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Modern distribution network with high penetration of intermittent renewable sources and the so-called prosumers requires more reliable distribution automation (DA) system for safe operation and control. The design of optimal feeder terminal units (FTU) placement is critical and economical for the effective DA application. Previously proposed solutions of optimal FTU placement aiming to ensure the accuracy of state estimation (SE), typically include the following two main shortcomings: 1) only to obtain the optimal FTU placement in quantity, and the analysis of FTU location is not considered yet; 2) few consider the uncertainty of intermittent power injections in the analysis of state estimation. In this paper, a modified methodology of FTU placement is proposed not only aiming to ensure the accuracy of state estimation with the minimum number of meters, but also finding those specific FTU locations to guarantee the power service reliability. Moreover, the uncertainty models of those intermittent power injections are also considered by using probability density function (PDF). The resultant optimization problem is addressed by using the covariance matrix adaptation evolution strategy (CMA-ES). Case studies demonstrate the feasibility and effectiveness of the proposed methodology.

When high voltage direct current (HVDC) transmission adopting mono-polar ground return operation mode or unbalanced bipolar operation mode, the invasion of DC current into neutral point of alternating current (AC) transformer will cause core saturation, temperature increasing, and vibration acceleration. Based on the MPG-200D soft magnetic measurement system, the influence of DC bias on magnetic performance of 0.23 mm and 0.27 mm series (P 1.7 =0.70–1.05 W/kg, B 8 >1.89 T) grain-oriented (GO) silicon steels under condition of AC / DC hybrid excitation were systematically realized in this paper. For the high magnetic induction GO steels (core losses are the same), greater thickness can lead to stronger ability of resisting DC bias, and the reasons for it were analyzed. Finally, the magnetostriction and A-weighted magnetostriction velocity level of GO steel under DC biased magnetization were researched.

After investigation and analysis, this project studies the intelligent identification matching technology of the physical device model, the overlay technology of the lightweight model and the physical equipment, and realized the panoramic layered display of the internal and external three-dimensional models of the physical device. According to the specific needs of the operation and maintenance of the physical device, the component-level monitoring data (operating conditions, thyristors, water level, temperature and humidity, etc.) of the physical device are overlaid onto the corresponding equipment model.

The uncertainty of renewables penetration brings challenges to the operation of the integrated energy system (IES). This paper focuses on the development of adjustable robust dispatch for this purpose. Firstly, IES is modeled based on the energy hub. Then an adjustable robust dispatching model is designed with the goal of minimum cost, where the decision variables are obtained to ensure a feasible solution for all scenarios according to the uncertainty set. On this basis, the model is further transformed into a mixed-integer linear program (MILP) so that it can be easily solved by the existing solvers. Finally, it is shown that the optimum scheme obtained by the proposed method reduces the reserve capacity and cost compared with the static robust optimization.

In recent years, Virtual Synchronizer Generator (VSG), which simulates the inertia and damping of the synchronous generator, has been gradually applied in microgrid. This paper studies the microgrid inverter using the VSG control strategy. Due to the existence of virtual inertia and damping coefficient in the VSG, the frequency and output active power of the microgrid inverter will oscillate when the active power scheduling command is abrupt, and the inverter will break down in severe cases due to over-current protection. Aiming at this problem, based on the root trajectory analysis of active power closed-loop transfer function, the influence of damping coefficient on VSG stability and dynamic performance is studied, combined with the physical meaning of the power angle curve and damping coefficient of VSG, the fuzzy algorithm is proposed to configure virtual damping coefficient. The VSG adaptive control algorithm of the coefficient is given, and the membership function and fuzzy rule table are given. Finally, the correctness and effectiveness of the control strategy are verified by constructing the simulink simulation model.

