The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who ha...The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.展开更多
A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing t...A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.展开更多
Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−freque...Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.展开更多
Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat...Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.展开更多
Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transien...Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.展开更多
Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhance...Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.展开更多
The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)h...The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.展开更多
We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers...We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.展开更多
This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obsta...This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.展开更多
Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on im...Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on improving the controllability and efficiency of SynRM.Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque.Shaft-mounted position sensors are generally undesirable because of their high cost,sensitivity to harsh operating conditions,maintenance requirements,and reduced reliability in environments characterized by high vibration.Consequently,sensorless control techniques that estimate rotor position using measured stator currents and voltages have attracted increasing attention.However,magnetic saturation,parameter nonlinearities,and cross-coupling effects significantly degrade position estimation accuracy and may compromise the stability of sensorless SynRM drives.In this paper,a nonlinear SynRM model is developed using finite element analysis(FEA)to accurately capture magnetic saturation and cross-coupling effects,thereby providing a precise representation of the machine’s electromagnetic behavior under varying load and flux conditions.A series of magnetostatic FEA simulations is performed.To reduce computational complexity,only one motor pole is analyzed by applying anti-periodic boundary conditions along the domain sides and enforcing a zero magnetic vector potential on the external stator boundary.Nonlinear iron material properties are modeled using the appropriate B-H curve.The simulations are carried out by imposing d-and q-axis current components and computing the corresponding flux linkages and electromagnetic torque.Based on these results,both apparent and incremental inductances are extracted and incorporated into the control algorithm.An advanced fictitious flux linkage method combined with a phase-locked loop(PLL)is employed for accurate rotor position estimation.Simulation results confirm that the proposed sensorless control strategy ensures stable operation and high position estimation accuracy over the entire speed range.展开更多
Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,envi...Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,environmental monitoring,biomedicine,and chemical analysis.While thermal detectors are commonly used,their limitations in sensitivity and response time render them less suitable for next-generation MWIR and LWIR applications.These advanced applications necessitate the use of narrow bandgap semiconductor-based photodetectors,which offer tunable optoelectronic properties and higher specific detectivity compared to thermal detectors.In this review,we provide a detailed analysis of the operational principles and manufacturing strategies of infrared photodetectors based on narrow bandgap semiconductors,which enable high-performance detection in the MWIR and LWIR regions.Our focus is specifically on scalable fabrication of MWIR and LWIR photodetectors,emphasizing devices with active areas ranging from millimeters to centimeters.Researches on large-scale fabrication of infrared photodetectors using quantum dots,two-dimensional(2D)van der Waals(vdW)materials,and three-dimensional(3D)bulk semiconductors are investigated.Finally,we summarize the remaining challenges in developing scalable narrow bandgap semiconductor-based MWIR and LWIR photodetectors for commercialization.By addressing the obstacles such as the difficulty in large-scale unform film synthesis,the requirement for cryogenic device operation,and the introduction of high-density of defect states during the hybridization processes,MWIR and LWIR photodetectors based on narrow bandgap semiconductors will pave the way for designing new sensory systems and applications in a wavelength regime that has been less developed compared to the visible and near-infrared(NIR)ranges.展开更多
The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mo...The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.展开更多
Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows rais...Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.展开更多
The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading ...The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.展开更多
This paper presents a frequency support strategy for the diode rectifier unit(DRU)-high-voltage direct current(HVDC)-based offshore wind power integration system,which coordinates multiple power sources without commun...This paper presents a frequency support strategy for the diode rectifier unit(DRU)-high-voltage direct current(HVDC)-based offshore wind power integration system,which coordinates multiple power sources without communication to reduce receiving grid frequency fluctuations.First,based on the deduced DRU's frequency transfer characteristic,a fine-designed ripple carrying frequency information is superimposed on the HVDC link,transferring the onshore frequency to offshore wind turbines(WTs)via the DC ripple and coupled AC harmonic without communication.Second,multiple power sources are utilized for frequency support,including HVDC capacitance and grid-forming WTs combined with energy storage systems,and appropriate sources are activated in the order specified by the designed thresholds.Finally,the effectiveness of the proposed frequency support strategy is verified by simulations in PSCAD/EMTDC.展开更多
Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(...Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.展开更多
As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation c...As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.展开更多
In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along...In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.展开更多
Pricing competition between virtual power plants(VPPs)for distributed energy resources(DERs)is considered in this paper.Due to limited amount of DERs in one distributed area,VPPs have to compete for the rights to work...Pricing competition between virtual power plants(VPPs)for distributed energy resources(DERs)is considered in this paper.Due to limited amount of DERs in one distributed area,VPPs have to compete for the rights to work with DERs and then sell electricity from internal DERs in the wholesale market.To address this pricing problem,a Markov decision process(MDP)with continuous state and action spaces is formulated for the VPP to consider future rewards brought by contract statuses of DERs.Deep deterministic policy gradient(DDPG)algorithm is applied to solve the pricing problem in MDP form.To deal with the non-stationary environment in the training process brought by competing VPP,a fictitious adversary method is put forward in this paper to combine with DDPG algorithm for the first time.The proposed fictitious adversary method can help the VPP in finding competitive and robust pricing strategies under competition.Numerical results demonstrate effectiveness of the proposed methodology in finding satisfying pricing strategies that consider competitor behavior and long-term values of DERs.展开更多
Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously o...Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.展开更多
文摘The human brain is asymmetrical in function, with each of its two hemispheres being somewhat responsible for distinct cognitive and motor tasks, to include writing. It stands to reason that engineering students who have established entrance into their upper-division programs will have demonstrated cognitive proficiency in math and logical operations, abstract and analytical reasoning and language usage, to include writing. In this study the question was asked: is there a correlation between an upper-division electrical engineering students’ analytical reasoning ability and their descriptive writing ability? Descriptive writing is taken here to mean a students’ ability to identify key physical aspects of a mathematical model and to express—in words—a concise and well-balanced description that demonstrates a deep conceptual understanding of the model. This includes more than a description of the variables or the particular application to an engineering problem;it includes a demonstrated recognition of the basic physics that govern the model, certain limitations (idealizations) inherent in the model, and an understanding of how to make practical experimental measurements to verify the governing physics in the model. A student at this level may demonstrate proficiency in their analytical reasoning skills and hence be capable of correctly solving a given problem. However, this does not guarantee that the same student is skilled in associating equations with their physical meaning on a deep conceptual level or in understanding physical limitations of the equation. Consequently, such a student may demonstrate difficulty in mapping their comprehension of the model into written language that demonstrates a sound conceptual understanding of the governing physics. The findings represent a sample of two independent class sections of Electrical and Computer Engineering junior’s first course in Microe-lectronic Devices and Circuits during fall semesters 2012 and 2013 at a private mid-size university in NW Oregon. A total of three exams were administered to each of the 2012/2013 groups. Correlations between exam scores that students achieved on their descriptive writing of microelectronics phenomena and their analytical problem-solving abilities were examined and found to be quite significant.
文摘A wind-turbine power system is often challenged by voltage instability,reactive power imbalance,and limited fault ride-through capability under grid disturbances.Doubly Fed Induction Generator based wind farms,owing to their partial coupling with the grid,are particularly vulnerable to voltage dips and excessive reactive power absorption during fault events.This study proposes an adaptive control strategy based on Model Reference Adaptive Control integrated with stator flux-oriented vector control to regulate active and reactive power of a DFIG-based wind farm connected to a standard IEEE 9-bus power system under fault conditions.The proposed control scheme is developed and validated using detailed MATLAB/Simulink modeling under normal operation,symmetrical three-phase fault conditions,and post-fault recovery scenarios.A three-phase-to-ground fault is applied at the wind farm interconnection bus for a duration of 150 ms to evaluate transient performance.Simulation results demonstrate that the adaptive controller ensures fast power tracking,effective reactive power support,and enhanced voltage recovery compared to a conventional proportional–integral controller.Quantitatively,the proposed method improves voltage recovery time by approximately 45%,reduces active power overshoot by 38%,and lowers total harmonic distortion by 52% following fault clearance.Furthermore,the adaptive controller maintains stable operation under variations in wind speed and machine parameters without requiring retuning,highlighting its robustness against system uncertainties.The results confirm that the proposed control strategy significantly enhances fault ride-through capability,power quality,and dynamic stability of grid-interfaced wind farms.These findings demonstrate the practical applicability of adaptive control techniques for improving the reliability and resilience of modern power systems with high wind energy penetration.
