Target Power Research Articles

Blinded sample size recalculation in multiple composite population designs with normal data and baseline adjustments.

The increasing interest in subpopulation analysis has led to the development of various new trial designs and analysis methods in the fields of personalized medicine and targeted therapies. In this paper, subpopulations are defined in terms of an accumulation of disjoint population subsets and will therefore be called composite populations. The proposed trial design is applicable to any set of composite populations, considering normally distributed endpoints and random baseline covariates. Treatment effects for composite populations are tested by combining p-values, calculated on the subset levels, using the inverse normal combination function to generate test statistics for those composite populations while the closed testing procedure accounts for multiple testing. Critical boundaries for intersection hypothesis tests are derived using multivariate normal distributions, reflecting the joint distribution of composite population test statistics given no treatment effect exists. For sample size calculation and sample size, recalculation multivariate normal distributions are derived which describe the joint distribution of composite population test statistics under an assumed alternative hypothesis. Simulations demonstrate the absence of any practical relevant inflation of the type I error rate. The target power after sample size recalculation is typically met or close to beingmet.

Field evaluation of electrofishing response thresholds for adult Grass Carp

AbstractObjectiveOptimal electrofishing waveforms and parameters have been established for juvenile Grass Carp Ctenopharyngodon idella in controlled laboratory trials, but effective settings for adult Grass Carp in riverine systems remain to be determined. This work builds upon existing research by making opportunistic use of data collected in the Cuyahoga River, Ohio, to refine power standards for boat electrofishing that is used to target adult Grass Carp.MethodsOver 12 days of boat electrofishing surveys, we used a gradient of voltage settings at various ambient water conductivities to generate an electrofishing goal table for adult Grass Carp.ResultOur target power was 3550 W (1447–6507 W) higher, on average, than the target power recommended in previous studies for coolwater and warmwater fish species with 60 Hz. Results also show that a 40‐A control box is inadequate in locations where ambient water conductivities exceed 576 μS/cm and when power is limited.ConclusionRefining power standards for boat electrofishing that targets Grass Carp may increase catch rates to levels needed to suppress populations in the Great Lakes region and North America.

Mechanical and electrochemical properties of NbMoSiC gradient films deposited by adjusting sputtering power of carbon target

NbMoSiC gradient films were successfully deposited on 316L stainless steel by adjusting sputtering power of carbon target, and their microstructure, mechanical and electrochemical properties were comprehensively investigated through SEM, XRD, XPS, Raman, nanoindenter and electrochemical workstation. The results show that the NbMoSiC gradient films exhibit a carbon composition gradient across the thickness direction, which impedes the continuous columnar growth and results in a compact and flawless microstructure. The films also possess multiphase structures of Nb2C, MoSi2, silicon and amorphous carbon, leading to a significant improvement in hardness value and elastoplasticity. Furthermore, the electrochemical experiments demonstrated a substantial reduction in the corrosion current density for the NbMoSiC gradient films (3.008 × 10−8 A/cm2) compared to bare 316L substrate (3.687 × 10−7 A/cm2) in artificial seawater medium. The charge transfer resistance of the gradient films (3.482 × 106 Ω cm2) has increased by an order of magnitude, indicating an enhanced inhibition effect on the ion transport during the corrosion reaction. The improved corrosion resistance of 316L stainless steel is mainly attributed to strong passivation ability and the dense microstructure formed by the carbon composition gradient structure.

Optimal configuration for regional integrated energy systems with multi-element hybrid energy storage

This paper proposes a configuration method for a multi-element hybrid energy storage system (MHESS) to address renewable energy fluctuations and user demand in regional integrated energy systems (RIES). To reduce the investment cost of energy storage applications in RIES, a multi-timescale capacity configuration model is formulated, containing a day-ahead power planning model to optimize the power output of energy supply equipment on the hour-level scale and a day-in power operation model that considers the power response characteristics of MHESS to smooth out renewable energy fluctuations on the minute-level scale. In addition, an active energy storage operation strategy is proposed to minimize the configuration investment of MHESS in the day-ahead planning stage. The empirical mode decomposition algorithm is employed to decompose the target power of MHESS to derive the optimal capacity configuration and power output of each energy storage unit. The simulation results indicate that the proposed method is successful in reducing the investment cost when compared to both a passive and a simple Li-ion battery method. Specifically, the proposed method reduced the investment cost by 8.8% and 183.64%, respectively. This work provides a valuable reference for RIES optimization and further exploration of the potential for MHESS development.

