arXiv subjects

Ning Lu

Publications and source records attributed to Ning Lu.

At least 73 records · Page 4Linked to original sources

arXiv2022

A TCN-based Hybrid Forecasting Framework for Hours-ahead Utility-scale PV Forecasting

This paper presents a Temporal Convolutional Network (TCN) based hybrid PV forecasting framework for enhancing hours-ahead utility-scale PV forecasting. The hybrid framework consists of two forecasting models: a physics-based trend forecasting (TF) model and a data-driven cloud-event forecasting (CF) model. Three TCNs are integrated in the framework for: i) blending the inputs from different Numerical Weather Prediction sources for the TF model to achieve superior performance on forecasting hourly PV profiles, ii) capturing spatial-temporal correlations between detector sites and the target site in the CF model to achieve more accurate forecast of intra-hour PV power drops, and iii) reconciling TF and CF results to obtain coherent hours-ahead PV forecast with both hourly trends and intra-hour fluctuations well preserved. To automatically identify the most contributive neighboring sites for forming a detector network, a scenario-based correlation analysis method is developed, which significantly improves the capability of the CF model on capturing large power fluctuations caused by cloud movements. The framework is developed, tested, and validated using actual PV data collected from 95 PV farms in North Carolina. Simulation results show that the performance of 6 hours ahead PV power forecasting is improved by approximately 20% compared with state-of-the-art methods.

eess.SP↗

arXiv2022

On-Demand Resource Management for 6G Wireless Networks Using Knowledge-Assisted Dynamic Neural Networks

On-demand service provisioning is a critical yet challenging issue in 6G wireless communication networks, since emerging services have significantly diverse requirements and the network resources become increasingly heterogeneous and dynamic. In this paper, we study the on-demand wireless resource orchestration problem with the focus on the computing delay in orchestration decision-making process. Specifically, we take the decision-making delay into the optimization problem. Then, a dynamic neural network (DyNN)-based method is proposed, where the model complexity can be adjusted according to the service requirements. We further build a knowledge base representing the relationship among the service requirements, available computing resources, and the resource allocation performance. By exploiting the knowledge, the width of DyNN can be selected in a timely manner, further improving the performance of orchestration. Simulation results show that the proposed scheme significantly outperforms the traditional static neural network, and also shows sufficient flexibility in on-demand service provisioning.

eess.SY↗

arXiv2022

Training Quantized Deep Neural Networks via Cooperative Coevolution

This work considers a challenging Deep Neural Network(DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations. Most previous quantization approaches are not applicable to this task since they rely on full-precision gradients to update network weights. To fill this gap, in this work we advocate using Evolutionary Algorithms (EAs) to search for the optimal low-bits weights of DNNs. To efficiently solve the induced large-scale discrete problem, we propose a novel EA based on cooperative coevolution that repeatedly groups the network weights based on the confidence in their values and focuses on optimizing the ones with the least confidence. To the best of our knowledge, this is the first work that applies EAs to train quantized DNNs. Experiments show that our approach surpasses previous quantization approaches and can train a 4-bit ResNet-20 on the Cifar-10 dataset with the same test accuracy as its full-precision counterpart.

cs.NE↗

arXiv2022

Ultra-High Lithium Storage Capacity of Al2C Monolayer under Restricted Multilayered Growth Mechanism

Designing anode materials with high lithium specific capacity is crucial to the development of high energy-density lithium ion batteries. Herein, a distinctive lithium growth mechanism, namely, the restricted multilayered growth for lithium, and a strategy for lithium storage are proposed to achieve the balance between the ultra-high specific capacity and the need to avert uncontrolled dendritic growth of lithium. In particular, based on first-principles computation, we show that the Al2C monolayer with planar tetracoordinate carbon structure can be an ideal platform for realizing the restricted multilayered growth mechanism as a 2D anode material. Furthermore, the Al2C monolayer exhibits ultra-high specific capacity of lithium of 4059 mAh/g, yet with a low dif-fusion barrier of 0.039-0.17 eV as well as low open circuit voltage in the range of 0.002-0.34 V. These novel properties endow the Al2C monolayer a promising anode material for future lithium ion batteries. Our study offers a new way to design promising 2D anode materials with high specific capacity, fast lithium-ion diffusion, and safe lithium storage mechanism.

