High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

Azab, Mohamed

doi:10.1186/s41601-021-00204-z

Cited by 14 publications

(9 citation statements)

References 73 publications

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“…However, the voltage level was not reduced by switched Z-source converter. FCS-MPC was developed in [18] to control three-phase grid-connected strings depending on the Power Quality (PQ) control scheme. Finite Control Set-Model Predictive Control (FCS-MPC) was employed for attaining high-performance decoupled control of active and reactive powers injected into the grid from Distributed Energy Resources (DER).…”

Section: Related Workmentioning

confidence: 99%

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Samikannu¹,

Vinoth²,

Rao³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit. Renewable energy sources are playing a significant role in the modern energy system with rapid development. In renewable sources like fuel combustion and solar energy, the generated voltages change due to their environmental changes. To develop energy resources, electric power generation involved huge awareness. The power and output voltages are plays important role in our work but it not considered in the existing system. For considering the power and voltage, Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier (GPIC-MDCNNC) Model is introduced for the grid-connected renewable energy system. The input information is collected from two input sources. After that, input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model. Hidden layer 1 is transferred to hidden layer 2. Gaussian activation is employed for determining the output voltage with help of the controller. At last, the output layer offers the last value in GPIC-MDCNNC Model. The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages. GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works. The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

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Section: Related Workmentioning

confidence: 99%

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Samikannu¹,

Vinoth²,

Rao³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…where J 1 and J 2 are the value of the cost function shown in (5), which are obtained by substituting u 1 and u 2 into (3), respectively.…”

Section: Fcs-mpc With Virtual Vectormentioning

confidence: 99%

“…Although the carrier modulated PWM has been used, the MPC strategy gets attention widely because it does not have the complex proportional integral controller requirements [2,3]. In addition, FCS-MPC has the advantage of fast dynamic response, excellent steady-state performance, and straightforward implementation [4][5][6][7]. FCS-MPC is suitable for various power electronics applications, including motor drives, active power filters, and grid-tied and stand-alone converters [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Virtual Vector Optimal Selection Strategy for NPC Three‐Level Grid‐Tied Converter with Finite Control Set Model Predictive Control

Xie

et al. 2022

IEEJ Transactions Elec Engng

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Virtual vector is applied in neutral-point-clamped (NPC) three-level converter finite control set model predictive control (FCS-MPC) strategy due to its good performance of less current ripples and high power efficiency. However, in the process of adding virtual vectors, some redundant virtual vectors will be selected. Excessive virtual vector will increase the computational burden and affect grid-tied current performance. A virtual vector optimal selection strategy for model predictive control to determine the vectors is proposed. The strategy calculates the fluctuation of grid-tied current through theoretical analysis. Based on the analysis of amplitude fluctuation of grid-tied current, the graphics of coverage angle visualization can be obtained under different virtual vector. The angle of the required virtual vectors is obtained through the graphics of coverage angle visualization. The number of virtual vector can be determined when the error current angle range covers the entire vector graph. The proposed control strategy was compared with the conventional control strategies, including excessive virtual vectors or with no any virtual vector. Experimental results show that the proposed control strategy can significantly reduce THD, grid-tied current error and computational burden.

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“…Since the back-EMFs have relatively slow variation compared to the sampling and switching frequencies, the back-EMF components eα and eβ can be considered constant during the sampling period, i.e., ) [41,42].…”

Section: Computation Of the Motor Back-emfs In (α-β) Coordinatesmentioning

confidence: 99%

“…One such modern scheme is the finite control set model predictive control approach (FCS-MPC) [28][29][30][31][32][33][34][35][36][37][38][39][40]. In the FCS-MPC technique, the future behavior of the system is predicted for a finite time frame [41,42]. Accordingly, the optimum future control action is applied to the system to satisfy a customized goal function [37], where the FCS-MPC algorithm is repeated at every sampling period.…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of BLDC Motor Drives with Different Approaches: FCS-Model Predictive Control and Hysteresis Current Control

Azab

2022

WEVJ

Self Cite

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The control techniques of the brushless DC (BLDC) motor have gained a large amount of interest in recent years, with their use being implemented in order to achieve a high-performance drive, including quick transient response and high-quality waveforms at the steady state. This paper provides a comparative study between three control schemes of BLDC motors: the direct power control scheme using a finite control set model predictive control (FCS-MPC) approach, the stator current controlled scheme using an FCS-MPC approach, and the stator current controlled scheme using ON–OFF hysteresis current controllers. The three systems were studied and investigated under the same operating conditions. The comparative study included investigating the performance of the BLDC drive in both steady state and transient operations. Qualitative and quantitative analyses were performed on the results obtained with each control scheme. The obtained results demonstrate the validity and effectiveness of the three investigated schemes in controlling the motor speed to the desired value under sudden load changes and achieving satisfactory quick transient responses. However, the results indicate the superiority of the direct power control scheme using an FCS-MPC approach over the others in terms of its minimum torque ripple, lowest torque and speed pulsations, minimum active and reactive power ripples, and high-quality waveforms of the stator currents drawn by the motor with minimum THD.

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High performance decoupled active and reactive power control for three-phase grid-tied inverters using model predictive control

Cited by 14 publications

References 73 publications

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Virtual Vector Optimal Selection Strategy for NPC Three‐Level Grid‐Tied Converter with Finite Control Set Model Predictive Control

Comparative Study of BLDC Motor Drives with Different Approaches: FCS-Model Predictive Control and Hysteresis Current Control

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