Design and analysis for the SPICE parameters of waveform-selective metasurfaces varying with the incident pulse width at a constant oscillation frequency

In this study, we numerically demonstrate how the response of recently reported circuit-based metasurfaces is characterized by their circuit parameters. These metasurfaces, which include a set of four diodes as a full wave rectifier, are capable of sensing different waves even at the same frequency in response to the incident waveform, or more specifically the pulse width. This study reveals the relationship between the electromagnetic response of such waveform-selective metasurfaces and the SPICE parameters of the diodes used. In particular, we draw conclusions about how the SPICE parameters are related to (1) the high-frequency operation, (2) input power requirement and (3) dynamic range of waveform-selective metasurfaces with supporting simulation results. First, we show that reducing a parasitic capacitive component of the diodes is important for realization of the waveform-selective metasurfaces in a higher frequency regime. Second, we report that the operating power level is closely related to the saturation current and the breakdown voltage of the diodes. Moreover, the operating power range is found to be broadened by introducing an additional resistor into the inside of the diode bridge. Our study is expected to provide design guidelines for circuit-based waveform-selective metasurfaces to select/fabricate optimal diodes and enhance the waveform-selective performance at the target frequency and power level. Our results are usefully exploited to ensure the selectivity based on the pulse duration of the incident wave in a range of potential applications including electromagnetic interference, wireless power transfer, antenna design, wireless communications, and sensing.

Research on distributed photovoltaic power prediction based on spatiotemporal information ensemble method

Distributed photovoltaic power generation can efficiently utilize idle resources and reduce carbon emissions. In order to reduce the impact of grid-connected distributed photovoltaic power fluctuations on grid operation, this paper simultaneously exploits the temporal dependence of power series and the spatial correlation of meteorological data to propose a combined prediction model with temporal characteristics and spatial relationships fused for distributed photovoltaic power plants with spatiotemporal information. First, in the study of time-dependent prediction, we propose a long and short-term memory neural network ensemble prediction model based on genetic algorithm-natural gradient boosting, which efficiently fits multiple sets of temporal characteristics of distributed photovoltaic. In the study of spatial correlation prediction, the meteorological data affecting photovoltaic power generation are selected by κ correlation coefficients, the target power plant and spatial reference power plant meteorological data are reconstructed into a two-dimensional matrix, and a two-dimensional convolutional neural network spatial feature extraction power prediction model is designed. Finally, the advantages of the two prediction models of temporal information and spatial features are fused by multiple error evaluation criteria improved information entropy, and a distributed photovoltaic power plant is constructed and implements highly accurate spatiotemporal information combination prediction model. The effect of the forecasting model in this study is validated using the photovoltaic cluster dataset in Hebei Province, China. Compared with other models, the results of this study show that the five prediction performance evaluation metrics of the proposed combined spatiotemporal information model are better.

Analog-Circuit Domain Cancellation with Optimal Feedback Path Selection on Full-Duplex Relay Systems

This paper proposes a scheme that reduces residual self-interference significantly in the analog-circuit domain on wireless full-duplex relay systems. Full-duplex relay systems utilize the same time and frequency resources for transmission and reception at the relay node to improve spectral efficiency. Our proposed scheme measures multiple responses of the feedback path by changing the direction of the main beam of the transmitter at the relay, and then selecting the optimal direction that minimizes the residual self-interference. Analytical residual self-interference is derived as the criterion to select the optimal direction. In addition, this paper considers the target of residual self-interference power before the analog-to-digital converter (ADC) dependent on the dynamic range in the analog-circuit domain. Analytical probability that the residual interference exceeds the target is derived to help in determining the number of measured responses of the feedback path. Computer simulations validate the analytical results, and show that in particular, the proposed scheme with ten candidates improves the residual self-interference by approximately 6 dB at the probability of 0.01 that the residual self-interference exceeds target power compared with a conventional scheme with the feedback path modeled as Rayleigh fading.

