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The electric vehicle (EV) charging station is a critical part of the infrastructure for the wide adoption of EVs. Real-time simulation of an EV station plays an essential role in testing its operation under different operating modes. However, the large numbers of high-frequency power electronic switches contained in EV chargers pose great challenges for real-time simulation. This paper proposes a compact electromagnetic transient program (C-EMTP) algorithm for FPGA-based real-time simulation of an EV station with multiple high-frequency chargers. The C-EMTP algorithm transforms the traditional EMTP algorithm into two parallel sub-tasks only consisting of simple matrix operations, to fully utilize the high parallelism of FPGA. The simulation time step can be greatly reduced compared with that of the traditional EMTP algorithm, and so the simulation accuracy for high-frequency power electronics is improved. The EV chargers can be decoupled with each other and simulated in parallel. A CPU-FPGA-based real-time simulation platform is developed and the proposed simulation of the EV station is implemented. The control strategy is simulated in a CPU with 100 μs time-step, while the EV station circuit topology is simulated in a single FPGA with a 250 ns time-step. In the case studies, the EV station consists of a two-level rectifier and five dual-active bridge (DAB) EV chargers. It is tested under different scenarios, and the real-time simulation results are validated using PSCAD/EMTDC.

The proliferation of photovoltaics (PV) in distribution systems brings about voltage stability problems. This paper proposes an algorithm to calculate extreme bifurcation intervals considering deep PV penetration levels. The paper applies the sandglass-hyper-ellipsoid-cone model to analyze the correlations among net bus loads representing bidirectional power flows for different PV penetration levels. Using the net bus load model, three extreme bifurcation points on voltage stability limit hypersurface are formulated where two of these points represent the voltage stability bifurcation interval in the forecasted net bus loading direction. An iterative method is applied to obtain bifurcation solutions. The validity of the proposed method is verified by using the IEEE 3-bus system, the IEEE 33-bus system and the IEEE 123-node test feeder. The numerical results demonstrate that the proposed sandglass-hyper-ellipsoid-cone net bus load model and solutions are effective in managing correlated net bus load forecast errors in distribution systems with PV integration.

The increasing integration of wind power generation brings more uncertainty into the power system. Since the correlation may have a notable influence on the power system, the output powers of wind farms are generally considered as correlated random variables in uncertainty analysis. In this paper, the C-vine pair copula theory is introduced to describe the complicated dependence of multidimensional wind power injection, and samples obeying this dependence structure are generated. Monte Carlo simulation is performed to analyze the small signal stability of a test system. The probabilistic stability under different correlation models and different operating conditions scenarios is investigated. The results indicate that the probabilistic small signal stability analysis adopting pair copula model is more accurate and stable than other dependence models under different conditions.

To evaluate the changes of left atrial (LA) geometry and function in patients with rheumatoid arthritis (RA) by conventional echocardiography and two-dimensional speckle tracking imaging (2D-STI). We enrolled 46 RA patients with a duration of

Residents with advanced measuring and controlling appliances can participate in demand response (DR) by the home energy management system (HEMS), thereby improving the reliability and flexibility of the power system. However, the domestic incentive mechanism is deficient, and there is short of home energy optimization model that comprehensively considers price-based and incentive-based DR. First at all, a variety of flexible appliances such as distributed photovoltaic (PV), electric vehicle (EV), and energy storage (ES) were considered, a mixed-integer load model under a complex power consumption environment was classified and established. Secondly, the price-based DR based on the time-of-use electricity price and the incentive-based DR based on the user agreement were considered comprehensively. Taking reducing household electricity costs and household net load power fluctuations as joint optimization targets, a day-ahead scheduling model considering users' willingness to participate in incentive-based DR was established. Finally, the effectiveness of the proposed model was verified based on the MATLAB.

This study proposes a novel robust transmission-constrained unit commitment model with adjustable conservatism (denoted as RAC-TCUC). Ellipsoidal uncertainty set (EUS) is adopted in this model to well fit the spatial-temporal correlated wind power. The affine policy is utilised in the generation dispatch process of the model for the sake of computational tractability. To reduce the conservatism, a novel criterion for budget value selection of the EUS is presented. Moreover, this study discusses a crucial, yet barely addressed issue in the literature: the feasibility of the solution against the realisation of uncertainties beyond the prescribed EUS. To prove the validity of the criterion, an analytical relationship between the budget value of the EUS and the actual probability of solution's feasibility against all possible scenarios of uncertainties is presented. Finally, the testing results demonstrate the effectiveness and economic benefits of the proposed method.