At present, the traditional grid 3D model is huge, and the running software and hardware environment is complex. It is difficult to apply across computer platforms, the model rendering efficiency is low, and the mobile terminal is difficult to load and run. The core of 3D engine is the real-time high-speed rendering technology of 3D model. The traditional 3D model needs a lot of post-rendering after modeling. It needs to calculate and process the material, texture, and illumination of the model, which consumes a lot of computing resources and labor cost. This paper innovatively adopts the rendering algorithm based on grid business characteristics and realizes the high speed of 3D power equipment model on mobile terminal equipment through the application of modules and algorithms such as material management, particle system, bulletin board, HDR environment, and illumination. Rendering solves the problem of high cost of post-production rendering of traditional 3D models.

As the essential part of high voltage direct current (HVDC) system, modular multi-level converter (MMC) can be controlled properly, which is related to the safety and stability of the whole system. In actual operation, the influence of different working conditions on the core components is not the same. In this paper, the influence of different working conditions on electronic components is analyzed based on the working principle of MMC. When establishing the system reliability model, considering different backup strategies, k/n (G) model and Gamma distribution are adopted respectively. The effects of two different backup strategies on the system are compared and the effects of the redundancy of submodules on the reliability are analyzed. If the component failure rate is determined, the increase of redundant modules can improve the reliability of the system. Under the same redundancy rate, the cold standby strategy improves the system reliability more obviously.

Modular Multilevel Converter (MMC) valves are widely used in high-voltage direct current (HVDC) power transmission projects, and their reliability is one of the research priorities which is closely related to IGBT. Excessive junction temperature is one of the important causes of IGBT damage. Therefore, it is necessary to further study the key factors affecting IGBT junction temperature. Firstly, based on the temperature-sensitive parameter method, the thermal impedance model of IGBT is established, and the parameters of thermal impedance model under different working conditions are analyzed. Secondly, based on the numerical calculation model, the losses of the core component IGBT module of the converter valve under different working conditions are calculated. Then, the junction temperature of the IGBT is calculated by the thermal network method. Finally, the junction temperature of IGBT in the actual engineering of the MMC converter valve is calculated theoretically, the loss and junction temperature under different working conditions are analyzed.

Voltage quality is an important aspect of power quality and a stable voltage amplitude is significant for network security and operational economy. Reactive voltage compensation is mostly paid attention on the transmission grid side, while the reactive voltage on the distribution grid side is rarely noticed. Compensating the reactive voltage of each node is uneconomical, so using a small amount of treatment equipment to meet the voltage requirements of each node of the distribution network is quite necessary. In this paper, a genetic algorithm-based method is developed for positioning of reactive voltage compensator. The effectiveness of the algorithm is verified by a 4-node system. The results indicate that the proposed genetic algorithm-based method can effectively calculate the position of the compensator.

The traditional control strategies of shunt active power filter (SAPF) is primarily focused on the compensation of local non-linear loads, which may become uneconomical for the network with a number of distributed non-linear loads. In this paper, two methods applied to the allocation and sizing of multiple shunt active filters in distribution network are proposed and compared, including an improved particle swarm optimization (PSO) algorithm with one mixed objective function and a multi-objective particle swarm optimization (MOPSO) algorithm. To evaluate the capability of the proposed methods, the IEEE 18-bus test system is employed in simulation. Simulation results confirms that both methods can achieve the goal but the MOPSO-based algorithm is more efficient and universal in the allocation and sizing of multiple SAPFs compared with the PSO based algorithm.

A ppb-level CO sensor based on multi-comb optical-feedback cavity enhanced absorption spectroscopy with a 2.3 μm diode laser was developed for SF6 decomposition analysis in electric power system. The effective optical path reached to 4.5 km within 35 cm length cavity. Besides, through modulating the cavity length five times automatically, the spectral resolution was improved to 0.0015 cm−1 from 0.0071 cm−1. Targeting the R(6) line of CO first overtone band at 4285.01 cm−1, which is interference free from absorption spectra of SF6 mixtures (SF6, SO2, H2S, SO2F2, HF, CF4, CO2, COS, O2 and H2O), the minimum detection limit and detection precision under different gas pressures were performed. At optimum integration time of 30 s determined by Allan deviation analysis and gas pressure of 40 torr, the minimum detection limit and detection precision of CO were better than 18 ppb and 150 ppt, respectively.