基金funded by the Science and Technology Program of State Grid Corporation of China(5500-202356358A-2-1-ZX).
文摘Arc faults within the transformers can generate sudden pressure surges,constituting significant hazards that may precipitate oil tank explosions and severely compromise power system stability.Conventional power−frequency arc discharge experiments encounter limitations in isolating pressure wave characteristics due to persistent gas generation and arc reignition.To circumvent these challenges,an oil-immersed impulse voltage discharge platform was conceived and engineered to investigate pressure wave propagation dynamics.A pressure numerical simulation model and theoretical model of oil−solid interface reflection and refraction were subsequently established to elucidate the pressure propagation mechanism.The experimental and simulation results show that the pressure wave generated by pulsed arc discharge in oil propagates radially in the form of spherical waves.Due to the viscous loss and wave front expansion of transformer oil,the peak pressure decays exponentially with distance,with a decay coefficientβ=1.15.When pressure waves encounter metal obstacles inside transformer oil,there are two propagation paths:direct transmission through and multiple reflections through,and a mode transformation of pressure waves occurs at the oil−solid interface,mainly propagating through obstacles in the form of transverse waves.This work quantitatively delineates the energy pressure wave coupling,propagation dynamics,and attenuation mechanisms,providing critical insights for assessing and mitigating arc fault-induced transformer explosion risks.
基金supported in part by the National Natural Science Foundation of China(No.52407115)State Key Laboratory of Power System Operation and Control(61011000223).
文摘Advanced adiabatic compressed air energy storage(AA-CAES),with its dual capability for electricity-heat cogeneration and energy storage,offers significant potential as an energy hub for integrated electricity and heat systems(IEHS).While synergies in the electricity-heat market are known to enhance economic efficiency,it is hard to achieve cooperative operation due to the inherent differences among participants of IEHS and the absence of an incentive-compatible mechanism.To address this challenge,this paper proposes a Nash bargaining-based cooperative operation strategy for IEHS with AA-CAES.First,a cooperative alliance framework based on the Nash bargaining is proposed to optimize energy trading.Second,to overcome computational complexity,the non-convex,nonlinear Nash bargaining problem is decomposed into a two-stage optimization approach.In the first stage,a joint planning model maximizes the total profit of the alliance,determining the optimal energy interaction for each participant.In the second stage,a subsequent model ensures fair profit distribution by optimizing pricing and benefit-sharing mechanisms.Subsequently,a distributed solution strategy based on the self-adaptive alternating direction method of multipliers is utilized to preserve operator privacy and improve computational efficiency.Finally,case studies demonstrate that within the electricity-heat co-supply mode,the daily profit of AA-CAES can improve by approximately 4137.45 CNY.Meanwhile,through the proposed cooperative strategy,participants in the IEHS can obtain greater profits,which validates the effectiveness of this strategy.
基金supported in part by Northeast Branch of State Grid Corporation of China(52992624000W).
文摘Grid-forming(GFM)control is a key technique for power systems with high penetration of converter-interfaced generation.However,its application to photovoltaic(PV)systems faces challenges related to DC voltage transient stability.This paper investigates a common countermeasure involving a PI-based DC voltage controller for GFM-PV systems,revealing that their small-signal stability is sensitive to parameter tuning.The study develops a generalized DC voltage-dominated 2nd-order GFM model and successfully conducts complex torque analysis,showing that this approach can be effectively extended to other dynamics governed by DC voltage-dominated GFM systems.Subsequently,the paper establishes a stability criterion for GFM-PV systems and proposes a parameter tuning method for DC voltage controllers that incorporates damping margin considerations.The performance of the tuned single-machine-infinite-bus GFM-PV system is validated on the RT-LAB real-time simulation platform under scenarios involving solar irradiance fluctuations and grid frequency disturbances.The proposed method proves effective in ensuring the stability of the GFM-PV system,with robust theoretical support.
