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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

IEEE Transactions on power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern power systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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Data-driven power Flow Method Based on Exact Linear Regression Equations

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Journal of Modern power systems and Clean Energy 2022年第3期10卷 800-804页

作者： Yanbo Chen Chao Wu Junjian Qi State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical&Electronic EngineeringNorth China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ 07030USA

power flow(PF)is one of the most important calculations in power *** widely-used PF methods are the Newton-Raphson PF(NRPF)method and the fast-decoupled PF(FDPF)*** smart grids,power generations and loads become intermittent and much more uncertain,and the topology also changes more frequently,which may result in significant state shifts and further make NRPF or FDPF difficult to *** address this problem,we propose a data-driven PF(DDPF)method based on historical/simulated data that includes an offline learning stage and an online computing *** the offline learning stage,a learning model is constructed based on the proposed exact linear regression equations,and then the proposed learning model is solved by the ridge regression(RR)method to suppress the effect of data *** online computing stage,the nonlinear iterative calculation is not *** results demonstrate that the proposed DDPF method has no convergence problem and has much higher calculation efficiency than NRPF or FDPF while ensuring similar calculation accuracy.

关键词： Data driven exact linear regression equation Fast-decoupled power flow Newton-Raphson method

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Time-domain and Frequency-domain Analyses of PETT Oscillation in Press Pack IGBTs

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CSEE Journal of power and Energy systems 2023年第2期9卷 682-695页

作者： Jiayu Fan Xiang Cui Xinling Tang Cheng Peng Zhibin Zhao Xiangrui Meng Xuebao Li Zhong Chen State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electrical Power UniversityBeijing 102206China Power Semiconductor Devices Research Institute State Key Laboratory of Advanced Power Transmission TechnologyGlobal Energy Interconnection Research Institute Co.Ltd.Beijing 102209China Department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA

Plasma extraction transit time(PETT)oscillation might appear in IGBT devices,which is harmful to the electromagnetic compatibility(EMC)of a renewable energy *** eliminate this oscillation,its frequency-domain characteristics in wire-bonded IGBT devices have been extensively ***,the time-domain analysis of PETT oscillation,especially in Press Pack IGBT(PPI)devices,has not attracted enough attention *** this paper,PETT oscillations with multi-chips in PPI devices are systematically investigated by *** is first reported there are multiple resonant oscillations at the tail period when multi-chips turn *** overlap in the time domain waveforms,which lead PETT oscillation to be more serious in ***,PETT oscillation is divided into three different feedback states for the first *** the IGBT chip in the PETT oscillation,its physical based model and behavior model are proposed,which further form the equivalent circuit as the two-port ***,it is indicated that only parallel resonances can lead to PETT oscillation,which is consistent with experiment results.

关键词： PETT oscillation press pack IGBTs timedomain analysis two-port network

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Abnormal Data Identification and Reconstruction Based on Wind Speed Characteristics

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CSEE Journal of power and Energy systems 2025年第2期11卷 612-622页

作者： Mao Yang Tian Peng Wei Zhang Xin Su Chao Han Fulin Fan Key Laboratory of Modern Power System Simulation and Control&Renewable Energy Technology Ministry of EducationNortheast Electric Power UniversityJilin 132012China Department of Electronic and Electrical Engineering University of StrathclYdeGlasgow G11XWUnited Kingdom

High availability of wind power data is the basis for wind power research, but there are a large number of abnormal data in actual collected data, which seriously affects analysis of wind power law and reduces prediction accuracy. Measured power data of wind farm are analyzed, influence of wind speed fluctuation characteristics on wind power is discussed, and abnormal points are identified for data of different wind types. The Cluster-Based Local Outlier Factor (CLOF) algorithm based on K-means is used to identify outlier abnormal points, and conditional constraints based on physical background are used to identify accumulation abnormal points. Reconstructed data segment is divided according to fluctuation of wind speed. The Bidirectional Gate Recurrent Unit (BiGRU) model with wind speed as input reconstructs fluctuation segment data, and bi-directional weighted random forest model reconstructs stationary segment data. Based on analysis of measured data of a wind farm, results show the method can effectively identify various abnormal data, and complete high-quality reconstruction of data, thereby improving accuracy of wind power prediction.