In the development of on-line monitoring technique for transformer aging status, detecting concentration of methanol dissolved in transformer oil by the method of surface enhanced Raman spectroscopy (SERS) is a novel and promising technique. By use of Raman spectroscopy technique, we can improve various shortcomings of traditional detection technology. In this paper, the concentration of methanol dissolved in 2 mL/L methanol-oil solution was effectively detected by surface enhanced Raman spectroscopy. In the first, Raman spectra of single methanol molecule and its SERS were simulated to analyze the Raman peaks of methanol. A SERS substrate was prepared with selective enhancement. Based on prepared SERS substrate, concentration of methanol dissolved in transformer oil with content of 2 mL/L have been effectively detected. This helped us to further study the lower concentration of methanol in oil and make quantitative analysis.

How to accurately detect the concentration of furfural in transformer oil plays an important role in the analysis of the aging state of the transformer. As a fast and non-destructive detection method, Raman spectroscopy is the key to improving the sensitivity of Raman detection. In this paper, based on the extraction technique, the deionized water and methanol are used as extractants to carry out the comparative experiment of furfural Raman spectroscopy in oil. Firstly, the deionized water and methanol extract of furfural in oil are prepared and obtained. The Raman characteristic peaks of furfural are 1372 cm-1 and 1411 cm-1 and 1675 cm-1 by Raman detection, and qualitative analysis is discussed. The experimental results show that deionized water as an extractant has certain feasibility for Raman detection, and it has the lower limit of detection for Raman detection compared with methanol as extractant, which makes a certain contribution in order to improve the sensitivity of furfural Raman detection in transformer oil.

In some of the China’s urban grid, the transmission congestion problems are usually mitigated by reconfiguring the topological structure of the high voltage distribution network (HVDN). Whereas the growing amount of the electric vehicles (EV) and the large scale photovoltaic (PV) generations that integrate to the grid result in the frequent reconfiguration of the HVDN. However, the development of the energy storage technology provides an efficient method to handle this problem. But the high cost of the energy storage devices and the limited installation room for large scale energy storage systems (ESS) are still a tricky problem. Thus the size and the location of the ESS should be elaborately selected. In order to fully exploit the advantages of the ESS, this paper proposed a two-layer ESS planning model which considered the coordination scheme of the charge and discharge strategy of the ESS and the HVDN reconfiguration. The optimal size and location of the ESS is determined in the upper layer model. The lower level model is used to calculate the operation cost which can be considered as the adaptability of the decision produced by the upper layer model. The two layer optimization model is solved by the NSGA-II algorithm. A testing system extracted from an urban grid in China is used to verify the validity of the proposed method. Numerical results shows that with the assistant of the ESS control scheme, the reconfiguration times of the HVDN is significantly reduced while the reconfigurability of the HVDN is also conducive to save the investment on ESS.

Due to the increasingly fast growth of electricity load, insufficient static capacity of 110kV-220kV urban transmission grid poses challenges to effectively eliminate congestion risk at peak period. In practice, 110kV network which possesses high reconfigurability is dispatched flexibly to transfer substation load aiming to ensure operational margin of 220kV grid. Traditionally, mixed integer programming OPF-based models were used to provide optimized strategy. However, the solution of these methods is usually intractable due to non-convexity and non-linearity, which is not applicable for online routines. In light of this, a novel data-driven approach which has highly efficient computing performance for yielding reconfiguration strategy of 110kV network while satisfying the N-1 contingency is presented. First, the large-scale samples reflecting the feasible connectivity among the grouped 110kV and 220kV bus are produced and then fed into DNNs to learn 0-1 topology constraints. Second, numerical relations among 220kV power flow and bus injection as well as the N-1 constraints are also trained by DNN. At last, a heuristic evolutionary algorithm is applied to solve power congestion problem with embedded well-trained neural networks as surrogated physical constraints. The case study validates the effectiveness of the presented methodology.