文摘Hybrid commutation converters(HCCs)utilizing reverse-blocking integrated gate commutation thyristors(IGCTs)have gained significant attention due to their immunity to commutation failure.Leveraging the recovery enhancement characteristics of IGCTs,HCCs demonstrate superior performance at reduced extinction angles,thereby minimizing reactive power consumption.This study presents a comprehensive investigation into reactive power control strategies for HCCs operating at small extinction angles.First,the topological configuration and commutation principle of HCC are elucidated.Subsequently,the mechanism of HCC reactive power control is analyzed,and a reactive power control strategy is proposed by combining the converter transformer taps with extinction angles.Moreover,the relationship between transformer taps and reactive power exchange under different rated extinction angles is calculated,and the theoretically rated extinction angle is proposed.Finally,to validate the proposed control strategy,a four-terminal ultra-high voltage direct current power grid incorporating HCC technology is modeled and sim-ulated using PSCAD/EMTDC.The simulation results demonstrate that the proposed strategy effectively supports AC systems by reducing reactive power absorption in HCCs,while simultaneously exhibiting enhanced reli-ability and economic efficiency.
文摘The increasing penetration of inverter-based resources(IBRs)and renewable energy resources poses significant challenges to the stability and controllability of modern power systems.Dynamic virtual power plants(DVPPs)have emerged as a transformative solution for aggregating and controlling heterogeneously distributed energy resources(DERs)flexibly and dynamically.This paper presents a comprehensive review of DVPPs,covering their conceptual evolution—from microgrids to virtual power plants(VPPs)and fast-acting VPPs—culminating in the dynamic DVPP paradigm.This review explores key architectural frameworks,including grid-forming and grid-following roles,as well as AC/DC interfacing strategies.Emphasis is placed on secondary frequency and voltage control mechanisms,dynamic-based and market-based disaggregation,and control methodologies tailored to DERs.
基金funded by Scientific Research Deanship at University of Ha’il-Saudi Arabia through project number(RG-24014).
文摘We present a computer-modeling framework for photovoltaic(PV)source emulation that preserves the exact single-diode physics while enabling iteration-free,real-time evaluation.We derive two closed-form explicit solvers based on the Lambert W function:a voltage-driven V-Lambert solver for high-fidelity I–V computation and a resistance-driven R-Lambert solver designed for seamless integration in a closed-loop PV emulator.Unlike Taylor-linearized explicit models,our proposed formulation retains the exponential nonlinearity of the PV equations.It employs a numerically stable analytical evaluation that eliminates the need for lookup tables and root-finding,all while maintaining limited computational costs and a small memory footprint.The R-Lambert model is integrated into a buck-converter emulator equipped with a discrete PI regulator,which generates current references directly from sensed operating points,thus supporting hardware-constrained implementation.Comprehensive numerical experiments conducted on six commercial modules from various technologies(mono,poly,and multicrystalline)demonstrate significant accuracy improvements under the IEC EN 50530 near-MPP criterion:the V-Lambert solver reduces the±10%Vmpp band error by up to 61 times compared to an explicit-model baseline.Dynamic simulations under varying irradiance,temperature,and load conditions achieve millisecond-scale settling with accurate trajectory tracking.Additionally,processor-in-the-loop experimental validation on an embedded microcontroller supports the simulation results.By unifying exact analytical modeling with embedded realization,this work advances computer modeling for PV emulation,MPPT benchmarking,and controller verification in integrated renewable energy systems.
基金supported by the National Science and Technology Council of under Grant NSTC 114-2221-E-130-007.
文摘This paper presents an intelligent patrol and security robot integrating 2D LiDAR and RGB-D vision sensors to achieve semantic simultaneous localization and mapping(SLAM),real-time object recognition,and dynamic obstacle avoidance.The system employs the YOLOv7 deep-learning framework for semantic detection and SLAM for localization and mapping,fusing geometric and visual data to build a high-fidelity 2D semantic map.This map enables the robot to identify and project object information for improved situational awareness.Experimental results show that object recognition reached 95.4%mAP@0.5.Semantic completeness increased from 68.7%(single view)to 94.1%(multi-view)with an average position error of 3.1 cm.During navigation,the robot achieved 98.0%reliability,avoided moving obstacles in 90.0%of encounters,and replanned paths in 0.42 s on average.The integration of LiDAR-based SLAMwith deep-learning–driven semantic perception establishes a robust foundation for intelligent,adaptive,and safe robotic navigation in dynamic environments.