关键词： Abnormal data bidirectional weighted random forest BiGRU CLOF reconstruction wind power prediction wind speed characteristics

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Salp Swarm Algorithm Based Optimal Network Reconfiguration with Distributed Generation for power Loss Minimization and Voltage Profile Improvement

Salp Swarm Algorithm Based Optimal Network Reconfiguration w...

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2023 International Conference on Sustainable Technology and engineering, i-COSTE 2023

作者： Alende, Azmerawu Argawu School of Electrical and Computer Engineering Hawassa University Department of Power System and Energy Engineering Hawassa Ethiopia

ISBN: (纸本)9798350329711

In a distribution system, power loss and voltage deviation are the main concern for the customers and utility. The distribution system has a lower voltage level and higher amount of flowing current than the transmission system, so that, the percentage real power loss in the distribution system is higher. This study addresses power loss and voltage deviation concerns in distribution systems. It optimizes the Yirgalem-Ethiopia radial distribution network using the Salp Swarm Algorithm (SSA) and solar Distributed Generation (DG). SSA outperforms other algorithms in reducing real and reactive power losses and improving the voltage profile. The approach minimizes network costs and maintains voltage within acceptable limits. It proves cost-effective, demonstrating significant power loss reduction and improved voltage profiles. The resource feasibility of solar and wind power in Yirgalem city was analyzed and the outcomes showed that solar power generation is more desirable. For this reason, the solar-type Distributed Generation (DG) is used. The proposed SSA algorithm was compared with Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) in three various scenarios (only optimal network reconfiguration, only optimal DG size and site, and simultaneous optimal network reconfiguration and DG allocation) for the Aposto feeder. As stated, the SSA method performs better in terms of reducing both real and reactive power losses and improvement of voltage profile. The model has been formulated to minimize the total cost of the network by determining the optima of the substation locations and power, the load transfers between the demand centers, the feeder routes and the load flow in the network subject to a set of constraints. From the point of view of economic evaluations, the proposed approach is cost-effective. Generally, the simulation results show that the proposed technique is effective to maintain all buses voltage magnitude within the IEEE acceptable lim

关键词： Distributed power generation

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Comparative Analysis of Defective Solar PV Module Inspection Using Thermal Infrared and Electroluminescence Imaging Techniques 4

Comparative Analysis of Defective Solar PV Module Inspection...

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4th International Conference on High Voltage engineering and power systems, ICHVEPS 2023

作者： Nakagawa, Kota Kaligambe, Abraham Fujita, Goro Shibaura Institute of Technology Power System Laboratory Department of Electrical Engineering Tokyo Japan

ISBN: (纸本)9798350318678

The demand for renewable energy has led to a rapid increase in the production and installation of solar photovoltaic (PV) systems worldwide. However, during transportation, installation, and exposure to adverse weather conditions, PV panels are prone to defects that reduce their lifespan and efficiency. Visual and I-V curve test inspection methods are commonly used to maintain the smooth operation of PV systems, but they are limited by the inability to detect most flaws, the need for expertise, and long inspection times. Hence, there is a need for cost-effective and efficient inspection methods to identify defects in solar panels. The objective of this paper is to analyze and compare the benefits and effectiveness of two solar PV inspection methods, namely thermal infrared (IR) imaging and electroluminescence (EL) imaging. Indoor and outdoor IR images are captured using a FLIR Pro thermal camera and EL images are captured using a low-cost converted Panasonic GF3 camera. The results of our study demonstrate that while each method has its advantages, EL imaging is the most effective method for detecting a wide variety of solar cell defects. Thermal IR imaging is useful for detecting defects that affect the temperature of the panel but may not detect minute defects such as microcracks. The simplicity and low-cost nature of the developed EL setup make it a viable option for small and large PV systems, contributing to the advancement of the solar PV inspection field. © 2023 IEEE.