A new non-isolated interleaved bidirectional soft-switching DC-DC converter (NIBC) with a low current and low voltage stress auxiliary zero-voltage-transaction (ZVT) cell is proposed in this paper. Both main switches and auxiliary switches can realize zero-current-switching (ZCS) turn-on and ZVT turn-off, and all diodes achieve soft-switching condition. Furthermore, auxiliary switches can help to recycle the magnetizing current energy to high voltage side of the converter. Compared with conventional NIBCs with soft-switching cell, in the proposed NIBC, the current and voltage stress of auxiliary switches can be reduced lower than the input current and high voltage side, and it is feasible to select switches with low rated power, low voltage and low current stress as auxiliary switches, thus improve the efficiency and reduce the cost. The operation and design of the proposed NIBC is analyzed in this paper, and simulation and experiments are conducted to verify their validity

A ppb-level CO sensor based on multi-comb optical-feedback cavity enhanced absorption spectroscopy with a 2.3 μm diode laser was developed for SF6 decomposition analysis in electric power system. The effective optical path reached to 4.5 km within 35 cm length cavity. Besides, through modulating the cavity length five times automatically, the spectral resolution was improved to 0.0015 cm−1 from 0.0071 cm−1. Targeting the R(6) line of CO first overtone band at 4285.01 cm−1, which is interference free from absorption spectra of SF6 mixtures (SF6, SO2, H2S, SO2F2, HF, CF4, CO2, COS, O2 and H2O), the minimum detection limit and detection precision under different gas pressures were performed. At optimum integration time of 30 s determined by Allan deviation analysis and gas pressure of 40 torr, the minimum detection limit and detection precision of CO were better than 18 ppb and 150 ppt, respectively.

The development of information display interaction from planar 2D to stereoscopic 3D has become increasingly mainstream. In the future, there will be more and more 3D content creators and consumers. The core model engine is standardized, lightweight, cross-platform and 3D-based. The interactive content library has become a hot topic in the IT industry. This paper studies the mainstream model file conversion compression technology based on the characteristics of the power grid business, and realizes the compression and weight reduction of the three-dimensional model. In this paper, compressive sensing-based 3D model compression method is used to achieve rapid compression of server-side models, which can greatly reduce the model space and increase the loading speed of the model. Compressed sensing compresses data at the beginning of data acquisition. When sampling, it does not follow the frequency twice as high as the maximum cut-off frequency of the initial signal as described by Shannon Nyquist Theorem, which greatly reduces the amount of data sampled, and does not require subsequent data recompression, which can greatly increase the model’s compression efficiency and quality. First, get the original model and split the model data into material data, animation data, grid data, node tree data, and map data. According to the model data classification, compression-sensing algorithm based on the business characteristics of power grid services was used to compress the model for sampling. Get a discrete representation of the model after sampling. Obtain the compressed model, and combine the material data, animation data, grid data, node tree data, and map data according to the characteristics of the power business and associate it with the unique identifier of the business model. The compression of mainstream model files based on the characteristics of power grid services can effectively improve the operational efficiency of 3D models. The research results lay a solid foundation for the engine technology in the future development of interactive business systems for displaying 3D power equipment based on 3D model engines. The development of a prototype of a 3D model engine can provide basic support for the construction of a unified power 3D model library for the future grid and 3D interaction of power services.

Leaf surface fertilization with liquid fertilizer produced from amino acids constitutes a potentially important source of nitrogen and is important for plant production. However, few reports have focused on the plant growth promotion by novel liquid fertilizers created by new amino acid resources, let alone the influence on leaf microbiota. In this study, the effects of liquid fertilizer, created by amino acids hydrolyzed from animal hairs with or without the PGPR strain Bacillus amyloliquefaciens SQR9, on crop yield and leaf microbiota were investigated. The results showed that leaves sprayed with amino acid liquid fertilizer (AA) and liquid biological fertilizer (AA9) persistently increased cowpea yields compared to the control amended with chemical fertilizer (CF). Fertilization with amino acid fertilizer showed no significant difference in microbial composition compared with the CF treatment; however, the introduction of functional microbes altered the microbial composition. Pearson correlation analysis, VPA analysis and SEM models all revealed that the amino acids liquid fertilizer application, but not the functional strain or the altered microbiota, performed as the direct driver attributing to yield enhancement. We conclude that leaf fertilization with a novel amino acid liquid fertilizer can greatly enhance the crop yield and that the addition of beneficial microbes may perform the role in further altering the composition of leaf microbiota.