cond-mat.mtrl-sci↗

arXiv2022

Anisotropic Electrene T'-Ca2P with Electron Gas Magnetic Coupling as Anode Material for Na/K Ion Batteries

There is an urgently need for the high-performance rechargeable electrical storage devices as supplement or substitutions of lithium ion batteries due to the shortage of lithium in nature. Herein we propose a stable 2D electrene T'-Ca2P as anode material for Na/K ion batteries by first-principle calculations. Our calculated results show that T'-Ca2P monolayer is an antiferromagnetic semiconducting electrene with spin-polarized electron gas. It exhibits suitable adsorption for both Na and K atoms, and its anisotropic migration energy barriers are 0.050/0.101 eV and 0.037/0.091 eV in b/a direction, respectively. The theoretical capacities for Na and K are both 482 mAh/g, while the average working voltage platforms are 0.171-0.226 V and 0.013-0.267 V, respectively. All the results reveal that the T'-Ca2P monolayer has promised application prospects as anode materials for Na/K ion batteries.

cond-mat.mtrl-sci↗

arXiv2022

SA-HMTS: A Secure and Adaptive Hierarchical Multi-timescale Framework for Resilient Load Restoration Using A Community Microgrid

Distribution system integrated community microgrids (CMGs) can partake in restoring loads during extended duration outages. At such times, the CMG is challenged with limited resource availability, absence of robust grid support, and heightened demand-supply uncertainty. This paper proposes a secure and adaptive three-stage hierarchical multi-timescale framework for scheduling and real-time (RT) dispatch of CMGs with hybrid PV systems to address these challenges. The framework enables the CMG to dynamically expand its boundary to support the neighboring grid sections and is adaptive to the changing forecast error impacts. The first stage solves a stochastic extended duration scheduling (EDS) problem to obtain referral plans for optimal resource rationing. The intermediate near-real-time (NRT) scheduling stage updates the EDS schedule closer to the dispatch time using newly obtained forecasts, followed by the RT dispatch stage. To make the dispatch decisions more secure and robust against forecast errors, a novel concept called delayed recourse is proposed. The methodology is evaluated via numerical simulations on a modified IEEE 123-bus system and validated using OpenDSS/hardware-in-loop simulations. The results show superior performance in maximizing load supply and continuous secure CMG operation under numerous operating scenarios.

eess.SY↗

arXiv2022

ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles

It is a common practice for utilities to down-sample smart meter measurements from high resolution (e.g. 1-min or 1-sec) to low resolution (e.g. 15-, 30- or 60-min) to lower the data transmission and storage cost. However, down-sampling can remove high-frequency components from time-series load profiles, making them unsuitable for in-depth studies such as quasi-static power flow analysis or non-intrusive load monitoring (NILM). Thus, in this paper, we propose ProfileSR-GAN: a Generative Adversarial Network (GAN) based load profile super-resolution (LPSR) framework for restoring high-frequency components lost through the smoothing effect of the down-sampling process. The LPSR problem is formulated as a Maximum-a-Prior problem. When training the ProfileSR-GAN generator network, to make the generated profiles more realistic, we introduce two new shape-related losses in addition to conventionally used content loss: adversarial loss and feature-matching loss. Moreover, a new set of shape-based evaluation metrics are proposed to evaluate the realisticness of the generated profiles. Simulation results show that ProfileSR-GAN outperforms Mean-Square Loss based methods in all shape-based metrics. The successful application in NILM further demonstrates that ProfileSR-GAN is effective in recovering high-resolution realistic waveforms.

eess.SP↗

arXiv2022

The Regulated NiCu Cycles with the new $^{57}$Cu(p,$γ$)$^{58}$Zn reaction rate and the Influence on Type-I X-Ray Bursts: GS 1826$-$24 Clocked Burster

