ANN-based SHEPWM using a harmony search on a new multilevel inverter topology
ANN-based SHEPWM using a harmony search on a new multilevel inverter topology
This article presents the application of the harmony search (HS) optimization algorithm for selective harmonic elimination PWM (SHEPWM) in a new topology of multilevel inverters with reduced number of electronic switching elements. The main objective of the harmonic elimination strategy is eliminating undesired low-rank harmonics in order to improve the quality of the output waveform. The harmonic elimination strategy is achieved by solving a system of nonlinear equations. In this paper harmony search optimization is applied using arti cial neural networks (ANNs) on a new 21-level inverter topology. The algorithm is based on a music improvisation process. MATLAB programming software is used to develop a harmony search optimization program for harmonic elimination. A small-scale laboratory of the proposed 21-level inverter is built to validate the simulation results and to prove the efficiency of the proposed control scheme.
___
- [1] Shalchi AR, Nazarpour D, Hosseini SH, Sabahi M. Novel topologies for symmetric, asymmetric, and cascade switched-diode multilevel converter with minimum number of power electronic components. IEEE T Ind Electron 2014; 61: 5300-5310.
- [2] Patel HS, Hoft RG. Generalized techniques of harmonic elimination and voltage control in thyristor inverters: part I{harmonic elimination, IEEE T Ind Appl 1973; 9: 310-317.
- [3] Gnana Sundari M, Rajaram M, Balaraman S. Application of improved re y algorithm for programmed PWM in multilevel inverter with adjustable DC sources. Appl Soft Comput 2016; 41: 169-179.
- [4] Hiendro A. Multiple Switching Patterns for SHEPWM Inverters Using Differential Evolution Algorithms. Interna- tional Journal of Power Electronics and Drive Systems 2011; 1: 94-103.
- [5] Taleb R, Helaimi M, Benyoucef D, Boudjema Z. Genetic algorithm application in asymmetrical 9-level inverter. International Journal of Power Electronics and Drive Systems 2016; 7: 521-530.
- [6] Ganesan K, Barathi K, Chandrasekar P, Balaji D. Selective harmonic elimination of cascaded multilevel inverter using BAT algorithm. Procedia Technology 2015; 21: 651-657.
- [7] Wang X, Gao XZ, Zenger K. An Introduction to Harmony Search Optimization Method. Heidelberg, Germany: Springer, 2015.
- [8] Geem ZW, Kim JH, Loganathan GV. A new heuristic optimization algorithm: harmony search. Simulation 2001; 76: 60-68.
- [9] Geem ZW, Kim JH, Loganathan GV. Harmony search optimization: application to pipe network design. Interna- tional Journal of Modelling and Simululation 2002; 22: 125-133.
- [10] Ambia MN, Hasanien HM, Al-Durra A, Muyeen SM. Harmony search algorithm-based controller parameters optimization for a distributed-generation system. IEEE T Power Deliver 2015; 30: 246-255.
- [11] Maiti S, Verma V, Chakraborty C, Hori Y. An adaptive speed sensorless induction motor drive with arti cial neural network for stability enhancement. IEEE T Ind Inform 2012; 8: 757-766.
- [12] Filho F, Maia HZ, Mateus THA, Ozpineci B, Tolbert LM, Pinto JOP. Adaptive selective harmonic minimization based on ANNs for cascade multilevel inverters with varying DC sources. IEEE T Ind Electron 2013; 60: 1955-1962.
- [13] Ayala HVH, Coelho LdS, Mariani VC, Luz MVFd, Leite JV. Harmony search approach based on Ricker map for multi-objective transformer design optimization. IEEE T Magn 2015; 51: 1-4.
- [14] Hoang DC, Yadav P, Kumar R, Panda SK. Real-Time Implementation of a harmony search algorithm-based clustering protocol for energy-efficient wireless sensor networks. IEEE T Ind Inform 2014; 10: 774-783.
- [15] Kim JH, Lee HM, Yoo DG. Investigating the convergence characteristics of harmony search. Adv Intel Sys Comput 2015; 382: 3-10.
- [16] Askarzadeh A, Rezazadeh A. An innovative global harmony search algorithm for parameter identi cation of a PEM fuel cell model. IEEE T Ind Electron 2012; 59: 3473-3480.