Development of an Enhanced Selective Harmonic Elimination for a Single-Phase Multilevel Inverter with Staircase Modulation

Govind, S.; Chappa, Anilkumar; Rao, K. D. M.; Dawn, Subhojit; Ustun, Taha Selim

doi:10.3390/electronics11233902

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…From Fig. 7, an 11-level output voltage waveform,5 notches are created, and each notch represents angles α1, α2, α3, α4, α5 and for the quarter wave symmetry, the fundamental value of voltage can be obtained using Fourier transform [17], [18].…”

Section: Sigma-delta Selective Harmonic Eliminationmentioning

confidence: 99%

Sigma-Delta Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters Using FPGA-Enabled Recurrent Neural Network-Newton Raphson Algorithm

Dakka¹,

Patthi²,

Rao³

et al. 2023

Preprint

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Harmonics produce nonlinearity, compromising system stability. Numerical approaches are effective yet time-consuming for solving dynamic nonlinear harmonic issues. This paper suggests employing a mix of the Recurrent Neural Network and Newton-Raphson (RNN-NR) techniques to address nonlinear harmonic problems. Inspired by the brain, RNNs are powerful problem prediction and pattern modelling algorithms. RNNs are able to effectively handle newly collected inputs even in the absence of NR-based mathematical models. Harmonics can be eliminated by Sigma-Delta Selective Harmonic Elimination (SD-SHE) PWM, which is used by Cascaded H-Bridge Multilevel Inverters (CHB-MLI). SD-SHE PWM notches the cascaded multilevel inverter's output waveform at varying angles to decrease harmonics. The neural network is trained using the MATLAB RNN programme. To implement its stages, Xilinx Vivado transfers the RNN to a Field Programmable Gate Array (FPGA). The nonlinearity problem is solved when the neural network is operated on an FPGA, and the design can be easily modified to meet the requirements of different applications.

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Section: Sigma-delta Selective Harmonic Eliminationmentioning

confidence: 99%

Sigma-Delta Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters Using FPGA-Enabled Recurrent Neural Network-Newton Raphson Algorithm

Dakka¹,

Patthi²,

Rao³

et al. 2023

Preprint

View full text Add to dashboard Cite

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Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers

Joga,

Sinha,

Paul

et al. 2024

Front. Smart Grids

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The paper introduces a novel method for identifying the location of harmonic-generating sources in smartgrids. The method utilizes a Dual-Tree Complex Wavelet Transform (DTCWT) of voltage and current signals measured at a specific point in the network. By applying DTCWT Transform, the signals are decomposed, and three non-active power quantities are extracted to represent the harmonic components within the system exclusively. These chosen non-active power quantities serve as indicators of the presence of harmonics in the system. Through analysis and comparison of these quantities, the method enables determining the precise location of the dominant harmonic generating source. This information is valuable for effectively addressing and mitigating harmonic issues in the network. Leveraging DTCWT and focusing on non-active power quantities provides a valuable tool for power system engineers and operators to diagnose and mitigate harmonic issues, ultimately improving power quality and system performance. This study presents a new feature extraction method to compute Non-active power quantities based on DTCWT due to its shift-invariant property.

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Investigation of Recent Metaheuristics Based Selective Harmonic Elimination Problem for Different Levels of Multilevel Inverters

2023

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Multilevel inverters (MLI) are popular in high-power applications. MLIs are generally configured to have switches reduced by switching techniques that eliminate low-order harmonics. The selective harmonic elimination (SHE) method, which significantly reduces the number of switching, determines the optimal switching moments to obtain the desired output voltage and eliminates the desired harmonic components. To solve the SHE problem, classical methods are primarily employed. The disadvantages of such methods are the high probability of trapping in locally optimal solutions and their dependence on initial controlling parameters. One solution to overcome this problem is the use of metaheuristic algorithms. In this study, firstly, 22 metaheuristic algorithms with different sources of inspiration were used to solve the SHE problem at different levels of MLIs, and their performances were extensively analyzed. To reveal the method that offers the best solution, these algorithms were first applied to an 11-level MLI circuit, and six methods were determined as a result of the performance analysis. As a result of the evaluation, the outstanding methods were SPBO, BMO, GA, GWO, MFO, and SPSA. As a result of the application of superior methods to 7-, 11-, 15-, and 19-level MLIs according to the IEEE 519—2014 standard, it has been shown that BMO outperforms in 7-level MLI, GA in 11-level MLI, and SPBO in 15- and 19-level MLIs in terms of THD, while in terms of output voltage quality, GA in 7-level MLI, BMO in 11-level MLI, GA and SPSA in 15-level MLI, and SPSA in 19-level MLI come forward.

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Development of an Enhanced Selective Harmonic Elimination for a Single-Phase Multilevel Inverter with Staircase Modulation

Cited by 4 publications

References 22 publications

Sigma-Delta Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters Using FPGA-Enabled Recurrent Neural Network-Newton Raphson Algorithm

Sigma-Delta Selective Harmonic Elimination in Cascaded H-Bridge Multilevel Inverters Using FPGA-Enabled Recurrent Neural Network-Newton Raphson Algorithm

Identification of harmonic sources in smart grid using systematic feature extraction from non-active powers

Investigation of Recent Metaheuristics Based Selective Harmonic Elimination Problem for Different Levels of Multilevel Inverters

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