During the X-ray bursts of GS 1826$-$24, "clocked burster", the nuclear reaction flow that surges through the rapid-proton capture process path has to pass through the NiCu cycles before reaching the ZnGa cycles that moderate the further extent of hydrogen burning in the region above germanium and selenium isotopes. The $^{57}$Cu(p,$γ$)$^{58}$Zn reaction located in the NiCu cycles plays an important role in influencing the burst light curves as found by Cyburt et al. (2016). We deduce the $^{57}$Cu(p,$γ$)$^{58}$Zn reaction rate based on the experimentally determined important nuclear structure information, isobaric-multiplet-mass equation, and large-scale shell model calculations. Based on the isobaric-multiplet-mass equation, we propose a possible order of $1^+_1$ and $2^+_3$ dominant resonance states and constrain the resonance energy of the $1^+_2$ state. The latter reduces the contribution of the $1^+_2$ dominant resonance state. The new reaction rate is up to a factor of four lower than the Forstner et al. (2001) rate recommended by JINA REACLIB v2.2 at the temperature regime sensitive to clocked bursts of GS 1826$-$24. Using the simulation from the one-dimensional implicit hydrodynamic code, KEPLER, to model the thermonuclear X-ray bursts of GS 1826$-$24 clocked burster, we find that the new $^{57}$Cu(p,$γ$)$^{58}$Zn coupled with the latest $^{56}$Ni(p,$γ$)$^{57}$Cu and $^{55}$Ni(p,$γ$)$^{56}$Cu reaction rates redistributes the reaction flow in the NiCu cycles and strongly influences the burst ash composition, whereas the $^{59}$Cu(p,$α$)$^{56}$Ni and $^{59}$Cu(p,$γ$)$^{60}$Zn reactions suppress the influence of the $^{57}$Cu(p,$γ$)$^{58}$Zn reaction and diminish the impact of nuclear reaction flow that by-passes the important $^{56}$Ni waiting point induced by the $^{55}$Ni(p,$γ$)$^{56}$Cu reaction on burst light curve.

astro-ph.HE↗

arXiv2022

The Impact of the New $^{65\!}$As(p,$γ$)$^{66\!}$Se Reaction Rate on the Two-Proton Sequential Capture of $^{64}\!$Ge, Weak GeAs Cycles, and Type-I X-Ray Bursts such as the Clocked Burster GS 1826$-$24

We re-assess $^{65}$As(p,$γ$)$^{66}$Se reaction rates based on a set of proton thresholds of $^{66}$Se, $S_\mathrm{p}$($^{66}$Se), estimated from the experimental mirror nuclear masses, theoretical mirror displacement energies, and full $pf$-model space shell-model calculation. The self-consistent relativistic Hartree-Bogoliubov theory is employed to obtain the mirror displacement energies with much reduced uncertainty, and thus reducing the proton-threshold uncertainty up to 161 keV compared to the AME2020 evaluation. Using the simulation instantiated by the one-dimensional multi-zone hydrodynamic code, KEPLER, that closely reproduces the observed GS 1826$-$24 clocked bursts, the present forward and reverse $^{65}$As(p,$γ$)$^{66}$Se reaction rates based on a selected $S_\mathrm{p}$($^{66}$Se) = 2.469$\pm$0.054 MeV, and the latest $^{22}$Mg($α$,p)$^{25}$Al, $^{56}$Ni(p,$γ$)$^{57}$Cu(p,$γ$)$^{58}$Zn, $^{55}$Ni(p,$γ$)$^{56}$Cu, and $^{64}$Ge(p,$γ$)$^{65}$As reaction rates, we find that though the GeAs cycles is weakly established in the rapid-proton capture process path, the $^{65}$As(p,$γ$)$^{66}$Se reaction still strongly characterizes the burst tail end due to the two-proton sequential capture on $^{64}$Ge, not found by Cyburt et al. (2016) sensitivity study. The $^{65}$As(p,$γ$)$^{66}$Se reaction influences the abundances of nuclei $A$ = 64, 68, 72, 76, and 80 up to a factor of 1.4. The new $S_\mathrm{p}$($^{66}$Se) and the inclusion of the updated $^{22}$Mg($α$,p)$^{25}$Al reaction rate increases the production of $^{12}$C up to a factor of $4.5$ that is not observable and could be the main fuel for superburst. The waiting point status of and two-proton sequential capture on $^{64}$Ge, weak-cycle feature of GeAs at region heavier than $^{64}$Ge, and impact of other possible $S_\mathrm{p}$($^{66}$Se) are also discussed.