Integrated reactive power optimization for distribution systems considering electric vehicle dis-/charging support

Electric vehicle (EV) charging, and discharging have an important reactive power support capability for the distribution network, for which a reactive power optimization method considering EV dis-/charging is proposed. Firstly, the principle of charger’s reactive power regulation is analysed,​ and a charger state constraint model is established to satisfy the constraints on the power factor angle by modifying the charger state for the battery capacity and the target power of the users. Secondly, a quantification method for user reactive power optimization subsidies is proposed to establish network-side and user-side demand indicators respectively, and to integrate reactive power optimization target function for the distribution system. Then, bi-directional charger’s PF is taken as the optimization variable, and the charger power factor angle optimization method is proposed based on the Particle Swarm Optimization (PSO) algorithm, so as to establish a reactive power optimization model considered the orderly dis-/charging, in order to realize the active/reactive power regulation between the distribution network and Owners of the EV. Finally, simulations are carried out using the IEEE-33 model as an example. Results proved that presented orderly dis-/charging method can increase voltage profile and decrease loss rate of the network to 40%, but also significantly decrease customer’s expenditure to 13%, while satisfying the temporary travel demand of customers.

An AI-Based Power Reserve Control Strategy for Photovoltaic Power Generation Systems Participating in Frequency Regulation of Microgrids

In this paper, a novel AI-based power reserve control strategy is proposed for photovoltaic (PV) power generation systems participating in the frequency regulation (FR) of microgrids. The proposed strategy uses a frequency response module to determine the target power reserve ratio of the PV system based on microgrid frequency deviation, as well as a power reserve control module to obtain the target duty cycle, which is input to the BOOST converter. The use of artificial neural networks (ANN) in the power reserve control module enables the PV system to work at a specified power reserve ratio, producing appropriate power and mitigating frequency fluctuations in the microgrid. Additionally, a deep reinforcement learning (DRL) algorithm is employed as the decision maker for variable step-size control and initial power reserve ratio determination. Simulations were performed to validate the effectiveness of the proposed method, demonstrating a significant reduction in average frequency deviation by 72.36% when subjected to random variations in irradiance intensity and load conditions. Overall, the proposed AI-based power reserve control strategy has good potential for practical applications in real-world microgrids, promoting the absorption of new energy led by PV and reducing the phenomenon of light abandonment.

A Robust Adaptive Objective Power Allocation in Cognitive NOMA Networks.

Cognitive radio (CR) is a candidate for opportunistic spectrum implementation in wireless communications, allowing secondary users (SUs) to share the spectrum with primary users (PUs). In this paper, a robust adaptive target power allocation strategy for cognitive nonorthogonal multiple access (NOMA) networks is proposed, which involves the maximum transmission power of each SU and interference power threshold under PU constraints. By introducing the signal-to-interference-plus-noise ratio (SINR) adjustment factor, the strategy enables single-station communication to achieve energy efficiency (EE) or high throughput (HT), thus making the target function more flexible. In the same communication scenario, different cognitive users can choose different communication targets that meet their needs. Different QoS can be selected by the same cognitive user at different times. In the case of imperfect channel state information (CSI), semi-infinite (SI) constraints with bounded uncertainty sets are transformed into an optimization problem under the worst case, which is solved by the dual decomposition method. Simulation results show that this strategy has good adaptive selectivity and robustness.

Formation of pure anatase TiO2 using the reactive pulsed dc magnetron sputtering method for controlling the target poisoning state

