Generation Scheme Research Articles

Rooftop PV Development Suitability and Carbon Benefits: An Anhui Province Case Study

As one of the most rapidly developing provinces in China in the past two decades, Anhui Province has seen an increasing demand for clean energy in recent years due to industrial transformation and the requirements of dual carbon targets. This paper opts to investigate roof-mounted distributed photovoltaics, which are more suitable for development in densely populated areas. Current research on distributed photovoltaics largely focuses on vague estimations of power generation potential, without adequately considering the specific development conditions of different regions. This paper starts from the actual situation affecting the development of roof-mounted distributed photovoltaics and selects a smaller number of factors that are more in line with reality for hierarchical analysis, constructing a relatively simple but practical evaluation system (“meteorological-geographical-socio-economic”). At the same time, this paper innovatively proposes different schemes for the full lifecycle power generation and emission reduction benefits of roof-mounted distributed photovoltaics and compares them, providing a foundation for subsequent in-depth research. Key findings include the following: The northern regions of Anhui Province exhibit higher suitability for rooftop distributed PV, with residential areas being the primary influencing factor, followed by solar radiation considerations; the annual power generation potential of rooftop distributed PV in Anhui Province constitutes around 80% of the total electricity consumption in 2021, but the potential is predominantly concentrated in rural areas, resulting in spatial disparities in power generation and consumption across the province; developing the rooftop distributed PV industry based on suitability can yield substantial power generation and emission reduction benefits, translating to an estimated reduction of approximately 1.28 × 108 tCO2 annually, representing around one-third of Anhui Province’s carbon emissions in 2021.

Efficient Reaction‐Based Approaches for Gas‐Phase Enthalpy of Formation Prediction and their Application to Large (C32) Polycyclic Aromatic Hydrocarbons

AbstractIn this work, an efficient and generally applicable scheme for the automatic generation of the minimal set of independent model reactions to be used for the calculation of enthalpies of formation is presented. A post‐processing procedure targeting the selection of the most suitable model reactions by assigning them larger weights is suggested. The developed computational protocol exploiting high‐level ab initio calculations and accurate reference enthalpies of formation retrieved from the Active Thermochemical Tables reproduces with chemical accuracy well‐established enthalpies of formation of 15 relatively small (C10–C24) polycyclic aromatic hydrocarbons (PAHs). A promising alternative single‐reaction strategy encompassing all reference species is outlined. Both methods are then applied to predict for the set of 43 larger (C32) PAHs, and revealed significant deviation (Mean Unsigned Error, MUE = 23.2 kJ mol−1) compared to the earlier theoretical results obtained from B3LYP calculations with subsequent group‐based empirical corrections. In the absence of the experimental data, these evaluations of are expected to be more realistic as the approach employed in this work demonstrated better performance on the benchmarking set of 15 smaller PAHs with the MUE of 1.3–1.5 versus 6.2 kJ mol−1.

Regional evaluation of simulated waves during tropical storm events in the Gulf of Mexico

High waves in the Gulf of Mexico are mainly originated in tropical cyclones and winter storms. The availability of accurate estimations of high wave characteristics is an essential prerequisite for marine spatial planning, coastal zone management, and marine developments over the Northeastern Gulf of Mexico (NeGoM). Moreover, accurate wave simulation relies upon accurate wind field estimations. The wind field data used as input for numerical wave models is typically obtained from global and regional atmospheric model outputs. However, these model outputs may exhibit higher bias relative to observations, especially during fast-traveling tropical weather events. This study evaluates wave sensitivity over the NeGoM to different input wind speed and direction sources using unstructured SWAN model simulations. Five wind data products were employed in this study from ECMWF and NCEP global and regional models to examine the impact of wind forcing on wave simulations. The quality of wind field input data from these sources was evaluated relative to available observations (wind speed and direction) at National Data Buoy Center stations. A detailed sensitivity analysis is carried out to determine optimal wave model configuration. The model's performance is evaluated by comparing its wave simulation results with observational data during selected periods, simultaneously assessing the quality and sufficiency of wind data inputs from various sources. Our study shows that wind fields from FNL and NAM datasets are better correlated with observational data, in terms of vector correlation. Simulations using ECMWF – Real-Time product, combined with Janssen white-capping scheme better estimate wave conditions over the NeGoM compared with the other wind forcings used in this study. Moreover, Janssen white-capping scheme seems to be more realistic for estimations during Hurricane Michael; while for Hurricane Ida, Janssen generation scheme tends to overestimate the wave heights and Komen provides more accurate results.