Transpiration as one of the plant's vital movements can also be seen as a pump that pumps nutrients such as water and inorganic salts from the roots to the leaves. Learning of this process might find many applications in nanotechnology, materials science or bioscience. Here, we were inspired by the transpiration of plants and the remarkable photothermal conversion characteristic of MXene Ti3C2Tx, constructing a biomimetic transpiration ion pump based on macroscale evaporation induces nanofluidic ionic transportation. This device can pump ions with an inverse concentration gradient of 200-fold under asymmetric infrared light. Besides, such a device with reversible regulation and fast light response can also act as an energy conversion set-up, which generates a stable current and voltage of about 7.3 μA and 13.6 mV, respectively, at an infrared light intensity of ∼600 mW cm−2. This conception of a biomimetic ion pump with a simple construction process might provide an idea for the potential research of power harvesting and energy conversion.

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generation. This study determines the optimal allocation of wind capacity in microgrids, with considerations of the impact of generation uncertainty on both static and dynamic performance of the system, for reducing the possible violations of operation limits caused by wind power fluctuations. The system performance under uncertainty is evaluated using a zonotope-based set-theoretic method. The uncertain variations of wind power are modelled by a zonotope, then the uncertainty sets of static and dynamic state variables, which capture all possible values of the system states, are further calculated by set operations and are embedded in the optimisation model as constraints for system performance. The proposed method is applied to a 33-bus microgrid; the results prove the validity of the method and show that the system can be guaranteed to operate within acceptable security regions in all possible situations when subjected to certain level of uncertainty.

Under axial pressure or shear load, thin-walled plate and shell structures are easily destroyed by buckling. This paper presents the design method for finding the optimal stiffener layout on thin-walled plate and shell structures against the buckling by using the adaptive growth method (AGM), which is based on the growth mechanism of branch systems in nature. Firstly, a mathematical model for optimization of the stiffener layout against buckling is established, and a Karush-Kuhn-Tucker (KKT)-based iteration formula is derived. Next, the stiffeners grow or degenerate from “seeds” according to the adaptive growth principle. Several examples, including imperforated and perforated rectangular plates with unilateral axial pressure and shear loading, are demonstrated to validate the effectiveness of the suggested method, and well-defined stiffener layouts are obtained. The results show that the stiffener layout is clear, and the buckling resistance performance of the optimized stiffened structures is greatly improved.

The modular multilevel converter (MMC) now is considered as the most promising converter in the high-voltage direct current system. An improved indirect predictive control applied in the MMC is proposed in this paper. With the proposed predictive control, the burden of communication can be reduced. The predictive precision can be improved with the proposed controller. Furthermore, the control accuracy for the system can be improved. In this paper, the mathematical model of the MMC is presented at first. Based on the model, the stability analysis of current control loop is obtained and the gain boundaries of the controllers are deduced. Afterward, the indirect predictive control is applied in the control loop. The predictive errors caused by the circuit parameters uncertainties are further deduced. Moreover, in order to eliminate the predictive error and improve the predictive precision, the repetitive controller is introduced in the submodule voltage balance controller. At last, the validity and advantages of the proposed method are verified by a bunch of simulating and experimental results.

Background: The purpose of this study was to investigate the relationship between left atrial function and left atrial stiffness in type 2 diabetic patients.Methods: A total of 41 healthy subjects (controls) and 39 patients with type 2 diabetes mellitus (DM2 patients) were enrolled in this study. The left atrial volume index was measured by biplanar Simpson method. The early and late diastolic velocities (e'and a') were measured by tissue Doppler in lateral and septal boeder of the mitral annulus. Lateral and septal myocardial velocities were averaged. E/eʹ ratio was computed.The peak atrial longitudinal strain (PALS) was analyzed by tow-dimensional speckle tracking imaging (2D-STI) technique.The ratio of E/e ' to PALS was used as an indicator of LASI.Results:The maximum volume (LAVImax), minimum volume (LAVImin) and pre-contraction volume (LAVIpre) of the left atrium in DM2 patients were significantly higher than those in the control groups, and the differences were statistically significant (PConclusion: Compared with the controls, PALS decreased and LASI increased in DM2. PALS and LASI were significantly correlated with left atrial volume and E/e’. LASI showed the highest diagnostic efficacy in the diagnosis of diabetes. PALS and LASI can be used as indicators for early evaluation of left atrial function in patients with DM2.Trial registration number:+86-17856931224;date of registration:May 3,2021

In order to reduce the error of short-term photovoltaic (PV) power forecast without irradiance data, a prediction model based on daily feature matrix and long short term-memory (LSTM) deep neural network is proposed. Firstly, various factors affecting PV output are analyzed to select model inputs effectively. On this basis, a new similar day selection method considering the internal and external factors under multi-source data integration scenarios is introduced. Based on weather forecast information and day-ahead PV power data, daily feature matrices can be constructed to determine similar days by calculating the distances between the matrices. Then, the similar historical PV power vector is used as an input of a LSTM deep neural network, combined with meteorological forecast information to realize the final power prediction. Finally, the feasibility of the proposed method can be validated with the actual data of residential PV systems in North America.

