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<p>The efficiency of photovoltaic systems decreases during partial shading due to the inaccurate maximum power point tracking (MPPT). Thus, a novel hybrid MPPT technique based on GPC-ANFIS (Giza pyramid construction optimization- Adaptive neuro-fuzzy inference system) is proposed to address the partial shading issues. GPC is an ancient-inspired metaheuristic technique which optimizes the global maximum power point under dynamic shading conditions. Further ANFIS helps in addressing the nonlinear characteristics of the photovoltaic system. The optimization strategy of GPC combined with the offline trained ANFIS results in the optimum duty cycle in less iterations which further leads to improved tracking accuracy. This significantly reduces the computational burden making it suitable for real-time deployment and parallel optimization. Five metaheuristic algorithms namely Harris Hawk Optimization (HHO), Particle Swarm Optimization (PSO), Grasshopper Optimization (GOA), Cuckoo Search (CS), Grey wolf Optimization (GWO) and three hybrid algorithms, i.e., PSO-ANFIS, GWO-ANFIS and MPSO-ANFIS are also considered for the comparative analysis. The proposed GPC-ANFIS-based MPPT is designed for a 1.2&#xa0;kW PV system in MATLAB Simulink. The system is simulated for four different irradiance patterns, i.e., uniform, partial shading conditions, step varying irradiance and the real atmospheric data. The simulation results demonstrate that GPC-ANFIS achieves tracking efficiency in the range of 93.8–99.1% for different irradiance patterns. These results confirm the robustness and efficiency of the proposed hybrid MPPT system in different test conditions. The considered PV system is also analyzed for suitability of deployment on advanced embedded and high-performance computing platforms for the next-generation real-time PV systems.</p>

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A novel metaheuristic-based hybrid MPPT technique for photovoltaic systems under dynamic shading conditions

  • 期刊论文

  • 发表日期：

    2026年7月16日

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  • Harshita Agnihotri,
  • Asha Rani

摘要

The efficiency of photovoltaic systems decreases during partial shading due to the inaccurate maximum power point tracking (MPPT). Thus, a novel hybrid MPPT technique based on GPC-ANFIS (Giza pyramid construction optimization- Adaptive neuro-fuzzy inference system) is proposed to address the partial shading issues. GPC is an ancient-inspired metaheuristic technique which optimizes the global maximum power point under dynamic shading conditions. Further ANFIS helps in addressing the nonlinear characteristics of the photovoltaic system. The optimization strategy of GPC combined with the offline trained ANFIS results in the optimum duty cycle in less iterations which further leads to improved tracking accuracy. This significantly reduces the computational burden making it suitable for real-time deployment and parallel optimization. Five metaheuristic algorithms namely Harris Hawk Optimization (HHO), Particle Swarm Optimization (PSO), Grasshopper Optimization (GOA), Cuckoo Search (CS), Grey wolf Optimization (GWO) and three hybrid algorithms, i.e., PSO-ANFIS, GWO-ANFIS and MPSO-ANFIS are also considered for the comparative analysis. The proposed GPC-ANFIS-based MPPT is designed for a 1.2 kW PV system in MATLAB Simulink. The system is simulated for four different irradiance patterns, i.e., uniform, partial shading conditions, step varying irradiance and the real atmospheric data. The simulation results demonstrate that GPC-ANFIS achieves tracking efficiency in the range of 93.8–99.1% for different irradiance patterns. These results confirm the robustness and efficiency of the proposed hybrid MPPT system in different test conditions. The considered PV system is also analyzed for suitability of deployment on advanced embedded and high-performance computing platforms for the next-generation real-time PV systems.