Generative adversarial network for optimization of operational parameters based on shield posture requirements

To mitigate the potential hazards of shield tunneling misalignment (STM) caused by tunneling posture deviation, a method for optimizing operational parameters tailored to tunneling posture adjustment is developed. This paper presents a generative adversarial network (GAN) framework that incorporate a conditional generative adversarial network (CGAN) and two distinct discriminators (WGAN and Path GAN) to enhance the performance of the multi operational parameter generator. Based on engineering data, comparative experiments are designed to investigate the impact of the feature extraction methods, discriminators, and training strategies on the generation performance. Research has shown that an optimal generator scheme, comprising independent convolutional neural networks (CNNs), a summation feature fusion strategy, and a shared decoder, achieves remarkable performance with an MAE of 0.009, RMSE of 0.012, and average error scope of 0.073. Applications of the model confirm its ability to provide optimization suggestions for shield tunneling posture adjustments in engineering scenarios.

Scotogenic gauge mechanism for neutrino mass and dark matter

Scotogenic is a scheme for neutrino mass generation through the one-loop contribution of an inert scalar doublet and three sterile neutrinos. This work argues that such inert scalar doublet is a Goldstone boson mode associated with a gauge symmetry breaking. Hence, the resultant scotogenic gauge mechanism is very predictive, generating neutrino mass as contributed by a new gauge boson doublet that eats such Goldstone bosons. The dark matter stability is manifestly ensured by a matter parity as residual gauge symmetry for which a vector dark matter candidate is hinted.

Numerical experiment of phase-matched ion-based high-order harmonic generation in the water-window x-ray spectral region via electromagnetic envelope equation.

We present a phase-matching scheme for efficient high-order harmonic generation in the water-window x-ray spectral region using a 405-nm driving pulse. A high-intensity pulse (∼1016 W/cm2) is used to produce He1+ ions as the target medium, increasing the cutoff photon energy to the water-window x-ray spectral region. By adjusting the driving pulse divergence, the positive dipole phase variation balances the negative plasma dispersion and geometrical phase shift, achieving phase matching. Using the electromagnetic envelope equation coupled with the Keldysh ionization model, numerical experiments identify the optimal conditions. Results show that the relative conversion efficiencies of the 95th harmonic (4.26 nm) and the 169th harmonic (2.4 nm) reach 66% and 79% of the perfect phase-matching conditions, respectively.

QSKCG: Quantum‐based secure key communication and key generation scheme for outsourced data in cloud

AbstractIn the era of digital proliferation, individuals opt for cloud servers to store their data due to the diverse advantages they offer. However, entrusting data to cloud servers relinquishes users' control, potentially compromising data confidentiality and integrity. Traditional auditing methods designed to ensure data integrity in cloud servers typically depend on Trusted Third Party Auditors. Yet, many of these existing auditing approaches grapple with intricate certificate management and key escrow issues. Furthermore, the imminent threat of powerful quantum computers poses a risk of swiftly compromising these methods in polynomial time. To overcome these challenges, this paper introduces a Quantum‐based Secure Key Communication and Key Generation Scheme QSKCG for Outsourced Data in the Cloud. Leveraging Elliptic Curve Cryptography, the BB84 secure communication protocol, certificateless signature, and blockchain network, the proposed scheme is demonstrated through security analysis, affirming its robustness and high efficiency. Additionally, performance analysis underscores the practicality of the proposed scheme in achieving post‐quantum security in cloud storage.