Accurate detection of dissolved aging features in transformer oil is the key to judging the aging degree of oil-paper insulation. In this work, in order to realize in situ detection of furfural dissolved in transformer oil, silver nanoparticles were self-assembled on the surface of gold film with P-aminophenylthiophenol (PATP) as a coupling agent. Rhodamine-6G (R6G) was used as the probe molecule to test the enhancement effect. By optimizing the molecular concentration, molecular deposition time, and silver sol deposition time of PATP, the nanoparticles were made more uniform and compact, and an enhanced substrate with rich hot spots was obtained. The optimum substrate was developed, and surface-enhanced Raman spectroscopy (SERS) detection of trace furfural dissolved in transformer oil was realized. The results showed that the substrate prepared under the conditions of 0.1 mol/L PATP, 5 h deposition in PATP and 12 h immersion in silver sol, had the best reinforcement effect (that is, uniform and compact particle arrangement and no particle clusters). By use of this substrate, the minimum detectable concentration of furfural in transformer oil was about 1.06 mg/L, which provides a new method for fast and nondestructive detection of transformer aging diagnosis.

Numerical methods can provide sufficient information for thermal dynamics of district heating (DH) network. However, high computation burden restricts its application, especially for large-scale DH networks. This dilemma can be overcome by establishing fast numerical calculation procedure and properly selecting the calculation steps without reduction of numerical accuracy. The first-order implicit upwind method is an iteration-free numerical approach for fast simulation of DH pipeline thermal dynamics, while the first-order precision restricts its numerical performance. To further improve the simulation performance, two iteration-free numerical methods with high-order precision, the second-order implicit upwind method and third-order semi-implicit QUICK method are developed. Validations of the three numerical methods are conducted with the measured data of a real DH pipeline. Preferred calculation steps of the three methods are studied via comprehensive numerical experiments. Based on the preferred step size, simulation comparisons of the numerical methods are performed. Results show that the outlet temperature fluctuations of DH pipeline can be feasibly predicted by these methods with satisfying accuracy. The second-order implicit upwind method perform best considering least computation burden (0.003 s) within acceptable error. With the application of the second-order implicit upwind method, simulation analyses on DH pipeline under varying flow velocities are performed.

Efficient numerical simulation of thermo-hydraulic coupled conditions is critical for analysis and control of DH network. However, for most of meshed DH network, the flow directions of its pipelines may change, which will lead to difficulties in effective numerical calculation of the thermo-hydraulic conditions. This paper established an efficient thermo-hydraulic coupled calculation method, which utilizes the topology sorting algorithm to obtain the calculation sequence of all pipelines during numerical thermal transient calculation, according to the flow directions and flow rates of all pipelines. The flow directions and flow rates are solved by hydraulic condition calculation at each time step. The proposed method is applied to a real meshed DH network for thermal dynamic simulation. Independence analysis of hydraulic computation frequency is conducted, and convergences of the simulated temperature variations are observed when hydraulic computation frequency gets larger. Thermal dynamics of the meshed DH network are analyzed with 10-min hydraulic computation interval. Results show that the temperature decay and time delay can be up to 2.7 °C and 210 min for the simulated network. Thermal dynamic characteristics of supply temperature waves are various considering the distance from heat source, fluctuations of outdoor temperature and the variations of substation flow rate.

High Tc Superconducting (HTS) DC cable is a promising solution for large-scale power transmission over long distance. However, the indispensable refrigeration system for HTS cable might be undesirable. Considering that LNG (liquid natural gas) now is one kind of clean energy that is used more widely and widely, it is possible to integrate the HTS cable and LNG transportation into an energy pipeline, resulting an acceptable new energy transportation system. In this paper, the principle and structure of the energy pipeline are discussed firstly, and then the basic physical properties of HTS tapes and liquid insulation media in LNG temperature range are analyzed. On the base as above, the general design of ±100 kV/1kA energy pipeline is made.

