein, we report a novel and facile strategy to fabricate rechargeable and biocidal air filtration materials by creating advanced

The steady state performance of the dual active bridge converter has been widely researched before. This paper focuses on the modulation strategy for optimized transient performance of the dual active bridge converter under reverse of the power flow direction. Firstly, normal modulation methods to reverse the power flow direction are presented. The transition time under the normal methods is relatively long and it can cause a DC component in the high frequency inductor current, which can compromise the normal operation of the high frequency transformer. To overcome the drawbacks, this paper presents a novel strategy to modulate the dual active bridge converter during the reverse of the power flow direction. The transition gets faster and the DC component in the inductor current is eliminated based on the proposed modulation strategy.

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