The establishment of an integrated charging station with PV, energy storage and battery swapping not only meets the different charging and replacement needs of electric vehicle users, but also takes into account the economic benefits of the charging station operating company and the safety of the power grid. In this paper, by analyzing the electrical structure and operation mode of the integrated charging station, and establishing the operation model of each system, this paper proposes a collaborative optimization Dispatching strategy that aims at the minimum operating cost of the integrated charging station, using particle swarm optimization (PSO) algorithm to solve the problem, and optimize the grid and energy storage output plan according to the relevant constraints and combined with the grid power price and electric vehicle charging and replacement demand and other relevant predicted values, thereby reducing the operating cost of the integrated charging station and reducing the load of the charging station peak-valley difference.

With the widespread application of new energy, energy storage system, large scale electric vehicles (EVs) in power distribution, bidirectional charging piles with energy storage, and overflow characteristics is set up on a large-scale. These greatly increase the uncertainty and randomness of the dynamic load of the regional comprehensive energy system (RIES) and bring great challenges to its effective load management. In view of the above problems, this paper considers the large-scale electric vehicle (EV) as a mobile energy storage unit to participate in the energy optimization management model of the (RIES). The model can realize the balance of energy supply and demand of RIES, reduce the fluctuation of load demand, operation management cost, and environmental pollution by regulating the charging and discharging power of EV users and the output of controllable power generation unit in the region. Finally, the improved imperialist competition algorithm (ICA) is utilized to solve the model, and different scenarios are simulated and analyzed. According to the simulation results, it can be seen that the model can realize effective management of regional load, which verifies the effectiveness and feasibility of the model.

The large-scale development of electric vehicles and the increase in the proportion of renewable energy power generation are effective ways for our country to transform its low-carbon economic development model. In this paper, from the perspective of synergy between large-scale electric vehicles and renewable energy power generation, comprehensively considering the characteristics of the randomness of electric vehicle loads and the intermittency of renewable energy, the two cooperative interaction technologies are studied. The electric vehicle (EV)-photovoltaic power generation (PPG) collaborative interactive dynamic programming model is optimized using hybrid particle swarm optimization algorithm (BreedPSO). Finally, the data of a certain Shanghai area are simulated and analyzed. The results show that the access of electric vehicles can effectively suppress grid load fluctuations and reduce the system load variance.

Day-ahead multi-period scheduling can provide reasonable operation plans for various power sources and energy storage systems in distribution networks, so as to reduce costs and improve energy utilization. In this paper, the dynamic power flow optimization model of radial distribution network with wind/PV/energy storage is established firstly. The model takes the output of various power sources and the charge-discharge power of energy storage systems as decision variables, which belongs to nonconvex nonlinear programming problems. Then the second-order cone relaxation is applied to the power flow equation, and the problem is transformed into a second-order cone programming model which can be solved strictly and efficiently. Finally, the extended IEEE 30-node system is simulated, and the software Gurobi is used to obtain the optimal scheduling scheme, which verifies the effectiveness of this method. Test results show that the operating cost is reduced by adding wind/PV/energy storage into networks, and the consumption of renewable energy is increased by considering the punishment for wind and PV abandonment.

Under the influence of the idea of globalization and modernism planning, in the process of urban development, inefficient use of land, supremacy of functionalism and traffic congestion have become important problems in Chinese cities because of the expansion of land scale, the transformation of urban functions and the leading of maneuverable traffic. As the component unit of the city, urban block is also faced with the problems of unordered environment and the loss of culture. To realize the comprehensive and sustainable development of economy, society and ecological environment in the city, it is of great significance to study the corresponding urban block patterns suitable for the city. This paper starts from the perspective of local culture and conducts in-depth discussion on the internal causes of the current problems of urban blocks in China. On this basis, the paper puts forward the countermeasures of the sustainability of urban block patterns from three aspects of society, technology and management and attempts to explore the construction path of sustainable development for urban blocks in the future.

