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<p>The rapid adoption of photovoltaic (PV) systems has highlighted the need for efficient power electronic interfaces that can deliver high voltage gain, robust maximum power point tracking (MPPT), and superior grid integration. Conventional DC–DC converters such as boost, SEPIC, Luo, and Cuk topologies suffer from efficiency degradation, high device stress, and poor performance at high duty ratios, limiting their suitability for modern PV applications. Furthermore, classical MPPT algorithms, including Perturb and Observe (P&amp;O) and Incremental Conductance (INC), often exhibit oscillations around the maximum power point under dynamic conditions, while inverter control with traditional proportional–integral (PI) regulators is prone to degraded performance and higher total harmonic distortion (THD) under parameter variations. To address these challenges, this paper proposes an integrated framework for grid-connected PV systems based on a high-gain Landsman converter combined with an adaptive neuro-fuzzy inference system (ANFIS)-based MPPT algorithm. The Landsman converter provides a voltage gain of 1:16, surpassing conventional converters such as Boost (1:1.5), SEPIC (1:8), and Luo (1:12), while ensuring continuous input current and reduced ripple. The ANFIS-based MPPT ensures fast and oscillation-free tracking of the maximum power point under rapidly varying irradiance. A 31-level asymmetrical cascaded multilevel inverter (MLI) is employed to generate high-quality sinusoidal output, while an ANN-assisted PI controller regulates the inverter, enhancing dynamic response and achieving robust grid synchronization. The fuzzy logic controller further supports nonlinear system handling without requiring an exact mathematical model. Simulation studies in MATLAB/Simulink demonstrate that the proposed system significantly improves overall efficiency and power quality. The 31-level MLI achieves sinusoidal output with reduced switching components and maintains voltage and current THD values below 4%. Comparative analysis validates that the proposed Landsman–ANFIS–ANN control framework outperforms conventional converter and control approaches in terms of voltage gain, MPPT accuracy, harmonic suppression, and dynamic stability. These findings highlight the suitability of the proposed design for reliable, high-performance grid-connected PV applications.</p>

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Performance measurement of high gain Landsman converter with ANFIS based MPPT and cascaded H-bridge thirty-one multilevel inverter in a single-phase grid-connected PV system

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月25日

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  • N. Bharath Kumar,
  • Uma Maheswara Rao M,
  • P. Lakshminarayana,
  • Ch N. Narsimha Rao,
  • J. Veeranjaneyulu

摘要

The rapid adoption of photovoltaic (PV) systems has highlighted the need for efficient power electronic interfaces that can deliver high voltage gain, robust maximum power point tracking (MPPT), and superior grid integration. Conventional DC–DC converters such as boost, SEPIC, Luo, and Cuk topologies suffer from efficiency degradation, high device stress, and poor performance at high duty ratios, limiting their suitability for modern PV applications. Furthermore, classical MPPT algorithms, including Perturb and Observe (P&O) and Incremental Conductance (INC), often exhibit oscillations around the maximum power point under dynamic conditions, while inverter control with traditional proportional–integral (PI) regulators is prone to degraded performance and higher total harmonic distortion (THD) under parameter variations. To address these challenges, this paper proposes an integrated framework for grid-connected PV systems based on a high-gain Landsman converter combined with an adaptive neuro-fuzzy inference system (ANFIS)-based MPPT algorithm. The Landsman converter provides a voltage gain of 1:16, surpassing conventional converters such as Boost (1:1.5), SEPIC (1:8), and Luo (1:12), while ensuring continuous input current and reduced ripple. The ANFIS-based MPPT ensures fast and oscillation-free tracking of the maximum power point under rapidly varying irradiance. A 31-level asymmetrical cascaded multilevel inverter (MLI) is employed to generate high-quality sinusoidal output, while an ANN-assisted PI controller regulates the inverter, enhancing dynamic response and achieving robust grid synchronization. The fuzzy logic controller further supports nonlinear system handling without requiring an exact mathematical model. Simulation studies in MATLAB/Simulink demonstrate that the proposed system significantly improves overall efficiency and power quality. The 31-level MLI achieves sinusoidal output with reduced switching components and maintains voltage and current THD values below 4%. Comparative analysis validates that the proposed Landsman–ANFIS–ANN control framework outperforms conventional converter and control approaches in terms of voltage gain, MPPT accuracy, harmonic suppression, and dynamic stability. These findings highlight the suitability of the proposed design for reliable, high-performance grid-connected PV applications.