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<p>This paper describes a stability performance of MPPT techniques for PV, focusing on the efficacy of fuzzy, artificial neural networks and a novel hybrid neural-fuzzy approach. To improve tracking accuracy and system stability in dynamic environmental settings, the proposed hybrid neural-fuzzy MPPT blends the adaptive reasoning of fuzzy logic with the learning ability of artificial neural networks. The simulation outcomes show that the hybrid neural-fuzzy method achieves faster convergence to the MPP and sustains greater stability across fluctuating temperature and irradiance levels compared to standalone ANN and fuzzy logic strategies. This innovative method expressively amends the stability and general enactment of solar PV systems, demonstrating its reliability and efficiency. ANN, fuzzy, and the suggested hybrid neural-fuzzy technique are thoroughly compared based on tracking speed, tracking efficiency, power loss, mean squared error, mean absolute error, root mean square error, mean absolute percentage error, and mean squared error. Under dynamic input variations, the NHNF controller performs more steadily and consistently, while the fuzzy controller performs more accurately in steady-state scenarios. The paper provides insightful information for improving MPPT algorithms in renewable energy systems.</p>

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A Novel Dynamic Tracking and Power Optimization Based Intelligent MPPT Algorithm for PV Systems

  • 期刊论文

  • 发表日期：

    2026年4月10日

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  • Jayanta Kumar Sahu,
  • Subhendu Sekhar Sahoo,
  • Saroj Kumar Mishra,
  • Baratam Arundhati

摘要

This paper describes a stability performance of MPPT techniques for PV, focusing on the efficacy of fuzzy, artificial neural networks and a novel hybrid neural-fuzzy approach. To improve tracking accuracy and system stability in dynamic environmental settings, the proposed hybrid neural-fuzzy MPPT blends the adaptive reasoning of fuzzy logic with the learning ability of artificial neural networks. The simulation outcomes show that the hybrid neural-fuzzy method achieves faster convergence to the MPP and sustains greater stability across fluctuating temperature and irradiance levels compared to standalone ANN and fuzzy logic strategies. This innovative method expressively amends the stability and general enactment of solar PV systems, demonstrating its reliability and efficiency. ANN, fuzzy, and the suggested hybrid neural-fuzzy technique are thoroughly compared based on tracking speed, tracking efficiency, power loss, mean squared error, mean absolute error, root mean square error, mean absolute percentage error, and mean squared error. Under dynamic input variations, the NHNF controller performs more steadily and consistently, while the fuzzy controller performs more accurately in steady-state scenarios. The paper provides insightful information for improving MPPT algorithms in renewable energy systems.