A popular method to harvest solar power is to convert light to heat, then to the kinetic energy of water, and finally to electricity, so-called concentrating solar power. However, it requires highly bulky components, limiting the applications. In this study, we present a miniaturized nanofluidic version—simply placing a MXene film on ionic solution and exposing part of the film to light. The distinguished photothermal property of MXene converts the asymmetric light irradiation to a water evaporation gradient, pumping water through the nanofluidic channels in the film, which transports cations and results in an ionic current. We expect this study to inspire more studies to explore the possibility of nanofluidic photothermal electricity as an alternative solar electricity technology for dispatched electricity generation and powering microfluidic/nanofluidic devices.

In this paper, a Lyapunov function-based decentralized control scheme is proposed for a single-phase parallel-connected inverters system within islanded micro-grid. With this novel method, a proper voltage regulation (e.g., amplitude and waveform quality) and excellent power and current sharing performance can be well ensured even in the presence of unknown line impedance and unbalanced LC filter parameters. In addition, high reliability and robustness can be guaranteed since no inter-communication among inverters as well as no centralized controller is demanded, which also delivers advantages of scalability and redundancy. The stability of the closed-loop system is ensured by standard Lyapunov method. The designation of the decentralized controller is simple to implement but rather effective. Detailed simulation and hardware-in-the-loop experimental results are presented to validate the feasibility of the proposed scheme under different operating conditions.

Wind power outputs will bring larger computational error in actual operation without considering the correlation. This study presents an affinely adjustable robust optimisation method for AC-DC optimal power flow (AC-DC AAROPF) considering the correlation of wind power. Affine policies are utilised in the re-dispatch process to ensure the feasibility of the infinite scenarios of uncertainties. After transforming AC-DC AAROPF to a tractable model, a novel approach based on simplified pair copula to consider multiple dependence of wind power is presented. The AC-DC AAROPF is evaluated on the modified AC-DC IEEE 30-bus and 118-bus system. Results show a modest increase in expected cost for reasonable levels of uncertainty representing forecast error confidence intervals, which obtains a more robust solution with higher successful rates.

The triple phase shift (TPS) modulation scheme, which provides three control freedoms, is of great importance for the optimized operation of a dual active bridge (DAB) isolated bidirectional dc/dc converter. First of all, this paper introduces an accurate, universal model to describe the analytic expressions of the DAB converter under TPS control. Based on this, six operating modes of the DAB converter are further discussed. Afterwards, the concept of global optimal condition (GOC) equations is proposed to derive the closed form of analytic expressions of an optimal modulation scheme that makes the DAB converter operate with minimized root-mean-square (rms) current during whole power range with different operating modes. According to the GOC equations, the physical explanation of the proposed modulation scheme is further given in details, and the complex interaction among the control variables, the transferred power, and rms current is revealed. The real-time optimization process of the proposed method is also specified. Finally, the proposed methods are applied to a laboratory prototype. The experimental results confirm the theoretical analysis and practical feasibility of the proposed strategies.

Microstructure design is an essential procedure to realize high performance wearable strain sensors. In this paper, we proposed a simple and effective assembly approach to prepare a reduced graphene oxide (RGO) nanosheet-wrapped aligned thermoplastic polyurethane (TPU) fibrous mat through ultrasonication to construct a uniform 3D conductive network. By combining the advantages of this conductive network and elastomers, a highly stretchable and ultrasensitive strain sensor was prepared by embedding this aligned fibrous network in flexible polydimethylsiloxane (PDMS). In comparison with vertical directional conductive RGO/TPU networks, the parallel directional fibrous network-based flexible sensor exhibited remarkable mechanical strength and responsivity, showing an interesting anisotropic response behavior. Our flexible strain sensors possessed a broad sensing range (0–150% with reversible responses), high sensitivity (the gauge factor was 593), excellent cycling stability, fast response and stable resistance to humidity and temperature stimuli, thus fulfilling the requirements of remarkable strain sensors. As a prototype, the parallel directional fiber-based flexible strain sensor was assembled onto various parts of the human body to monitor the motions via the response of electrical signals, indicating its tremendous potential applications in wearable electronics and intelligent robots.