Hardware Accelerated AES: RTL to GDS

This paper presents a hardware-accelerated Advanced Encryption Standard (AES) implementation featuring an 11-stage pipelined architecture and an optimized subkey generation scheme for enhanced performance. This design is implemented in Verilog and verified through waveform analysis. Operating at a maximum clock frequency of 100 MHz, our implementation achieves significant efficiency. The final design occupies an area of 1.44 mm2 and consists of 32,424 standard cells, with a power consumption of 32 mW at the typical corner. Furthermore, the entire design flow, from synthesis to layout (GDSII), is achieved using OpenLane, an open-source Electronic Design Automation (EDA) tool. This democratization of hardware design empowers a wider range of participants, particularly students and researchers, to efficiently implement their designs and gain hands-on experience without the constraints of expensive commercial tools. This fosters innovation and accelerates progress by allowing a broader community to contribute to technological advancements. Index Terms—OpenLane, Open-source , EDA, Pipelined AES, RTL to GDSII

Improving Object Detection Accuracy with Self-Training Based on Bi-Directional Pseudo Label Recovery

Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper presents a novel approach to generate high-quality pseudo labels for unlabeled data. It utilizes predictions with high- and low-confidence levels to generate refined labels and then validates the accuracy of those predictions through bi-directional object tracking. The bi-directional object tracker leverages both past and future information to recover missing labels and increase the accuracy of the generated pseudo labels. This method can also substantially reduce the effort and time needed in label creation compared to the conventional manual labeling. The proposed method utilizes a buffer to accumulate detection labels (bounding boxes) predicted by the object detector. These labels are refined for accuracy though forward and backward tracking, ultimately constructing the final set of pseudo labels. The method is integrated in the YOLOv5 object detector and tested on the BDD100K dataset. Through the experiments, we demonstrate the effectiveness of the proposed scheme in automating the process of pseudo label generation with notably higher accuracy than the recent state-of-the-art pseudo label generation schemes. The results show that the proposed method outperforms previous methods in terms of mean average precision (mAP), label generation accuracy, and speed. Using the bi-directional recovery method, an increase in mAP@50 for the BDD100K dataset by 0.52% is achieved, and for the Waymo dataset, it provides an improvement of mAP@50 by 8.7% to 9.9% compared to 8.1% of the existing method when pre-training with 10% of the dataset. An improvement by 2.1% to 2.9% is achieved as compared to 1.7% of the existing method when pre-training with 20% of the dataset. Overall, the improved method leads to a significant enhancement in detection accuracy, achieving higher mAP scores across various datasets, thus demonstrating its robustness and effectiveness in diverse conditions.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.

A lossless quantization approach for physical-layer key generation in vehicular ad hoc networks based on received signal strength

Vehicular Ad Hoc Networks (VANETs) provide various benefits and play a crucial role in improving efficiency and ensuring human safety across different applications. However, these advantages also give rise to security challenges and privacy concerns, necessitating a thorough examination of security attacks. Current key generation schemes, particularly those based on the Physical-Layer Model (PLM), face limitations such as low key generation rates and inadequate randomness. This paper introduces an innovative key generation method that utilizes adaptive physical-layer techniques and lossless quantization. The method involves an eight-level quantization process, which enables precise granularity and adaptive selection of quantization thresholds to tailor the computation of Received Signal Strength (RSS) measurements to individual needs. The adaptive approach ensures the retention of information within RSS measurements, resulting in reduced bit disagreement rates and enhanced randomness of the generated keys. Simulated evaluations demonstrate the effectiveness of the proposed method, showing superior performance in terms of bit generation, entropy, and secrecy rates, while also minimizing the occurrence of unnecessary measurements. This advancement holds significant promise for strengthening the security framework within VANETs.

Fractal OAM Generation and Detection Schemes

Fractal OAM Generation and Detection Schemes

A DWT-Based PHY Key Generation Scheme for the Cross-Layer Security in IoT Networks

A DWT-Based PHY Key Generation Scheme for the Cross-Layer Security in IoT Networks

Performance evaluation of solar-PV integrated hybrid fuzzy-logic controlled multi-functional UPQC for enhancing PQ features

To improve distribution system voltage and current quality, a newly built solar-PV system connected multi-functional universal power quality compensator (MFUPQC) has been extensively used. The proposed MFUPQC mitigates both load and source-side concerns in a three-phase distribution system. Furthermore, as part of the distributed generation scheme, active power from solar PV is injected into the grid or source when solar PV is available. In this context, the proposed MFUPQC was tested in both PQ enhancement and DG integration modes using a feasible control scheme. The proportional-integral controller is used for shunt- voltage-source inverter (VSI) DC-link control, which is not suitable for regulating DC-link voltage at the desired level due to incorrect gain value selection. In this work, an intelligent hybrid-fuzzy-logic DC-link control of MFUPQC evidences the intelligent knowledge base for better regulation of power-quality issues. The suggested hybrid fuzzy-logic controlled MFUPQC device's performance for both power quality (PQ) improvement and DG integration is validated using the MATLAB/Simulink software tool, and simulation results are provided with an appealing comparison analysis.

