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<p>Maximum power point tracking (MPPT) algorithms are critical for maximizing the power output of photovoltaic (PV) systems under dynamic and complex environmental conditions. Conventional adaptive drift-free hill climbing (ADFHC) MPPT performs effectively under uniform irradiance but struggles in partial shading conditions (PSCs) because of its inability to differentiate the global maximum power point (GMPP) from multiple local maximum power points (LMPPs). Particle swarm optimization (PSO) addresses these limitations through robust global search capabilities; however, it often stagnates after convergence and adapts poorly to dynamic environmental changes. This paper presents a novel hybrid MPPT algorithm, iPSO-iADFHC, which integrates the global exploration strength of improved PSO (iPSO) with the local exploitation precision of improved ADFHC (iADFHC). Key innovations include deterministic initialization, exponential inertia weight reduction, real-time environmental change detection, and adaptive step-size adjustment. These features collectively enhance GMPP detection accuracy, tracking speed and adaptability under PSCs. Extensive simulations and hardware experiments validate the proposed algorithm. Results demonstrate that iPSO-iADFHC improves MPPT efficiency by 46.93% in simulation and 42.57% in hardware compared with standard PSO while reducing tracking time by 3.8 and 2.6&#xa0;s, respectively. These advancements highlight the practical applicability of the hybrid algorithm in improving the performance and reliability of PV systems under diverse PSC scenarios.</p>

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Hybrid iPSO-iADFHC MPPT algorithm for PV systems under various partial shading conditions

  • 期刊论文

  • 发表日期：

    2025年6月27日

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  • Norazlan Hashim,
  • Rahimi Baharom,
  • Muhammad Murtadha Othman,
  • Dalina Johari,
  • Azhan Ab. Rahman

摘要

Maximum power point tracking (MPPT) algorithms are critical for maximizing the power output of photovoltaic (PV) systems under dynamic and complex environmental conditions. Conventional adaptive drift-free hill climbing (ADFHC) MPPT performs effectively under uniform irradiance but struggles in partial shading conditions (PSCs) because of its inability to differentiate the global maximum power point (GMPP) from multiple local maximum power points (LMPPs). Particle swarm optimization (PSO) addresses these limitations through robust global search capabilities; however, it often stagnates after convergence and adapts poorly to dynamic environmental changes. This paper presents a novel hybrid MPPT algorithm, iPSO-iADFHC, which integrates the global exploration strength of improved PSO (iPSO) with the local exploitation precision of improved ADFHC (iADFHC). Key innovations include deterministic initialization, exponential inertia weight reduction, real-time environmental change detection, and adaptive step-size adjustment. These features collectively enhance GMPP detection accuracy, tracking speed and adaptability under PSCs. Extensive simulations and hardware experiments validate the proposed algorithm. Results demonstrate that iPSO-iADFHC improves MPPT efficiency by 46.93% in simulation and 42.57% in hardware compared with standard PSO while reducing tracking time by 3.8 and 2.6 s, respectively. These advancements highlight the practical applicability of the hybrid algorithm in improving the performance and reliability of PV systems under diverse PSC scenarios.