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model predictive controller-Based Voltage and Frequency Regulation in Renewable Energy Integrated Power System Coordinated with Virtual Inertia and Redox Flow Battery

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2023年第1期47卷 159-176页

作者： Kumar, Vineet Sharma, Veena Naresh, R. Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur 177005 Himachal Prades India

This manuscript discusses the concurrent voltage and frequency control problem for an interconnected system having three areas, each containing thermal and diesel units in conjunction with renewable integration from a separate source. In the first case study, renewable sources like wind, solar, and geothermal have been incorporated using a static transfer function-based model without considering variability in wind and solar. In the second case study, the dynamic model of wind and solar has been considered after incorporating the deterministic drifts and stochastic fluctuations. The paper presents Harris hawks optimized model predictive controller (MPC-HHO) to curtail the frequency and voltage fluctuations caused due to the sudden change in loading pattern. Additionally, the authors have incorporated various dynamic energy storage devices like virtual inertia and redox flow battery (VI-RFB) in coordination with the MPC-HHO controller. From the obtained results, it has been validated that the transient response from the proposed MPC-HHO controller coordinated with VI-RFB has superior characteristics when compared with state-of-the-art methodologies available in the existing literature. Further, the most prevalent nonlinearities present in the realistic power system have been considered and their effects on the controller's performance have been investigated and the robustness of the proposed MPC-HHO controller coordinated with VI-RFB has been successfully validated.

关键词： Automatic voltage control Harris hawks optimization algorithm Load frequency control model predictive controller

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model predictive controller-Based Levothyroxine Dosing Strategy for Patients Suffering from Hashimoto's Thyroiditis 12

Model Predictive Controller-Based Levothyroxine Dosing Strat...

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12th IFAC Symposium on Biological and Medical Systems (BMS)

作者： Sharma, Ravi Goede, Simon Lucas Theiler-Schwetz, Verena Reichhartinger, Markus Graz Univ Technol Inst Automat & Control Graz Austria Med Univ Graz Dept Internal Med Div Endocrinol & Diabetol Graz Austria

Hashimoto's thyroiditis is the primary cause of hypothyroidism, resulting in insufficient thyroid hormone production, mainly thyroxine and triiodothyronine. Treatment involves achieving desired hormone levels through the oral intake of thyroxine hormone, primarily levothyroxine. Achieving adequate patient-specific thyroid hormone concentrations typically requires several visits with medical professionals. In this study, a patient-specific levothyroxine dosage has been identified using a model-predictive controller-based levothyroxine dosing strategy. The proposed study demonstrates that the designed levothyroxine dosing strategy yields a reliable response and even considers variations in the patient's biological parameters. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://***/licenses/by-nc-nd/4.0/)

关键词： Hashimoto's thyroiditis Set-point theory Mathematical thyroid model model predictive controller

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Optimal electrical vehicle-to-grid integration: three-phase three-level AC/DC converter with model predictive controller based bidirectional power management scheme

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ELECTRICAL ENGINEERING 2024年 1-23页

作者： Srivastava, Abhinav Manas, Munish Dubey, Rajesh Kumar Cent Univ Haryana Dept Elect Engn Mahendragarh Haryana India

The control and modeling of an electric vehicle charging station with a three-level converter are discussed in this study from both the grid side and the EV side. The primary subject of discussion is the control systems for charging stations with a bidirectional DC/DC charging regulator and a three-level AC/DC power conversion. In order to manage the duty cycle of the converter during the AC-to-DC conversion stage, three alternative controller techniques-proportional-integral, proportional-integral-derivative, and model predictive controller-are developed in this study. The goal of the study is to understand how these controller techniques impact the battery's charging and discharging processes in a bidirectional charging system. To evaluate and compare the different controller schemes, the simulation results and assesses their performance based on various criteria including charging/discharging efficiency, voltage and current, battery state-of-charge regulation, and response time. The simulation results show that in terms of peak overshoot and settling time, the MPC controller performs better than the conventional proportional-integral and proportional-integral-derivative controllers. Also, an experimental validations were carried out on a laboratory-scale three-phase two-stage bidirectional electric vehicle (EV) charger, featuring a 48 V 4 kW Li-ion battery, under the control of a dSPACE controller. These experiments show that the method achieves rapid dynamics and robust steady-state performance. The significance of the controller selection in bidirectional charging systems, emphasizing the benefits of using model predictive control for better battery charging and discharging outcomes.