The Au/ZnSe/ZnO heterojunction material with less transition zone on the two-phase interface was successfully prepared by the in-situ replacement of zinc oxide by Se2-. Moreover, the heterojunction formed by in-situ substitution can effectively reduce the interface transfer resistance and increase the life of carriers. The photocurrent of Au/ZnSe/ZnO is almost twice that of Au/ZnO, due to the formation of heterojunction ZnSe interface. As a result, the carrier lifetime of the material is increased by nearly 2.7 times (126.26 s/46.83 s) compared to Au/ZnO. Ascorbic acid is a very important compound for human metabolism, but there is no simple and rapid method to detect ascorbic acid. Au/ZnSe/ZnO is used as the photoanode of the photoelectric non-enzymatic sensor system for AA detection. The sensitivity of Au/ZnSe/ZnO/FTO-based PEC AA sensor is 113.75 μA mM−1 cm−2, exhibiting its advantages of fast, sensitive and high response.

Modern power systems are experiencing an escalation in the penetration of wind power sources which have both positive and negative impact on small signal stability. Wind power being stochastic in nature introduces uncertainties which make it necessary to account for such uncertainties during power system modeling and analysis. Probabilistic methodologies are appropriate choices in accounting for such uncertainties. These methodologies have recently developed concerns with regard to the probabilistic small signal stability analysis of power system integrated with wind power generation. Accounting for the dependence existing between wind power sources is considered as an important option to aid in simulating the impact of the wind power integration towards small signal stability. Numerous dependence modeling methods exist, each with its strengths and weaknesses. This paper presents an overview of the probabilistic methods applied in small signal stability analysis and methodologies used to model dependence or correlation in the probabilistic stability analysis. A comparative analysis of different dependence models is performed. The model accuracy is compared under different scenarios and the efficiency is discussed.

This thesis presents the application of a minimal radial basis function (RBF) neural network, referred to as MRAN (Minimal Resource Allocation Network) for speaker verification. Extension of MRAN to elliptical basis functions has been studied too. MRAN is a sequential learning algorithm for radial basis function neural networks. During the training, MRAN allows hidden neurons to be added or removed thus to realize a minimal network. MRAN recruits hidden neurons based on the novelty of the input data. If all of the novelty criteria can not be satisfied, the existing network parameters are updated by extended Kalman filter (EKF). Additionally, MRAN’s pruning strategy removes hidden neurons from the network if their contributed output to the output layer is insignificant. In this way, MRAN is adapted to fit the dynamics of the input data closely. MASTER OF ENGINEERING (EEE)

Clinical efficacy of aspirin combined with clopidogrel in treating cerebral infarction and its influence on serum high-sensitivity C-reactive protein (hs-CRP), soluble intercellular adhesion molecule-1 (sICAM-1) and tumor necrosis factor-α (TNF-α) were explored. Ninety patients with acute cerebral infarction treated in Yidu Central Hospital of Weifang were analyzed, and those treated with aspirin alone were group A (n=40) and those treated with aspirin and clopidogrel were group B (n=50) according to the different treatment plans. The NIHSS score, total effective rate and incidence rate of adverse reactions after treatment and admission were compared between the two groups. The expression level of hs-CRP was detected by enzyme-linked immunosorbent assay, and the expression levels of sICAM-1 and TNF-α were analyzed by radioimmunoassay before treatment and three weeks after surgery, respectively, and they were analyzed and compared. After treatment, the total effective rate of patients in group B was significantly higher than that of group A (P

The idea of ‘energy router’ (ER) is put forward as a critical application of the energy Internet (EI), especially in the new generation of electric power system to fulfil the power management and to deal with the integration of massive distributed generations. Furthermore, AC/DC hybrid network becomes a kind of development trend since it can improve the power quality and efficiency compared with adding converters for DC loads. This article proposes a decentralised control method for multi-port converter (MPC)-based ERs to achieve flexible power regulation and ‘plug and play’ operation. With the proposed schemes, DC bus voltage can be maintained together by multi-ER through appropriately adjusting their transmitted power according to the pre-set power-sharing ratio. Furthermore, the sharing ratio can be online changed for flexible power regulation. The stability of the system is ensured by Lyapunov method, detailed simulation results are presented to validate the feasibility of the proposed scheme under different operating conditions.

Due to the increasing penetration, intermittent renewable energy sources (RESs), such as wind power, have added additional uncertainties to AC–DC power system operation. The strong correlation between wind power will bring larger computational errors. It makes generation dispatching incompatible with the current single-period optimal power flow practice. This study presents an AC–DC affinely adjustable robust dynamic optimal power flow (AARDOPF) model. Ellipsoidal uncertainty set is adopted to well fit the spatial-temporal correlated wind power. Affine policies are utilised in the re-dispatch process. To apply AC–DC AARDOPF, the base-point generation is calculated and determined to match the power with forecasted RES output. And once the uncertainty is revealed, generators reschedule its output through participation factors responding to the uncertain fluctuation of RES output to ensure a feasible solution for all realisations of RES output. Then original non-linear models are transformed into SOCP models. Finally, GUROBI is used to solve this model. Numerical results are obtained on modified AC–DC IEEE 30-bus and 118-bus system to minimise the expected operational cost. Comparing with stochastic dynamic optimal power flow (DOPF), this model is proved to be more practical and effective.

