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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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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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Intelligent Control of Mobile Robot Via Waypoints Using Nonlinear model predictive controller and Quadratic Bezier Curves Algorithm

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2020年第4期15卷 1857-1870页

作者： Al-Mayyahi, Auday Aldair, Ammar A. Rashid, Abdulmuttalib T. Univ Basrah Coll Engn Dept Elect Engn Basrah Iraq

This article introduces a new control methodology to intelligently drive the motion of a mobile robot via given waypoints. A nonlinear model predictive controller (NMPC) is utilized to track the waypoints that are placed randomly at different positions in a given environment. Hence, various tracking paths can be generated based on locations of waypoints. Additionally, a Quadratic Bezier Curves algorithm has been applied for obstacle avoidance. It is combined with the NMPC via a switching mechanism. Hence, the transportation of the mobile robot will take the priority of avoiding obstructing obstacles if exist before moving forward to the next target waypoint. Single and multiple mobile robots have been simulated into several scenarios to investigate the performance of the developed control scheme.

关键词： model predictive controller Quadratic bezier curves algorithm Waypoints Obstacle avoidance

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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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Controlling of Proton Exchange Membrane Fuel Cell by model predictive controller Based on ANFIS model

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FUEL CELLS 2016年第5期16卷 530-537页

作者： Karimi, M. Yahyazadeh, J. Rezazade, A. Shahid Beheshti Univ Dept Elect & Comp Engn GC Tehran 198396311 Iran

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro-fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro-fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch-and-bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.

关键词： Adaptive Neuro-Fuzzy Inference System Artificial Neural Network Fuel Cell Fuzzy modeling model predictive controller

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Stochastic wind energy integrated multi source power system control via a novel model predictive controller based on Harris Hawks optimization

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ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS 2022年第4期44卷 10694-10719页

作者： Kumar, Vineet Sharma, Veena Arya, Yogendra Naresh, R. Singh, Amita Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur Himachal Prades India JC Bose Univ Sci & Technol YMCA Elect Engn Dept Faridabad Haryana India Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur 177005 Himachal Prades India

Combined voltage and frequency control is a critical control problem of modern power system to avoid blackouts. This paper discusses the simultaneous voltage and frequency control for an interconnected multi-source multi-area power system having two areas of equal generating capacity, each containing thermal, diesel, and wind units. A centralized model predictive controller (MPC) scheme is suggested to minimize voltage and frequency fluctuations. For effective control, a normalized performance criterion has been considered, and recently appeared Harris hawks optimization (HHO) algorithm has been integrated for the optimal tuning of MPC weights for the first time. The effectiveness of the proposed MPC-HHO controller has been verified after comparing its transient response performance indices with that of various existing controllers available in the literature. Quantitatively, the proposed controller yields a minimum performance index value, i.e. 0.000741, and it has been observed that the proposed MPC-HHO controlled system has 79.28%, 68.89%, 77.19%, and 81.96% better settling time for frequency deviation in area 1 when compared with PI lambda DF-LSA, FOIDF-LSA, MFO-FOPID, and PID-FA controllers, respectively. Further, the stochasticity in wind power generation has been considered with the help of Monte Carlo simulation of Autoregressive Integrated Moving Average model (ARIMA) model for practical wind speed data from National Renewable Energy Laboratory. The stability of the proposed controller has been verified using eigenvalues and Bode plot analysis, where the MPC-HHO controlled system was having more gain and phase margin values as compared to the PID controlled system. Furthermore, the robustness of the approach has been successfully evaluated by considering the nonlinearities, and parameter sensitivity analysis has been conducted after varying the parameters of voltage and frequency control loops and reevaluating the transient performance of the controlled sys

关键词： Automatic Voltage Control meta-heuristic optimization algorithm load frequency control model predictive controller wind power plant

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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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Control of an irrigation branch canal using model predictive controller

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CURRENT SCIENCE 2020年第8期118卷 1255-1264页

