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Article type: Research Article
Authors: Thangavel, Jayakumara; * | Chinnaraj, Gnanavelb | Chandrasekaran, Gokulc | Kumarasamy, Vanchinathanc
Affiliations: [a] Department of Electrical and Electronics Engineering, Nandha Engineering College, Erode, Tamil Nadu, India | [b] Department of Electrical and Electronics Engineering, AMET Deemed to be University, Chennai, Tamil Nadu, India | [c] Department of Electrical and Electronics Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
Correspondence: [*] Corresponding author. Jayakumar Thangavel, Department of Electrical and Electronics Engineering, Nandha Engineering College, Erode, Tamil Nadu, India. E-mail: [email protected].
Abstract: This paper presents the design and development of Modular Multilevel Inverter (MMI) to reduce Total harmonic distortion (THD) using intelligent techniques towards marine applications. Many researchers have described the additional advantage of inverter control challenges such as voltage imbalance, increasing the number of voltage levels, power quality issues, reducing the number of semiconductors switches and achieving higher efficiency. Under the intelligent techniques, the implementation is carried out with aid of Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS) to calculate the modulation index (ma) and switching angles (θ) for MMI. Based on the calculation, it is trained to form a mapping between inputs and outputs for obtaining reduced Total Harmonics Distortion (THD). The objective of the intelligent controller is to control the inverter for regulating the output voltage with lowest THD. The proposed control structure has been estimated and compared for better robustness in terms of switching angle and modulation index with least THD in the inverter. Simulations and prototype models are made to analyze the controller’s performance, for inverter output voltage and harmonics. This proposed system is designed for marine lighting load application. The FPGA performance with all intelligent methods are analyzed by in SPARTAN3E500 FPGA device.
Keywords: Artificial Neural Networks (ANN), Fuzzy Logic Controller (FLC), Adaptive Neuro-Fuzzy Inference System (ANFIS), Modular Multilevel Inverter (MMI), Total Harmonics Distortion (THD)
DOI: 10.3233/JIFS-220190
Journal: Journal of Intelligent & Fuzzy Systems, vol. 44, no. 2, pp. 1807-1821, 2023
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