A Breakthrough Authentication Protocol for Fortifying IoT-Cloud Security

Abstract: Physical layer key generation, leveraging the reciprocity and randomness of wireless fading channels, has garnered significant research interest in recent years. While theoretical studies have demonstrated its potential to generate informationtheoretically secure keys, challenges remain in translating theory into practice. This paper provides an overview of the physical layer key generation process and discusses its practical challenges. Various passive and active attacks are analyzed and evaluated through numerical studies. A new key generation scheme using random probing signals and combining usergenerated randomness with channel randomness is introduced to counteract active attacks. Numerical results demonstrate that the proposed scheme achieves higher security strength than existing schemes using constant probing signals under active attacks. Secret-key generation relying on wireless channel reciprocity is an intriguing solution as it can be efficiently implemented at the physical layer of emerging wireless communication networks, while providing information-theoretic security guarantees. In this paper, we investigate and compare the secret-key capacity based on the sampling of the entire complex channel state information (CSI) or only its envelope, the received signal strength (RSS).

A new practical physical layer secret key generation in the presence of an untrusted relay

Physical layer secret key generation (SKG) has recently been introduced as a lightweight and efficient solution for sixth-generation (6G) networks. In this area, schemes based on local random generators are used for high-rate key generation. One of these schemes is random phase injection, where channel probe signals with random phases are exchanged between communication parties (source and destination). This paper proposes an SKG scheme in the presence of an untrusted relay, which helps the SKG process while cannot extract the secret key. To make the scheme operational, for the first time, the channel probe signals are considered discrete random phase based on M-PSK signals and a multi-bit quantizer is used in the reception. In addition, to reduce the key error rate, quantization with guard bands (GB) is used for key extraction. For such a scenario, we derive expressions for key agreement rate, key mismatch rate (KMR), key discarding rate (KDR) and key generation rate (KGR). Additionally, for the first time, this work examines the context of geometric secrecy for the proposed discrete phase key generation scheme for both direct and relaying scenarios. Through simulations, several engineering insights are presented to enhance the quality of the proposed SKG and its security.

СУЧАСНІ ТЕНДЕНЦІЇ РОЗВИТКУ СИСТЕМ КОМПЕНСАЦІЇ РЕАКТИВНОЇ ПОТУЖНОСТІ

This paper surveys current trends and developments in reactive power compensation systems, elucidating their significance in enhancing system capacity and flexibility. By analyzing the capacity factor enhancement enabled by reactive power compensation, the study underscores its role in optimizing electricity delivery, thus conserving energy resources and facilitating the integration of renewable energy sources. It explores the key models and concepts driving advancements in reactive power compensation within power grids. As modern electrical systems grow in complexity, ongoing research into improving compensation methods becomes imperative to ensure the reliability and efficiency of industrial power grids. Future endeavors will involve developing a model utilizing real-world enterprise data to investigate the effects of technological variations in filter parameters on the performance of complex reactive power compensation schemes, encompassing both filters and capacitor banks. Keywords: reactive power, compensation systems, filter-compensating circuit, industrial power grid, electric power. Context. The paper considers the current trends used in the field of reactive power compensation, and the main directions of development of modern methods of reactive power compensation. Objective. The purpose of the work is to overview of current trends in the development of reactive power compensation systems, as well as an overview of possible ways of their effective application in various industrial networks. Methods. The paper presents a key aspect of reactive power compensation is its ability to increase system capacity and flexibility. By improving the capacity factor, utilities can deliver electricity more efficiently, reducing the need for excess capacity and thus conserving energy resources. In addition, this process plays an important role in supporting the integration of renewable energy sources such as wind and solar power, which often introduce volatility into electricity generation schemes. Results. The paper presents the main models and concepts in the field of reactive power compensation in power grids. Conclusions. Given the growing complexity of modern electrical systems, it is important to continue research into improving methods and means of reactive power compensation to ensure the reliability and efficiency of industrial power grids. In further research, it is planned to develop a model using a real enterprise as an example and investigate the impact of technological deviations of filter parameters on the steady-state and transient performance of complex reactive power compensation schemes, including not only filters but also capacitor banks.