The increasing wind capacity integration brings new challenges to power system planning and operation, due to the intermittency and uncertainty of its power generations. This paper determines the optimal allocation of wind capacity with considerations of the impact of uncertainty in wind generation using the zonotope-based method, for the sack of reducing the possible violations of operation limits caused by wind power fluctuations. The uncertain variations of wind power are bounded by a zonotope and then propagated through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The state-bounding zonotope is then added to the optimal model to quantify and assess the impact of generation uncertainty on system static performance. The proposed method is applied to IEEE 33-bus system, the results prove the validity of the method and show that the zonotope based set-theoretic method is more efficient than probabilistic methods when focusing on the operation limits with considerations of the uncertainty of multiple renewable energy sources.

The rising penetration of renewable generation as a result of environmental concerns generates increased uncertainties in power systems. This necessitates probabilistic analyses of the system performance, which include probabilistic power flow (PPF). The PPF suffers from the curse of dimensionality due to a large number of random loads. To address this issue, a multivariate dimension-reduction (MDR) method is proposed for PPF studies in this paper. The MDR decomposes the PPF problem into lower dimensional PPF subproblems which are further solved with promising accuracy. The computation time of the proposed method is proportional to the number of wind farms, which noticeably facilitates computation. The proposed method is applied to the IEEE 118-bus system and 2383-bus system. Simulation results demonstrate the accuracy and effectiveness of the proposed method.

Probabilistic load flow (PLF) is a powerful and widely used tool to address the random nature of renewable energy resources for the planning and operation of power systems. In this study, a new approach using a combination of the multiple integral method (MIM) and the cumulant method (CM) is proposed for PLF studies. The PLF problem with high dimensionality is decomposed into a bi-level problem and solved by MIM and CM to reduce the computational burden significantly while achieving satisfactory accuracy. The proposed method is applied to the IEEE 118-bus system and is compared with CM and point estimate method. Simulation results show that the proposed method achieves great computational efficiency and accuracy.

Most wind turbine generators installed in large wind farms are variable speed types which operate at the maximum power point tracking mode in order to increase the power generation. Due to this fact and regarding the random nature of the wind speed, the output power of the wind farm fluctuates randomly. Fluctuating power affects network operation and needs to be smoothed. In order to mitigate the output power fluctuations of a wind farm, a 4-step coordinated control technique based on ADALINE (ADAptive Linear NEuron) is proposed in this paper which uses a small BESS (Battery Energy Storage System) capacity. At first the on-line tracking of the WFOP (Wind Farm Output Power) is carried out by ADALINE. Afterwards, two constraints for maximum permissible fluctuations are imposed on the ADALINE output. Two states of charging feedback control strategies are implemented in the third and fourth steps. Reducing the battery capacity in proposed coordinated control technique is fulfilled through the accurate tracking performed by ADALINE and also by maintaining the level of BESS saved energy within the batteries safe performance region performed by state of charging feedback control strategies. Simulation results run by real data verify that the performance of the proposed approach is considerably better than the basic approach.

Wind farm power fluctuations resulted from the wind random nature bring a significant challenge to the wind turbine generators operating in the maximum power point tracking. Furthermore, the smoothing process of a large wind farm in which the Battery Energy Storage System (BESS) is used, needs a considerable initial investment cost. Utilizing Smart Parking Lots (SPLs) can be considered as an applicable solution. In this paper, assuming conventional parking lots in Tehran converted to an SPL set in a near future and thus establishing an enormous charging/discharging capacity, the smoothing process is performed on a 51 MW wind farm. For this purpose, a coordinated control system based on two control algorithms is proposed. The first proposed algorithm chooses eligible SPLs for charging/discharging activity before receiving a new sample of the wind farm output power. Afterwards, the second proposed algorithm determines qualified vehicles in selected SPLs. The main aim is to minimize the number of SPLs (vehicles) taking part in the process. According to the simulation results, the required BESS capacity in the power smoothing process of a typical wind farm decreases considerably when the proposed approach is applied. Thus, the investment cost of BESS is reduced significantly.