AbstractThe use of pulsed dc‐sputtering sources for reactive magnetron sputtering with oxygen offers a possibility to suppress the negative effects of target poisoning (such as arcing). This results in a wide process range for the selection of a desired operating point. The control of target poisoning plays a major role in maintaining constant coating properties and affects the stoichiometry of the reactive coating, as well as the coating rate and the economic impact of the coating process. In a hysteresis, the target poisoning during the reactive sputtering of titanium under oxygen addition proceeds nonlinearly. Without the use of a suitable target poisoning control technique, the sputtering process can abruptly change to an unstable state. As a result, variations of stoichiometry can occur during the deposition process. A proven method for maintaining a stable reactive sputtering process is the control of oxygen flow with the input variable target voltage. By determining the typical oxygen hysteresis at constant target power and constant argon flow, an operating point for the control loop is derived. The desired target voltage then serves as the input variable for the control loop of the target poisoning. The controlling technique for target poisoning is a basic requirement for the production of the photolytic active anatase phase of titanium dioxide (TiO2) using reactive magnetron sputtering. The photocatalytic equipment of surfaces with a titanium dioxide coating in the anatase phase can be realized with the reactive pulsed dc magnetron sputter ion plating process (DC‐MSIP). The pulsed DC‐MSIP process facilitates coating a variety of surfaces at temperatures below 200°C in an environmentally friendly manner.

Spatio-temporal PV power forecasting considering the time-shift correction and the information fusion strategy of multi-stations

Accurate prediction of PV power is essential to ensuring the safe and economic operation of power systems with high PV penetration. The current PV power prediction scheme considering the spatio-temporal correlation characteristics is relatively simple in data processing, resulting in low prediction accuracy; at the same time, the missing data also poses a great problem to the prediction. Therefore, in order to improve the prediction accuracy and solve the problem of missing data, this paper proposes a PV power spatio-temporal prediction model considering time-shift correction and a multi-station information fusion strategy Firstly, relevant power station clusters are constructed using hierarchical clustering, and a similar daily data filtering model considering the variation characteristics of daily power characteristic curves is proposed to filter the data; Secondly, multiple BP neural network models are constructed and multiple reference power stations with high relevance are predicted using irradiance information; Thirdly, the prediction results of multiple reference power stations are input to the data processing module for time-shift analysis and spatial correlation information fusion correction, which solves the missing data problem of the target power station to be predicted. Finally, it is input to One-dimensional Convolutional Neural Network(1DCNN) to achieve the power prediction of the target power station with missing data. The simulation analysis shows that the root mean square error (RMSE) of a sunny day forecast is 3.31%; the RMSE of a non-sunny day forecast is 9.65%, which proves the accuracy of this two-layer neural network is higher compared to other model structures, so the proposed scheme has certain reliability and accuracy in the prediction of PV power with missing data.

Structure and electrocatalytic hydrogen evolution performance of Mo2C thin films prepared by magnetron sputtering

Mo2C, which has a unique electronic structure similar to the electronic structure of Pt, is considered as the material with the greatest potential to replace Pt as a catalyst for the electrocatalytic hydrogen evolution reaction (HER). However, Mo2C thin films have not attracted enough attention in the field of electrocatalysis. This work proposes a method for preparing Mo2C thin films as a catalyst for electrocatalytic HER through radiofrequency magnetron sputtering. The HER activity of the Mo2C thin film in acidic and alkaline media is studied by changing the deposition power of the Mo2C target and doping Ni for structural modification. Results show that increasing the deposition power of Mo2C can significantly enhance the HER activity of the films in acidic and alkaline media, and metal Ni doping can further enhance the HER activity of the Mo2C films. In an alkaline environment at a current density of 10 mA cm−2, the films demonstrate an overpotential of as low as 163 mV with a Tafel slope of 107 mV·dec−1. In acidic media, the films present the corresponding overpotential of 201 mV and a Tafel slope of as low as 96 mV·dec−1. Moreover, the Ni-doped Mo2C films have excellent HER stability. The synergy between doped Ni and Mo vacancies optimizes the strength of the Mo–H bond and the adsorption and desorption equilibrium of active H, thus enhancing HER kinetics. This work guides the possible structural design of Mo2C thin films for electrocatalytic HER.