With no efficient method widely recognized for controlling or preventing tomato bacterial wilt, a devastating disease caused by the soil-borne bacterium Ralstonia solanacearum, tomato production is severely hindered. Therefore, the objective was thus to provide a strategy based on fumigation using ammonium bicarbonate along with organic amendment to reduce disease severity, and the impact of this treatment on the soil microbiome as well as the underlying mechanism leading to disease suppression were evaluated using high-throughput sequencing. Results showed that this combined strategy effectively controls tomato bacterial wilt disease despite the high abundance of R. solanacearum in both the bulk and rhizosphere soil in all treatments. The treatment led to significant changes in the soil bacterial and fungal communities, and at harvest time, fumigation and organic amendment equally affected the variation in the rhizosphere microbiome. Further, a shift in the rhizosphere bacterial community acted as the key factor directly and indirectly suppressing R. solanacearum and controlling bacterial wilt disease. In addition, Rhodanobacter, Terrimonas and Chitinophaga in the rhizosphere were the potential taxa involved in disease suppression. Both fumigation and organic amendment contributed to disease suppression by decreasing the abundance of R. solanacearum and altering the bacterial composition in combination with the stimulation of key bacterial taxa.

With the rapid growth of energy consumption, how to utilize energy in an efficient and cheap way becomes an intensive problem. This paper proposes an optimal operation strategy to reduce system fuel costs and increase system stability by independently considering cooling loads and adjusting CHP heat to power ratio seasonally. In this paper, a mathematical model of CHP operation is introduced to reveal the relationship between the supplementary volume of diesel oil and CHP heat to power ratio. Meanwhile, by analyzing the influence of seasonal factor on energy consumption, CHP heat to power ratio is optimized seasonally. Then, by independently considering the impacts of the cooling loads on system operation, the particle swarm optimization (PSO) algorithm is used to optimize the operation strategy of each device. Finally, this paper validates the positive effects of storage devices on improving system economy and stability under the premise of the time-of-use gas price. Results show that system fuel costs can be reduced by 5.2% if the seasonal factor is considered. Additionally, by optimizing the operation strategy, the peak valley gap of electrical loads in summer reduces by 40.7%. Moreover, the proposed strategy successfully utilizes storage capacity to shift loads and respond to gas price.

Transmission congestion is considered as a serious security problem that limit the efficiency of the transmission systems. Due to the lack of controlling measures in some part of the transmission network, much load shedding amount and controlling cost would be required to alleviate the congestion. As more and more renewable energy resources integrate into the grid, the transmission lines are prone to be overload. Whereas in high voltage distribution network (HVDN), each substation usually has one or two reserve lines that connect to another power supply point thus greatly enrich the operation flexibility of HVDN. By changing the topological structure of HVDN, the load in transmission network can be greatly redistributed. In order to improve the transmission efficiency and reduce the total controlling cost, a generator rescheduling scheme considering line switching of HVDN was proposed in this paper. The model consists of a generator rescheduling model and a second order cone programming (SOCP) based HVDN line switching model. An urban test system in China was utilized to verify the effectiveness of the proposed method. Numerical results show that utilizing the HVDN line switching to transfer the load dramatically reduce the load shedding amount and controlling cost.

The growing penetration level of photovoltaic (PV) and electric vehicles (EV) increase the operational risk of the urban power system. Besides, the unbalanced load distribution at time-spatial scale also impacts the hosting capacity of the PVs and causes severe transmission congestion. Thus this paper proposed a bi-level optimization model to manage the transmission congestion and enhance the PV hosting capacity considering the reconfigurable capability of the high voltage distribution network (HVDN). In the upper level, the optimal topological structure of HVDN is calculated aiming at minimizing the total operational cost. In the lower level, the maximum hosting capacity of PVs is achieved by a second-order cone programming model. The proposed method was verified by an urban power system in China. Numerical results demonstrated that reconfiguring the HVDN topological structure provide huge benefits of enhancing PV penetration level and facilitating the integration of the EV.