Mesoscale smoothed particle hydrodynamics simulation of seizure and flash temperature for dry friction of elastoplastic solids in a newly developed model

This study developed a simulation model using a smoothed particle hydrodynamics (SPH) method targeted to seizure process at the mesoscale. The mechanisms of wear, adhesion, and heat generation leading to seizure at the mesoscale were modelized without assumptions or theories based on empirical rules. In particular, we targeted on flash temperature during seizure process, which is difficult to measure directly in experiment and has not been simulated without using friction theory. Our model consisted of both a macroscopic elastoplastic consideration and a microscopic interfacial interaction consideration, and the heat generation scheme that 90% of the plastic strain energy is converted to heat energy were adopted in the model. The simulation demonstrated the seizure process in which the contact state is maintained by the strong interfacial interaction as the plastic strain progresses and the temperature rapidly rises. The flash temperature by the simulation provided a reasonable quantitative match at order level to a temperature estimated by substituting true contact area and interfacial heat flux obtained by the simulation into a theoretical formula of flash temperature.

Optimization of the Coupling between Water and Energy Consumption in a Smart Integrated Photovoltaic Pumping Station System

Agricultural irrigation requires significant consumption of freshwater resources and energy. The integration of photovoltaic power generation into irrigation systems has been extensively investigated in order to save the cost of energy. However, current research often neglects the coupling relationship between photovoltaic power generation and irrigation schemes. This study presented a novel smart integrated photovoltaic pump station system to effectively address the issue associated with water and energy consumption in irrigation. An optimization model was proposed to synchronize the energy consumption of irrigation pump stations with photovoltaic power generation, accurately meeting the irrigation water demand while maximizing solar energy utilization. The optimization model incorporates power balance, grid-connected power, and total water demand as constraints while considering pump speed as the decision variable and aiming to minimize daily operational costs. Finally, a high-standard farmland was used as a case study to validate the efficacy of the optimization strategy through two photovoltaic grid-connected policies—one allowing for the sale of surplus power and the other prohibiting it. An improved dynamic programming method was employed to solve for optimal energy consumption schemes under different water demand conditions; the results were compared against traditional methods, revealing potential cost savings ranging from 6.2% to 30.5%. The optimization model and method propose a new operational concept for the irrigation system with photovoltaic generation, effectively utilizing the distinctive features of both irrigation and photovoltaics to optimize water and energy resources.

Power Harmonic Suppression based direct vector control for robust DFIG operation during grid voltage distortions

AbstractThis paper proposes a Power Harmonic Suppression (Damping) (PHS) based reference current generation scheme, which focuses on simultaneous mitigation of harmonic pulsations in electromagnetic torque, stator real and reactive powers, and DC‐link voltage while retaining sinusoidal stator and grid currents and unity power factor at grid side. The crux of this PHS method lies in its ability to eliminate the harmonic powers developed in the DFIG stator and grid at 100Hz, 200Hz, 300Hz, 400Hz, and 600 Hz owing to second, fifth, and seventh harmonic grid voltages. This is achieved as a result of the elimination of natural fluxes developed in the DFIG air gap due to second, fifth, and seventh harmonic distortions in grid voltage. For this, a single concurrent reference current generation technique is developed for RSC and GSC to mitigate the oscillations of electromagnetic torque, stator real and reactive powers, and ripples in DC‐link voltage. Further, the reference rotor currents (positive, negative, fifth, and seventh sequence) are compared with the actual rotor currents using a PI controller to retain sinusoidal stator and grid currents. The proposed scheme's efficacy is validated with PSCAD/EMTDC simulations and using OPAL‐RT OP4510 real‐time simulator, on a 2.3 kVA DFIG.