关键词： Electric vehicles model predictive controller PI and PID controller AC and DC converter

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Cooperative approach for electric vehicle charging: a multi-polynomial regression-based model predictive controller with advanced techniques

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SOFT COMPUTING 2024年 1-22页

作者： Sahw, Probeer Maity, Tanmoy Yadav, R. K. IIT ISM Dhanbad Dept Elect Engn Police Line RdMain Campus IIT ISM Dhanbad 826004 Jharkhand India Raj Kumar Goel Inst Technol Dept Elect & Commun Engn 5KM Stone DelhiMeerut RdNear Raj Nagar Extens Rd Ghaziabad 201003 Uttar Pradesh India

The market share of Electric Vehicles (EVs) is steadily increasing, benefiting the environment and energy crisis. However, the widespread adoption of EVs can negatively impact the smart grid, leading to stress, frequency swings, and power variations. To address this, a multi-polynomial regression-based model predictive controller using the XGBOOST + Relief + AOA technique is developed. It enhances the performance of dispersion frameworks while managing Energy and Voltage Control (EVC) for EV coordination. Initial information outlines input data, including harmonic distortion, unbalanced grids, transformer lifespan, voltage swings, and power loss. Data processing resolves issues, such as improper scaling, missing data, and imbalance. An oversampling technique based on aggregative clustering handles imbalanced data, and independent features are standardized using the concordance correlation coefficient-based power transform (CCC-PT). The proposed EV charging model utilizes an optimization objective function, incorporating XGBOOST, Relief, and AOA techniques. The resulting optimization maintains a balance between exploration and exploitation, minimizing errors and time complexity. The multipolynomial regression-based model predictive controller focuses on voltage and energy control to ensure charge balance. Overall, this implementation optimizes distributed state functioning by considering the impact of EV coordination on EVC. The effectiveness and applicability of this methodology are evaluated across three separate scenarios.

关键词： Electric vehicles Control strategy Clustering Power transform Feature selection model predictive controller

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An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

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ELECTRIC POWER SYSTEMS RESEARCH 2024年 226卷

作者： Fernando, Warnakulasuriya Sonal Prashenajith Mahmud, Md Apel Northumbria Univ Fac Engn & Environm Tyne NE1 8ST England Northumbria Univ Fac Engn & Environm Wear NE1 8ST England

This paper presents a nonlinear extended state observer (NLESO)-based nonlinear model predictive controller (NMPC) design for resonant grounding devices called rapid earth fault current limiters (REFCLs) to compensate arc faults in power distribution networks covering bushfire prone areas. The proposed NLESO is designed to estimate disturbances which are represented through changes in the neutral voltage due to unknown faults and changes in leakage parameters of distribution networks during hot summer days. The NMPC in this work compensates the fault current and subsequently, the faulty phase voltages irrespective of fault impedances. The dynamical model of REFCLs is transformed into a suitable model to design NLESO and nonlinearities are modeled in such a way that these fall within different sectors depending on the value of the observer gain and estimation errors while ensuring the desired recovery of states during severe transients. The estimated disturbances are then incorporated into the NMPC to achieve the expected compensation level for both fault current and faulty phase voltage. The proposed NLESO-based NMPC's stability is analyzed by representing the whole system in form of a Lure system. Finally, the performance of the proposed scheme is evaluated through simulations using MATLAB/Simulink and OPAL-RT real-time simulator following the Victorian regulatory impact statement for reducing powerline bushfires.

