Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network

Rasheed, Mohammed; Alakkad, Moataz M.A.; Omar, Rosli; Sulaiman, Marizan; Halim, Wahidah Abd

doi:10.11591/ijeecs.v20.i3.pp1148-1158

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Artificial neural network ANN has used in different application for the active power filter such as the identification and calculate the reference courants [21]- [23]. However, this paper presented an investigation of a five-phase shunt APF to compensate the reactive power and to enhance the quality of the power in a five-phase embarked electrical network.…”

Section: Introductionmentioning

confidence: 99%

Neural network controller for five phases shunt active power filter applied for five phase embarked electrical network

Laala

Amami

Rabhi

et al. 2023

IJPEDS

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This paper discusses neural network ANN controller applied for five phase shunt active power filter. Three-phase shunt active power filters are widely studied, however in this paper we investigate to extend their application to five phase embarked electrical network. Two-level five phase inverter is used as a shunt active power filter to reduce harmonics and compensate reactive power injected by a five-phase diode rectifier. An artificial neural network controller has been used to regulate the DC side of the inverter, thereby contributing to the calculation of the reference harmonic currents on the one side and controlling the injected harmonic currents on the other. Self-tuning filter (STF) is used to calculate the reference current. By using STF allow to extract the voltage and current fundamental components in the α-β-axis directly without the use of a phase-locked loop (PLL). Simulation results shows that the ANN controller presents appreciable performances in references tracking and the proposed five-phase shunt active power filter provides a sinusoidal supply current in phase with the line voltage with low harmonic distortion.

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Section: Introductionmentioning

confidence: 99%

Neural network controller for five phases shunt active power filter applied for five phase embarked electrical network

Laala

Amami

Rabhi

et al. 2023

IJPEDS

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“…Numerous strategies and analytical methodologies are developed and investigated in the scientific literature to address the SHE issues associated with inverters with multiple levels. All suggested solutions may be broadly grouped into three categories: repetitive procedures, consequence theory, and adaptive algorithms [9][10][11]. Newton Raphson (NR) is a highly popular recursive method.…”

Section: Introductionmentioning

confidence: 99%

Design of Switched Capacitor Multilevel Inverter with Selective Harmonic Elimination

Buddinni,

Usha

2023

IOP Conf. Ser.: Mater. Sci. Eng.

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In this paper, Selective Harmonic Elimination (SHE) technique is applied to control multilevel inverters that can be used to eliminate the low order dominant harmonics. This is considered a low frequency technique, in which the switching angles are predetermined based on solving a system of transcendental equations. Newton Raphson is one among the heuristic techniques utilised for selective harmonic elimination. The proposed SHE strategy is simulated along with Sinusoidal Pulse Width Modulation (SPWM) technique and the harmonic distortions are measured and compared for both techniques in MATLAB/Simulink software.

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“…A multilevel inverter is an electronic circuit with multi-level power, this inverter/circuit can be converted into a direct power (DC) output supply and supplies an alternate voltage (AC) waveform. "Renewable sources of energy such as photovoltaics, wind and water turbines are now essential energy sources due to their renewable and environmental friendliness" [1]- [3]. Most of these renewable pathways generate a DC waveform as an output that is not appropriate for transmissions, distributions and uses.…”

Section: Introductionmentioning

confidence: 99%

Management switching angles real-time prediction by artificial neural network

Al-Hiealy¹,

Shikh²,

Jalil³

et al. 2021

IJEECS

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Artificial neural networks (ANNs) is an efficient way for different types of real-world prediction problems. In the past decade, it has given a tremendous surge in a global research activities. ANNs embody much certainty and provide a great deal of promise This paper has present artificial neural network (ANN) technique analysis and prediction for management switching angles real-time. The proposes to be used ANN for prediction and selected obtine angles for implement the timing diagram for mulitlvel inverter circuit. In order to control the fundamental component, ANNs are used to solve the analysis of non-linear equation of the output timing diagram in order to determine the switching angles. Substantially, the number of switching devices are reducing as possible basically for reducing a switching loss in the system, also have been used ANNs technique to optimize a switching angles behavior to reduce total harmonic distortion (THD) at voltage and current output waveform equal THDV 8.05% THDA 5.1%. For the proposed controllers, the performance and results by the ANNs were obtained and compared by using MATLAB software.

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Enhance the accuracy of control algorithm for multilevel inverter based on artificial neural network

Cited by 4 publications

References 18 publications

Neural network controller for five phases shunt active power filter applied for five phase embarked electrical network

Neural network controller for five phases shunt active power filter applied for five phase embarked electrical network

Design of Switched Capacitor Multilevel Inverter with Selective Harmonic Elimination

Management switching angles real-time prediction by artificial neural network

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