astro-ph.HE↗

arXiv2021

Reinforcement Learning for Volt-Var Control: A Novel Two-stage Progressive Training Strategy

This paper develops a reinforcement learning (RL)approach to solve a cooperative, multi-agent Volt-Var Control (VVC) problem for high solar penetration distribution systems. The ingenuity of our RL method lies in a novel two-stage progressive training strategy that can effectively improve training speed and convergence of the machine learning algorithm. In Stage 1(individual training), while holding all the other agents inactive, we separately train each agent to obtain its own optimal VVC actions in the action space: {consume, generate, do-nothing}. In Stage 2 (cooperative training), all agents are trained again coordinatively to share VVC responsibility. Rewards and costs in our RL scheme include (i) a system-level reward (for taking an action), (ii) an agent-level reward (for doing-nothing), and(iii) an agent-level action cost function. This new framework allows rewards to be dynamically allocated to each agent based on their contribution while accounting for the trade-off between control effectiveness and action cost. The proposed methodology is tested and validated in a modified IEEE 123-bus system using realistic PV and load profiles. Simulation results confirm that the proposed approach is robust and computationally efficient; and it achieves desirable volt-var control performance under a wide range of operation conditions.

eess.SY↗

arXiv2021

Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack

Over the past few years, various word-level textual attack approaches have been proposed to reveal the vulnerability of deep neural networks used in natural language processing. Typically, these approaches involve an important optimization step to determine which substitute to be used for each word in the original input. However, current research on this step is still rather limited, from the perspectives of both problem-understanding and problem-solving. In this paper, we address these issues by uncovering the theoretical properties of the problem and proposing an efficient local search algorithm (LS) to solve it. We establish the first provable approximation guarantee on solving the problem in general cases.Extensive experiments involving 5 NLP tasks, 8 datasets and 26 NLP models show that LS can largely reduce the number of queries usually by an order of magnitude to achieve high attack success rates. Further experiments show that the adversarial examples crafted by LS usually have higher quality, exhibit better transferability, and can bring more robustness improvement to victim models by adversarial training.

cs.CL↗

arXiv2021

A Novel Grid-forming Voltage Control Strategy for Supplying Unbalanced Microgrid Loads Using Inverter-based Resources

This paper presents a novel grid-forming voltage control strategy for a battery energy storage system to maintain balanced three-phase output voltages when serving unbalanced loads. A stationary reference frame ($αβ$) based control scheme is proposed to regulate positive-sequence and negative-sequence voltages. Compared with the conventional rotating reference frame ($dq$) based control scheme, the proposed scheme shows better dynamic performance. Then, we analyze the system zero-sequence network and propose adding a grounding transformer to the Y/Yg connected output transformer to further reduce the voltage unbalance at the point of common coupling. The simulation results demonstrate the effective performance of the proposed voltage control scheme. Based on the results, the power-voltage unbalance curve is derived for different output transformer configurations to establish the relationship between the power unbalance limit and the voltage unbalance limit for microgrid power scheduling.

eess.SY↗

arXiv2021

A Novel Data Segmentation Method for Data-driven Phase Identification

This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase are weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.

eess.SY↗

arXiv2021

Novel Real-Time EMT-TS Modeling Architecture for Feeder Blackstart Simulations

This paper presents the development and benchmarking of a novel real-time electromagnetic-transient and transient-stability (EMT-TS) modeling architecture for distribution feeder restoration studies. The work presents for the first time in the literature a real-time EMT-TS testbed in which the grid-forming unit is simulated in EMT domain, operating as the slack bus of the phasor domain while including unbalanced voltage conditions. To evaluate the performance and limitations of the proposed model, an equivalent EMT testbed is developed to serve as a benchmark. First, the co-simulation framework and the domain coupling methodology are introduced. Next, the steady-state operation of the EMT-TS and EMT models are matched. Then, tests are conducted to analyze the transient performance of the proposed EMT-TS model when compared to its EMT benchmark. Results reveal that when utilizing a battery energy storage system (BESS) as the grid-forming unit, the EMT-TS testbed can maintain high accuracy for typical load steps.

eess.SY↗

arXiv2021

A Data-driven Probabilistic-based Flexibility Region Estimation Method for Aggregated Distributed Energy Resources