关键词： Fault current State observer model predictive controller Fault current limiters Resonant grounding

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Enhanced the Hosting Capacity of a Photovoltaic Solar System Through the Utilization of a model predictive controller

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IEEE ACCESS 2024年 12卷 62480-62491页

作者： Mabrook, M. Mourad Donkol, A. A. Mabrouk, A. M. Hussein, Aziza I. Barakat, Mohamed Beni Suef Univ Space Commun Dept Fac Nav Sci & Space Bani Suwayf 62511 Egypt Beni Suef Natl Univ Space & Nav Appl Sci Program Bani Suwayf 62511 Egypt South Valley Univ Fac Engn Elect Engn Commun & Elect Dept Qena 83523 Egypt Nahda Univ Fac Engn Elect Engn Dept Bani Suwayf 62764 Egypt King Salman Int Univ KSIU Fac Engn South Sinai 46612 El Tur Egypt Effat Univ Elect & Comp Engn Dept Jeddah 22332 Saudi Arabia Canadian Int Coll CIC Fac Engn ECE Dept Giza 12511 Egypt Almaaqal Univ Coll Engn Control & Comp Dept Basrah 61014 Iraq

The global expansion of solar-powered within distribution networks with Low Voltage (LV) is experiencing substantial expansion. Despite the various advantages offered by solar photovoltaic generation, surpassing the constraints on Hosting Capacity (HC) within these networks persist a significant technical problem in system operation, especially in relation to voltage operation. This research delves into the effectiveness of improving the Hosting Capacity (HC) of a photovoltaic (PV) system within an LV distribution system. It utilizes a model predictive controller (MPC) to achieve this enhancement and contrasts its performance with reactive power control. The study examines scenarios encompassing both linear and non-linear loads to assess the impact of these control strategies on the PV system's harmonic current in the LV distribution network. Through detailed analysis, the MPC controller demonstrates superior adaptability and responsiveness, maintaining stable active power at 95.5 kW before accommodating a 100% PV system penetration and experiencing a substantial increase to 192 kW. The hosting capacity, thereby, sees a notable 101.05% improvement under MPC control. Additionally, the study reveals that MPC optimizes reactive power utilization, resulting in a 17.9% reduction in reactive power and an 18.3% enhancement in bus voltage compared to reactive power control. Notably, MPC exhibits superior adaptability to both linear and non-linear loads, emphasizing its potential as an effective solution for optimizing the performance of PV systems within LV distribution grids. This research underscores the significance of advanced control strategies in facilitating the integration of renewable energy systems while ensuring grid stability and reliability.

关键词： model predictive controller hosting capacity solar power distribution network grid constraints

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Frequency stabilization for interconnected renewable based power system using cascaded model predictive controller with fractional order PID controller

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IET RENEWABLE POWER GENERATION 2023年第16期17卷 3836-3855页

作者： Sibtain, Daud Rafiq, Turab Bhatti, Mehdi Hassan Shahzad, Sulman Kilic, Heybet Harbin Elect Co Ltd Harbin Peoples R China Natl Transmiss & Despatch Co Ltd NTDCL Lahore Pakistan Dicle Univ Dept Elect Power & Energy Syst TR-21280 Diyarbakir Turkiye Dicle Univ Dept Elect Power & Energy Syst TR-21280 Diyarbakir Turkiye

In today's electrical grid, frequency cannot be ignored. The proliferation of renewable energy sources into a power system degrades its frequency;hence, the need for frequency regulation has been increased as a result. The second major factor is the perturbation in the load that originates the frequency fluctuation which needs to be suppressed in minimum time to avoid any system collapse. The designing of an optimal controller is indispensable because of increasing power system complexity. In order to maintain the stability of the power system, this paper presents the cascaded design of the model predictive controller with fractional order PID controller (MPC-FOPIDN) to mitigate the frequency oscillations due to disruption in load. The combination of the predictive capabilities of model predictive control (MPC) and fractional order control enhances control abilities, making it an optimal control strategy for load frequency control (LFC). The grasshopper optimization algorithm (GOA) is applied to obtain optimal gains values for the FOPID controller. The efficacy of the controller has effectively mitigated frequency fluctuations caused by a change in demand or any uncertainty in the power system. The designing of an optimal controller is indispensable because of increasing power system complexity. In order to maintain the stability of the power system, this paper presents the cascaded design of the model predictive controller with fractional order PID controller (MPC-FOPIDN) to mitigate the frequency oscillations due to disruption in load. The predictive ability of MPC and improved control ability in terms of fractional operation amalgamate it as an optimal control for load frequency control (LFC). The grasshopper optimization algorithm (GOA) is applied to obtain optimal values for the FOPID controller. image