关键词： Solar panels

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Coupon-based Demand Response for Consumers Facing Flat-rate Retail Pricing

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CSEE Journal of power and Energy systems 2024年第5期10卷 1887-1900页

作者： Hongxun Hui Yi Ding Kaining Luan Tao Chen Yonghua Song Saifur Rahman State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao 999078China. College of Electrical Engineering Zhejiang UniversityHangzhou 310027China State Grid Jiangsu Electric Power Co. LTD.Nanjing 210000China School of Electrical Engineering Southeast UniversityNanjing 210096China Advanced Research Institute and Department of Electrical and Computer Engineering Virginia Polytechnic Institute and State UniversityVA 22203USA

Even though smart meters have been widely used in power systems around the world,many consumers are still finding it hard to participate in demand response(DR)due to flat-rate retail pricing *** address this issue,this paper proposes a coupon-based demand response(CDR)scheme to achieve equivalent dynamic retail prices to inspire consumers’inherent ***,a security-constrained unit commitment optimization model is developed in the day-ahead market to obtain coupon rewards,which are then broadcast to consumers to motivate them to reschedule their power consumption *** evaluate the adjustment value of consumers’power consumption,a collective utility function is proposed to formulate the relationship between power quantity and coupon *** this basis,the security-constrained economic dispatch model is developed in the intra-day market to reschedule generating units’output power according to real-time load demands and fluctuating renewable *** the operation interval,a settlement method is developed to quantify consumers’electricity fees and coupon benefits on a monthly *** proposed CDR scheme avoids real-time iterative bidding process and effectively decreases the difficulty of massive,small consumers participating in *** proposed CDR is implemented in a realistic DR project in China to verify consumers’energy cost and renewables’curtailment can both be decreased.

关键词： Coupons demand response flat-rate retail pricing renewable energies small consumers

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Optimal Offering Strategy of Virtual power Plant with Hybrid Renewable Ocean Energy Portfolio

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CSEE Journal of power and Energy systems 2023年第6期9卷 2040-2051页

作者： Siyuan Guo Bin Zhou Ka Wing Chan Siqi Bu Canbing Li Nian Liu Cong Zhang College of Electrical and Information Engineering Hunan UniversityChangsha 410082China Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China

This paper proposes a hybrid ocean energy sys-tem to form a virtual power plant(VPP)for participating in electricity markets in order to promote the renewable ocean energy utilization and *** the proposed system,solar thermal energy is integrated with the closed-cycle ocean thermal energy conversion(OTEC)to boost the temperature differences between the surface and deep seawater for efficiency and flexibility improvements,and the thermodynamic effects of seawater mass flow rates on the output of solar-boosted OTEC(SOTEC)are exploited for deploying SOTEC as a renewable dispatchable *** optimal tidal-storage operation model is also developed to make use of subsea pumped storage(SPS)with hydrostatic pressures at ocean depths for mitigating the intermittent tidal range energy in order to make the arbitrage in the electricity ***,a two-stage coordinated scheduling strategy is presented to optimally control seawater mass flow rates of SOTEC and hydraulic reversible pump-turbines of SPS for enhancing the daily VPP *** studies have been investigated to confirm the superiority of the developed methodology in various renewable ocean energy and electricity market price scenarios.

关键词： Ocean thermal energy conversion rolling optimization subsea pumped storage system tidal power generation virtual power plant

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A Holistic State Estimation Framework for Active Distribution Network with Battery Energy Storage system

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Journal of Modern power systems and Clean Energy 2022年第3期10卷 627-636页

作者： Shaojian Song Huangjiao Wei Yuzhang Lin Cheng Wang Antonio Gómez-Expósito IEEE School of Electrical Engineering Guangxi UniversityNanning 530004China Department of Electrical and Computer Engineering University of MassachusettsLowellMA 01852USA State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Laboratory of Engineering for Energy and Environmental Sustainability University of SevilleSpain

Battery energy storage systems(BESSs)are expected to play a crucial role in the operation and control of active distribution networks(ADNs).In this paper,a holistic state estimation framework is developed for ADNs with BESSs integrated.A dynamic equivalent model of BESS is developed,and the state transition and measurement equations are *** on the equivalence between the correction stage of the iterated extended Kalman filter(IEKF)and the weighted least squares(WLS)regression,a holistic state estimation framework is proposed to capture the static state variables of ADNs and the dynamic state variables of BESSs,especially the state of charge(SOC).A bad data processing method is also *** simulation results show that the proposed holistic state estimation framework improves the accuracy of state estimation as well as the capability of bad data detection for both ADNs and BESSs,providing comprehensive situational awareness for the whole system.

关键词： Active distribution network(ADN) anomaly detection battery energy storage system(BESS) Kalman filtering situational awareness state estimation state of charge(SOC)

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