文摘Synchronous reluctance motors(SynRM)are widely employed in industrial applications due to their high robustness,low cost,and absence of permanent magnets.In recent years,significant research efforts have focused on improving the controllability and efficiency of SynRM.Accurate rotor position information is essential for the controller to generate appropriate current and voltage references corresponding to the desired speed and load torque.Shaft-mounted position sensors are generally undesirable because of their high cost,sensitivity to harsh operating conditions,maintenance requirements,and reduced reliability in environments characterized by high vibration.Consequently,sensorless control techniques that estimate rotor position using measured stator currents and voltages have attracted increasing attention.However,magnetic saturation,parameter nonlinearities,and cross-coupling effects significantly degrade position estimation accuracy and may compromise the stability of sensorless SynRM drives.In this paper,a nonlinear SynRM model is developed using finite element analysis(FEA)to accurately capture magnetic saturation and cross-coupling effects,thereby providing a precise representation of the machine’s electromagnetic behavior under varying load and flux conditions.A series of magnetostatic FEA simulations is performed.To reduce computational complexity,only one motor pole is analyzed by applying anti-periodic boundary conditions along the domain sides and enforcing a zero magnetic vector potential on the external stator boundary.Nonlinear iron material properties are modeled using the appropriate B-H curve.The simulations are carried out by imposing d-and q-axis current components and computing the corresponding flux linkages and electromagnetic torque.Based on these results,both apparent and incremental inductances are extracted and incorporated into the control algorithm.An advanced fictitious flux linkage method combined with a phase-locked loop(PLL)is employed for accurate rotor position estimation.Simulation results confirm that the proposed sensorless control strategy ensures stable operation and high position estimation accuracy over the entire speed range.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2022R1C1C100923512)by the Yonsei University Research Fund of 2023-22-0076supported by the POSCO Science Fellowship from POSCO TJ Park Foundation。
文摘Mid-wavelength infrared(MWIR)and long-wavelength infrared(LWIR)detectors,which operate within the 3-14µm wavelength range,have been extensively employed in various fields,including military,space exploration,environmental monitoring,biomedicine,and chemical analysis.While thermal detectors are commonly used,their limitations in sensitivity and response time render them less suitable for next-generation MWIR and LWIR applications.These advanced applications necessitate the use of narrow bandgap semiconductor-based photodetectors,which offer tunable optoelectronic properties and higher specific detectivity compared to thermal detectors.In this review,we provide a detailed analysis of the operational principles and manufacturing strategies of infrared photodetectors based on narrow bandgap semiconductors,which enable high-performance detection in the MWIR and LWIR regions.Our focus is specifically on scalable fabrication of MWIR and LWIR photodetectors,emphasizing devices with active areas ranging from millimeters to centimeters.Researches on large-scale fabrication of infrared photodetectors using quantum dots,two-dimensional(2D)van der Waals(vdW)materials,and three-dimensional(3D)bulk semiconductors are investigated.Finally,we summarize the remaining challenges in developing scalable narrow bandgap semiconductor-based MWIR and LWIR photodetectors for commercialization.By addressing the obstacles such as the difficulty in large-scale unform film synthesis,the requirement for cryogenic device operation,and the introduction of high-density of defect states during the hybridization processes,MWIR and LWIR photodetectors based on narrow bandgap semiconductors will pave the way for designing new sensory systems and applications in a wavelength regime that has been less developed compared to the visible and near-infrared(NIR)ranges.