关键词： fractional order PID controller grasshopper optimization algorithm load frequency control model predictive controller

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model predictive controller-Based Levothyroxine Dosing Strategy for Patients Suffering from Hashimoto's Thyroiditis

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IFAC-PapersOnLine 2024年第24期58卷 275-280页

作者： Ravi Sharma Simon Lucas Goede Verena Theiler-Schwetz Markus Reichhartinger Institute of Automation and Control Graz University of Technology Austria Private Research Lochem The Netherlands Division of Endocrinology and Diabetology Department of Internal Medicine Medical University of Graz Austria

关键词： Hashimoto's thyroiditis Set-point theory Mathematical thyroid model model predictive controller

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model predictive controller-based Convolutional Neural Network controller for optimal frequency tracking of resonant converter-based EV charger

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RESULTS IN ENGINEERING 2024年 24卷

作者： Manoharan, Rajalakshmi Wahab, Razia Sultana Vellore Inst Technol Sch Elect Engn Vellore India

The rapid establishment of e-transportation is decided by two factors, namely the range and charging time of the Electric Vehicle (EV). The smart and fast charging topology are the main concerns to increase the range and reduce the charging time. The controllers are the heart of the smart EV chargers which decide the charger's optimized performance and reliability. Since the AI based smart charger performs well for fast and dynamic charging of battery, in this article model predictive controller (MPC) based Convolutional Neural network (CNN) is used as the controller. The EV charger with a Totem pole PFC circuit followed by an LLC resonant DC-DC converter is the proven topology for the efficient and high-power handling capacity of the charger. Initially the PI controller is used for controlling the frequency of the LLC converter and the MPC is developed, finally the CNN controller is structured, trained, validated, and tested to emulate MPC with a similar performance to reduce the computational burden and complexity of the real time implementation. A 7.2 kW LLC-based EV charger with <5 % current ripple and <12 % THD is designed, and the performance is analysed under various load conditions for constant current and constant voltage charging. The detailed analysis of the EV charger for PI, MPC and DNN controllers is done, and the measurement of the performance parameters ensures the robustness.

关键词： Deep neural network EV charger LLC resonant converter model predictive controller

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Wave-motion compensation for USV-UAV cooperation: A model predictive controller approach 20

Wave-motion compensation for USV-UAV cooperation: A model pr...

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20th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA)

作者： Martins, Jose Pereira, Pedro Campilho, Raul Pinto, Andry Polytech Porto ISEP Sch Engn Ctr Robot & Automat Syst INESC TEC Porto Portugal Univ Porto Fac Engn Ctr Robot & Automat Syst INESC TEC Porto Portugal

ISBN: (纸本)9798331516246;9798331516239

Due to the difficult access to the maritime environment, cooperation between different robotic platforms operating in different domains provides numerous advantages when considering Operations and Maintenance (O&M) missions. The nest Uncrewed Surface Vehicle (USV) is equipped with a parallel platform, serving as a landing pad for Uncrewed Aerial Vehicle (UAV) landings in dynamic sea states. This work proposes a methodology for short term forecasting of wave-behaviour using Fast Fourier Transforms (FFT) and a low-pass Butterworth filter to filter out noise readings from the Inertial Measurement Unit (IMU) and applying an Auto-Regressive (AR) model for the forecast, showing good results within an almost 10-second window. These predictions are then used in a model predictive Control (MPC) approach to optimize trajectory planning of the landing pad roll and pitch, in order to increase horizontality, consistently mitigating around 80% of the wave induced motion.

关键词： model predictive controller Auto-Regressive models Fast Fourier Transform low-pass Butterworth filter wave-motion mitigation USV

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