Hybrid AC/DC distribution network has developed rapidly owing to its convenience for renewable energy integration, thus the authors are interested in quantifying the impact of uncertainty from renewable power on system static performance. This paper proposes a zonotope-based set-theoretic method for quantifying the impact of renewable power fluctuations on system-state variables in hybrid AC/DC distribution network, both for the assessment of the capabilities for integrating renewable resources by hybrid AC/DC system, and for the appropriate selection of operating method in hybrid power systems. The authors bound the uncertain variations of generation by a zonotope and propagate it through a linearised power flow to get another zonotope, which captures all possible values of the system state variables. The zonotope-based method is computationally tractable and takes less computation than probabilistic methods especially when considering the generation uncertainty of multiple renewable energy resources. The proposed method is applied to a hybrid AC/DC test system with different control strategies of converters. The results show that hybrid AC/DC system with proper control mode may help reduce the impact of generation uncertainty.

In application of video surveillance system, the algorithm of object detection is affected by occlusion easily, and the results on small object are not satisfying. This paper develops a multi-local feature object detection method based on deep neural network. The image is used as input to calculate the position and category probability of the object through a single network calculation, which improves the operating efficiency. The core of the method is to extract multiple local features of the target for detection. When the target is partially occluded, it can identify the target by the unoccluded patch. In addition, the high-level and low-level features in the convolutional network integrate to improve the detection effect on small targets. Experimental results show that the proposed method has a good effect on the detection of occluded targets and small objects.

Inn for electric vehicles (EVs) and photovoltaic (PV) is a typical integration of EVs locally consuming renewable energy sources (RES), with features of EV charging, energy storage, and RES consumption. In the existing research works, scholars focus on the energy storage system (ESS) as a medium to charge EVs and send back the power to the utility grid and alleviating the information asymmetry between EV owners and grid dispatching agencies with lower costs of distribution network. In this study, a mixed operational mode based on inn for EV and PV with a replaceable EV battery is analysed. The fragmented energy transferring can shift the load more effectively, balance fluctuation of RES, of great significance to optimise the operation of distribution network system. In summary, a comprehensive model of power flow in distribution network with inn for EV and PV is proposed. Based on an improved IEEE 33-node case, the effects of fragmented energy management mode are analysed. Numerical results show that replacing the full-charged battery for EV is more effective for peak load time-shifting and improving consuming of RES power based on the interaction among RES, ESS, and the replaceable EV battery.

With the increasing penetration of intermittent renewable energy sources (RESs) into microgrids, the original operation mode of power generation determined by load demand faces severe challenges due to the uncertainties of the RESs power output. The electric springs(ESs), as an emerging technology has been verified to be effective in enabling load demand to follow power generation and stabilizing fluctuation of RESs output. This paper presents a new mode of economic operation for island microgrids including non-critical loads with embedded electric springs. Its connotation includes that i) the capacity of energy storage can be reduced through the interaction of the energy storage system (ESS) and the electric springs, ii) the electric springs reduce the stress of peak load regulation and operational cost and iii) the demand of microgrids system for ramping ability of generation units is reduced with the buffer of the electric springs. Numerical results show that the coordinated operation between electric springs and energy storage system of microgrids can bring down the investment cost for the ESS and short-term operational cost in the aspect of economic dispatch, reducing requirements for the capacity and ramp ability of the energy storage system in microgrids. Energy buffering can be achieved with lower cost and the load demand can follow power generation in the new operational mode of islanded microgrids using electric springs.

This chapter introduces the principles of doubly‐fed induction generators (DFIGs). It presents PQ control and direct torque control (DTC) of DFIGs. The chapter describes low‐voltage ride through (LVRT) of the DFIG. The DFIG consists of a wound rotor induction generator (WRIG) with the stator windings directly connected to the constant‐frequency three‐phase grid and with the rotor windings mounted to a bidirectional back‐to‐back voltage source power converter. The power converter consists of two parts: the rotor‐side converter and grid‐side converter. PQ control is widely used in the practice, giving decoupled active power and reactive power. DTC is considered to be a simple and robust control scheme that achieves quick and precise torque control response. A major drawback of DFIGs is their LVRT capability during grid faults. Faults in the power system can cause a voltage dip at the connection point of the wind turbine.

