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Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review

Author

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  • Memon, Mudasir Ahmed
  • Mekhilef, Saad
  • Mubin, Marizan
  • Aamir, Muhammad

Abstract

Observing present scarcity of fossil fuel and emissions of greenhouse gases, electricity generated from Renewable Energy (RE) sources turns out to be the best alternative for generating the power. In RE system, the inverter is normally used to condition the DC power into AC to meet the requirements of load and transmission system. The inverter offers myriad benefits; however, the presence of harmonics (particularly low-order) in the output voltage affects the efficiency and performance of inverter, causes switching losses and decreases the lifetime of the system. In last three decades, significant research has been done to develop the efficient control technique for eliminating the unwanted harmonics. The preliminary review of existing control techniques revealed that the selective harmonic elimination pulse-width modulation (SHEPWM) is more proficient to eliminate the low-order harmonics. However, non-linear transcendental equations used in this technique pose a challenge to solve particularly for calculus-based methods. With the advent of powerful and low-cost computers, bio-inspired intelligent algorithms (BIAs) seem to be a better approach for solving these complex equations. This review paper presents the detailed principle operation of nine well-known BIAs and discusses their application in inverters for harmonic elimination (HE). Moreover, different objective functions are also discussed in this paper which is used by the researchers for HE. Additionally, the performance of five renowned BIAs, namely, Imperialist Competitive Algorithm, Particle Swarm Optimization, Differential Evolution, Bee Algorithm and Genetic Algorithm is critically evaluated. Their performance is analyzed in terms of accuracy, computational complexity, convergence speed, and a number of control parameters. The conclusion has been made on the basis of information extracted from the literature and evaluation results with future recommendations. This single paper covers all the essential information regarding HE in inverters, which will help researchers to design the efficient RE conversion system.

Suggested Citation

  • Memon, Mudasir Ahmed & Mekhilef, Saad & Mubin, Marizan & Aamir, Muhammad, 2018. "Selective harmonic elimination in inverters using bio-inspired intelligent algorithms for renewable energy conversion applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2235-2253.
    • Handle: RePEc:eee:rensus:v:82:y:2018:i:p3:p:2235-2253
    DOI: 10.1016/j.rser.2017.08.068
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    3. Yin, Linfei & Gao, Qi & Zhao, Lulin & Wang, Tao, 2020. "Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids," Energy, Elsevier, vol. 191(C).
    4. Faris E. Alfaris & Essam A. Al-Ammar & Ghazi A. Ghazi & Ahmed A. Al-Katheri, 2024. "Design Enhancement of Grid-Connected Residential PV Systems to Meet the Saudi Electricity Regulations," Sustainability, MDPI, vol. 16(12), pages 1-29, June.
    5. Muhyaddin Rawa & Marif Daula Siddique & Saad Mekhilef & Noraisyah Mohamed Shah & Hussain Bassi & Mehdi Seyedmahmoudian & Ben Horan & Alex Stojcevski, 2019. "Design and Implementation of a Hybrid Single T-Type Double H-Bridge Multilevel Inverter (STDH-MLI) Topology," Energies, MDPI, vol. 12(9), pages 1-15, May.
    6. Jie Wu & Lizhong Bie & Nan Jin & Leilei Guo & Jitao Zhang & Jiagui Tao & Václav Snášel, 2020. "Dual-Frequency Output of Wireless Power Transfer System with Single Inverter Using Improved Differential Evolution Algorithm," Energies, MDPI, vol. 13(9), pages 1-15, May.
    7. Yandi G. Landera & Oscar C. Zevallos & Rafael C. Neto & Jose F. da Costa Castro & Francisco A. S. Neves, 2023. "A Review of Grid Connection Requirements for Photovoltaic Power Plants," Energies, MDPI, vol. 16(5), pages 1-24, February.
    8. Md. Tariqul Islam & Hady H. Fayek & Eugen Rusu & Md. Fayzur Rahman, 2021. "A Novel Hexagonal-Shaped Multilevel Inverter with Reduced Switches for Grid-Integrated Photovoltaic System," Sustainability, MDPI, vol. 13(21), pages 1-15, October.
    9. Akhter, Muhammad Naveed & Mekhilef, Saad & Mokhlis, Hazlie & Ali, Raza & Usama, Muhammad & Muhammad, Munir Azam & Khairuddin, Anis Salwa Mohd, 2022. "A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems," Applied Energy, Elsevier, vol. 307(C).

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