Synthesis and Characterization of Radio-Frequency-Sputtered Delafossite Copper Indium Oxide (CuInO2) Thin Films

The deposition of CuInO2 films was achieved for the first time through a dual-target RF magnetron sputtering technique using Cu and In2O3 targets. It is found that the sputtering power of the Cu target has a significant effect on the structural, morphological, and optical characteristics of the films. The sputtering power of Cu was varied from 5 W to 50 W while the sputtering power of In2O3 was held constant at 50 W. From the XRD analysis, it was confirmed that pure-phase CuInO2 was obtained at a Cu sputtering power of 5 W. XPS analysis revealed that Cu exists in the oxidation state of 1+ in the films sputtered with 5 W Cu, further confirming the successful synthesis of single-phase CuInO2. The oxidation states of Cu and In, full-width half maximum (FWHM), peak positions, satellite peak positions, and their respective binding energies have been elucidated through XPS analysis. Nanocrystalline growth was confirmed through SEM analysis performed on all the post-deposition annealed films. Further, it was observed that grain size increased from 18.49 nm to 63.34 nm as Cu sputtering power is increased from 5 W to 50 W. The optical studies were performed on the post-deposition annealed films in the 300–800 nm wavelength range. The optical transmission and the optical bandgap were found to decrease with an increase in the Cu sputtering power. The highest transmission of ∼75% and the highest bandgap of 3.62 eV was obtained for the films showing single-phase CuInO2.

Joint target assignment and power allocation in the netted C-MIMO radar when tracking multi-targets in the presence of self-defense blanket jamming

The netted radar system (NRS) has been proved to possess unique advantages in anti-jamming and improving target tracking performance. Effective resource management can greatly ensure the combat capability of the NRS. In this paper, based on the netted collocated multiple input multiple output (C-MIMO) radar, an effective joint target assignment and power allocation (JTAPA) strategy for tracking multi-targets under self-defense blanket jamming is proposed. An architecture based on the distributed fusion is used in the radar network to estimate target state parameters. By deriving the predicted conditional Cramer-Rao lower bound (PC-CRLB) based on the obtained state estimation information, the objective function is formulated. To maximize the worst case tracking accuracy, the proposed JTAPA strategy implements an online target assignment and power allocation of all active nodes, subject to some resource constraints. Since the formulated JTAPA is non-convex, we propose an efficient two-step solution strategy. In terms of the simulation results, the proposed algorithm can effectively improve tracking performance in the worst case.

Research on energy storage allocation strategy considering smoothing the fluctuation of renewable energy

Energy storage technology can effectively solve the problems caused by large-scale grid connection of renewable energy with volatility and uncertainty. Due to the high cost of the energy storage system, the research on capacity allocation of energy storage system has important theoretical and application value. In this paper, an optimization method for determining the capacity of energy storage system for smoothing the power output of renewable energy is proposed. First, based on the actual data of Ulanqab, the output characteristics of wind power and photovoltaic power generation are studied, and the K-means algorithm is used to select typical days. Then, the energy storage configuration model is built according to the objective function and constraints. Finally, genetic algorithm is used to solve the optimization model, obtain the corresponding parameters, and complete the configuration of energy storage capacity. Based on the results of renewable energy spectrum analysis, the minimum capacity of the energy storage system that meets the constraint of target power output volatility after compensation by the energy storage system can be optimized. The simulation results show that at 1 and 10 min, the flattened volatility is about 2% and 5%, while the actual penetration volatility is about 20% and 30%. The volatility of the optimized model is greatly reduced, which proves the effectiveness of the proposed strategy.

Modulating the Combinatorial Target Power of MgSnN2 via RF Magnetron Sputtering for Enhanced Optoelectronic Performance: Mechanistic Insights from DFT Studies.

The unique structural features of many ternary nitride materials with strong chemical bonding and band gaps above 2.0 eV are limited and are experimentally unexplored. It is important to identify candidate materials for optoelectronic devices, particularly for light-emitting diodes (LEDs) and absorbers in tandem photovoltaics. Here, we fabricated MgSnN2 thin films, as promising II-IV-N2 semiconductors, on stainless-steel, glass, and silicon substrates via combinatorial radio-frequency magnetron sputtering. The structural defects of the MgSnN2 films were studied as a function of the Sn power density, while the Mg and Sn atomic ratios remained constant. Polycrystalline orthorhombic MgSnN2 was grown on the (120) orientation within a wide optical band gap range of ∼2.20-2.17 eV. The carrier densities of 2.18× 1020 to 1.02 × 1021 cm-3, mobilities between 3.75 and 2.24 cm2/Vs, and a decrease in resistivity from 7.64 to 2.73 × 10-3 Ω cm were confirmed by Hall-effect measurements. These high carrier concentrations suggested that the optical band gap measurements were affected by a Burstein-Moss shift. Furthermore, the electrochemical capacitance properties of the optimal MgSnN2 film exhibited an areal capacitance of 152.5 mF/cm2 at 10 mV/s with high retention stability. The experimental and theoretical results showed that MgSnN2 films were effective semiconductor nitrides toward the progression of solar absorbers and LEDs.