文摘The detection of high-impedance faults(HIFs)in microgrid feeders is a serious issue due to low fault current levels during HIFs.This issue becomes especially problematic when microgrids are operating in an islanded mode integrated with inverter-based distributed generators(IBDGs).This paper proposes an innovative relaying scheme for the protection of microgrid feeders using incremental negative sequence power.After a fault occurs,the negative sequence voltage and currents are collected at the ends of the protected feeder and then its incremental values are measured.After that,incremental negative sequence real power is obtained at the ends of the feeder to obtain the proposed relaying feature,i.e.,a ratio of the sum of incremental negative sequence real power(∆RSNSP).The∆RSNSP is defined as the ratio of the sum of incremental negative sequence real power at the two ends of the feeder to the minimum of the powers among the ends.Simulation studies on a modified IEEE 13-bus system have shown that this scheme can detect HIFs and low-impedance faults(LIFs).It has been rigorously tested under various operating conditions,like variations in fault inception angles,faults during islanding,simultaneous faults,evolving faults,composite faults,capacitors,and load switching.This scheme is not only fast and accurate but also performs well even in noisy conditions,changes in topologies(i.e.,radial or mesh),synchronization errors,and transient faults.A comparative chart comparing its performance with other recent schemes is also included.Finally,the scheme is also validated on a real-time simulator which proves that the proposed scheme can work effectively under various fault conditions.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Generative Adversarial Networks(GANs)have become valuable tools in medical imaging,enabling realistic image synthesis for enhancement,augmentation,and restoration.However,their integration into clinical workflows raises concerns,particularly the risk of subtle distortions or hallucinations that may undermine diagnostic accuracy and weaken trust in AI-assisted decision-making.To address this challenge,we propose a hybrid deep learning framework designed to detect GAN-induced artifacts in medical images,thereby reinforcing the reliability of AI-driven diagnostics.The framework integrates low-level statistical descriptors,including high-frequency residuals and Gray-Level Co-occurrence Matrix(GLCM)texture features,with high-level semantic representations extracted from a pre-trained ResNet18.This dual-stream approach enables detection of both pixel-level anomalies and structural inconsistencies introduced by GAN-based manipulation.We validated the framework on a curated dataset of 10,000 medical images,evenly split between authentic and GAN-generated samples across four modalities:MRI,CT,X-ray,and fundus photography.To improve generalizability to real-world clinical settings,we incorporated domain adaptation strategies such as adversarial training and style transfer,reducing domain shift by 15%.Experimental results demonstrate robust performance,achieving 92.6%accuracy and an F1-score of 0.91 on synthetic test data,and maintaining strong performance on real-world GAN-modified images with 87.3%accuracy and an F1-score of 0.85.Additionally,the model attained an AUC of 0.96 and an average precision of 0.92,outperforming conventional GAN detection pipelines and baseline Convolutional Neural Network(CNN)architectures.These findings establish the proposed framework as an effective and reliable solution for detecting GAN-induced hallucinations in medical imaging,representing an important step toward building trustworthy and clinically deployable AI systems.
文摘The increasing integration of electric vehicle(EV)loads into power systems necessitates understanding their impact on stability.Small-magnitude perturbations,if persistent,can cause low-frequency oscillations,leading to synchronism loss and mechanical stress.This work analyzes the effect of voltage-dependent EV loads on this small-signal stability.The study models an EV load within a Single-Machine Infinite Bus(SMIB)system.It specifically evaluates the influence of EV charging through the DC link capacitor of a Unified Power Flow Controller(UPFC),a key device for damping oscillations.The system’s performance is compared to a modified version equipped with both a UPFC and a Linear Quadratic Regulator(LQR)controller.Results confirm the significant influence of EV charging on the power network.The analysis demonstrates that the best performance is achieved with the SMIB system utilizing the combined UPFC and LQR controller.This configuration effectively dampens low-frequency oscillations,yielding superior results by reducing the system’s rise time,settling time,and peak overshoot.
基金supported by the State Grid Corporation of China Science and Technology Project(No.5500-202319103A-1-1-ZN).
文摘This paper presents a frequency support strategy for the diode rectifier unit(DRU)-high-voltage direct current(HVDC)-based offshore wind power integration system,which coordinates multiple power sources without communication to reduce receiving grid frequency fluctuations.First,based on the deduced DRU's frequency transfer characteristic,a fine-designed ripple carrying frequency information is superimposed on the HVDC link,transferring the onshore frequency to offshore wind turbines(WTs)via the DC ripple and coupled AC harmonic without communication.Second,multiple power sources are utilized for frequency support,including HVDC capacitance and grid-forming WTs combined with energy storage systems,and appropriate sources are activated in the order specified by the designed thresholds.Finally,the effectiveness of the proposed frequency support strategy is verified by simulations in PSCAD/EMTDC.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.
基金supported by Science and Technology Project of China Southern Power Grid Company(036000KK52222007(GDKJXM20222121)).
