Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads

Valencia-Rivera, Gerardo Humberto; Merchan-Villalba, Luis Ramon; Tapia-Tinoco, Guillermo; García, José Merced Lozano; Ibarra-Manzano, Mario Alberto; Aviña-Cervantes, Juan Gabriel

doi:10.3390/math8071096

Cited by 13 publications

(9 citation statements)

References 32 publications

(30 reference statements)

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“…The extensive use of power generation using wind has forced countries to establish a set of rules for the operation and connection to the grid of wind generators. Amongst all the standards, those related to smart networks aim to guarantee a safe supply, and ensure the reliability and quality of the energy generated [25,26]. With the strong growth of grid-connected wind power plants, there is a need for grid-integrated wind farms with the ability to withstand grid voltage and frequency also during perturbations in the network [18].…”

Section: Introductionmentioning

confidence: 99%

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

et al. 2021

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Wind-generated energy is a fast-growing source of renewable energy use across the world. A dual-feed induction machine (DFIM) employed in wind generators provides active and reactive, dynamic and static energy support. In this document, the droop control system will be applied to adjust the amplitude and frequency of the grid following the guidelines established for the utility’s smart network supervisor. The wind generator will work with a maximum deloaded power curve, and depending on the reserved active power to compensate the frequency drift, the limit of the reactive power or the variation of the voltage amplitude will be explained. The aim of this paper is to show that the system presented theoretically works correctly on a real platform. The real-time experiments are presented on a test bench based on a 7.5 kW DFIG from Leroy Somer’s commercial machine that is typically used in industrial applications. A synchronous machine that emulates the wind profiles moves the shaft of the DFIG. The amplitude of the microgrid voltage at load variations is improved by regulating the reactive power of the DFIG and this is experimentally proven. The contribution of the active power with the characteristic of the droop control to the load variation is made by means of simulations. Previously, the simulations have been tested with the real system to ensure that the simulations performed faithfully reflect the real system. This is done using a platform based on a real-time interface with the DS1103 from dSPACE.

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Section: Introductionmentioning

confidence: 99%

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

et al. 2021

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“…Dynamic model of the micro grid is shown in Figure (1). This micro grid consists of a PV, battery, super capacitor and DC load.…”

Section: Dynamic Model Of the Micro Gridmentioning

confidence: 99%

“…Furthermore, different control ways for the system are reviewed, and their pros and cons of each approach are explained in the following parts. In [1,2], compound PID_LQR controller has been applied for micro grid with linear and nonlinear loads. Although the linear controller in this reference has the benefits such as structural simplicity and ease of implementation, it loses its performance in the presence of MG's uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Applying Adaptive Super-Twisting Controller for Voltage Regulation in a DC Micro Grid

Ashtary

Hakimi

Naghavi³

et al. 2023

Preprint

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Micro grids mainly operate in two modes of on-grid and off-grid. The main problems regarding off-gird are voltage and frequency oscillation or instability. In this case, a control system must be used to adjust the voltage and frequency of the micro grid. According to nonlinear features, uncertainty parameters and variations of the load, applying nonlinear and robust methods will be a suitable approach for the system. In this paper, a nonlinear adaptive super-twisting controller based on the sliding mode theory is used to control the micro grid. The principal reason for utilizing this controller is to control distortion in presence of parameters uncertainty. For this purpose, system dynamic equations will be extracted. Afterwards, by using sliding mode theory, a controller will be designed to control the entire system. To address chattering problem, an adaptive super-twisting method is proposed. Closed loop stability has been proved by Lyapunov theorem. The proposed method is evaluated by MATLAB/Simulink software, and a comparison between the simulation results and the other methods has been made. The comparison studies show that the proposed adaptive Super-Twisting controller is more applicable than conventional methods.

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“…For that, we need to define the scale, which used to measure the efficiency of the energy consumption that standard, called the power factor (𝑃 𝑓 ) [18] for the active power (𝐴 𝑝 ). We need to know the actual power transmitted to nonlinear loads [19]- [21], such as electric appliances, lighters, heaters, and laptops (kW); therefore, to calculate it we must first learn that the current (I) lagging by 90 degrees behind the voltage (V) by an angle for the electrical loads that be used in our houses, the vector diagram which is being used to describe currents and voltages that are perfectly sinusoidal signals. The current vector can be divided into two components in this vector diagram: one in phase with the voltage vector (component 𝐼 𝑎 ) and the second is lagging 90 degrees (component 𝐼 𝑟 ) [2] usually the power factor (𝑃 𝑓 ) is given in (3):…”

Section: Electrical Energy Modelmentioning

confidence: 99%

A new smart approach of an efficient energy consumption management by using a machine-learning technique

Hasan

Kadhim

2022

IJEECS

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Many consumers of electric power have excesses in their electric power consumptions that exceed the permissible limit by the electrical power distribution stations, and then we proposed a validation approach that works intelligently by applying machine learning (ML) technology to teach electrical consumers how to properly consume without wasting energy expended. The validation approach is one of a large combination of intelligent processes related to energy consumption which is called the efficient energy consumption management (EECM) approaches, and it connected with the internet of things (IoT) technology to be linked to Google Firebase Cloud where a utility center used to check whether the consumption of the efficient energy is satisfied. It divides the measured data for actual power (A_p ) of the electrical model into two portions: the training portion is selected for different maximum actual powers, and the validation portion is determined based on the minimum output power consumption and then used for comparison with the actual required input power. Simulation results show the energy expenditure problem can be solved with good accuracy in energy consumption by reducing the maximum rate (A_p ) in a given time (24) hours for a single house, as well as electricity’s bill cost, is reduced.

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Hybrid LQR-PI Control for Microgrids under Unbalanced Linear and Nonlinear Loads

Cited by 13 publications

References 32 publications

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid

Applying Adaptive Super-Twisting Controller for Voltage Regulation in a DC Micro Grid

A new smart approach of an efficient energy consumption management by using a machine-learning technique

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