This paper presents a data-driven, distributionally robust chance-constrained optimization method for estimating the real and reactive power controllability of aggregated distributed energy resources (DER). At the DER-level, a two-dimensional flexibility region can be formed based on the real and reactive power regulating limits of each DER considering forecast uncertainty. At the feeder-level, an aggregated flexibility region is computed via a multi-directional search method. In each search direction, extend the real and reactive power of each controllable DER towards its operational limits until: i) all DERs' maximum operational limits are reached or, ii) one or more of the distribution network operational limits are violated. The method enables three key features for operating aggregated DER resources: controllability estimation, visualization, and risk quantification. Simulation results demonstrated the effectiveness of the algorithm and quantified the impact of different parameter settings on the flexibility region boundary changes. The proposed algorithm is robust and computationally efficient and can meet real-time computing needs.

eess.SY↗

arXiv2021

A Unified Power-Setpoint Tracking Algorithm for Utility-Scale PV Systems with Power Reserves and Fast Frequency Response Capabilities

This paper presents a fast power-setpoint tracking algorithm to enable utility-scale photovoltaic (PV) systems to provide high quality grid services such as power reserves and fast frequency response. The algorithm unites maximum power-point estimation (MPPE) with flexible power-point tracking (FPPT) control to improve the performance of both algorithms, achieving fast and accurate PV power-setpoint tracking even under rapid solar irradiance changes. The MPPE is developed using a real-time, nonlinear curve-fitting approach based on the Levenberg-Marquardt algorithm. A modified adaptive FPPT based on the Perturb and Observe technique is developed for the power-setpoint tracking. By using MPPE to decouple the impact of irradiance changes on the measured PV output power, we develop a fast convergence technique for tracking power-reference changes within three FPPT iterations. Furthermore, to limit the maximum output power ripple, a new design is introduced for the steady-state voltage step size of the adaptive FPPT. The proposed algorithm is implemented on a testbed consisting of a 500 kVA three-phase, single-stage, utility-scale PV system on the OPAL-RT eMEGASIM platform. Results show that the proposed method outperforms the state-of-the-art.

eess.SY↗

arXiv2021

Maximum Power Reference Tracking Algorithm for Power Curtailment of Photovoltaic Systems

This paper presents an algorithm for power curtail-ment of photovoltaic (PV) systems under fast solar irradiance intermittency. Based on the Perturb and Observe (P&O) technique, the method contains an adaptive gain that is compensated in real-time to account for moments of lower power availability. In addition, an accumulator is added to the calculation of the step size to reduce the overshoot caused by large irradiance swings. A testbed of a three-phase single-stage, 500 kVA PV system is developed on the OPAL-RT eMEGAsim real-time simulator. Field irradiance data and a regulation signal from PJM (RTO) are used to compare the performance of the proposed method with other techniques found in the literature. Results indicate an operation with smaller overshoot, less dc-link voltage oscillations, and improved power reference tracking capability.

eess.SY↗

arXiv2021

A Two-Stage Coordinative Zonal Volt/VAR Control Scheme for Distribution Systems with High Inverter-based Resources

This paper presents a two-stage zonal Volt/VAR control scheme for coordinating inverter-based resources (IBR) with utility-owned voltage regulators (VR) to regulate voltage in unbalanced 3-phase distribution systems. First, correlations between nodal voltages are derived from nodal voltage sensitivity studies. Then, the feeder is partitioned into non-overlapping, weakly-coupled voltage control zones based on nodal voltage correlations. IBR are used in the first stage to regulate voltage changes continuously and VR are used in the second stage to regulate large voltage deviations. An online VR voltage setpoint tuning strategy is developed to reduce excessive tap changes and avoid large voltage fluctuations without retrofitting existing VR controllers. In addition, the proposed algorithm uses real-time voltage measurements only from the critical nodes (typically less than 4% of total nodes) to reduce the sensing and communication needs. Actual distribution feeder topologies and load and PV time-series data are used to verify the performance of the algorithm. Because the method is a rule-based approach, it runs extremely fast, requires fewer measurements, and requires no retrofit to the existing VR control mechanisms. Simulation results show that the performance of the proposed method in terms of voltage control results and average numbers of VR tap changes are satisfactory.

eess.SY↗