With large-scale wind power integrating into power grid, it's increasingly important to consider uncertainty factors into power system operation scheduling. In actual operation, wind power outputs are relatively strong correlation. It will bring larger computational error without considering these factors. Current researches are inadequate to model multiple dependent wind power outputs. Recently, presented stochastic optimal power flow models have lower computational efficiency and not able to assure the convergence. To solve these problems, a second-order cone dynamic stochastic optimal power flow model is proposed based on Simplified Pair Copula model. In this model, original non-linear models are transformed into SOCP models after constructing multiple correlated wind power outputs. Improved point estimate method and GUROBI are used to solve this model. Comparing with the program without considering the correlation of wind speed, the proposed method is proved to be effectiveness and practicability.

This paper proposes an affinely adjustable robust optimization (AARO) approach to account for the uncertainty of wind power in microgrid optimal dispatch. It proposes an affinely adjustable robust optimal dispatch formulation where the base-point generation of energy storage system (ESS) and diesel generators is calculated to serve the forecasting load which is not balanced by wind power in island microgrid. The affinely adjustable robust dispatch framework is solved using the second-order cone programming, and the optimal base-point output and participation factors of energy storage and diesel generator in each dispatch interval are obtained on the wind fluctuation according to an uncertainty set. Numerical results show that the optimal operating cost increases slightly with the increase of wind power output uncertainty with higher operating costs using AARO method, but the more robust dispatch scheme, compared to deterministic cases. AARO can effectively cope with wind power prediction errors with a high success rate in day-ahead dispatch, especially when it lacks short-term renewable energy forecast data.

WLANs are susceptible to interference and not suitable for transmission of the integrated services of urban rail guided transportation. With the rapid deployment of 4G, it is highly concerned if the LTE can be used for urban rail transit. In this paper, we present the latest field test we made on the performance of Time Division Long Term Evolution(TD-LTE) to transmit the integrated services of urban rail transportation. It is found the effects of train speed and handover on the packet loss rate, delay and throughput are minimal. The test result prove that the performance of TD-LTE at high speed satisfy the Quality of Service(QoS) requirements of integrated services of urban rail guided transit.

Allergic asthma is characterized by airway hyperresponsiveness, inflammation, and a cellular infiltrate dominated by eosinophils. Numerous epidemiological studies have related the exacerbation of allergic asthma with an increase in ambient inhalable particulate matter from air pollutants. This is because inhalable particles efficiently deliver airborne allergens deep into the airways, where they can aggravate allergic asthma symptoms. However, the cellular mechanisms by which inhalable particulate allergens (pAgs) potentiate asthmatic symptoms remain unknown, in part because most in vivo and in vitro studies exploring the pathogenesis of allergic asthma use soluble allergens (sAgs). Using a mouse model of allergic asthma, we found that, compared with their sAg counterparts, pAgs triggered markedly heightened airway hyperresponsiveness and pulmonary eosinophilia in allergen-sensitized mice. Mast cells (MCs) were implicated in this divergent response, as the differences in airway inflammatory responses provoked by the physical nature of the allergens were attenuated in MC-deficient mice. The pAgs were found to mediate MC-dependent responses by enhancing retention of pAg/IgE/FcεRI complexes within lipid raft–enriched, CD63(+) endocytic compartments, which prolonged IgE/FcεRI-initiated signaling and resulted in heightened cytokine responses. These results reveal how the physical attributes of allergens can co-opt MC endocytic circuitry and signaling responses to aggravate pathological responses of allergic asthma in mice.

A novel Lyapunov function based decentralized control method is proposed for three-phase parallel connected inverters system within islanded micro-grid. A proper voltage regulation and excellent power sharing performance can be well ensured even under unknown line impedance and unbalanced LC filter parameters. Moreover, high reliability and robustness can be guaranteed since no inter-communication among inverters as well as no centralized controller is demanded, which also derives the advantages of scalability and redundancy. The stability of the entire system is ensured by standard Lyapunov method. The implementation is simple and effectiveness without multi-loop controller. The analysis is confirmed via some simulation and hardware-in-the-loop (HIL) experimental results.

In the grid-connected modular multilevel converter (MMC), grid faults not only cause the current to be higher than the rated current, but also bring the unbalanced issues to the inner unbalanced currents and arm voltages. In this paper, a multi-variable coordinated control method which is employed in the d-q coordinate is presented. First, a parameter is introduced in the calculation of the grid currents references. Afterwards, the relationships between the system operating parameters and the parameter are derived. Furthermore, the influences of parameter on the safe operation areas are analyzed in details. In addition, the P-Q diagrams of the MMC are deduced in terms of the safe operating areas under unbalanced conditions, and an adaptive multi-variable coordinated control is proposed. Finally, the simulating results from MATLAB/Simulink are presented to verify the theoretical analysis.