文摘As renewable energy penetration continues to rise,enhancing power system flexibility has become a critical requirement.Photovoltaic–storage–charging stations(PSCSs)are key components for enhancing local regulation capability and promoting renewable integration.However,evaluating the adjustable capability of such hybrid stations while considering security constraints remains a major challenge.This paper first analyzes the adjustable capabilities of all the resources within such a station based on the power-energy boundary(PEB)model.Then,an optimal formulation is proposed to obtain the adjusted parameters of the aggregate feasible region(AFR)model,which embeds low-dimensional linear models within high-dimensional linear models to improve the accuracy.To solve this formulation,it is transformed using duality theory and an alternating optimization algorithm is designed to obtain the solution.Finally,a multi-station adjustable capability aggregation method considering security constraints is introduced.Simulation results verify that the proposed method effectively reduces infeasible regions and improves smoothness of aggregated boundaries,providing an accurate and practical tool for flexibility evaluation in PSCSs and offering guidance for aggregators and system planners.
文摘In real industrial microgrids(MGs),the length of the primary delivery feeder to the connection point of the main substation is sometimes long.This reduces the power factor and increases reactive power absorption along the primary delivery feeder from the external network.Besides,the giant induction electro-motors as the working horse of industries requires remarkable amounts of reactive power for electro-mechanical energy conversions.To reduce power losses and operating costs of the MG as well as to improve the voltage quality,this study aims at providing an insightful model for optimal placement and sizing of reactive power compensation capacitors in an industrial MG.In the presented model,the objective function considers voltage profile and network power factor improvement at the MG connection point.Also,it realizes power flow equations within which all operational security constraints are considered.Various reactive power compensation strategies including distributed group compensation,centralized compensation at the main substation,and distributed compensation along the primary delivery feeder are scrutinized.A real industrial MG,say as Urmia Petrochemical plant,is considered in numerical validations.The obtained results in each scenario are discussed in depth.As seen,the best performance is obtained when the optimal location and sizing of capacitors are simultaneously determined at the main buses of the industrial plants,at the main substation of the MG,and alongside the primary delivery feeder.In this way,74.81%improvement in power losses reduction,1.3%lower active power import from the main grid,23.5%improvement in power factor,and 37.5%improvement in network voltage deviation summation are seen in this case compared to the base case.
基金supported by the National Key R&D Program of China(2020YFB09060002020YFB0906005).
文摘Pricing competition between virtual power plants(VPPs)for distributed energy resources(DERs)is considered in this paper.Due to limited amount of DERs in one distributed area,VPPs have to compete for the rights to work with DERs and then sell electricity from internal DERs in the wholesale market.To address this pricing problem,a Markov decision process(MDP)with continuous state and action spaces is formulated for the VPP to consider future rewards brought by contract statuses of DERs.Deep deterministic policy gradient(DDPG)algorithm is applied to solve the pricing problem in MDP form.To deal with the non-stationary environment in the training process brought by competing VPP,a fictitious adversary method is put forward in this paper to combine with DDPG algorithm for the first time.The proposed fictitious adversary method can help the VPP in finding competitive and robust pricing strategies under competition.Numerical results demonstrate effectiveness of the proposed methodology in finding satisfying pricing strategies that consider competitor behavior and long-term values of DERs.
基金supported by the Smart Grid-National Science and Technology Major Project of China(2024ZD0802000).
文摘Although wind energy is volatile,the output of a wind-storage plant is partially dispatchable,making it a promising paradigm on the generation side.A grid-friendly wind-storage plant ought to be able to continuously output the desired power over a certain period of time.This paper proposes a dependable dynamic capacity provision scheme of a wind-storage plant over a daily horizon.It stipulates a minimum number of periods during which the committed capacity must be fulfilled and a maximum mismatch during the remaining periods when the desired power output is not achievable.In the general case,the day-ahead piecewise constant capacity provision results in a two-stage stochastic program formulated as a mixed-integer linear program.Specifically,for constant capacity provision,a decomposition algorithm is developed to determine the global optimal solution,and the complexity grows linearly with the number of scenarios.Given the committed capacity trajectory,the real-time operation problem is modeled as a four-state stochastic dynamic program.The discrete state-action values are derived recursively via the principle of optimality.Real-time dispatch actions are generated by using the action-value tabular leveraging inexact ultra-short-term forecasts.Numerical tests over one year demonstrate that the proposed method successfully fulfills reliable operation on 355 days and achieve an optimality gap of 9.47%compared with the ex-post optimum,which is comparable to model predictive control using exact 2–3-hour-ahead wind power forecasts.
