kumar Abhinav’s Post

Congratulations to kumar Abhinav and other co-authors for publishing an article in reputed journal 𝐄𝐥𝐞𝐜𝐭𝐫𝐢𝐜 𝐏𝐨𝐰𝐞𝐫 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 (Q1, IF: 4). Interested ones may go through the article. Abstract: This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony’s system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach’s effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances. https://lnkd.in/dt5yy7rk

Fault ride-through (FRT) operation is an important capability of wind turbine, which must maintain uninterrupted operation in presence of grid faults and inject reactive power into power grid to support voltage restoration. The benefits of finite control set model predictive control (FCS-MPC) to improve operation performance of high-power wind power converter during grid faults and comply with next-generation grid code requirements in terms of reactive current provision are investigated in my research. I am glad to contribute with this paper as part of my industrial PhD in collaboration between Ingeteam Wind Energy and Aalborg University. "Advanced Fault Ride-Through Operation Strategy Based on Model Predictive Control for High Power Wind Turbine," has been published in IEEE Transactions on Sustainable Energy journal, It can be accessed at: https://lnkd.in/dd7b44MB Thanks to my supervisor and co-supervisor Prof. zhe chen and Yanbo Wang from Aalborg University, and Joseba Arza Alonso from Ingeteam; for their efforts and valuable contributions in this investigation. #offshorewind #powergeneration #aau #aauenergy #ingeteam

#mdpienergies #highlycitedpaper A Fuzzy Logic-Based Emulated Inertia Control to a Supercapacitor System to Improve Inertia in a Low Inertia Grid with Renewables 👉https://lnkd.in/gKNn4fJe #frequencycontrol; #inertia; #fuzzylogic; #supercapacitor; #emulatedinertia control; #hardware-in-loop

🌊⚡ Enhanced Real-Time Multi-Terminal HVDC Power System Benchmark Models 🌍 Exciting news! Our paper, titled "Enhanced Real-Time Multi-Terminal HVDC Power System Benchmark Models with Performance Evaluation Strategies," has been accepted for publication in the CIGRE Science & Engineering Journal (CSE). Look out for it in February 2024. In the global pursuit of carbon neutrality, the European energy transition stands out as a pioneering effort, aiming to make Europe climate-neutral by 2050. Offshore wind energy in the North Sea is instrumental in achieving this goal, with the European Union setting ambitious targets of 60 GW by 2030 and 300 GW by 2050. 🌬️ TenneT's 2GW Program: TenneT, a offshore transmission operator, is at the forefront of this movement. Their vision: 14 high-voltage direct current (HVDC) offshore grids with 2 GW transmission each, delivering green energy to the Dutch and German North Sea by 2031. 🛠️ Real-Time Testing: Prior to the energization of these grids, rigorous real-time testing is essential. Our enhanced real-time simulation model running in RTDS Technologies Inc. platform steps in, filling critical gaps in benchmark models. It offers: 📚 Key Features: • State of the art ±525 kV submarine and land cables model. • Wind farm/wind turbine dynamics based on Real-time wind gusts using SiL setup. • Electrical and Control parameter perturbation using an automatic script. • An average model of VARC DC CB.  • Expandable models.  • Computational insights. 🔗📅 Early Access to Our Model🚀: https://lnkd.in/eP2FGPhh Our sincere gratitude to co-author Tanumay Karmokar, supervisors Marjan Popov, and Aleksandra Lekić  for their invaluable contributions to this work. #HVDC #EnergyTransition #RenewableEnergy #RealTimeSimulationModel #RTDS🌟🌍

#Modeling and #Control of #HVDC Electrical Grids

⚡ Our new publication as part of the #PRIN #PNNR 2022 project OESIS 4 ENERGY funded by the #EuropeanUnion - #NextGenerationEU 💡 Project title: "A novel market design for the optimal allocation of multiple ancillary services in future low inertia power networks". In this work, Asja Alic and I propose a novel system scheduling model which incorporates a detailed description of the inertial and governor’s response of conventional generators as well as the system-level frequency dynamics. The methodology is able to consider the typical overshoot of the governor’s response to optimally allocate enough headroom among generators. In addition, a set of constraints on the frequency deviation and on its rate of change allows to optimize the energy production whilst maintaining the security of the system. Furthermore, the paper develops a constraint on the rise time of the governor’s response. Hence, the proposed methodology allows to enforce specific dynamics concerning the governor’s response. https://lnkd.in/ddug35S4

