Offline Selective Harmonic Elimination With (2N+1) Output Voltage Levels in Modular Multilevel Converter Using a Differential Harmony Search Algorithm

(2N+1) selective harmonic elimination pulse-width modulation (SHE-PWM) is an effective switching strategy of the modular multilevel converter in medium-voltage cases. In these cases, the number of sub-modules (SMs) is not high. Compared to the traditional (N+1) SHE-PWM, (2N+1) SHE-PWM has N more vol...

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Vydáno v:IEEE access Ročník 8; s. 121596 - 121610
Hlavní autoři: Xin, Yayun, Yi, Jin, Zhang, Kai, Chen, Canfeng, Xiong, Jian
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
(2<italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">N +1) SHE-PWM
Algorithms
Ant colony optimization
Configuration management
Converters
Electric potential
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Harmonic analysis
Harmonic distortion
harmony search algorithm
Heuristic methods
Machine learning
Mathematical model
Medium voltage
modular multilevel converter
Modulation
Multilevel converters
Optimization
Particle swarm optimization
Pulse duration modulation
Robustness (mathematics)
Search algorithms
Switches
Switching
Voltage
ISSN:2169-3536, 2169-3536
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Popis
Shrnutí:(2N+1) selective harmonic elimination pulse-width modulation (SHE-PWM) is an effective switching strategy of the modular multilevel converter in medium-voltage cases. In these cases, the number of sub-modules (SMs) is not high. Compared to the traditional (N+1) SHE-PWM, (2N+1) SHE-PWM has N more voltage levels, thus it can yield much better harmonic performance. However, the task of selective harmonic elimination also becomes much more complicated since there are more switching angles to be determined. This paper proposes a differential harmony search algorithm (DHS) with a novel harmony improvisation procedure for solving this problem. The Bayesian optimization method is applied to find the optimal parameter configuration for DHS. The performance of DHS is compared with 6 other metaheuristic algorithms including differential evolution (DE), harmony search (HS), genetic algorithm (GA), particle swarm optimization (PSO), teaching and learning-based optimization (TLBO), and ant colony optimization (ACO). The comparison is conducted on a set of 100 (2N +1) SHE-PWM instances by varying the modulation index from 0.01 to 1.0 with a step of 0.01. The numerical results show that the proposed DHS outperforms other compared methods in terms of objective function values, algorithm robustness, the magnitude of fundamental harmonic, and the calculated total harmonic distortion values. The switching angles obtained by DHS are further validated by both Matlab/Simulink simulation and hardware experiment.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3007022