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An improved binary differential evolution algorithm for optimizing PWM control laws of power inverters

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Abstract

Stochastic optimization methods inspired by biological evolution system have been widely employed to optimize PWM control laws of power inverters. But the existing approaches impose a serious computational burden and difficult parameter tuning issues. However, the differential evolution (DE) algorithm has the superiority of simple implementation and few parameters to tune. Thus, we propose an improved binary DE (IBDE) algorithm for optimizing PWM control laws of power inverters. The proposed algorithm focuses on the designs of the adaptive crossover and parameterless mutation strategies without imposing an additional computational burden. In numerical experiments, a single-phase full-bridge and two-level three-phase inverters are considered, and the optimal PWM control law is calculated to maximize the closeness of the controlled inductor current to sinusoidal reference current by using the proposed algorithm. Experimental results indicate that IBDE can obtain high quality output waveform that is a very good approximation to the sinusoidal reference waveform. Moreover, the spectrum analysis for the optimal PWM control law obtained by IBDE indicates that the lower odd-order harmonics are eliminated, while the existing peer algorithms cannot do well. We also carry out experiments on sensitivity analysis with respect to several important parameters.

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Abbreviations

DC:

Direct current

AC:

Alternating current

(S)PWM:

(Sinusoidal) Pulse width modulation

DSP:

Digital signal processor

THD:

Total harmonic distortion

BVI:

Bootstrap variable inductance

FACTS:

Flexible AC transmission systems

A :

Amplitude of reference current

C :

Capacitor

L :

Inductance

R :

Resistance

DE:

Differential evolution

B(R)DE:

Binary-coded (real-coded) DE

SaDE:

Self-adaptive DE

DBDE:

Discrete binary-coded DE

BLDE:

Binary learning DE

JADE:

Adaptive differential evolution

GA(s):

Genetic algorithm(s)

IA(s):

Immune algorithm(s)

SSA(s):

Simulated annealing algorithm(s)

F :

Scale factor

CR :

Crossover rate

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Acknowledgements

The authors acknowledge the support from the National Natural Science Foundation of China under Grants 61304146, 61403194, 71461027 and 71471158. Provincial Science and Technology Foundation of Guizhou of China under Grants (Qian ke he [2015]2002) and the Provincial excellent creative talents of science and technology reward program of Guizhou of China under Grants 2014255. Science and technology talent training object of Guizhou province outstanding youth (Qian ke he ren zi [2015]06). Guizhou province natural science foundation in China (Qian Jiao He KY [2014]295). 2013, 2014 and 2015 Zunyi 15851 talents elite project funding. Zhunyi innovative talent team (Zunyi KH201538). Project of teaching quality and teaching reform of higher education in Guizhou province (Qian Jiao gaofa [2013]446, [2015]337).

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Authors and Affiliations

  1. College of Automatic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Shuqu Qian, Yongqiang Ye & Guofeng Xu

  2. Anshun University, Anshun, 561000, China

    Shuqu Qian

  3. School of Mathematics and Computer Science, Zunyi Normal College, Zunyi, 563002, China

    Yanmin Liu

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  1. Shuqu Qian
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  2. Yongqiang Ye
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  3. Yanmin Liu
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  4. Guofeng Xu
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Correspondence to Shuqu Qian.

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Qian, S., Ye, Y., Liu, Y. et al. An improved binary differential evolution algorithm for optimizing PWM control laws of power inverters. Optim Eng 19, 271–296 (2018). https://doi.org/10.1007/s11081-017-9354-5

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  • DOI: https://doi.org/10.1007/s11081-017-9354-5

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