Ultra-high frequency (UHF) detection, due to the advantages such as noiseless measurement, high sensitivity, wide detection range and 3-D localization capability, has been widely applied in power equipment. The propagation characteristics of electromagnetic (EM) waves should be investigated, because it serves as the substantial guides to improve the sensitivity of UHF detection and the efficiency of online monitoring by UHF detection. In this paper, a 110/35/10 kV three-phase oil-filled transformer with complete internal structure was reconstructed to investigate the propagation characteristics of EM waves. The amplitude, frequency band energy and time-frequency property were analyzed. The results show that the amplitude of EM waves decreases nonlinearly and the attenuation rate slows down with the increasing distance between antennas and PD source. During the propagation of EM waves, the higher frequency bands suffer the greater attenuation leading to the less energy ratio and the energy mainly concentrates on the 300 MHz-600 MHz. The time-frequency analysis also proves that higher frequency bands suffer the greater attenuation. These results are considered to be useful for the decisions of the detection sensitivity, sensor positions and the band width of sensors.

Hypothesis The microfluidic technology can drive molecules to organize into aggregates with nano-structures, and gives a possibility to control aggregate morphologies by adjusting hydrodynamic parameters of microfluidics. COMSOL Multiphysics is a useful software to simulate the mixing situation of solutions in microfluidic. Here, experiments and simulation are combined to study the self-assembly of gradient copolymers in the microfluidic device. Experiments Fluorinated gradient copolymers self-assembled in a three-dimensional co-flow focusing microfluidic device (3D CFMD). Hydrodynamic parameters of 3D CFMD were adjusted to control morphologies and the sizes of copolymer aggregates. A simulation software, COMSOL Multiphysics, was used to simulate the mixing and diffusion of outer phase stream and inner phase stream to explore the mixing kinetics of two streams in the microchannels. Findings 3D CFMD offered a novel platform for the continuous and controllable self-assembly of fluorinated gradient copolymer. Various morphologies of copolymer aggregates were obtained in 3D CFMD, but just spherical micelles were formed by a traditional solvent-inducing method. The flow velocity, initial water content of outer-phase stream, and the copolymer concentration of inner-phase stream had great effects on the morphology and size of copolymer aggregates. The simulation results made us a better understanding on the microfluidic self-assembly.

Ice flashover threatens the security and reliability of power transmission. However, the ice flashover mechanism of insulators remains poorly understood. This study analyses water droplet freezing and ion distribution in ice layer under alternating electric field. It also investigates ion migration during icing process of insulator string under alternating electric field and its effects on insulator flashover. Results showed that the average freezing time of water droplets was related to electric field strength. The extent of ion migration during freezing decreased with increasing electric field strength. The maximal melting water conductivity of the ice layer and icicle of the insulator formed under energized condition was higher than the corresponding freezing water conductivity but lower than that under non-energized condition. Furthermore, the hanging location of each insulator significantly affected the melting water conductivity of the ice layer. The surface conductivity of the ice layer increased because of the conductive ion migration in freezing water during freezing, which was an important factor that decreased the flashover voltage of the ice-covered insulator. The existence of alternating electric field would impact the extent of ion migration. This study may serve as a reference for updating prediction flashover model of ice-covered insulators during the melting period.

In this paper, we derive the Bayes-Optimal estimator based on approximate message passing (AMP) algorithm in massive multiple-input multiple-output (MIMO) systems, which requires statistical channel state information (CSI). According to the analysis of channel model in beam domain, the covariance matrix is derived for CSI acquisition. With the aid of statistical CSI, the convergence of the proposed algorithm has significant improvement in comparison with which use the expectation-maximization (EM) algorithm to fit the statistical CSI. Simulations show great mean squared error (MSE) performance that approximates the Minimum Mean Square Error (MMSE) estimator, and better convergence performance than other AMP algorithm can be achieved. Besides, the results prove that performance of the random pilot in this algorithm is close to that of the orthogonal pilot based on Zadoff-Chu sequences.