作者： Menon, Jailakshmi Mudgal, B., V Guruprasath, M. Sivalingam, S. Anna Univ Saveetha Engn Coll Chennai 602105 Tamil Nadu India Anna Univ Ctr Water Resources Chennai 600025 Tamil Nadu India Smarta Opti Solut Chennai 600073 Tamil Nadu India Publ Works Dept Coimbatore 641001 Tamil Nadu India

Sustainable use of the available water resource is an important challenge for food security. An irrigation canal system consists of a number of pools connected to each other by control structures. Unsteady flow Saint-Venant equations are used to model the flow in the canal system and these equations are solved using implicit finite difference methods. The canal system is represented as linear time invariant system and the resulting equations are the dynamic equations in state space form. These linearized equations are modelled in Matlab (R) environment. The control strategy adopted in this research is the downstream control method of operation and the control structures adopted are radial gates and modified radial gates. The unsteady flow model is applied to the chosen study area and the results obtained from the model are used to simulate the dynamics of the canal system using model predictive controller toolbox. The results show that any closed loop controller will work efficiently with the hydraulic model developed.

关键词： Finite difference irrigation channels linear time invariant model predictive controller unsteady flow

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A model predictive controller for improvement in power quality from a hybrid renewable energy system

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SOFT COMPUTING 2019年第8期23卷 2627-2635页

作者： Muthukumar, R. Balamurugan, P. Mt Zion Coll Engn & Technol Dept Elect & Elect Engn Pudukkottai Tamil Nadu India

Hybrid renewable energy systems are one among the key areas of research in the fields of sustainable energy in recent times. With the ever-increasing demand in need for energy, renewable sources of energy are much sought after as they are also environment friendly in nature when compared with their non-renewable energy system counterparts. A solar-wind turbine hybrid system is proposed in this research paper, and a generalized predictive controller has been implemented to improve the stability and power quality of the proposed hybrid system. Survey of related works indicates certain limitations in implementing renewable energy systems with respect to stability, reliability and flexibility in fuel which have been effectively addressed in this research article. A three-level inverter has been implemented to fine-tune the power factor which is a combined power factor in the proposed work. A FFT analysis on the power indicates a reduced total harmonic distortion in the proposed work and has been justified over the conventional MPPT-based power generation systems without a properly designed controlling method. The constraints in designing a hybrid system with respect to power quality act as input to the model predictive controller in the proposed work. The work could also be implemented in cascading two or more number of heterogeneous renewable power sources.

关键词： HRES Stability and power quality model predictive controller Total harmonic distortion

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An interpolation-based model predictive controller for input-output linear parameter varying systems

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INTERNATIONAL JOURNAL OF DYNAMICS AND CONTROL 2022年第5期10卷 1539-1552页

作者： Hadian, Mohsen Ramezani, Amin Zhang, Wenjun Univ Saskatchewan Coll Engn Dept Mech Engn Saskatoon SK Canada Tarbiat Modares Univ Coll Elect & Comp Engn Dept Control Tehran Iran

This paper developed a controller for constrained nonlinear MIMO systems, which reduced computation and conservatism. Firstly, a linear parameter varying (LPV) model was chosen to capture the system's nonlinear/time-variant characteristics with scheduling variables, having less complexity than traditional nonlinear models. Secondly, this LPV model was accompanied by a model predictive controller (MPC) to deal with constrained MIMO systems. In contrast to nonlinear MPC, an LPV-MPC had a linear model in each sampling time, leading to a less complicated designing process and a lower online computation load. Moreover, the LPV-MPC was constructed in an input-output configuration not to require the state measurement. Thirdly, an interpolation-based MPC (IMPC) was supplemented for the sake of conservatism, where control signals were derived from interpolation between several precalculated control gains. If this strategy is implemented, the online calculation will be moderated, and conservatism will be improved through an increased attraction region. The efficiency of the proposed method was thoroughly examined in two numerical examples. The findings showed that not only did the approach perform brilliantly in setpoint tracking and disturbance rejection, but also it was deeply tractable considering online computation and conservatism.

关键词： model predictive controller MPC LPV Interpolation-based controllers Feasibility

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