Model predictive current control method for four-leg three-level converter operating as shunt active power filter and grid connected inverter

Antoniewicz, Kamil; Rafał, K.

doi:10.1515/bpasts-2017-0065

Cited by 12 publications

(10 citation statements)

References 20 publications

(21 reference statements)

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“…Sehingga, MPC cocok diterapkan pada konverter daya karena efisiensi sistem dan kualitas keluaran lebih baik (Rashid, 2014). Kendali MPC ini mulai diterapkan dalam filter daya hybrid (Andang, dkk., 2022) (Antoniewicz & Rafal, 2017) inverter (Andang, dkk., 2022) (Mohamed, dkk., 2019), dan konverter DC (Dehghanzadeh, dkk, 2018) (Zhou, dkk, 2020).…”

Section: Pendahuluanunclassified

Pemodelan Active Front End Converter (AFE) Tiga-Fasa Tiga-Kaki menggunakan Kendali Model Predictive Control (MPC)

2023

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ABSTRAKPenelitian ini membahas Active Front End Converter (AFE) tiga-fasa tiga-kaki menggunakan kendali Model Predictive Control (MPC) pada pensakelaran converter yang dapat mengurangi nilai harmonisa dan memperbaiki faktor daya pada jaringan. Kendali MPC digunakan untuk melakukan prediksi tegangan dan arus AFE dengan cara memasukkan vektor tegangan yang dihasilkan oleh sakelar konverter ke dalam model state-space diskrit dan dievaluasi menggunakan cost function. Simulasi model dilakukan pada beberapa kondisi, yaitu simulasi beban resistif, beban induktif, beban kapasitif, beban nonlinear, dan variable frequency drive (VFD) dengan motor induksi. Simulasi beban resistif, THDv dan THDi secara berurutan nilai rata-ratanya sebesar 0.02% dan 0.45%, beban induktif, 0.01% dan 1.92%, beban kapasitif, 0.01% dan 1.92%, beban nonlinear, 0.03% dan 1.23%, dan beban VFD dengan motor induksi sebesar 0.04% dan 1.18%. Faktor daya pada simulasi beban bervariasi menghasilkan unity. Dari hasil simulasi didapatkan kesimpulan kendali MPC pada AFE mampu meningkatkan kualitas daya listrik.Kata kunci: Harmonisa, Faktor Daya, AFE, MPC, Model Vektor Ruang ABSTRACTThis study discusses a three-phase three-leg Active Front End Converter (AFE) using Model Predictive Control (MPC) which controls converter switching to reduce harmonic values and improve the power factor on the network. MPC is used to predict the AFE voltage and current by entering the voltage vector generated by the converter switch into a discrete state-space model and evaluated using a cost function. The simulations of the model have been done under several conditions, resistive loads, inductive loads, capacitive loads, nonlinear loads, and variable frequency drive (VFD) with an induction motor. Resistive load simulations, THDv and THDi respectively, the average values are 0.02% and 0.45%, inductive load, 0.01% and 1.92%, capacitive load, 0.01% and 1.92%, nonlinear load, 0.03% and 1.23%, and load VFD with induction motor is 0.04% and 1.18%. The power factor in the varying load simulation results is unity. From the simulation results, it can be concluded that MPC control on AFE can improve electrical power quality. Keywords: Harmonics, Power Factor, AFE, MPC, Space Vector Model

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Section: Pendahuluanunclassified

Pemodelan Active Front End Converter (AFE) Tiga-Fasa Tiga-Kaki menggunakan Kendali Model Predictive Control (MPC)

2023

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“…The model is extended with an additional delay state to model the delay between the instant the state variables x e k are evaluated and the time when a new control action u e (k+1) is made available (11). Considering the new state vector…”

Section: Control Relevant Modellingmentioning

confidence: 99%

“…Due to this, NMPC, especially on systems with strict computational constraints, is not suitable for fast, real-time operation. Usually sequential quadratic programming (SQP) algorithm or its modified versions prove to be effective as a nonlinear, non-convex optimization problem solver in the NMPC [9][10][11][12]. But it greatly depends on the strong initial guesses for the starting of computations which may lead to the local minimum solution and the resulting NMPC control may be far from an optimal point.…”

Section: Introductionmentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Diwan,

Deshpande

2022

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As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm.

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“…The substitution of ( 18) and ( 19) in (17) gives: * * * * In (20) represents the predicted inverter output voltage expressed in terms of the reference and the actual supply currents [12], [13], [23]. The main characteristic of this control strategy is the selection of the Int J Pow Elec & Dri Syst ISSN: 2088-8694  voltage vector that minimizes a quality function g [9], [23].…”

Section: Phase Locked Loop (Pll)mentioning

confidence: 99%

High-performance active power filter implementation based on predictive current control

Rahima

Goléa

Toufik

et al. 2019

IJPEDS

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This paper presents the use of the predictive strategy concept to improve the Active Power Filter (APF) performance, by compensation of the reactive power and elimination of the harmonic currents generated by non-linear loads. Predictive control is generating considerable interest when it comes to implementing current control strategies in active power filter. The proposed strategy provides a simple controller incorporating Phase Locked Loop (PLL) independency. The prediction is evaluated using a cost function that quantiﬁes the desired system behavior. The cost function used in this work evaluates the ﬁltered error of the currents. This strategy minimized the number of sensors, ease of practical implementation and reduced system size and cost. The effectiveness of the proposed controller is confirmed through simulation and experimental validation using a hardware prototype based on dSPACE-1104

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Model predictive current control method for four-leg three-level converter operating as shunt active power filter and grid connected inverter

Cited by 12 publications

References 20 publications

Pemodelan Active Front End Converter (AFE) Tiga-Fasa Tiga-Kaki menggunakan Kendali Model Predictive Control (MPC)

Pemodelan Active Front End Converter (AFE) Tiga-Fasa Tiga-Kaki menggunakan Kendali Model Predictive Control (MPC)

Bulletin of the Polish Academy of Sciences: Technical Sciences

High-performance active power filter implementation based on predictive current control

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