Distributed fiber optic sensing holds enormous potential to meet the monitoring needs of rapidly emerging new energy resources. An advanced portfolio of distributed temperature, strain and acoustic sensing-based monitoring solutions can deliver the highest resolution, continuous, real-time data over long distances even in the most hostile of environments.  See us present at the 4th EAGE Digitalization Conference on 26 March 2024 in Paris. Glynn Williams, Advisor to Luna, will present an overview of the technology together with a series of short case studies featuring various applications from across the energy sector. He will demonstrate how techniques that were originally developed for the harsh environments of Oil & Gas operations have been adapted for use in new green energy applications. He will start with applications in #CCS that form part of the net zero roadmap before turning to Enhanced Deep Geothermal Systems (EGS), shallow #geothermal operations, and hydroelectric power through the use of short case studies.  #Fiberoptics #DistributedSensing #CCUS #energytransition #energy #EAGEDIGITAL2024 EAGE (European Association of Geoscientists and Engineers)

✔ 📰 New book chapter for the Ingenium Team! "Prediction and Classification of different wind turbine alarms using K-nearest neighbors" by Ana María Peco Chacón Isaac Segovia Ramírez and Fausto Pedro García Márquez in Elsevier Congratulations!! Abstract: The new machine learning techniques offer improvements in the monitoring control of wind farms. Monitoring systems regularly report wind turbine conditions, this information can be used by classification algorithms to detect possible failures. The objective of this research is the classification of different types of alarms by means of different K-nearest neighbor algorithms. The approach is applied to an actual dataset of a wind turbine. The comparison between different validation techniques indicates that the best accuracy is 98.7% with weighted K-nearest neighbor algorithm and 10-fold crossvalidation. The results show that it can predict and classify alarms of the specification type of environmental states and conditions, with true positive rates of 89.4% and 75.9%, respectively. These results demonstrate that the proposed algorithms are efficient to classify this type of alarm. For future research, the methodology can be used with other algorithms and analyze their correlation with specific supervisory control and data acquisition signals. https://lnkd.in/ddHgxbZQ #windenergy #research #knearestneighbors #artificialintelligence #faultprediction #faultclassification #windturbine

To what extent can we predict dynamic control effects in wind turbine wakes with a simple, physics-based model? Published yesterday; check out my latest open-access article here: https://lnkd.in/eEEYjpkT We aim to improve wind farm performance by reducing wake effects using the control degrees of freedom that wind turbines already have. Using experimental data from wind tunnel experiments, we assess three wind turbine models for predicting the effects of yaw control on the power production of downstream turbines. These models simulate the wake using free-vortex wake methods, showing good agreement with measurements in the mid to far wake under both steady and time-varying conditions. The level of accuracy and the computational efficiency show potential for using these models for control optimisation. Many thanks to my co-authors - Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem Van Wingerden - for their contributions to the model development, experimental data, and article revision!

📣 We are happy to announce the release of our technical report, ‘Reference Economic Model for Airborne Wind Energy Systems’. 📝 Co-authored with Filippo Trevisi, this report is part of the IEA Wind TCP Task 48 initiative, representing a collaborative effort between industry and academia.   💡 Highlights of the Report 1. Parametric Cost Models: Our report and the accompanying computer code provide detailed parametric cost models validated with industry data to estimate both capital expenditure (#CapEx) and operational expenditure (#OpEx) for each component of airborne wind energy systems (AWESs). 2. Design Metrics: We identify key design metrics beyond #LCoE that can be used as objectives for optimizing and refining AWES designs. These metrics will help evaluate the performance of AWESs and guide future R&D efforts. 3. Market Potential: The report explores potential markets where #AWESs could significantly contribute to the global energy supply mix. 📙 Read the full report: https://lnkd.in/dA6E2-8R 💾 Access the code repository: https://lnkd.in/dFgXFCqg   We thank Airborne Wind Europe for facilitating this work and supporting data collection, storage, and dissemination.   #AirborneWindEnergy #RenewableEnergy #CleanTech #SustainableEnergy #LCoE #systemDesign #Innovation #TUDelft #Polimi