RAC-GAN-Based Scenario Generation for Newly Built Wind Farm

Due to the lack of historical output data of new wind farms, there are difficulties in the scheduling and planning of power grid and wind power output scenario generation. The randomness and uncertainty of meteorological factors lead to the results of traditional scenario generation methods not having the ability to accurately reflect their uncertainty. This article proposes a RAC-GAN-based scenario generation method for a new wind farm output. First, the Pearson coefficient is adopted in this method to screen the meteorological factors and obtain the ones that have larger impact on wind power output; Second, based on the obtained meteorological factors, the Grey Relation Analysis (GRA) is used to analyze the meteorological correlation between multiple wind farms with sufficient output data and new wind farms (target power stations), so that the wind farm with high meteorological correlation is selected as the source power station. Then, the K-means method is adopted to cluster the meteorological data of the source power station, thus generating the target power station scenario in which the cluster information serves as the label of the robust auxiliary classifier generative adversarial network (RAC-GAN) model and the output data of the source power station is considered as the basis. Finally, the actual wind farm output and meteorological data of a region in northeast China are employed for arithmetic analysis to verify the effectiveness of the proposed method. It is proved that the proposed method can effectively reflect the characteristics of wind power output and solve the problem of insufficient historical data of new wind farm output.

Potential of Residential Storage Battery Demand Response in Tertiary Balancing Market

The recent increase in residential photovoltaic (PV) power generation has become a major factor in the electricity supply instability. As a result, power balancing at the distribution level is becoming increasingly important. In Japan, storage batteries installed in residential homes along with PV systems has reached nearly 3GW and are expected to contribute to the power system stability. In this paper, we propose a method for a group of residential consumers to participate in the balancing market by managing their total meter value to match the target power. This method consists of the following two stages: bid volume optimization, which optimizes the bid volume based on the past behavior of the consumers, and demand response (DR) dispatch optimization, which quickly determines the dispatch of charging and discharging requests every 5min when DR is requested. Evaluation simulations based on the actual 1-min power data and consumer response model showed that, by aggregating 1600 consumers, the success criteria of the balancing market can be met. In other words, the total metered power in 1-min and 5-min intervals can be controlled within ±10% of the bid volume against the target power. From these experiments we have shown that the aggregation of as large as 1600 residential consumers has the potential to participate in the balancing market.

SOLPS-ITER modeling of Ar and N seeded discharges in ASDEX upgrade and ITER

In this paper SOLPS-ITER simulations of Ar and N seeded discharges are presented for H-mode conditions on ASDEX Upgrade and for burning plasmas on ITER. These discharges are additionally compared with Ne seeded cases for both tokamaks. It is demonstrated that Ar and N are retained in the divertor, and that the amount of seeded impurity (measured both by the seeding rate in atoms/s and the averaged separatrix density) necessary to achieve the same divertor conditions is lower for Ar than for N. This is attributed to almost the same values of first ionization potential of Ar and N (if compared to e.g. Ne) and to higher Ar radiation efficiency. If the radiated power fraction within the numerical simulation grid is of the order of 50%, which is sufficient to achieve partial detachment at the outer target in both devices, radiation losses from the confined region remain small for Ar seeded ITER cases (about 6% of power from the outer core region included in the SOLPS-ITER computational domain with total volume 135 m3). The fuel dilution by Ar remains lower than that by N and Ne, which corresponds to an Ar averaged separatrix concentration of 0.4%. From the point of view of target power handling, the boundary simulations thus demonstrate that Ar may be a suitable alternative seed impurity on ITER.