This paper studies the statistical channel state information (CSI) acquisition in single-cell for massive multiuser multiple-input multiple-output (MIMO) system in the beam domain. Compared with the Bayesian channel estimation which models the channels by an independent and identically Gaussian-mixture (GM) distribution, we model the channels by an independent and non-identically Gaussian distribution exploiting the statistical channel characteristics, and provide the acquired statistical CSI for the approximate message passing (AMP) channel estimation that can reduce computational complexity. Meanwhile, we show that the statistical CSI for the user scheduling in the beam division multiple access (BDMA) transmission is the same as that of the AMP channel estimation. Due to the heavy orthogonal pilot overhead when serving a large number of user terminals (UTs) in massive MIMO system, the Bayesian channel estimation that don't need the orthogonal pilots is used to estimate the channel parameters in the statistical CSI acquisition. Simulation results show that the mean square error (MSE) of the AMP channel estimation using the estimated statistical CSI is better than that of the Bayesian channel estimation and the iteration number has a significant reduction. Besides, the user scheduling using the estimated statistical CSI can reach a good sum rate of the scheduled UTs.

The structure of the extended fuzzy basis function network (EFBFN) is firstly proposed, and the least squares (LS) method is used to design it by fixing the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel evolutionary algorithm based on LS and the hybrid of evolutionary programming and particle swarm optimization (LS-EPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. The enhanced EFBFN whose parameters are refined automatically using LS-EPPSO is thus called adaptive EFBFN. In the simulation part, the proposed method to construct AEFBFN is employed to model a three input nonlinear function and to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.

Centralized control using system-wide data has been suggested to enhance the dynamic performance of large interconnected power system. However, the impact of time delays in acquisition and transmission of key signals cannot be ignored. A novel direct method (DM) based on cluster treatment of characteristic roots (CTCR) is introduced in this paper to analyze the stability of a power system considering the feedback transmission delay. The complete stability view of a time delayed single-machine infinite-bus (SMIB) system is obtained through the direct method in time delay space. The validity of this method is verified by time domain simulation results.

WAMS will play a vital role in the monitoring, analysis, modeling and control of interconnected power system. However, the impact of time delays introduced by WAMS canpsilat be ignored. A novel framework called the cluster treatment of characteristic roots (CTCR) is introduced to analyze the stability of a power system considering the feedback transmission delays. The ldquobuilding blockrdquo concept yields a very practical and efficient procedure for the stability assessment of multiple time delayed systems. This novel perspective serves very well for the preparatory steps of the CTCR method. The complete stability picture of a multiple time delayed single-machine infinite bus (SMIB) system is obtained through the combination of ldquobuilding blockrdquo concept and the CTCR method in time delay space. The validity of this method is verified by time domain simulation results.

This paper presents a new kind of Evolutionary Fuzzy System (EFS) based on the Least Squares (LS) method and a hybrid learning algorithm: Adaptive Evolutionary-programming and Particle-swarm-optimization (AEPPSO). The structure of the Extended Fuzzy Basis Function Network (EFBFN) is firstly proposed, and the LS method is used to design it with presetting the widths of the hidden units in EFBFN. Then, to enhance the performance of the obtained EFBFN ulteriorly, a novel learning algorithm based on least squares and the hybrid of evolutionary programming and particle swarm optimization (AEPPSO) is proposed, in which we use EPPSO to tune the parameters of the premise part in EFBFN, and the LS algorithm to decide the consequent parameters in it simultaneously. In the simulation part, the proposed method is employed to predict a chaotic time series. Comparisons with some typical fuzzy modeling methods and artificial neural networks are presented and discussed.

This paper presents a novel fuzzy modeling strategy using the hybrid algorithm EPPSO based on the combination of Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) for control of nonlinear dynamical systems. The EPPSO is used to automatically design fuzzy controllers for nonlinear dynamical systems. In the simulation part, one multi-input multi-output (MIMO) plant control problem is performed. The performance of the suggested method is compared to that of EP, PSO and HGAPSO in the fuzzy controllers design. Simulation results demonstrate the superiority of the proposed method.